Prediction of the likelihood of occurrence of an ovulation after miscarriage

Abstract

A method for predicting the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in a female individual, said method comprising at least: a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a βhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss, b) determining one or more likelihood score based on said set of data, c) predicting the likelihood of the occurrence of an ovulation in said female individual during a period of 6 weeks after a pregnancy loss based on said one or more likelihood score.

Description

PREDICTION OF THE LIKELIHOOD OF OCCURRENCE OF AN OVULATION AFTERMISCARRIAGEFIELDDespite one in four pregnancies ending with a pregnancy loss, there is limited knowledge about whento expect reproductive recovery. Stagnating ẞhCG limits chances of ovulation, thus inhibiting the onsetof menstrual bleeding, however with great individual variability creating a need for a more patient-specific approach.BACKGROUNDPregnancy loss, the spontaneous demise of a pregnancy before 22 weeks of gestation,¹ affects atleast 25% of all pregnancies.2 Despite associated physical and psychological comorbidities andimplications for future pregnancies and maternal health,3,4 research in the field of pregnancy loss isscarce. This is stressed by the Lancet Series Miscarriage Matters, imploring for further research tocatalyse development of health evaluation after miscarriage, as well as guidance regardingreproductive recovery and perspectives of a subsequent pregnancy.5 A recent study by Tessema etal. challenges the World Health Organization's recommendation of a minimum interpregnancy intervalof six months after pregnancy loss by finding no evidence linking conception within three months of apregnancy loss to increased risk of adverse obstetric complications. This suggests that patients canattempt subsequent conception when they feel ready and offering accurate guidance on reoccurrenceof ovulation becomes even more valuable.Donnet et al. followed 18 women experiencing first trimester pregnancy loss and found thatparticipants experienced ovulation at a mean of 29 days (min-max: 13-103 days) post pregnancy loss,7hinting at substantial individuality between patients, as well as involvement of other factors.Furthermore, Hallet's synthesis of various smaller studies indicates that in 67-90% of cases, initialmenstrual bleeding following spontaneous miscarriage is preceded by an ovulation.8Amongst others, a contributing factor to delay of a subsequent ovulation is thought to be stagnatinglevels of the glycoprotein hormone, human chorionic gonadotropin (hCG) from the lost pregnancy. 9ẞhCG is a heterodimeric molecule, where the alpha subunit is structurally equivalent to that ofluteinizing hormone (LH), follicle-stimulating hormone (FSH) and thyroid-stimulating hormone (TSH)and the beta subunit is unique for ẞhCG. 10 Because of this, as well as a shared LH / hCG receptor,ẞhCG levels following a pregnancy loss induce negative feedback in the pituitary, thus decreasingtranscription of gonadotropins FSH and LH, potentially hindering ovulation. 9 In a study, Stier et al.investigated the time from 35 surgical treatments of spontaneous miscarriages to complete clearanceof ẞhCG to be, however with an unknown cut-off level, 9 to 35 days (median = 19 days) posttreatment¹¹. Experiencing retained product of conception after treatment is correlated with a delayeddecline of ẞhCG levels,12-14 whereas other factors such as higher initial ẞhCG level are expected tohave same effect11. To our knowledge, no studies investigated the decline in ẞhCG concentrationsafter pregnancy loss in a population of this magnitude and none analysed maternal factors affectingthis.Patients experiencing pregnancy loss are typically advised to anticipate their next menstrual bleedingwithin 4-8 weeks. Nonetheless, the absence of clear scientific backing or uniform guidelines creates avagueness that does not take individual factors into account. The aim of this study was to investigatethe ẞhCG levels at time of pregnancy loss and six weeks after and factors affecting these levels. Withthese results we can provide patients with more personalized information and care regardingpregnancy loss and subsequent reproductive prospects, and we can improve understanding of returnof menstrual bleeding, catering to an unmet need.SUMMARYWe found that 6-7 weeks after a pregnancy loss, ẞhCG levels were negatively correlated with returnof menstrual bleeding and thereby a reproductive recovery. Surgical compared with both medical andexpectant treatment, maternal age, and gestational age were associated with higher ẞhCG. MaternalBMI, number of prior livebirths or pregnancy losses, and intrauterine insemination compared tospontaneous conception were associated with lower ẞhCG.These findings contribute towards a more personalized approach to supporting women who areexperiencing or have experienced pregnancy loss, facilitating their physical and reproductive recovery.An improved understanding of the return of fecundity following a pregnancy loss will facilitateappropriate and early intervention alongside expectation management between patient and provider.This could lower time to a subsequent cycle and potential pregnancy, hereby possibly increasingchances of a livebirth in such.It is an object of the present disclosure to provide estimation or prediction of the likelihood ofoccurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in afemale individual.In one aspect, the present disclosure relates to a method for predicting the likelihood of occurrence ofan ovulation during a period of 6 weeks after the detection of a pregnancy loss in a female individual,said method comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determining one or more likelihood score based on said set of data,c) predicting the likelihood of the occurrence of an ovulation in said female individual during aperiod of 6 weeks after a pregnancy loss based on said one or more likelihood score.In another aspect, the present disclosure relates to a computer-implemented method for predicting thelikelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancyloss in a female individual, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determining one or more likelihood score based on said set of data,c) predicting the likelihood of the occurrence of an ovulation in said female individual during aperiod of 6 weeks after a pregnancy loss, based on said one or more likelihood score,d) providing an output including a first output associated with the likelihood of the occurrence ofan ovulation during a period of 6 weeks after the detection of a pregnancy loss in said femaleindividual.In another aspect, the present disclosure relates to a computer-implemented method for training amachine learning model, such as a neural network, to process as inputs a set of data comprisingpregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or morelikelihood scores associated with the likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss in a female individual, the method comprising:a) obtaining, using at least one processor, a set of data comprising pregnancy loss dataassociated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least aẞhCG value (IU / L) in said female individual, measured at the time of the determination of thepregnancy loss,b) performing, using the at least one processor, a training comprising:i) generating, using the at least one processor and a machine-learning model, likelihood databased on the set of data,ii) obtaining, using the at least one processor, training data,iii) determining, using the at least one processor and one or more likelihood functions, one ormore likelihood score based on the set of data and the training data; andiv) training, using the at least one processor, the machine learning model based on one ormore likelihood score.In another aspect, the present disclosure relates to an electronic device for predicting the likelihood ofoccurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in afemale individual, the electronic device comprising an interface, one or more processors, and amemory, wherein the one of more processors are configured to:a) obtain a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determine one or more likelihood scores based on the set of data, the likelihood score beingindicative of occurrence of an ovulation during a period of 6 weeks after the detection of apregnancy loss in said female, andc) provide an output including a first output associated with the one or more likelihood scores.BRIEF DESCRIPTION OF THE FIGURESFig. 1 schematically illustrates an example system according to the present disclosure,Fig. 2 is a flow chart of an example computer implemented method according to the present disclosure,Fig. 3 is a flow chart of an example computer implemented method according to the present disclosure,Fig. 4 illustrates an example implementation of a machine learning model according to the disclosure.Fig. 5 illustrates the ẞhCG levels after pregnancy loss.Fig. 6 illustrates the odds-ratios for seven variables robustly predicting the ẞhCG value at follow-up,namely: ẞhCG at pregnancy loss, days since pregnancy loss, gestational age at pregnancy loss(calculated from last menstrual period), prior number of live births, creatine kinase, the ASAT / ALATratio (De Ritis ratio), and the treatment choiceFig. 7 illustrates the effect of the ASAT / ALAT ratio on the ẞhCG value.Fig. 8 illustrates the difference of performance between models using all variables, and models usinga reduced number of variables.Fig. 9 illustrates the difference of performance between models using all variables, and models usinga reduced number of variables, with external data sets from CUH Herlev.Fig. 10 illustrates the difference of performance between models using all variables, and models usinga reduced number of variables, with external data sets from CUH North Zealand.Fig. 11 summarizes the risk of excessive ẞhCG levels post pregnancy loss depending on a selectionof preferred variables.Fig. 12 illustrates the probability of return of menstrual cycle within 8 weeks after a pregnancy loss,depending on the ẞhCG levels measured at follow-up visit.Figure 13 illustrates the treatments yielding the highest probability of having a BETAHCG < 3 IU / L atfollow-up, depending on the ẞhCG level measured at the time of pregnancy loss.DETAILED DESCRIPTIONThe present disclosure present outcome prediction models for predicting the likelihood of occurrenceof an ovulation during a period of 6 weeks after the detection of a pregnancy loss in a female individual,using biochemistry and clinical variables. The model demonstrated strong performance metrics, thataligned well with the results from two external validation sites (Herlev University Hospital and NorthernZealand Hospital), underlining the model's generalizability. Furthermore, our association studies andmachine learning models implicate not only the placenta-produced hormone ẞ-hCG and gestationalage, but also maternal levels of enzymes and lipids, thyroid function, and liver.Taken together, this demonstrates that a blood sample can be used to estimate the likelihood ofoccurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in afemale individual and can be a powerful method to plan subsequent reproductive prospects, whichwomen should be offered after experiencing a pregnancy loss.The external validation performed on an independent dataset further substantiates the robustness andgeneralizability of our model. Despite these strengths, the cohort includes pregnancy losses, and themodels may need to be further validated in ongoing pregnancies, but the present data teach that theconcept is applicable in ongoing pregnancies.Various examples and details are described hereinafter, with reference to the figures when relevant.It should be noted that the figures may or may not be drawn to scale and that elements of similarstructures or functions are represented by reference numerals throughout the figures. It should alsobe noted that the figures are only intended to facilitate the description of the examples. They are notintended as an exhaustive description of the disclosure or as a limitation on the scope of the disclosure.In addition, an illustrated example needs not have all the aspects or advantages shown. An aspect oran advantage described in conjunction with a particular example is not necessarily limited to thatexample and can be practiced in any other examples even if not so illustrated, or if not so explicitlydescribed.The figures are schematic and simplified for clarity, and they merely show details which aidunderstanding the disclosure, while other details have been left out. Throughout, the same referencenumerals are used for identical or corresponding parts.It is to be understood that a description of a feature in relation to the system / electronic device is alsoapplicable to the corresponding feature in the method(s) of operating a system / electronic device asdisclosed herein and vice versa.The present disclosure relates to tools and methods for analysis, classification, monitoring, and / orprediction of the likelihood of occurrence of an ovulation during a period of 6 weeks after the detectionof a pregnancy loss in a female individual.An electronic device for prediction of the likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss in a female individual is disclosed. The electronic devicecomprises an interface, one or more processors, and a memory. The one or more processors areconfigured to obtain, e.g. retrieve from a database and / or receive via the interface, clinical dataincluding one or more of, such as two or all of, female data associated with a female; and pregnancyloss data, e.g. associated with a fetus and / or fetal tissue and / or pregnancy tissue. The one or moreprocessors are configured to determine one or more likelihood scores based on one or more of thefemale data, and the pregnancy loss data, the one or more likelihood scores associated with thelikelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancyloss in a female individual. The one or more processors are configured to provide an output includinga first output associated with the one or more likelihood scores.The one or more likelihood scores may comprise a first likelihood score, e.g. indicative of a status ofthe likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of apregnancy loss. The first output may be associated with, such as representative or indicative of, thefirst likelihood score.In one or more example electronic devices, the one or more processors are configured to obtain femaledata associated with a female; obtain pregnancy loss data associated with a fetus and / or fetal tissue;determine one or more likelihood scores based on one or more of the female data, and the pregnancyloss data, the one or more likelihood scores associated with the likelihood of occurrence of an ovulationduring a period of 6 weeks after the detection of a pregnancy loss in said female individual; and providean output including a first output associated with the one or more likelihood scores.In one or more examples, to provide an output including a first output associated with the one or morelikelihood scores comprises to display, on a display of the interface, one or more user interfaceelements indicative of, e.g. showing the value of, one or more likelihood scores, such as first userinterface element indicative of the first likelihood score. For example, a first primary user interfaceelement may be color-coded based on a first primary likelihood score, e.g. indicative of the likelihoodof occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss and / ora first secondary user interface element may be color-coded based on a first secondary likelihoodscore, e.g. indicative of the absence of likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss.In one or more examples, to provide an output including a first output associated with the one or morelikelihood scores comprises to transmit the one or more likelihood scores or a subset thereof to aremote server or database, e.g. via a network.In one or more examples, to provide an output including a first output associated with the one or morelikelihood scores comprises to store the one or more likelihood scores or a subset thereof in thememory or a database.Female dataFemale data also denoted maternal data are data indicative and / or associated with the female ormother of the foetus.In one or more example electronic devices, the female data comprises female blood data also denotedFBD of the female, e.g. first female blood data of a first female blood sample taken at a first time and / orsecond female blood data of a second female blood sample taken at a second time. The first time maybe less than 48 hours, such as less than 24 hours, after detection of pregnancy loss. The second timemay be larger than 1 week after detection of pregnancy loss, such as in the range from 2 weeks to 8weeks, e.g. 4 weeks to 6 weeks, after detection of pregnancy loss. In one or more example electronicdevices, to determine the one or more likelihood scores comprises to determine the one or morelikelihood scores, such as the first likelihood score, based on the female blood data, such as the firstfemale blood data and / or the second female blood data.The female blood data may comprise or be indicative of one or more of Whole Genome Sequencing(WGS), Cell-Free Fetal DNA (cffDNA), proteomics, metabolomics immunological profile, metabolicprofile, inflammatory markers, blood coagulation status, and RNA sequencing.The female blood data may comprise or be indicative of one or more of the creatine kinase level, theAspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio), the C-ReactiveProtein (CRP) level, the thyroid Peroxidase antibody level, the thyroglobulin antibody level, theGamma-Glutamyl Transferase (GGT) level, the Aspartate Transaminase (ASAT) level, the AlanineTransaminase (ALAT) level, the triglycerides level, the LDL Cholesterol level, the apolipoprotein Blevel, the creatinine level, the thyroid-Stimulating Hormone (TSH) level, the uric acid level, therheumatoid Factor level, the transferrin level, the iron level, the alipoprotein(a) level, the albumin level,the bilirubin level, the urea level, the HDL Cholesterol level, the TG Rich Lipoprotein Cholesterol (TRL-C) level, the lipoprotein(a) level, the triacylglycerol lipase level, and / or the Lactate Dehydrogenase(LDH) level.The first female blood sample may be taken at a first time within a first time period, e.g. within 48 hoursor within 24 hours of detection of pregnancy loss.The second female blood sample may be taken at a second time within a second time period, e.g. inthe range from 2 weeks to 8 weeks after detection of pregnancy loss. In one or more examples, thesecond female blood sample may be taken 4 weeks to 6 weeks after detection of pregnancy loss.In one or more example electronic devices, the female data comprises female biosample data alsodenoted FBSD of the female. The female biosample data FBSD may comprise one or more of vaginaldata associated with a vaginal (microbiome) sample, rectal data associated with a rectal sample, andurine data associated with a urine sample. In one or more example electronic devices, to determinethe one or more likelihood scores comprises to determine the one or more likelihood scores, such asthe first likelihood score, based on the female biosample data, such as one or more of the vaginaldata, the rectal data, and the urine data of the female biosample data.The vaginal data may comprise one or more vaginal parameters indicative of vaginal sample. Thevaginal data of the female biosample data may comprise or be indicative of vaginal microbiome, suchas 16S rRNA sequencing and / or shotgun-sequencing and / or vaginal immunology.The rectal data of the female data may comprise one or more rectal parameters indicative of rectalsample. The rectal data of the female biosample data may comprise or be indicative of Gutmicrobiome, such as 16S rRNA sequencing and / or shotgun-sequencing and / or rectal immunology.The urine data of the female biosample data may comprise or be indicative of one or more of endocrinedisrupters, infections, cannabinoids, illegal drugs, and cotinine.In one or more example electronic devices, the female data comprises ultrasound image data of anultrasound scanning of the uterus of the female. To determine the one or more likelihood scores maycomprise to determine the one or more likelihood scores, such as the likelihood score, based on theultrasound image data.The female data may comprise one or more of type of pregnancy loss, selected evacuation treatment(or management of miscarriage), and if and / or which pregnancy complications occurred. A type ofpregnancy loss may be selected from one or more of spontaneous complete miscarriage, spontaneousincomplete miscarriage, missed abortion, and anembryonic pregnancy.The female data may comprise one or more of age, Body Mass Index (BMI) (kg.m2), weight, height,hip-waist ratio, pulse and blood pressure (systolic and / or diastolic) of the female.The female data may comprise one or more of the types of management of miscarriage (i.e. the typeof treatment received after the determination of the pregnancy loss, for removing the foetal material),the number of days since pregnancy loss, the number of prior live births, the presence or absence ofvaginal bleeding at inclusion, the number of prior pregnancy loss, and / or the cycle duration prior to thepregnancy.The female data may comprise health data, such as one or more health parameters indicative offemale health. In one or more examples, the health data of the female data may comprise a healthparameter, e.g. a first health parameter, indicative of whether the female has, has been operated for,or has been diagnosed with endometriosis. In one or more examples, the health data of the femaledata may comprise a health parameter, e.g. a second health parameter, indicative of whether thefemale has or has been diagnosed with fibrom, such as uterine fibroms. In one or more examples, thehealth data of the female data may comprise a health parameter, e.g. a third health parameter,indicative of whether the female has, has been operated for, or has been diagnosed with hernia. Inone or more examples, the health data of the female data may comprise a health parameter, e.g. afourth health parameter, indicative of whether the female has, has been operated for, or has beendiagnosed with ovarian cyst. In one or more examples, the health data of the female data maycomprise a health parameter, e.g. a fifth health parameter, indicative of whether the female has, hasbeen operated for, or has been diagnosed with Appendicitis. In one or more examples, the health dataof the female data may comprise a health parameter, e.g. a sixth health parameter, indicative ofwhether the female has or has been diagnosed with genital infection. In one or more examples, thehealth data of the female data may comprise a health parameter, e.g. a seventh health parameter,indicative of whether the female has, has been operated for, or has been diagnosed with Ectopicpregancy. In one or more examples, the health data of the female data may comprise a healthparameter, e.g. an eighth health parameter, indicative of whether the female has, has been operatedfor, or has been diagnosed with Polyscystiuc Ovarian Syndrome. In one or more examples, the healthdata of the female data may comprise a health parameter, e.g. a ninth health parameter, indicative ofwhether the female has had a Caesarean section. In one or more examples, the health data of thefemale data may comprise a health parameter, e.g. a tenth health parameter, indicative of whether thefemale has had vaginal bleeding during pregnancy. The health data may be obtained as questionnairedata via a questionnaire and / or via a patient record.The female data may comprise information or data on one or more of current medications, reproductivehistory, lifestyle, physical health, and mental health of the female. For example, the female data maycomprise female questionnaire data also denoted FQD comprising or indicative of one or more ofgeneral information, reproductive history, health, medication and dietary supplements,communication, relationship, staff satisfaction, well-being, sexuality, health behavior, family,childhood, sociodemographic factors, and work. The questionnaire data of the female data maycomprise questionnaire data from different times, such first questionnaire data from questionnaireanswers at a first time and / or second questionnaire data from questionnaire answers at a second time.The questionnaire data may comprise third questionnaire data from questionnaire answers at a thirdtime. The questionnaire data of the female data may comprise a parameter indicative of alcohol intakeduring pregnancy. The questionnaire data of the female data may comprise a parameter indicative ofsmoking during pregnancy. The female data, such as questionnaire data, may comprise a partnerparameter indicative of the number of sexual partners.The female data, such as questionnaire data, may comprise a first dietary parameter, e.g. indicativeof whether the female has taken vitamin(s) prior to and / or during the pregnancy. For example, the firstdietary parameter may be a vector or matrix and may be indicative of type(s) and / or amount(s) ofvitamin supplements, such as vitamin D and / or vitamin E, taken prior to and / or during pregnancy.The female data, such as questionnaire data, may comprise a second dietary parameter, e.g.indicative of whether the female has taken fish oil prior to and / or during the pregnancy. For example,the second dietary parameter may be a vector or matrix and may be indicative of type(s) and / oramount(s) of fish oil taken prior to and / or during pregnancy.Pregnancy loss dataThe pregnancy loss data may be associated with or indicative of one or more of a foetus, foetal tissue,pregnancy tissue, and product of conception and / or tissue thereof.The pregnancy loss data may comprise the ẞhCG values (IU / L) at the time of detection of thepregnancy loss and / or during a follow-up visit.Said ẞhCG values may be measured from a first female blood sample taken at a first time and / orsecond female blood data of a second female blood sample taken at a second time. The first time maybe less than 48 hours, such as less than 24 hours, after detection of pregnancy loss. The second timemay be larger than 1 week after detection of pregnancy loss, such as in the range from 2 weeks to 8weeks, e.g. 4 weeks to 6 weeks, after detection of pregnancy loss. In one or more example electronicdevices, to determine the one or more likelihood scores comprises to determine the one or morelikelihood scores, such as the first likelihood score, based on the female blood data, such as the firstfemale blood data and / or the second female blood data. Said first and second blood samples may bethe same or different blood samples as the first and second blood samples used for determination ofthe female data.The pregnancy loss data may comprise one or more parameter of the gestational age at pregnancyloss (calculated from the last menstruation of said individual), or the diagnosis of the pregnancy loss(such as missed abortion, blighted ovum, spontaneous ongoing pregnancy loss, etc.).The pregnancy loss data may comprise one or more parameters indicative of one or more of methodor mode of conception, such as natural, IVF, or insemination, gestational age(s), e.g. based on lastmenstrual period (calculated from the first day of the last menstrual period) also denoted GA_LMand / or findings from diagnostic ultrasound (e.g. multiple gestations) also denoted GA_UL, andphenotype.The pregnancy loss data may comprise one or more parameters indicative whether a gestational ageestimated from crown-rump length could have been obtained and / or was not obtainable.The pregnancy loss data may comprise one or more parameters indicative of whether donor spermand / or donor oocyte was used for conception of the fetus.The pregnancy loss data may comprise one or more parameters indicative of whether donor spermand / or donor oocyte was used for conception of the fetus.The pregnancy loss data may comprise one or more of type of pregnancy loss, selected evacuationtreatment, and if and / or which complications occurred. A type of pregnancy loss may be selected fromone or more of spontaneous complete miscarriage, spontaneous incomplete miscarriage, missedabortion, and anembryonic pregnancy,The pregnancy loss data may comprise one or more of type of selected evacuation treatment or typeof management of miscarriage, e.g. surgical management of miscarriage (i.e. surgical removal offoetus / foetal tissues), medical management of miscarriage (i.e. medical removal of foetus / foetaltissues), and / or expectant management of miscarriage (i.e. natural removal of foetus / foetal tissues).The pregnancy loss data may comprise foetal biosample data of the foetus. The fetal biosample datamay be associated with or be determined from one or more of pregnancy tissue, atretic embryos, andoocytes. The foetal biosample data may comprise one or more of Whole Genome Sequencing (WGS)and Single-cell shallow sequencing.Machine learning modelIn one or more example electronic devices, to determine the one or more likelihood scores, such asthe first likelihood score, comprises to apply a machine learning model, e.g. to one or more of thefemale data FD, and the pregnancy loss data PLD. In other words, female data, and / or pregnancy lossdata may be fed to a machine learning model providing as output one or more likelihood scores, e.g.including first likelihood score. The female data, such as one or more of female blood data, femalebiosample data, and health data of the female as described herein, may be fed to the machine learningmodel for provision of one or more likelihood scores, e.g. including first likelihood score(s), as output.The pregnancy loss data, may be fed to the machine learning model for provision of one or morelikelihood scores, e.g. including first likelihood score(s), as outputIn one or more example electronic devices, the first likelihood score also denoted RS_1 comprises oris a first primary likelihood score also denoted RS_1_1, e.g. indicative of the likelihood of occurrenceof an ovulation during a period of 6 weeks after the detection of a pregnancy loss. The first likelihoodscore may be a single value, e.g. indicative of the likelihood of occurrence of an ovulation during aperiod of 6 weeks after the detection of a pregnancy loss.In one or more example electronic devices, the first likelihood score RS_1 comprises a first secondarylikelihood score also denoted RS_1_2, e.g. indicative of the absence of likelihood of occurrence of anovulation during a period of 6 weeks after the detection of a pregnancy loss. In other words, the firstlikelihood score may comprise a plurality of first likelihood scores including a first primary likelihoodscore indicative of the likelihood of occurrence of an ovulation during a period of 6 weeks after thedetection of a pregnancy loss and a first secondary likelihood score indicative of the absence oflikelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancyloss.The one or more likelihood scores may comprise a second likelihood score also denoted RS_2 and / ora third likelihood score also denoted RS_3.In other words, to determine one or more likelihood scores based on one or more of the female data,and the pregnancy loss data, may comprise to determine a second likelihood score and / or a thirdlikelihood score.To provide an output may comprise to provide an output including a second output associated withthe second likelihood score. To provide an output may comprise to provide an output including a thirdoutput associated with the third likelihood score.Computer-implemented methodA computer-implemented method for predicting the likelihood of occurrence of an ovulation during aperiod of 6 weeks after the detection of a pregnancy loss in a female individual is disclosed. Themethod comprises obtaining female data associated with a female. The method comprises obtainingpregnancy loss data associated with a foetus and / or foetal tissue. The method comprises determiningone or more likelihood scores based on one or more of the female data, and the pregnancy loss data,the one or more likelihood scores associated with the likelihood of occurrence of an ovulation duringa period of 6 weeks after the detection of a pregnancy loss in a female individual and including a firstlikelihood score indicative of a likelihood of occurrence of an ovulation during a period of 6 weeks afterthe detection of a pregnancy loss. The method comprises providing an output including a first outputassociated with the one or more likelihood scores.In one or more example computer-implemented methods, the female data FD comprises female blooddata FBD of the female. In one or more example methods for pregnancy loss classification,determining the one or more likelihood scores comprises determining the one or more likelihoodscores based on the female blood data.In one or more example computer-implemented methods, the female data FD comprises femalebiosample data FBSD of the female. In one or more example methods for pregnancy lossclassification, determining the one or more likelihood scores comprises determining the one or morelikelihood scores based on the female biosample data.In one or more example computer-implemented methods, the first likelihood score comprises a firstprimary likelihood score indicative of a likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss.In one or more example computer-implemented methods, the first likelihood score comprises a firstsecondary likelihood score indicative of an absence of likelihood of occurrence of an ovulation duringa period of 6 weeks after the detection of a pregnancy loss.A computer-implemented method for training a neural network to process as inputs one or more, suchas one or all of female data (FD) associated with a female; and pregnancy loss data (PLD) associatedwith a foetus and / or foetal tissue, and to provide as output one or more likelihood scores associatedwith the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of apregnancy loss is disclosed. The method comprises obtaining, using at least one processor, femaledata associated with a female; and pregnancy loss data associated with a foetus and / or foetal tissue.The method comprises performing, using the at least one processor, a training, wherein performingthe training comprises generating, using the at least one processor and a machine-learning model,likelihood data based on the female data, and the pregnancy loss data. Performing the trainingcomprises obtaining, using the at least one processor, training data; determining, using the at leastone processor and one or more likelihood functions, one or more likelihood parameters based on thefemale data, the pregnancy loss data, and the training data; and training, using the at least oneprocessor, the machine learning model based on the one or more likelihood parameters.It is to be understood that a description of a feature in relation to the electronic device is also applicableto the corresponding feature in the method(s) of operating an electronic device as disclosed herein.Fig. 1 illustrates a system implementing the electronic device and / or methods according to the presentdisclosure. The system 2 comprises one or more databases 4 storing clinical data including one ormore of female data FD, and pregnancy loss data PLD. The system 2 comprises an electronic device6 for classification of a likelihood of occurrence of an ovulation during a period of 6 weeks after thedetection of a pregnancy loss, the electronic device comprising an interface 8, one or more processorsincluding processor 10, and a memory 12, wherein the one of more processors are configured toobtain, via the interface 8, female data FD associated with a female; and obtain, via the interface 8,pregnancy loss data PLD associated with a fetus and / or fetal tissue. The one or more databases 4communicates with the electronic device 6 over network. The electronic device 6 may implementdatabase 4A in memory 12.The one of more processors are configured to determine one or more likelihood scores based on oneor more of the female data FD, and the pregnancy loss data PLD. The one or more likelihood scoresare associated with the likelihood of occurrence of an ovulation during a period of 6 weeks after thedetection of a pregnancy loss and includes a first likelihood score RS_1 indicative of a likelihood ofoccurrence of an ovulation. The one of more processors are configured to provide, via the interface 8,an output including a first output associated with one or more likelihood scores, such as the firstlikelihood score RS_1. The interface 8 of the electronic device optionally comprises a display 14,wherein to provide an output comprises to display a first user interface element 30 as a first outputassociated or representing the first likelihood score (RS_1=0.8 in the illustrated example). Forexample, to provide an output may comprise to display a first primary user interface element as a firstprimary output associated or representing the first primary likelihood score. For example, to providean output may comprise to display a first secondary user interface element as a first secondary outputassociated or representing the first secondary likelihood score.The electronic device 6 may be configured to perform any of the methods disclosed herein, such asthe method in any of Fig. 2 and / or Fig. 3. In other words, the electronic device 6 is configured foranalysis, classification, monitoring, and / or prediction of the likelihood of occurrence of an ovulationduring a period of 6 weeks after the detection of a pregnancy loss in a female individual.The processor circuitry 302 is optionally configured to perform any of the operations disclosed in Figs.2-3 (such as any one or more of: S104, S106, S108, S112, S112A, S112B, S112C, S112D, S112E,114, S114A, S114B, S202, S204, S204A, S204B, S204C, S204D). The operations of the electronicdevice 6 may be embodied in the form of executable logic routines (e.g., lines of code, softwareprograms, etc.) that are stored on a non-transitory computer readable medium (e.g., the memory 12)and are executed by the one or more processors / processor 10.Furthermore, the operations of the electronic device 6 may be considered a method that the electronicdevice 6 is configured to carry out. Also, while the described functions and operations may beimplemented in software, such functionality may as well be carried out via dedicated hardware orfirmware, or some combination of hardware, firmware and / or software.The memory 12 may be or comprise one or more of a buffer, a flash memory, a hard drive, a removablemedia, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitabledevice. In a typical arrangement, the memory 12 may include a non-volatile memory for long term datastorage and a volatile memory that functions as system memory for the processor 10. The memory 12may exchange data with the processor 10 over a data bus. Control lines and an address bus betweenthe memory 12 and the processor 10 also may be present (not shown in Fig. 1). The memory 12 isconsidered a non-transitory computer readable medium.The memory 12 may be configured to store one or more of female data, such as female blood dataand / or, female biosample data, pregnancy loss data, one or more likelihood scores, e.g. including afirst likelihood score, an output, e.g. including a first output, a female output, machine learning model,first primary likelihood score, first secondary likelihood score, risk data, training data and / or one ormore likelihood parameters in a part of the memory 12.Fig. 2 shows a flow chart of an example computer-implemented method for pregnancy lossclassification. The method comprises obtaining S104 female data associated with a female. Themethod comprises obtaining S106 pregnancy loss data associated with a foetus and / or foetal tissue.The method comprises determining S112 one or more likelihood scores based on one or more of thefemale data, and the pregnancy loss data, the one or more likelihood scores associated with thelikelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancyloss in a female individual and including a first likelihood score indicative of a likelihood of occurrenceof an ovulation during a period of 6 weeks after the detection of a pregnancy loss in a female individual.The method comprises providing S114 an output including providing S114A a first output associatedwith the first likelihood score.Fig. 3 shows a flow chart of an example computer-implemented method for training a machine learningmodel, such as a neural network, to process as inputs female data associated with a female; andpregnancy loss data associated with a foetus and / or foetal tissue, and for providing as output one ormore likelihood scores associated with likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss in a female individual. The method 200 comprisesobtaining S202, using at least one processor, female data associated with a female; and pregnancyloss data associated with a foetus and / or foetal tissue. The method comprises performing S204, usingthe at least one processor, a training. Performing S204 the training comprises generating S204A,using the at least one processor and a machine-learning model, such as a neural network, risk databased on the female data, and the pregnancy loss data. Performing S204 the training comprisesobtaining S204B, using the at least one processor, training data. Performing S204 the trainingcomprises determining S204C, using the at least one processor and one or more likelihood functions,one or more likelihood parameters based on the female data, the pregnancy loss data, and the trainingdata. Performing S204 the training comprises training S204D, using the at least one processor, themachine learning model / neural network based on the one or more likelihood parameters.Fig. 4 illustrates an example implementation of a machine learning model according to the presentdisclosure. The machine learning model 10A takes as input female data, and pregnancy loss dataPLD, the female data FD optionally including one or more of FBD, FBSD, and FQD. The machinelearning model 10A provides as output a first likelihood score RS_1, such as one or both of RS_1_1indicative of a likelihood of occurrence of an ovulation during a period of 6 weeks after the detectionof a pregnancy loss, and RS_1_2 indicative of an absence of likelihood of occurrence of an ovulationduring a period of 6 weeks after the detection of a pregnancy loss.DEFINITIONSIndividualIn the present context the term "individual" relates to the mother. The individual is preferably a personin experiencing miscarriage / pregnancy loss. Though the present examples describe themeasurements in a maternal sample, the present disclosure can be adapted to measurements directon the foetus.Method of conceptionThe term "method of conception" in the present disclosure may encompass natural methods ofconception, such as mating; or artificial methods of conception, such as IUI (Intrauterine insemination)or IVF (in vitro fertilization). IVF may consist of ICSI (Intra cytoplasmic sperm infection), donor sperm,or donor oocyte.Diagnosis of the pregnancy lossThe term "diagnosis of the pregnancy loss" in the present disclosure may encompass e.g. missedabortion, blighted ovum, or spontaneous ongoing pregnancy loss.Last menstruationThe time period since the last menstruation is calculated from the first day of the last menstrual period.Cycle durationThe term "cycle duration" in the present disclosure may encompass short cycle (<21 days), regularcycle (21-35 days), long cycle (>35 days), no cycle, or unknown cycle.IsoformsTwo isoenzymes are present in humans:GOT1 / CAST, the cytosolic isoenzyme derives mainly from red blood cells and heart.GOT2 / mAST, the mitochondrial isoenzyme is present predominantly in liver.These isoenzymes have evolved from a common ancestral AST via gene duplication, and they sharea sequence homology of approximately 45%. In the Examples below, both isoforms are measured viathe Siemens Atellica technology, but any means for identifying the ratio can be used including meansthat can differentiate between the two isoforms.SampleIn the Examples below, blood samples were collected at various times like for example either prior tothe removal of pregnancy tissue. The plasma component was separated through a dual-stagecentrifugation process and then preserved at low temperatures for subsequent analysis.cfDNA can be extracted from the plasma and added to library construction via PCR and sequencedand bioinformatics processing was performed as described by Hartwig et al (Schlaikjær Hartwig, T. etal. Cell-free foetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): aprospective cohort study. Lancet Lond. Engl. 401, 762-771 (2023).Briefly, reads across lanes were merged into one fastq file. Reads were aligned using bowtie2. Alignedreads with a quality < 1 were removed, and the remaining reads were sorted and deduplicated usingsamtools. The foetal fraction was determined using SeqFF (Kim, S. K. et al. Determination of foetalDNA fraction from the plasma of pregnant women using sequence read counts. Prenat. Diagn. 35,810-815 (2015)) by which small differences of sequencing behaviour for maternal and foetal cell-freeDNA and read counts were used for estimation.Maternal whole blood was collected in EDTA tubes or serum clot activator tubes and separated intoplasma and serum. The serum samples were used for biochemical analysis at the Department ofClinical Biochemistry, at Copenhagen University Hospital Herlev, measured using standard assays,such as ẞ-hCG (sandwich immunoassay by Siemens Atellica IM Analyzer), creatinine and cholesterol(Enzymatic reaction and absorbance by Siemens Atellica CH 930).In the present context, the term "sample" relates to any liquid or solid sample collected from anindividual to be analysed. Preferably, the sample is liquefied at the time of assaying.In one or more exemplary embodiments, the sample is selected from the group consisting of blood,serum, plasma, urine, faeces, rectal swab, rectal microbiome, vaginal microbiome, vaginal discharge,vaginal secretion, cervical discharge, cervical swab, vaginal swab, amniotic fluid and other secretedfluids from the vagina and / or uterus.In one or more exemplary embodiments, the sample is selected from the group consisting of blood,urine, faeces, rectal swab, rectal microbiome, vaginal microbiome, and vaginal discharge samples.In one or more exemplary embodiments, the sample is selected from the group consisting of blood,serum, plasma, and urine.The sample taken may be dried for transport and future analysis. Thus, the method of the presentdisclosure includes the analysis of both liquid and dried samples.To increase detection efficiency, the sample data and the gestational age may be compared to a setof reference data to determine whether the individual is at increased risk of for example pregnancyloss or carrying a foetus with e.g. Down syndrome.BloodIn one or more exemplary embodiments, the sample is a blood sample.In one or more exemplary embodiments, the blood sample is separated into plasma and serumsamples.PlasmaIn one or more exemplary embodiments, the sample is a plasma sample.SerumIn one or more exemplary embodiments, the sample is a serum sample.UrineIn one or more exemplary embodiments, the sample is a urine sample.FaecesIn one or more exemplary embodiments, the sample is a faeces sample.Rectal microbiomeIn one or more exemplary embodiments, the sample is a rectal microbiome sample.Rectal swabIn one or more exemplary embodiments, the sample is a rectal swab sample.Vaginal dischargeIn one or more exemplary embodiments, the sample is a vaginal discharge sample.Vaginal microbiomeIn one or more exemplary embodiments, the sample is a vaginal microbiome sample.Vaginal secretionIn one or more exemplary embodiments, the sample is a vaginal secretion sample.Cervical dischargeIn one or more exemplary embodiments, the sample is a cervical discharge sample.Cervical swabIn one or more exemplary embodiments, the sample is a cervical swab sample.Vaginal swabIn one or more exemplary embodiments, the sample is a vaginal swab sample.Amniotic fluidIn one or more exemplary embodiments, the sample is an amniotic fluid sample.Other secreted fluids from the vagina and / or uterusIn one or more exemplary embodiments, the sample is a secreted fluid from the vagina and / or uterus.Maternal and / or a paternal sample.In one or more exemplary embodiments, the sample is a maternal and / or a paternal sample.Timing of the sample takingThe sample can be preferably taken at the time of the detection of the pregnancy loss, or during afollow-up visit.In one or more exemplary embodiments, the sample is taken within 96 hours after detection of thepregnancy loss, such as but not limited to within 90 hours, 84 hours, 78 hours, 72 hours, 66 hours, 60hours, 54 hours, 48 hours, 42 hours, 36 hours, 30 hours, 24 hours, 18 hours, 12 hours, 11, hours, 10hours, 9 hours, 8 hours, 7, hours, 6 hours, 5 hours, 4 hours, 3 hours, 2 hours or 1 hour after of apregnancy loss.Thus, the sample can be taken at any time after the detection of the pregnancy loss, such as 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,87, 88, 89, 90, 91, 92, 93, 94, 95, or 96 hours after the detection of the pregnancy loss.In one or more exemplary embodiments, another sample can be taken during a follow-up visit that canhappen anytime during a period of 10 weeks, such as 4 to 8 weeks, such as 6 to 8 weeks after thedetection of the pregnancy loss, preferably 6 to 8 weeks, preferably 6 or 8 weeks after detection of thepregnancy loss.Thus, the follow-up sample can be taken at any time after the detection of the pregnancy loss, suchas within 1 to 10 weeks after the detection of the pregnancy loss, such as 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,69, or 70 days after the detection of the pregnancy loss; preferably within 8 weeks after the detectionof the pregnancy loss, such as within 6 to 8 weeks, such as 6 weeks or 8 weeks after the detection ofthe pregnancy loss.BiomarkersBeta Human chorionic gonadotropin (ẞhCG)ẞhCG is a heterodimeric molecule, where the alpha subunit is structurally equivalent to that ofluteinizing hormone (LH), follicle-stimulating hormone (FSH) and thyroid-stimulating hormone (TSH)and the beta subunit is unique for ẞhCG.Measurement of serum-ẞhCGBlood was drawn in 9 mL EDTA tubes (Greiner Bio-One, Austria) and centrifuged by the Blood Bankat Hvidovre Hospital, Denmark. Hereafter serum was pipetted into 2 x 1 mL matrix tubes (FisherScientific, Denmark) per participant and frozen to -80 °C until further analysis. Analysis of serum wasperformed at the Department of Clinical Biochemistry at Herlev Hospital, Denmark through a SiemensAtellica Solution Immunoassay & Clinical Chemistry Analyzer in the period January 2023 to February2024. The Atellica IM Total hCG assay implemented a 2-site sandwich immunoassay which utilizeddirect chemiluminometric technology. This involved two specific antibodies, goat polyclonal anti-hCGantibody labeled with acridinium ester and mouse monoclonal anti-hCG bound to paramagneticparticles. The assay detected both free ẞ-subunit and intactẞhCG with a measuring interval between2-0-200,000-0 IU / L. According to the laboratory specific reference interval, ẞhCG ≥ 5IU / L wereindicative of a pregnancy.Circulating cell free foetal DNA (cffDNA)Cell-free DNA are short fragments of DNA released into the bloodstream through a natural process ofcell death. During pregnancy, maternal blood contains cell-free DNA (cfDNA), both from her owntissue, and from the foetus via the placenta (cffDNA).Approximately 2-20% of total cfDNA in maternal blood is foetal, of placental origin. cfDNA derived fromthe placenta can be detected as early as 4+ weeks gestation and is undetectable within hourspostpartum. A non-invasive prenatal test (NIPT) analyses cfDNA from a maternal blood sample toscreen for common chromosomal conditions in the foetus.The percentage of total cell-free DNA (cfDNA) in a sample derived from the foetus or placenta (cffDNA)is called the foetal fraction—which can affect the ability of NIPT to detect foetal aneuploidy. Whenconsidering various cfDNA and / or cffDNA NIPT technologies, it's important to understand how foetalfraction is used and how it can affect test results.The skilled addressee knows that foetal DNA fraction from the plasma of pregnant women can bedetermined using sequence read counts. Thus, the cell-free foetal DNA was determined from the readcounts of the total cfDNA, using a previously established algorithm, SeqFF. Read counts across thegenome are binned and used as input features to a penalized regression model, which then predictsthe foetal fraction.Aspartate transaminase (AST)Aspartate transaminase (AST) or aspartate aminotransferase, also known as AspAT / ASAT / AAT or(serum) glutamic oxaloacetic transaminase (GOT, SGOT), is a pyridoxal phosphate (PLP)-dependenttransaminase enzyme (EC 2.6.1.1).AST catalyses the reversible transfer of an a-amino group between aspartate and glutamate and, assuch, is an important enzyme in amino acid metabolism. AST is found in the liver, heart, skeletalmuscle, kidneys, brain, red blood cells and gall bladder. Serum AST level, serum ALT (alaninetransaminase) level, and their ratio (AST / ALT ratio) are commonly measured clinically as biomarkersfor liver health. The tests are part of blood panels.AST is similar to alanine transaminase (ALT) in that both enzymes are associated with liverparenchymal cells. The difference is that ALT is found predominantly in the liver, with clinicallynegligible quantities found in the kidneys, heart, and skeletal muscle, while AST is found in the liver,heart (cardiac muscle), skeletal muscle, kidneys, brain, and red blood cells.Alanine aminotransferase (ALT)Alanine transaminase (ALT) is a transaminase enzyme (EC 2.6.1.2). It is also called alanineaminotransferase (ALT or ALAT) and was formerly called serum glutamate-pyruvate transaminase orserum glutamic-pyruvic transaminase (SGPT).ALT is found in plasma and in various body tissues but is most common in the liver. It catalyses thetwo parts of the alanine cycle. Serum ALT level, serum AST (aspartate transaminase) level, and theirratio (AST / ALT ratio) are routinely measured clinically as biomarkers for liver health.When used in diagnostics, AST and ALT are almost always measured in international units / litre (IU / Lor U / L) or ukat. While sources vary on specific reference range values for patients, 0-40 IU / L is thestandard reference range for experimental studies. In a clinical setting, the reference ranges forpregnant women are 16-40 U / L in gestational week 13-40 for AST and 8-36 U / L in gestational week13 to 35 for ALT. Both AST and ALT may be elevated during pregnancy.Fluctuation of ALT levels are normal over the course of the day, and they can also increase in responseto strenuous physical exercise. When elevated ALT levels are found in the blood, the possibleunderlying causes can be further narrowed down by measuring other enzymes.In 2000, the American Association for Clinical Chemistry determined that the appropriate terminologyfor AST and ALT are aspartate aminotransferase and alanine aminotransferase. The termtransaminase is outdated and no longer used.AST / ALT ratioThe AST / ALT ratio or De Ritis ratio is the ratio between the concentrations of the two enzymesaspartate transaminase and alanine aminotransferase in the blood of a human or an animal. TheAST / ALT ratio is measured by conventional analytical methods, such as immunological methodsknown to the art. It is traditionally used as one of several liver function tests, and typically measuredwith a blood test. AST and ALT as determined for this disclosure are analysed at a Siemens AtellicaCH 930 machine by enzyme spectrophotometry but can be determined by any means known to theskilled addressee.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring RNA (cell-free or intracellular), DNA, protein, and / or metabolite levels of the aspartate transaminase and alanineaminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring cell-free RNAlevels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring intracellularRNA levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring DNA levelsof the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring protein levelsof the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring metabolitelevels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.To determine the clinical severity variations of the AST / ALT ratio in the present context, means forevaluating the detectable signal of the AST / ALT ratio measured involves a reference or referencemeans. The reference also makes it possible to consider assay, kit and method variations, handlingvariations and other variations not related directly or indirectly to the AST / ALT ratio.In the context of the present invention, the term "reference" relates to a standard in relation to quantity,quality or type, against which other values or characteristics can be compared, such as e.g. a standardcurve.The reference data presented in the Examples below reflects the maternal blood AST / ALT ratio fromconfirmed intrauterine pregnancy loss before 22 weeks of gestation but could equally be maternalblood AST / ALT ratio from all pregnant women carrying viable foetuses.As will be generally understood by those of skill in the art, methods for screening for foetalabnormalities are processes of decision making by comparison. For any decision-making process,reference values based on individuals having the disease or condition of interest and / or individualsnot having the disease or condition of interest are needed.In the present disclosure, the reference values are the maternal blood level of the measured markeror markers, for example, the AST / ALT ratio, in both pregnant women carrying for example geneticallyabnormal foetuses, pregnant women carrying viable foetuses, and women having a pregnancy loss.A set of reference data is established by collecting the reference values for a number of samples. Aswill be obvious to those of skill in the art, the set of reference data will improve by including increasingnumbers of reference values.In one or more exemplary embodiments, the reference means is an internal reference means and / oran external reference means.In the present context the term "internal reference means" relates to a reference which is not handledby the user directly for each determination, but which is incorporated into a device for the determinationof the biomarker of interest, like for example the AST / ALT ratio, whereby only the 'final result' or the'final measurement' is presented. The terms the "final result" or the "final measurement" relates to theresult presented to the user when the reference value has been considered. In one or more exemplaryembodiments, the internal reference means is provided in connection to a device used for thedetermination of the biomarker in question.In the present context, the term "external reference means" relates to a reference which is handleddirectly by the user to determine the biomarker, before obtaining the 'final result' or the 'finalmeasurement'. In one or more exemplary embodiments, the external reference means are selectedfrom the group consisting of a table, a diagram and similar reference means where the user cancompare the measured signal to selected reference means. The external reference means relates toa reference used as a calibration, value reference, information object, etc. for the AST / ALT ratio andwhich has been excluded from the device used.In one or more exemplary embodiments, the reference level / predetermined value is indicative of anormal physiological condition of said individual.In one or more exemplary embodiments, the reference level / predetermined value is indicative of acondition is a foetal abnormality.Although any of the known analytical methods for measuring the AST / ALT ratio will function in thepresent invention, as obvious to one skilled in the art, the analytical method used for the AST / ALTratio must be the same method used to generate the reference data for the AST / ALT ratio. If a newanalytical method is used for the AST / ALT ratio, a new set of reference data, based on data developedwith the method, must be generated.In one or more exemplary embodiments, methods described herein is combined with levels of furtherbiomarkers selected from the group consisting of gestational age, beta-Human ChorionicGonadotropin (β-hCG), Pregnancy-associated plasma protein A (PAPP-A), Disintegrin andmetalloproteinase domain-containing protein 12 (ADAM12), ISM2, TFPI2, ERVV-1, LYPD3,EBIB3_IL27, CSH1, GDF15, ANGPT2, FBN2, PRG2, INSL4, LAIR2, TSHB, C1QTNF6, LHB,SIGLEC6, MMP12, Placental Growth Factor (PLGF1), Alanine Transaminase (ALT),Aspartattransaminase (AST), High-density lipoprotein cholesterol (HDL), Low-density lipoproteincholesterol (LDL), Apolipoprotein B, Uric Acid, Transferrin, Bilirubin, Creatin kinase and Lipoprotein Aalpha feto-protein (AFP), unconjugated oestrol (uE3), human chorionic gonadotrophin (hCG), freealpha sub-unit of hCG (free a-hCG), free beta sub-unit of hCG (free ẞ-hCG), beta-core hCG,hyperglycosylated hCG (ITG), placental growth hormonre (PGH), inhibin, preferably dimeric inhibin-A(inhibin A), pregnancy-associated plasma protein A (PAPP-A), Complexes of PAPP-A with proMBP(proform of major basic protein), ProMBP, ProMBP complexes with angiotensinogen and / orcomplement factors and split products Schwangerschaftsprotein 1(SP1), Cancer antigen 125(CA125),Prostate specific antigen (PSA), Leukocyte enzymes, foetal DNA, foetal RNA, foetal cells, stem cells,oestradiol, ultrasound markers, nuchal translucency, femur length, absence of nasal bone,hyperechogenic bowel, echogenic foci in the heart, choroids plexus cysts, hydronephrosis, foetalmalformations, steroids, peptides, chemokines, interleukins (e.g. IL-6, IL-4, IL-1), tumour necrosisfactor, transforming growth factor alpha and beta, acute phase reactants, C-reactive protein,Fibronectin, maternal or foetal single nucleotide polymorphisms, e.g. promoter region polymorphismsin TNFbeta and mannan- binding lectin, complement components, HLA-G, and / or HLA molecules inthe sample.Thus, in one embodiment, the present disclosure relates to a method as described herein, whereinthe AST / ALT ratio directly and / or use the AST / ALT ratio estimating the amount of circulating cell freefoetal DNA (cffDNA) amount is combined with values from at least one marker selected from the groupdefined above.In one or more exemplary embodiments, the AST / ALT ratio is combined with levels of furtherbiomarkers selected from the group consisting of gestational age, beta-Human ChorionicGonadotropin (β-hCG), Pregnancy-associated plasma protein A (PAPP-A), vascular endothelialgrowth factor (VEGF), soluble fms-like tyrosine kinase-1 (sFlt-1), Alpha-Fetoprotein (AFP), Disintegrinand metalloproteinase domain-containing protein 12 (ADAM12), ISM2, TFPI2, ERVV-1, LYPD3,EBIB3_IL27, CSH1, GDF15, ANGPT2, FBN2, PRG2, INSL4, LAIR2, TSHB, C1QTNF6, LHB,SIGLEC6, MMP12, Placental Growth Factor (PLGF1), Alanine Transaminase (ALT),Aspartattransaminase (ASAT), High-density lipoprotein cholesterol (HDL), Low-density lipoproteincholesterol (LDL), Apolipoprotein B, Uric Acid, Transferrin, Bilirubin, Creatin kinase and Lipoprotein A.in the sample.In one or more exemplary embodiments, the AST / ALT ratio is combined with levels of furtherbiomarkers selected from the group consisting of gestational age, β-hCG, Bilirubin, Creatin kinase andLipoprotein A.C-Reactive Protein (CRP)In one or more exemplary embodiments, the ẞhCG level is combined with the CRP level in the sample.Thyroid Peroxidase AntibodyIn one or more exemplary embodiments, the ẞhCG level is combined with the Thyroid PeroxidaseAntibody level in the sample.Thyroglobulin AntibodyIn one or more exemplary embodiments, the ẞhCG level is combined with the Thyroglobulin Antibodylevel in the sampleGamma-Glutamyl Transferase (GGT)In one or more exemplary embodiments, the ẞhCG level is combined with the GGT level in the sampleTriglyceridesIn one or more exemplary embodiments, the ẞhCG level is combined with the Triglycerides level inthe sampleLDL CholesterolIn one or more exemplary embodiments, the ẞhCG level is combined with the LDL Cholesterol levelin the sampleApolipoprotein BIn one or more exemplary embodiments, the ẞhCG level is combined with the Apolipoprotein B levelin the sampleCreatinineIn one or more exemplary embodiments, the ẞhCG level is combined with the Creatinine level in thesampleThyroid-Stimulating Hormone (TSH)In one or more exemplary embodiments, the ẞhCG level is combined with the TSH level in the sampleUric AcidIn one or more exemplary embodiments, the ẞhCG level is combined with uric acid level in the sampleRheumatoid FactorIn one or more exemplary embodiments, the ẞhCG level is combined with the Rheumatoid Factor levelin the sampleCreatine KinaseIn one or more exemplary embodiments, the ẞhCG level is combined with the Creatine Kinase levelin the sampleTransferrinIn one or more exemplary embodiments, the ẞhCG level is combined with the Transferrin level in thesampleIronIn one or more exemplary embodiments, the ẞhCG level is combined with the iron level in the sampleAlbuminIn one or more exemplary embodiments, the ẞhCG level is combined with the Albumin level in thesampleBilirubinIn one or more exemplary embodiments, the ẞhCG level is combined with the Bilirubin level in thesampleUreaIn one or more exemplary embodiments, the ẞhCG level is combined with the urea level in the sampleHDL CholesterolIn one or more exemplary embodiments, the ẞhCG level is combined with the HDL Cholesterol levelin the sampleTG Rich Lipoprotein Cholesterol (TRL-C)In one or more exemplary embodiments, the ẞhCG level is combined with the TRL-C level in thesampleLipoprotein(a)In one or more exemplary embodiments, the ẞhCG level is combined with the Lipoprotein(a) level inthe sampleTriacylglycerol LipaseIn one or more exemplary embodiments, the ẞhCG level is combined with the Triacylglycerol Lipaselevel in the sampleLactate Dehydrogenase (LDH)In one or more exemplary embodiments, the ẞhCG level is combined with the LDH level in the sampleStatistical analysisWe evaluated the predictiveness of using collected clinical and biochemistry data. Due to the extremeskew of the outcome variables and many censored values, i.e. below the detection limit 2 IU / L weused a Bayesian ordinal regression model, assuming proportional odds and a logistic link function(ref). The parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling,as implemented in Stan. The sampler was run for 4,000 iterations (2,000 warmup iterations), withdefault settings for the HMC sampler. Convergence was assessed by inspection of R-hat values (allR-hat < 1.01), treedepth (treedepth not exceeding 10 post warm-up), and divergences (nodivergences).We included clinical and biochemistry data, as defined in the prior paragraphs. Variables with a highskew were log2 transformed (see Table 1). The transformation of variables was done irrespective ofthe association with the outcome to avoid overfitting.To identify the most influential predictors, we employed a backward feature selection procedure witha bootstrap-resampling approach to enhance the robustness of the selected variables. Specifically,we generated 500 bootstrap samples from the original dataset and fit an ordinal regression model toeach sample. Backward feature selection was then applied iteratively within each bootstrap sample,removing features until the Akaike Information Criterion (AIC) no longer decreased. At each iteration,the feature resulting in the largest AIC reduction was eliminated. It would be too computationallyexpensive to use the Bayesian model, and we instead used a maximum likelihood ordinal regressionmodel. The final subset of predictors was determined by selecting those features that were retainedin at least 75% of the bootstrap samples.Only data from the Copenhagen University Hospital Hvidovre was used for model fitting and featureselection. The fitted models, and the comparison of the full feature set and reduced feature set, wascompared using the ROC-AUC and calibration at two thresholds, namely ẞhCG ≥ 3 and ẞhCG ≥ 5.The first threshold represents the low bound for what would be expected in a pre-menopausal non-pregnancy woman, and the second threshold is indicative of a pregnancy. Calibration was evaluatedby inspection of calibration curves, and we report the slope and intercept as quantitative measures ofcalibration, along with the 95% confidence interval. Accuracy, sensitivity, specificity, and the F1 scorewas evaluated by thresholding the predicted probabilities into "yes" or "no", based on the prevalencein the training cohort (Copenhagen University Hospital Hvidovre). The F1 score is the harmonic meanof the precision and recall.The models were externally validated using data collected from the Copenhagen University HospitalHerlev and North Zealand Hospital.Missing values were imputed as the mode. We report the 95% Bayesian Credible Interval for allestimates, unless elsewhere noted.EXAMPLESClinical dataClinical data was collected from the electronic health records, questionnaires, and through interviewswith clinical staff. For this project, we included 13 variables, described in Supplementary Table 1. Thevariables consist of both data describing the maternal, fetal, and paternal characteristics.Biochemistry dataBiochemistry data was generated using the serum sample collected at the inclusion to theCopenhagen Pregnancy Loss Study. More than 30 biomarkers were measured, covering the majororgan systems and placental / fetal growth. The variables are described in the file "Features_new.xlsx".Statistical AnalysisWe evaluated the predictiveness of using collected clinical and biochemistry data. Due to the extremeskew of the outcome variables and many censored values, i.e. below the detection limit 2 IU / L weused a Bayesian ordinal regression model, assuming proportional odds and a logistic link function(ref). The parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling,as implemented in Stan. The sampler was run for 4,000 iterations (2,000 warmup iterations), withdefault settings for the HMC sampler. Convergence was assessed by inspection of R-hat values (allR-hat < 1.01), treedepth (treedepth not exceeding 10 post warm-up), and divergences (nodivergences).We included clinical and biochemistry data, as defined in the prior paragraphs. Variables with a highskew were log2 transformed (see Table 1). The transformation of variables was done irrespective ofthe association with the outcome to avoid overfitting.To identify the most influential predictors, we employed a backward feature selection procedure witha bootstrap-resampling approach to enhance the robustness of the selected variables. Specifically,we generated 500 bootstrap samples from the original dataset and fit an ordinal regression model toeach sample. Backward feature selection was then applied iteratively within each bootstrap sample,removing features until the Akaike Information Criterion (AIC) no longer decreased. At each iteration,the feature resulting in the largest AIC reduction was eliminated. It would be too computationallyexpensive to use the Bayesian model, and we instead used a maximum likelihood ordinal regressionmodel. The final subset of predictors was determined by selecting those features that were retainedin at least 75% of the bootstrap samples.Only data from the Copenhagen University Hospital Hvidovre was used for model fitting and featureselection. The fitted models, and the comparison of the full feature set and reduced feature set, wascompared using the ROC-AUC and calibration at two thresholds, namely ẞhCG ≥ 3 and ẞhCG ≥ 5.The first threshold represents the low bound for what would be expected in a pre-menopausal non-pregnancy woman, and the second threshold is indicative of a pregnancy. Calibration was evaluatedby inspection of calibration curves, and we report the slope and intercept as quantitative measures ofcalibration, along with the 95% confidence interval. Accuracy, sensitivity, specificity, and the F1 scorewas evaluated by thresholding the predicted probabilities into "yes" or "no", based on the prevalencein the training cohort (Copenhagen University Hospital Hvidovre). The F1 score is the harmonic meanof the precision and recall.The models were externally validated using data collected from the Copenhagen University HospitalHerlev and North Zealand Hospital.Missing values were imputed as the mode. We report the 95% Bayesian Credible Interval for allestimates, unless elsewhere noted.RESULTSThe multivariable regression model follows that described in Juul et al. When doing feature selection,we identified seven variables robustly predicting the ẞhCG value at follow-up, namely: ẞhCG atpregnancy loss, days since pregnancy loss, gestational age at pregnancy loss (calculated from lastmenstrual period), prior number of live births, creatine kinase, the ASAT / ALAT ratio (De Ritis ratio),and the treatment choice (Figure 2). The effect sizes, summarized as the odds-ratios, are shown inFigure effects.png, and in the table effects.xlsxWe evaluated the model's capability to predict elevated ẞhCG levels at two levels, namely 3 IU / L and5 IU / L. We observed that the models utilizing all variables, and the model that only utilized the sevenvariables, had no difference in performance (Figure 8). When evaluating it on two external data setsfrom CUH Herlev and CUH North Zealand, respectively, the performance replicated nicely. Calibrationdid decrease slightly (Figure 9 + Figure 10).The accuracy, sensitivity, specificity, and F1 score were highly concordant between all data sets, andusing only the seven most important features did not affect predictive capability (Table 1 + Table 2).This indicates that the model generalize well and can be applied to new patient populations, usingonly the seven proposed variables.We present the findings as a nomogram, which can be efficiently used for summarizing the risk ofexcessive ẞhCG levels post pregnancy loss (Figure 11). Following are three examples of using theinvention in practice.ẞhCG cutoff values are also used for determining best treatment yielding the highest probability ofhaving a ẞhCG < 3 IU / L at follow-up (see figure 13).These data show that medical treatment is preferred over surgical treatment, if the ẞhCG levels are <58560 IU / L.Conversely, medical treatment should be favored over ẞhCG values are < 5572 IU / L.These values were determined using data strictly from one site, namely Hvidovre Hospital.Two external data sets were further used to confirm the good replication of these findings (see Table6). The conclusion is that these findings replicate well to external data, with acceptable accuracy,sensitivity, and specificity.Example 1:Patient prognosis. The "average patient" (defined in Table 3) has a 38.8% chance (33.6%; 43.9% 95%Bayesian Credible Interval) of a ẞhCG >= 5 IU / L, after 6 weeks. This can be calculated directly usingthe nomogram. Furthermore, a forecasting curve can be created by varying the number of weeks sincepregnancy loss (Figure 5). The "average patient" can be replaced with the exact patient characteristicsto provide an individualized prediction and curve.Example 2:Choice of treatment. By varying the choice of treatment in the nomogram, different possibilities can beconsidered. For example, for the "average patient":1.Surgical treatment has a 38.8% chance (33.6%; 43.9% 95% Bayesian Credible Interval) of aẞhCG >= 5 IU / L, after 6 weeks2.Medical treatment has a 28.8% chance (24.0%; 33.6% 95% Bayesian Credible Interval) of aẞhCG >= 5 IU / L, after 6 weeks3.Expectant management has a 37.3% chance (26.9%; 48.0% 95% Bayesian Credible Interval)of a ẞhCG >= 5 IU / L, after 6 weeksConsequently, for the "average patient", medical treatment would enable the fastest return of ovulation.Example 3:Women with elevated ASAT / ALAT ratio are poor metabolizers of ẞhCG, reflected by an increasedodds for each doubling in the ASAT / ALAT Ratio (odds ratio = 1.71, 1.39; 2.16 95% Bayesian CredibleInterval). An example is also given in Figure 7.We identified all women that had been to follow-up within 8 weeks (see table 5 and Figure 12). Here,the threshold refers to a specific ẞhCG value at follow-up, and the probability of having a return ofmenstrual bleeding for the two groups. The two groups are defined as either having a ẞhCG below thethreshold, or above-equal to the threshold.For instance, for the group with ẞhCG >= 3, 73% report return of menstrual bleeding. Conversely, forthe group with ẞhCG < 3, 82% report return of menstrual bleeding. The low and high columns are the95% confidence interval.For instance, for the group with ẞhCG >= 5, 65% report return of menstrual bleeding. Conversely, forthe group with ẞhCG < 5, 81% report return of menstrual bleeding. The low and high columns are the95% confidence intervalConsequently, a reduction in ẞhCG was associated with a 9.5 percentage point increase in theprobability of ovulation within six weeks of the pregnancy loss (ovulation occurs 14 days prior tomenstrual bleeding).Table 1: Performance for predicting ẞhCG >= 3 IU / LDataset HospitalAccuracy sensitivity SpecificityF1<chr><chr><db 1><dbl><dbl> <dbl>Full CUH Hvidovre0.7840.7760.789 0.716Reduced CUH Hvidovre0.7680.7640.769 0.697Full CUH Herlev0.7470.7010.778 0.688Reduced CUH Herlev0.7270.7010.744 0.671Full CUH North Zealand0.7250.8390.607 0.756Reduced CUH North Zealand0.7020.7930.607 0.730<chr>Table 2: Performance for predicting ẞhCG >= 5 IU / LDataset Hospital<chr>Accuracy Sensitivity specificityF1<db1><dbl><dbl> <dbl>Full CUH Hvidovre0.7850.7710.819 0.836Reduced CUH Hvidovre0.7770.7690.797 0.830Full CUH Herlev0.7110.7180.692 0.785Reduced CUH Herlev0.6910.6760.731 0.762Full CUH North Zealand0.8010.8390.545 0.880Reduced CUH North Zealand0.7890.8190.591 0.871Table 3: The "average patient"VariableValueTreatmentSurgical treatmentDays since pregnancy loss44 daysPrior number of live births0ẞhCG21920 IU / LCreatine kinase59 U / LASAT / ALAT Ratio0.86Table 4:NameMedian LowHighDays since pregnancy loss-0.12-0.14-0.11Gestational age, last menstruation (days)0.020.020.03Number of prior live births-0.32-0.49-0.15Medical treatment vs Expectant management-0.39-0.860.1Surgical treatment vs Expectant management0.07-0.390.55HCGBETA at time of pregnancy loss, log2 transformed 0.610.530.69(IU / L)ASAT / ALAT Ratio, log2 transformed (De Ritis ratio)0.550.340.78Creatinkinase, log2 transformed (U / L)-0.21-0.41-0.02Medical vs surgical treatment-0.45-0.69-0.2Table 5:ThresholdProbabilitylowhighBETAHCG >= 30.730.680.77BETAHCG <30.820.770.87BETAHCG >= 40.70.650.75BETAHCG < 40.810.760.84BETAHCG >= 50.650.580.72BETAHCG < 50.810.770.84BETAHCG >= 60.650.570.72BETAHCG <60.790.760.83BETAHCG >= 70.630.540.72BETAHCG <70.790.750.82BETAHCG >= 80.610.510.7BETAHCG <80.790.760.82BETAHCG >= 90.610.50.71BETAHCG < 90.790.750.82BETAHCG >= 100.580.470.69BETAHCG < 100.790.750.82BETAHCG >= 110.60.480.71BETAHCG < 110.780.750.81BETAHCG >= 120.60.470.72BETAHCG < 120.780.750.81BETAHCG >= 130.630.490.76BETAHCG < 130.770.740.8BETAHCG >= 140.60.450.74BETAHCG < 140.780.740.81BETAHCG >= 150.60.440.74BETAHCG < 150.770.740.81BETAHCG >= 160.580.420.72BETAHCG < 160.780.740.81BETAHCG >= 170.630.470.78BETAHCG < 170.770.740.8BETAHCG >= 180.580.410.74BETAHCG < 180.770.740.8BETAHCG >= 190.560.380.73BETAHCG < 190.770.740.8BETAHCG >= 200.560.380.73BETAHCG < 200.770.740.8Table 6MetricHvidovreHerlevNorth ZealandHospitalHospitalHospitalSensitivity0.660.70.53Specificity0.610.620.77Pos Pred Value0.760.750.7Neg Pred Value0.490.570.61Precision0.760.750.7Recall0.660.70.53F10.710.720.61Accuracy0.640.670.65ITEMS1. A method for predicting the likelihood of occurrence of an ovulation during a period of 6 weeksafter the detection of a pregnancy loss in a female individual, said method comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determining one or more likelihood score based on said set of data,c) predicting the likelihood of the occurrence of an ovulation in said female individual during aperiod of 6 weeks after a pregnancy loss based on said one or more likelihood score.2. A computer-implemented method for predicting the likelihood of occurrence of an ovulation duringa period of 6 weeks after the detection of a pregnancy loss in a female individual, comprising atleast:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determining one or more likelihood score based on said set of data,c) predicting the likelihood of the occurrence of an ovulation in said female individual during aperiod of 6 weeks after a pregnancy loss, based on said one or more likelihood score,d) providing an output including a first output associated with the likelihood of the occurrence ofan ovulation during a period of 6 weeks after the detection of a pregnancy loss in said femaleindividual.3. A computer-implemented method for training a machine learning model, such as a neural network,to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / orfoetal tissue and provide as output one or more likelihood scores associated with the likelihood ofoccurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in afemale individual, the method comprising:a) obtaining, using at least one processor, a set of data comprising pregnancy loss dataassociated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least aẞhCG value (IU / L) in said female individual, measured at the time of the determination of thepregnancy loss,b) performing, using the at least one processor, a training comprising:i) generating, using the at least one processor and a machine-learning model, likelihood databased on the set of data,ii) obtaining, using the at least one processor, training data,iii) determining, using the at least one processor and one or more likelihood functions, one ormore likelihood score based on the set of data and the training data; andiv) training, using the at least one processor, the machine learning model based on one ormore likelihood score.4. An electronic device for predicting the likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss in a female individual, the electronic devicecomprising an interface, one or more processors, and a memory, wherein the one of moreprocessors are configured to:a) obtain a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determine one or more likelihood scores based on the set of data, the likelihood score beingindicative of occurrence of an ovulation during a period of 6 weeks after the detection of apregnancy loss in said female, andc) provide an output including a first output associated with the one or more likelihood scores.5. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein:i) a ẞhCG value inferior to 10 IU / L is predictive of a likelihood of the occurrence of anovulation of at least 79%ii) a ẞhCG value inferior or equal to 5 IU / L is predictive of a likelihood of the occurrence of anovulation of at least 81%,iii) a ẞhCG value inferior or equal to 3 IU / L is predictive of a likelihood of the occurrence of anovulation of at least 82%.6. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein the ẞhCG value (IU / L) is obtained via a blood sample of said femaleindividual.7. The method, computer-implemented method, or electronic device according to item 6, wherein theblood sample is taken at the time of detection of the pregnancy loss.8. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein the set of data comprises at least one further foetal data selected fromthe group consisting of:i) the gestational age at pregnancy loss, calculated from the last menstruation of saidindividualii) the method of conception,iii) the diagnosis of the pregnancy loss, andiv) the ẞhCG values at follow-up visit (IU / L),preferably the gestational age at pregnancy loss, calculated from the last menstruation of saidindividual.9. The method, computer-implemented method, or electronic device according to item 8, wherein theẞhCG value (IU / L) at follow-up visit (IU / L) is obtained via a blood sample of said female individualtaken during said follow-up visit.10. The method, computer-implemented method, or electronic device according to item 9, wherein thefollow-up visit occurs during a period of 10 weeks, such as within 4 to 8 weeks, such as within 6to 8 weeks after the detection of the pregnancy loss, such as 6 or 8 weeks after detection of thepregnancy loss.11. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein the set of data further comprises at least one female data selected fromthe group consisting of:i)the type of management of miscarriage (i.e. the type of treatment received after thedetermination of the pregnancy loss, for removing the foetal material),ii)the number of days since pregnancy loss (calculated from the detection / diagnosis ofthe pregnancy loss),iii)the number of prior live births,iv) the creatine kinase level,v)the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritisratio).vi)the age of said female individual (in years),preferably:i)the type of management of miscarriage (i.e. the type of treatment received after thedetermination of the pregnancy loss, for removing the foetal material),ii)the number of days since pregnancy loss,iii)the number of prior live births,iv)the creatine kinase level, and / orv)the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).12. The method, computer-implemented method, or electronic device according to item 11, whereinthe following data are obtained via a blood sample of said female individual.i)the creatine kinase level,ii)the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritisratio),iii)the C-Reactive Protein (CRP) level,iv)the thyroid Peroxidase antibody level,v)the thyroglobulin antibody level,vi)the Gamma-Glutamyl Transferase (GGT) level,vii)the Aspartate Transaminase (ASAT) level,viii)The Alanine Transaminase (ALAT) level,ix)the triglycerides level,x)the LDL Cholesterol level,xi) the apolipoprotein B level,xii)the creatinine level,xiii) the thyroid-Stimulating Hormone (TSH) level,xiv) the uric acid level,xv)the rheumatoid Factor level,xvi) the transferrin level,xvii) the iron level,xviii) the alipoprotein(a) level,xix) the albumin level,xx)the bilirubin level,xxi) the urea level,xxii) the HDL Cholesterol level,xxiii) the TG Rich Lipoprotein Cholesterol (TRL-C) level,xxiv) the lipoprotein(a) level,xxv) the triacylglycerol lipase level, and / orxxvi) the Lactate Dehydrogenase (LDH) level.13. The method, computer-implemented method, or electronic device according to item 12, whereinthe blood sample is taken at the time of detection of the pregnancy loss.14. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein the set of data comprises at least one further foetal data and / or femaledata selected from the group consisting of:i) the type of management of miscarriage (i.e. the type of treatment received after thedetermination of the pregnancy loss, for removing the foetal material),ii) the number of days since pregnancy loss,iii) the gestational age at pregnancy loss, calculated from the last menstruation of saidindividualiv) the number of prior live births,v) the creatine kinase level, and / orvi) the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).15. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein the likelihood score is determined using a non-linear model or a linearmodel, preferably a linear model, such as a Bayesian partial odds ordinal regression model or aBayesian logistic regression model.16. The method, computer-implemented method, or electronic device according to item 15, whereinthe likelihood score is determined using a Bayesian ordinal regression model.17. The method, computer-implemented method, or electronic device according to item 16, whereinthe parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling.18. The method, computer-implemented method, or electronic device according to any one of thepreceding items, wherein the types of management of miscarriage comprise the expectantmanagement of miscarriage, the surgical management of miscarriage, or the medicalmanagement of miscarriage.REFERENCES1Bender Atik R, Christiansen OB, Elson J, et al. ESHRE guideline: recurrent pregnancy loss.Hum Reprod Open 2018; 2018: 1-12.2Macklon NS, Geraedts JPM, Fauser BCJM. Conception to ongoing pregnancy: the 'black box'of early pregnancy loss. Hum Reprod Update 2002; 8: 333-43.3Egerup P, Mikkelsen AP, Kolte AM, et al. Pregnancy loss is associated with type 2 diabetes: anationwide case-control study. Diabetologia 2020; 63: 1521-9.4Westergaard D, Nielsen AP, Mortensen LH, Nielsen HS, Brunak S. Phenome-wide analysis ofshort-and long-run disease incidence following recurrent pregnancy loss using data from a 39-year period. J Am Heart Assoc 2020; 9: 15069.5The Lancet. Miscarriage: worldwide reform of care is needed. The Lancet 2021; 397: 1597.6Tessema GA, Håberg SE, Pereira G, Regan AK, Dunne J, Magnus MC. Interpregnancy intervaland adverse pregnancy outcomes among pregnancies following miscarriages or inducedabortions in Norway (2008-2016): A cohort study. PLoS Med 2022; 19: e1004129.7Donnet ML, Howie PW, Marnie M, Cooper W, Lewis M. Return of ovarian function followingspontaneous abortion. Clinical endocrinology (Oxford) 1991; 46: 63-5.8Hallet RL. Cyclic ovarian function following spontaneous abortions. Am J Obstet Gynecol 1954;67:52-5.9Heffner LJ. The reproductive system at a glance, Fourth edi. Chichester, West Sussex: JohnWiley & Sons, Inc., 2014.10Cole LA. HCG, the wonder of today's science. Reproductive Biology and Endocrinology 2012;10:1-18.11STEIER JA, BERGSJOS P, MYKING OL. Human Chorionic Gonadotropin in Maternal PlasmaAfter Induced Abortion, Spontaneous Abortion, and Removed Ectopic Pregnancy. TheAmerican College of Obstetricians and Gynecologists 1984; 64: 391-4.12Rørbye C, Nørgaard M, Nilas L. Prediction of late failure after medical abortion from serial b-hCG measurements and ultrasonography. DOI:10.1093 / humrep / deh041.13Serdinšek T, Reljič M, Kovač V. Medical management of first trimester missed miscarriage: theefficacy and complication rate. J Obstet Gynaecol (Lahore) 2019; 39: 647-51.14Petersen SG, Perkins AR, Gibbons KS, Bertolone JI, Mahomed K. Utility of ẞhCG monitoringin the follow-up of medical management of miscarriage. Aust N Z J Obstet Gynaecol 2017; 57:358-65.15Braunstein GD. False-positive serum human chorionic gonadotropin results: Causes,characteristics, and recognition. Am J Obstet Gynecol 2002; 187: 217-24.16Gnoth C, Johnson S. Strips of hope: Accuracy of home pregnancy tests and new developments.Geburtshilfe Frauenheilkd 2014; 74: 661-9.17Melmed S, Kleinberg D, Ho K. Pituitary Physiology and Diagnostic Evaluation. WilliamsTextbook of Endocrinology, Twelfth Edition 2011; : 175-228.18Schwartz MW, Seeley RJ, Zeltser LM, et al. Obesity Pathogenesis: An Endocrine SocietyScientific Statement. Endocr Rev 2017; 38: 267.19Alpert MA, Hashimi MW. Obesity and the Heart. Am J Med Sci 1993; 306: 117-23.20Nwabuobi C, Arlier S, Schatz F, Guzeloglu-Kayisli O, Lockwood CJ, Kayisli UA. hCG: BiologicalFunctions and Clinical Applications. Int J Mol Sci 2017; 18. DOI:10.3390 / IJMS18102037.21Nisula BC, Blithe DL, Akar A, Lefort G, Wehmann RE. Metabolic fate of humanchoriogonadotropin. J Steroid Biochem 1989; 33: 733-7.22Piantanida E, Ippolito S, Gallo D, et al. The interplay between thyroid and liver: implications forclinical practice. J Endocrinol Invest 2020; 43: 885-99.23Ticconi C, Giuliani E, Veglia M, Pietropolli A, Piccione E, Di Simone N. Thyroid autoimmunityand recurrent miscarriage. Am J Reprod Immunol 2011; 66: 452-9.

Claims

CLAIMS1. A method for predicting the likelihood of occurrence of an ovulation during a period of 6 weeksafter the detection of a pregnancy loss in a female individual, said method comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determining one or more likelihood score based on said set of data,c) predicting the likelihood of the occurrence of an ovulation in said female individual during aperiod of 6 weeks after a pregnancy loss based on said one or more likelihood score.

2. A computer-implemented method for predicting the likelihood of occurrence of an ovulation duringa period of 6 weeks after the detection of a pregnancy loss in a female individual, comprising atleast:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determining one or more likelihood score based on said set of data,c) predicting the likelihood of the occurrence of an ovulation in said female individual during aperiod of 6 weeks after a pregnancy loss, based on said one or more likelihood score,d) providing an output including a first output associated with the likelihood of the occurrence ofan ovulation during a period of 6 weeks after the detection of a pregnancy loss in said femaleindividual.

3. A computer-implemented method for training a machine learning model, such as a neural network,to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / orfoetal tissue and provide as output one or more likelihood scores associated with the likelihood ofoccurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in afemale individual, the method comprising:a) obtaining, using at least one processor, a set of data comprising pregnancy loss dataassociated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least aẞhCG value (IU / L) in said female individual, measured at the time of the determination of thepregnancy loss,b) performing, using the at least one processor, a training comprising:i) generating, using the at least one processor and a machine-learning model, likelihood databased on the set of data,ii) obtaining, using the at least one processor, training data,iii) determining, using the at least one processor and one or more likelihood functions, one ormore likelihood score based on the set of data and the training data; andiv) training, using the at least one processor, the machine learning model based on one ormore likelihood score.

4. An electronic device for predicting the likelihood of occurrence of an ovulation during a period of 6weeks after the detection of a pregnancy loss in a female individual, the electronic devicecomprising an interface, one or more processors, and a memory, wherein the one of moreprocessors are configured to:a) obtain a set of data comprising pregnancy loss data associated with a foetus and / or foetaltissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said femaleindividual, measured at the time of the determination of the pregnancy loss,b) determine one or more likelihood scores based on the set of data, the likelihood score beingindicative of occurrence of an ovulation during a period of 6 weeks after the detection of apregnancy loss in said female, andc) provide an output including a first output associated with the one or more likelihood scores.

5. The method, computer-implemented method, or electronic device according to any one of thepreceding claims, wherein:i) a ẞhCG value inferior to 10 IU / L is predictive of a likelihood of the occurrence of anovulation of at least 79%ii) a ẞhCG value inferior or equal to 5 IU / L is predictive of a likelihood of the occurrence of anovulation of at least 81%,iii) a ẞhCG value inferior or equal to 3 IU / L is predictive of a likelihood of the occurrence of anovulation of at least 82%.

6. The method, computer-implemented method, or electronic device according to any one of thepreceding claims, wherein the ẞhCG value (IU / L) is obtained via a blood sample of said femaleindividual.

7. The method, computer-implemented method, or electronic device according to claim 6, whereinthe blood sample is taken at the time of detection of the pregnancy loss.

8. The method, computer-implemented method, or electronic device according to any one of thepreceding claims, wherein the set of data comprises at least one further foetal data selected fromthe group consisting of:i) the gestational age at pregnancy loss, calculated from the last menstruation of saidindividualii) the method of conception,iii) the diagnosis of the pregnancy loss, andiv) the ẞhCG values at follow-up visit (IU / L),preferably the gestational age at pregnancy loss, calculated from the last menstruation of saidindividual.

9. The method, computer-implemented method, or electronic device according to claim 8, whereinthe ẞhCG value (IU / L) at follow-up visit (IU / L) is obtained via a blood sample of said femaleindividual taken during said follow-up visit.

10. The method, computer-implemented method, or electronic device according to claim 9, whereinthe follow-up visit occurs during a period of 10 weeks, such as within 4 to 8 weeks, such as within6 to 8 weeks after the detection of the pregnancy loss, such as 6 or 8 weeks after detection of thepregnancy loss..

11. The method, computer-implemented method, or electronic device according to any one of thepreceding claims, wherein the set of data further comprises at least one female data selected fromthe group consisting of:i)the type of management of miscarriage (i.e. the type of treatment received after thedetermination of the pregnancy loss, for removing the foetal material),ii)the number of days since pregnancy loss,iii)the number of prior live births,iv) the creatine kinase level,v)the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritisratio).vi)the age of said female individual (in years),vii)the body mass index (BMI) of said female individual (kg.m2),viii)the presence or absence of vaginal bleeding at inclusion,ix)the number of prior pregnancy loss,x)the cycle duration prior to the pregnancy,xi)the C-Reactive Protein (CRP) level,xii)the thyroid Peroxidase antibody level,xiii)the thyroglobulin antibody level,xiv)the Gamma-Glutamyl Transferase (GGT) level,xv)the Aspartate Transaminase (ASAT) level,xvi) The Alanine Transaminase (ALAT) level,xvii) the triglycerides level,xviii) the LDL Cholesterol level,xix) the apolipoprotein B level,xx)the creatinine level,xxi) the thyroid-Stimulating Hormone (TSH) level,xxii) the uric acid level,xxiii) the rheumatoid Factor level,xxiv) the transferrin level,xxv) the iron level,xxvi) the alipoprotein(a) level,xxvii) the albumin level,xxviii) the bilirubin level,xxix) the urea level,xxx) the HDL Cholesterol level,xxxi) the TG Rich Lipoprotein Cholesterol (TRL-C) level,xxxii) the lipoprotein(a) level,xxxiii) the triacylglycerol lipase level, and / orxxxiv) the Lactate Dehydrogenase (LDH) level,preferably:i)the type of management of miscarriage (i.e. the type of treatment received after thedetermination of the pregnancy loss, for removing the foetal material),ii)the number of days since pregnancy loss,iii)the number of prior live births,iv) the creatine kinase level, and / orv)the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).

12. The method, computer-implemented method, or electronic device according to any one of thepreceding claims, wherein the set of data comprises at least one further foetal data and / or femaledata selected from the group consisting of:i) the type of management of miscarriage (i.e. the type of treatment received after thedetermination of the pregnancy loss, for removing the foetal material),ii) the number of days since pregnancy loss,iii) the gestational age at pregnancy loss, calculated from the last menstruation of saidindividualiv) the number of prior live births,v) the creatine kinase level, and / orvi) the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).

13. The method, computer-implemented method, or electronic device according to any one of thepreceding claims, wherein the likelihood score is determined using a non-linear model or a linearmodel, preferably a linear model, such as a Bayesian partial odds ordinal regression model or aBayesian logistic regression model.

14. The method, computer-implemented method, or electronic device according to claim 13, whereinthe likelihood score is determined using a Bayesian ordinal regression model.

15. The method, computer-implemented method, or electronic device according to claim 16, whereinthe parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling.

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