21 results about "Adverse outcomes" patented technology

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The present application relates to the medical technical field, specifically relates to a kind of early postoperative adverse outcome prediction method for craniopharyngioma patient, comprising: extracting preoperative data and label data of patient, store in database;The data in database is preprocessed, is randomly divided into training set and verification set according to preset proportion, and the training set is handled using the minority class oversampling technique, obtain the training set after processing;From the training set after processing, the most predictive value feature subset is identified and screened;Predictive model is constructed;The predictive model is evaluated and compared, and the best model is selected as the final deployment model;Receive new preoperative data of patient, input final deployment model, output prediction result.The present application fills the blank of existing prediction craniopharyngioma postoperative early overall adverse outcome model, overcomes the single data processing method in existing prediction technology, feature selection is not accurate enough, prediction accuracy is not high and lacks clinical usability and other problems.

A method of determining a therapeutic regimen in a patient with cancer comprising determining in a sample from the patient the tumor mutation burden (TMB) and loss of heterozygosity (LOH), wherein high TMB in combination with no LOH is indicative of a positive outcome when treated with a checkpoint inhibitor and high TMB with LOH is indicative of a poor outcome, is provided herein.

An Artificial Intelligence (AI) based computational system is used to non-invasively estimate the presence of a range of aneuploidies and mosaicism in an image of embryo prior to implantation. Aneuploidies and mosaicism with similar risks of adverse outcomes are grouped and training images are labelled with their group. Separate AI models are trained for each group using the same training dataset and the separate models are then combined, such as by using an Ensemble or Distillation approach to develop a model that can identify a wide range of aneuploidy and mosaicism risks. The AI model for a group is generated by training multiple models including binary models, hierarchical layered models and a multi-class model. In particular the hierarchical layered models are generated by assigning quality labels to images. At each layer the training set is partitioned in the best quality images and other images. The model at that layer is trained on the best quality images, and the other images are passed down to the next layer and the process repeated (so the remaining images are separated into next best quality images and other images). The final model can then be used to non-invasively identify aneuploidy and mosaicism and associated risk of adverse outcomes from an image of an embryo prior to implantation.

Method and systems for sorting potential hematopoietic stem cell transplant (HSCT) donors as a donor or non-donor using immunophenotyping of blood samples. The methods and systems can be used to identify a donor for a HSCT or to choose a donor to generate a HSCT blood product that is not likely to result in bad outcomes for a recipient. Also provided herein are methods and systems for training machine learning models that can be used in methods and systems for sorting potential HSCT donors.

FIELD: molecular biology.SUBSTANCE: method for predicting a high risk of adverse outcome in a patient with purulent lung disease (PLD) infected with gram-negative bacteria of the Klebsiella pneumoniae species is described. The method involves isolating DNA from peripheral venous blood and determining variants of the AQP4 rs1058424 gene. When the AQP4 rs1058424 AA genotype is detected in a patient with PLD and the presence of signs of Klebsiella pneumoniae infection, a high risk of death is determined.EFFECT: based on the determination of the AQP4 rs1058424 genotype for patients with PLD and identified gram-negative bacteria, it becomes possible to fairly early divide hospitalized patients with purulent infection into high-risk and low-risk groups for a fatal outcome.1 cl, 4 ex

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Targeting immune checkpoints, such as Programmed cell Death 1 (PD1), has improved survival in cancer patients by unleashing exhausted CD8+ T-cell thereby restoring anti-tumor immune responses. Most patients, however, relapse or are refractory to immune checkpoint blocking therapies. Here, the inventors show that NRP1 is recruited in the cytolytic synapse of PD1+CD8+ T-cells, interacts and enhances PD-1 activity. In mice, CD8+ T-cell specific deletion of Nrp1 improves spontaneous and anti PD1 antibody anti-tumor immune responses. Likewise, in human metastatic melanoma, the expression of NRP1 in tumor infiltrating CD8+ T-cells predicts poor outcome of patients treated with anti-PD1 (e.g. pembrolizumab). Finally, the combination of anti-NRP1 and anti-PD1 antibodies is synergistic in human, specifically in CD8+ T-cells anti-tumor response. Thus the therapeutic inhibition of NRP1 alone or combined with an immune checkpoint inhibitor (e.g. anti-PD1 antibody) could efficiently repress tumor growth in human cancer. The present invention also relates to multispecific antibodies comprising at least one binding site that specifically binds to an immune checkpoint molecule (e.g. PD-1), and at least one binding site that specifically binds to NRP-1. The present invention also relates to a population of cells engineered to express a chimeric antigen receptor (CAR) and wherein the expression of NRP-1 in said cells is repressed.

A method of determining a therapeutic regimen in a patient with cancer comprising determining in a sample from the patient the tumor mutation burden (TMB) and loss of heterozygosity (LOH), wherein high TMB in combination with no LOH is indicative of a positive outcome when treated with a checkpoint inhibitor and high TMB with LOH is indicative of a poor outcome, is provided herein.

A computer implemented method includes retrieving data from a patient's updated electronic medical record relevant to a diagnosis of a disease. The data includes medical images and at least one of the patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, and medical insurance information. The method includes generating available treatment path options including at least one surgical treatment or intervention, using at least one model to predict at least one implication for each of the available treatment path options based on the data and simulated adverse outcomes of the at least one surgical treatment or intervention simulated by predictive models including a biomechanical model based on the medical images. The method includes interactively updating and displaying a decision tree including the available treatment path options each with a corresponding at least one objective function.

A method comprising: receiving or otherwise obtaining input data for a subject with suspected or confirmed pre-eclampsia, wherein the input data comprises data representing a plurality of input variables comprising clinical data variables and / or other input variables associated with the subject and / or healthcare setting, wherein the input variables are grouped into a plurality of variable data groups, wherein the method comprises: processing the input data to identify each of the plurality of variable data groups as one of: empty, partially complete or complete; performing a data completion process to complete the input data for the one or more partially complete variable group; combining the data of the complete variable group and the completed data of the partially complete variables groups to form combined data; performing a model selection process based on the received or otherwise obtained input data to select at least one model from a plurality of trained models; applying the selected model to at least some of the combined data to obtain at least a risk level and / or probability associated with one or more adverse maternal outcomes for a subject with suspected or confirmed pre-eclampsia.

The invention discloses a VTE risk intelligent dynamic assessment method and device based on a large language model, which can process medical record writing habits of different medical institutions, consider semantic understanding and logic inference, and support dynamic risk prediction in combination with low-cost conventional indexes, so as to simply and conveniently integrate into clinical work, reduce medical care burden and improve the risk assessment efficiency. The problems of VTE risk factor definition standardization, information extraction accuracy, user interface interaction friendliness and VTE event dynamic monitoring ability in the prior art are solved, effective help and support are practically provided for patient management, the patient is helped to recover early, and bad outcome is reduced. The method comprises the following steps: (1) calling and preprocessing data; (2) identifying and judging risk factors; (3) performing dynamic risk prediction and joint modeling; (4) triggering and early warning; (5) performing doctor checking and error correction feedback; and (6) performing individualized intervention suggestion and disposal recommendation.

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

The invention discloses a dangerous stratification model for hypertrophic cardiomyopathy and a preparation method and application thereof, and belongs to the technical field of biological medicine. The invention provides application of a reagent for detecting the expression level of PDCD5 protein. Researches find that expression increase of the serum PDCD5 protein is not only a risk factor of bad outcome of HCM patients, but also is independent of other clinical factors, and a survival curve of risk stratification according to the serum PDCD5 protein also has a good discrimination degree. The PDCD5 protein improves a risk stratification model of HCM established based on clinical factors. The non-synonymous variation of the VWCE, SPAG5, PDGFRB and PITX3 genes is independently related to the increase of the adverse outcome risk of HCM patients; on the basis of a score 2 established based on serum PDCD5 protein and clinical factors, the non-synonymous variation of the genes is combined, and the established multi-dimensional risk stratification model has good prediction efficiency on the adverse outcome of the HCM patient.

The invention relates to an evaluation model and method for predicting a neonatal bad outcome based on antenatal examination. The main outcome of the neonatal adverse outcome comprises two parts of disease diagnosis and invasive treatment, and the neonatal adverse outcome is related to death and long-term health risk of children. The evaluation model comprises evaluation variables of weight gain during pregnancy, preeclampsia, umbilical artery S / D, congenital malformation and SGA, wherein the preeclampsia, the congenital malformation and the SGA are independent risk factors of neonatal adverse outcome. According to the scoring method, scoring is carried out based on the evaluation model, when at least one item of preeclampsia, congenital malformation, SGA and maternal congenital heart disease is' YES ', a neonatal adverse outcome high-risk group is prompted, and the higher the score is, the higher the probability of occurrence of the neonatal adverse outcome is. According to the invention, medical professionals can be rapidly helped to more effectively predict the neonatal adverse outcome, so that clinical decision-making is promoted.

A method for machine-learning-based prediction of adverse outcomes using measured time-varying values of physiological parameters generated from output of an implantable or wearable cardiac device includes receiving, as input to a trained machine learning model, measured time-varying values of physiological parameters generated from output of one or more embedded sensors of a wearable or implantable cardiac device worn by or implanted within an individual subject. The method further includes generating, as output from the trained machine learning model, a value indicating a personalized risk estimate of all-cause mortality or a composite event of all-cause mortality or heart failure hospitalization for the individual subject. The method further includes performing, based on the value indicating the personalized risk estimate of all-cause mortality or the composite event, an intervention for the individual subject.

Adverse outcomes of CAR-T such as hematotoxicity with prolonged cytopenia and infectious complications represent a challenging clinical problem after CAR-T therapy; however, current predictive models rely on blood counts and general inflammatory lab markers (ferritin, CRP) only and lack mechanistic / functional insights. Prolonged cytopenias after CAR-T are associated with endothelial alteration, characterized by reduced ANG1, E-selectin and MMP-1, and increased ANG2:ANG1 ratio, VCAM-1 and Thrombomodulin early after CAR-T. It was found that Patients with high baseline sIL-2R and VCAM-1 not only show more prolonged neutropenia and a more aplastic neutrophil recovery but also experience more severe infectious complications. High baseline VCAM-1 is associated with significantly worse peak CD8 CAR-T cell expansion and separates MM patients with worse overall response (Figure 5, Figure s5) Baseline sIL-2R and VCAM-1 have high predictive value for adverse outcomes, such as prolonged neutropenia, severe infections and death, and can further improve existing models.

The invention relates to the technical field of biological medicine, and discloses a method for constructing a pregnant woman health index evaluation system, which comprises the following steps: collecting original data of a pregnant woman in a perinatal period, cleaning the original data, removing abnormal values, filling missing values, and carrying out standardization processing to obtain preprocessed data; a random forest feature screening technology is adopted, variables with feature importance higher than an average value are selected, a composite perinatal period bad outcome prediction model is established based on an artificial neural network algorithm, and a prediction value output by the prediction model is converted into a pregnant woman health index through linear conversion; according to the method, features screened in the construction process of a composite perinatal period bad outcome prediction model are used as independent variables, a Logistic regression model is used for prediction, model fitting is carried out for different bad outcomes, respective Logistic regression equations are obtained, and a single bad outcome prediction model is constructed; according to the invention, the early warning capability is improved, the clinical management efficiency is enhanced, and the occurrence rate of bad outcomes in the perinatal period is reduced.

Adverse outcomes of CAR-T such as hematotoxicity with prolonged cytopenia and infectious complications represent a challenging clinical problem after CAR-T therapy; however, current predictive models rely on blood counts and general inflammatory lab markers (ferritin, CRP) only and lack mechanistic / functional insights. Prolonged cytopenias after CAR-T are associated with endothelial alteration, characterized by reduced ANG1, E-selectin and MMP-1, and increased ANG2:ANG1 ratio, VCAM-1 and Thrombomodulin early after CAR-T. It was found that Patients with high baseline sIL-2R and VCAM-1 not only show more prolonged neutropenia and a more aplastic neutrophil recovery but also experience more severe infectious complications. High baseline VCAM-1 is associated with significantly worse peak CD8 CAR-T cell expansion and separates MM patients with worse overall response (Figure 5, Figure s5) Baseline sIL-2R and VCAM-1 have high predictive value for adverse outcomes, such as prolonged neutropenia, severe infections and death, and can further improve existing models.

This disclosure relates to a method, apparatus, device, and medium for constructing a predictive model for adverse maternal and infant outcomes in pregnant women who are positive for anti-SSA / Ro and / or SSB / La antibodies. The method includes: acquiring a training set and multiple predictive factors related to adverse maternal and infant outcomes; performing regression analysis on each predictive factor based on the training set using a multivariate logistic regression analysis model to determine the regression coefficients of each predictive factor and construct a predictive model; plotting an ROC curve based on a pre-defined validation set and evaluating the predictive value of the predictive model based on the area under the ROC curve; and plotting a nomogram based on the predictive model if the predictive value reaches a pre-defined value. The nomogram includes: a score axis, a total score axis, and a risk probability axis for each predictive factor. This disclosure enables the construction of a predictive model that comprehensively considers the influence of multiple factors, improving the operability of the predictive model in clinical practice.