Prediction system, method, medium and equipment for survival probability of Parkinson's disease

Abstract

The invention relates to a Parkinson's disease survival probability prediction system and method, a medium and equipment. The prediction system comprises: an acquisition module configured to acquire six target indexes of a testee, the target indexes including an onset age, a diabetes state, a UPDRS III score, an HY stage, a depression state and a cognitive impairment state; and the prediction module is configured to predict the survival probability of the testee in the future set time based on all the target indexes in combination with a Parkinson's disease survival prediction column diagram. According to the technical scheme provided by the invention, the survival probability of the testee in the future set time can be quickly estimated by means of the pre-constructed Parkinson's disease survival prediction line diagram, clinical doctors can conveniently predict the survival prognosis of PD patients, and the method is higher in clinical benefit and stronger in reference of conclusions.

Description

Technical Field

[0001] The present invention relates to the field of Parkinson's disease prognosis assessment, and in particular to a prediction system, method, medium and device for the survival probability of Parkinson's disease. Background Art

[0002] Parkinson's disease (PD) is one of the degenerative diseases of the nervous system. Its incidence rate is about 0.5% to 1% in people aged 60 to 65, and rises to 1% to 3% in people over 80 years old. The incidence rate increases with age, and is slightly higher in men than in women. According to statistics, the direct treatment and care costs of a PD patient in the United States are about $10,349 per year, plus indirect costs such as loss of productivity and unpaid care by family members, which brings a heavy burden to society and the patient's family. Therefore, conducting survival surveys on PD patients has become an important part of understanding the burden of the disease.

[0003] In the related technology, Cox proportional hazard regression combined with Kaplan-Meier survival curve drawing is used to study the risk factors for death in PD patients. A large number of studies have shown that the age of death of PD patients usually ranges from 72.7 to 82.9 years old, and the median survival time is usually 10 to 20 years. However, at this stage, there are no definite research results on the risk factors for death in PD patients and whether PD affects death. For example, studies have shown that the risk factors for death that affect the survival of Parkinson's disease include high age of onset, cognitive impairment, and more severe motor symptoms, but whether factors such as gender, hallucinations, depression, autonomic dysfunction, smoking, and deep brain stimulation surgery affect the survival of PD patients. In addition, the SMR (Standardized Mortality Ratio) value of PD patients can also be used to explore whether PD affects survival. According to previous research results, the SMR value of PD patients is between 0.5 and 3.8 compared with normal people, and the SMR value also has regional differences in different countries and regions. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a system, method, medium and device for predicting the survival probability of Parkinson's disease in order to solve at least one problem existing in the background technology.

[0005] In a first aspect, an embodiment of the present application provides a system for predicting the survival probability of Parkinson's disease, comprising:

[0006] An acquisition module, which is configured to acquire six target indicators of the test subject, wherein the target indicators include age of onset, diabetes status, UPDRS III score, HY stage, depression status and cognitive impairment status;

[0007] The prediction module is configured to predict the survival probability of the test subject within a set time in the future based on all the target indicators and in combination with the Parkinson's disease survival prediction nomogram.

[0008] In conjunction with the first aspect of the present application, in an optional implementation manner, in the target indicator:

[0009] The age of onset is a categorical variable, and the value of the age of onset indicates whether the onset age of the subject is under 50 years old or over 50 years old;

[0010] The diabetes status is a categorical variable, and the value of the diabetes status indicates whether the subject suffers from diabetes;

[0011] The UPDRSⅢ score is a continuous variable, and the value of the UPDRSⅢ score is the score of the Unified Parkinson's Disease Rating Scale Ⅲ of the test subject;

[0012] The HY stage is a categorical variable, and the value of the HY stage represents the HY stage content of the test subject;

[0013] The depression state is a categorical variable, and the value of the depression state indicates whether the subject suffers from depression;

[0014] The cognitive impairment state is a categorical variable, and the value of the cognitive impairment state indicates whether the subject suffers from cognitive impairment.

[0015] In conjunction with the first aspect of the present application, in an optional implementation, the Parkinson's disease survival prediction nomogram includes a single score line segment, six target indicator line segments, a total score line segment, and a survival probability line segment within a future set time;

[0016] The value of the target indicator on the corresponding target indicator line segment corresponds to the score on the single score line segment;

[0017] The sum of the scores of all the target indicators is the score on the total score line segment;

[0018] The value of the survival probability within the future set time on the corresponding survival probability line segment corresponds to the score on the total score line segment;

[0019] The individual score line segment, target index line segment, total score line segment, and survival probability line segment within a future set time period each occupy a row on the Parkinson's disease survival prediction nomogram.

[0020] In conjunction with the first aspect of the present application, in an optional implementation, in the Parkinson's disease survival prediction nomogram, the score range of the single score line segment is 0 to 100;

[0021] If the onset age of the tested person is under 50 years old, the value of the onset age corresponds to 0 on the single score line segment; if the onset age of the tested person is over 50 years old, the value of the onset age corresponds to 100 on the single score line segment;

[0022] If the subject does not have diabetes, the value of the diabetes status corresponds to 0 on the single score line segment; if the subject has diabetes, the value of the diabetes status corresponds to 45.8 on the single score line segment;

[0023] If the UPDRSⅢ score of the test subject ranges from 0 to 108, the maximum value of the UPDRSⅢ score corresponds to 63.7 on the single item score line segment, the minimum value corresponds to 0, and the corresponding relationship between the value and the score is a linear relationship;

[0024] If the HY stage of the test subject is 1 stage, 2 stages, 2.5 stages, 3 stages, 4 stages or 5 stages, the value of HY stage corresponds to 0, 25.4, 36.6, 42.4, 50.3 or 68.0 on the single score line segment;

[0025] If the subject is not depressed, the value of the depression state corresponds to 0 on the single score line segment; if the subject suffers from depression, the value of the depression state corresponds to 16.3 on the single score line segment;

[0026] If the subject has no cognitive impairment, the value of the cognitive impairment state corresponds to 0 on the single score line segment; if the subject has cognitive impairment, the value of the cognitive impairment state corresponds to 22.8 on the single score line segment.

[0027] In conjunction with the first aspect of the present application, in an optional implementation, in the Parkinson's disease survival prediction nomogram, the survival probability line segment within the future set time includes a 10-year survival probability line segment and a 20-year survival probability line segment, the values ​​on the same survival probability line segment are unequally divided continuous variables, and the score range of the total score line segment is 0 to 300;

[0028] The value range of the 10-year survival probability line segment is 0.5-0.95, corresponding to 107-275 on the total score line segment, wherein the values ​​0.5, 0.7, 0.8, 0.9, and 0.95 correspond to 275, 232, 202, 154, and 107 on the total score line segment, respectively;

[0029] The value range of the 20-year survival probability segment is 0.1-0.95, corresponding to 14.7-259.1 on the total score segment, among which the values ​​0.1, 0.3, 0.5, 0.7, 0.8, 0.9, and 0.95 correspond to 259.1, 217.5, 182.0, 139.3, 109.2, 60.9, and 14.7 on the total score segment respectively.

[0030] In conjunction with the first aspect of the present application, in an optional embodiment, the optimal cutoff value of the Parkinson's disease survival prediction nomogram is 150.

[0031] In conjunction with the first aspect of the present application, in an optional implementation manner, the method further includes:

[0032] The display module is configured to display the predicted survival probability of the test subject within a set future time.

[0033] In a second aspect, an embodiment of the present application provides a method for predicting the survival probability of Parkinson's disease, which is applied to the prediction system for the survival probability of Parkinson's disease as described in the first aspect, and the prediction method includes:

[0034] Obtaining six target indicators of the test subject, including age of onset, diabetes status, UPDRSⅢ score, HY stage, depression status and cognitive impairment status;

[0035] Based on all the target indicators and in combination with the Parkinson's disease survival prediction nomogram, the survival probability of the test subject within a set time in the future is predicted.

[0036] A third aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method described in the second aspect.

[0037] A fourth aspect of the present application provides an electronic device, including:

[0038] Processor; and

[0039] A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method described in the second aspect.

[0040] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0041] The embodiment of the present application provides a prediction system for the survival probability of Parkinson's disease, including an acquisition module and a prediction module, wherein the acquisition module is configured to acquire multiple target indicators of the test subject, the target indicators include age of onset, diabetes status, UPDRSⅢ score, HY stage, depression status and cognitive impairment status, and the prediction module is configured to predict the survival probability of the test subject within a set time in the future based on all target indicators and in combination with the Parkinson's disease survival prediction nomogram. In the prediction system, according to the target indicator of the test subject, that is, the selected death risk factor, the survival probability of the test subject within a set time in the future is quickly estimated with the help of a pre-constructed Parkinson's disease survival prediction nomogram, which can be convenient for clinicians to use in the prediction of the survival prognosis of Parkinson's disease patients, and, compared with the conventional UPDRSⅢ score and HY stage to roughly infer the survival probability of Parkinson's disease patients, the clinical benefits of the embodiment of the present application are higher and the referenceability of the conclusion is stronger.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 A schematic diagram of the structure of a device for predicting the survival probability of Parkinson's disease provided in one embodiment of the present application;

[0045] Figure 2 A nomogram for predicting survival of Parkinson's disease provided in one embodiment of the present application;

[0046] Figure 3 A schematic diagram of survival curves of high-risk patients and low-risk patients in a test population based on a Parkinson's disease survival prediction nomogram according to an embodiment of the present application;

[0047] Figure 4 for Figure 3 Schematic diagram of the corresponding 10-year cumulative survival rate and 20-year cumulative survival rate for each level;

[0048] Figure 5 and Figure 6 These are respectively the calibration curves of the first and second verification groups in one embodiment of the present application;

[0049] Figure 7 and Figure 8 These are the ROC curves of the first and second validation groups in an embodiment of the present application;

[0050] Fig. 9The nomogram, UPDRSⅢ score, and DCA curve of HY stage for the first and second validation groups for 10-year survival time, and the nomogram, UPDRSⅢ score, and DCA curve of HY stage for the first and second validation groups for 20-year survival time;

[0051] Fig.10 A schematic diagram of a flow chart of a method for predicting the survival probability of Parkinson's disease provided in one embodiment of the present application;

[0052] Fig.11 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the technical solutions and beneficial effects of the present invention more clearly understandable, the following is a detailed description by listing specific embodiments. The drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features; unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical field to which this application belongs.

[0054] like Figure 1 As shown, an embodiment of the present application provides a system for predicting the survival probability of Parkinson's disease, including an acquisition module 101 and a prediction module 102.

[0055] The acquisition module 101 is configured to acquire six target indicators of the subject, including age of onset, diabetes status, UPDRSⅢ score, HY stage, depression status and cognitive impairment status.

[0056] In this embodiment, the risk factors for death related to the test subject are preferably age of onset, diabetes status, UPDRSⅢ score, HY stage, depression state and cognitive impairment state. Age of onset, diabetes status, HY stage, depression state and cognitive impairment state are all categorical variables, UPDRSⅢ score is a continuous variable, and UPDRSⅢ score is the Unified Parkinson's Disease Rating Scale III score, which is used to evaluate the motor function of patients with Parkinson's disease, including tremor, muscle rigidity, bradykinesia, postural balance disorders and other aspects. Each item in the scale is scored according to the severity of the symptoms, and the total score is the sum of the scores of each item, which is the UPDRSⅢ score. A lower UPDRSⅢ score usually indicates that the patient's motor dysfunction is mild, while a higher UPDRSⅢ score means that the motor dysfunction is more severe.

[0057] In the target indicators: the value of the age of onset indicates whether the onset age of the test subject is under 50 years old or over 50 years old; the value of the diabetes status indicates whether the test subject suffers from diabetes; the value of the UPDRSⅢ score is the score of the Unified Parkinson's Disease Rating Scale III of the test subject, and the value range of the UPDRSⅢ score of the test subject is 0-108; the HY stage, that is, the Hoehn-Yahr stage, is used to assess the severity of Parkinson's disease and is a categorical variable. The value of the HY stage can indicate the HY stage content of the test subject, which includes stage 1, stage 2, stage 2.5, stage 3, stage 4 and stage 5; the value of the depression state indicates whether the test subject suffers from depression; the value of the cognitive impairment state indicates whether the test subject suffers from cognitive impairment.

[0058] Obviously, the target indicators of age of onset, diabetes status, depression status and cognitive impairment status have two variable values, and the HY stage has 6 variable values. Preferably, the UPDRSⅢ score is a positive integer ranging from 0 to 108, which can be regarded as a continuous variable with several variable values.

[0059] The prediction module 102 is configured to predict the survival probability of the test subject within a set time in the future based on all target indicators and in combination with the Parkinson's disease survival prediction nomogram.

[0060] In the prediction module 102, the prediction module 102 includes a Parkinson's disease survival prediction nomogram. The Parkinson's disease survival prediction nomogram includes a single score line segment, six target indicator line segments, a total score line segment, and a survival probability line segment within a future set time; the value of the target indicator on the corresponding target indicator line segment corresponds to the score on the single score line segment; the sum of the scores of all target indicators is the score on the total score line segment; the value of the survival probability within the future set time on the corresponding survival probability line segment corresponds to the score on the total score line segment; wherein, the single score line segment, the target indicator line segment, the total score line segment, and the survival probability line segment within the future set time each occupy a row on the Parkinson's disease survival prediction nomogram.

[0061] It should be noted that the nomogram, as a visualization tool for predicting the probability of a specific event or evaluating a certain result, is usually composed of several line segments with scales. Each line segment represents a variable. By finding the value of the target indicator on the corresponding line segment and then drawing a vertical line upward, the single score corresponding to the target indicator can be obtained on the single score segment. Finally, all the single scores are added together, and the predicted event probability or result can be read on a specific survival prediction segment through the total score.

[0062] Furthermore, in the Parkinson's disease survival prediction nomogram, the score range of the single score segment is 0 to 100; if the age of onset of the tested person is under 50 years old, the value of the onset age corresponds to 0 on the single score segment; if the age of onset of the tested person is over 50 years old, the value of the onset age corresponds to 100 on the single score segment; if the tested person has no diabetes, the value of the diabetes status corresponds to 0 on the single score segment; if the tested person has diabetes, the value of the diabetes status corresponds to 45.8 on the single score segment; if the UPDRSⅢ score of the tested person ranges from 0 to 108, the maximum value of the UPDRSⅢ score corresponds to 63.7 on the single score segment, and the minimum value corresponds to 63.7 on the single score segment. The value corresponds to 0, and the corresponding relationship between the value and the score is a linear relationship; if the HY stage of the test subject is stage 1, stage 2, stage 2.5, stage 3, stage 4 or stage 5, the value of the HY stage corresponds to 0, 25.4, 36.6, 42.4, 50.3 or 68.0 on the single score line segment; if the test subject is not depressed, the value of the depression state corresponds to 0 on the single score line segment; if the test subject suffers from depression, the value of the depression state corresponds to 16.3 on the single score line segment; if the test subject has no cognitive impairment, the value of the cognitive impairment state corresponds to 0 on the single score line segment; if the test subject suffers from cognitive impairment, the value of the cognitive impairment state corresponds to 22.8 on the single score line segment. Therefore, the corresponding values ​​can be determined according to the characteristics corresponding to each target indicator of the test subject, and then the score on the single score segment can be found according to the value of the target indicator on the target indicator segment in the Parkinson's disease survival prediction nomogram, that is, the single score of the target indicator.

[0063] In the embodiment of the present application, the value of each target indicator has a corresponding score on the single score segment. For example, the single score of the test subject whose onset age is over 50 years old corresponds to 100 on the single score segment, and the mapping relationship between the value of the target indicator and the score can be referred to the above records and will not be repeated here in detail.

[0064] Furthermore, in the Parkinson's disease survival prediction nomogram, the survival probability line segments within the future set time include 10-year survival probability line segments and 20-year survival probability line segments. The values ​​on the same survival probability line segment are unequally divided continuous variables, and the total score line segment ranges from 0 to 300. The value range of the 10-year survival probability line segment is 0.5 to 0.95, corresponding to 107 to 275 on the total score line segment, among which the values ​​are 0.5, 0.7, 0.8, 0.9, 0. 95 corresponds to 275, 232, 202, 154, and 107 on the total score segment respectively. The value range of the 20-year survival probability segment is 0.1-0.95, corresponding to 14.7-259.1 on the total score segment, among which the values ​​0.1, 0.3, 0.5, 0.7, 0.8, 0.9, and 0.95 correspond to 259.1, 217.5, 182.0, 139.3, 109.2, 60.9, and 14.7 on the total score segment respectively.

[0065] In this embodiment, there are preferably two survival probability segments, namely, a 10-year survival probability segment and a 20-year survival probability segment. The 10-year survival probability segment is taken as an example. The value on the 10-year survival probability segment represents the probability of the test subject surviving 10 years from the age of onset. According to the actual situation of the test subject, a value that meets the actual situation of the test subject is selected in the target indicator. The value of the target indicator corresponds to the score on the single score segment. See the above description to determine the single score of all target indicators. In this embodiment, the sum of the single scores of all target indicators is equal to the score on the total score segment. Therefore, a total score can be found on the total score segment according to the sum of the single scores of all target indicators, and then the total score corresponds to the value on the 10-year survival probability segment and the 20-year survival probability segment.

[0066] It should be noted that if Figure 2 As shown in the figure, if the total score of the test subject is 5, the total score cannot find a clear value on the 10-year survival probability segment and the 20-year survival probability segment. It can be inferred that the 10-year survival probability of the test subject is much greater than 0.95, and the 20-year survival probability is greater than 0.95.

[0067] Preferably, the optimal cutoff value of the Parkinson's disease survival prediction nomogram is 150. In an embodiment of the present application, Parkinson's patients are divided into high-risk patients and low-risk patients according to the optimal cutoff value. Among them, the total score of the test subject, i.e., the risk value, can be calculated from the Parkinson's disease survival prediction nomogram. If the risk value is above the optimal cutoff value of 150, the test subject can be divided into a high-risk patient, i.e., the test subject has a high risk of death, otherwise it is a low-risk patient. It can be seen that the embodiment of the present application can also classify the test subject according to the actual situation of the test subject through the optimal cutoff value of the Parkinson's disease survival prediction nomogram, and divide it into high-risk patients and low-risk patients, which can also be regarded as a grade division. Then, high-risk patients can be included in the high-risk group, and low-risk patients can be included in the low-risk group.

[0068] In some embodiments, the prediction system further includes a display module 103, and the display module 103 is configured to display the predicted survival probability of the test subject within a future set time. In practical applications, the predicted survival probability of the test subject within a future set time can be displayed in a visual manner through the display module 103. Of course, the embodiments of the present application include but are not limited to displaying the survival probability, and can also visually display other related data.

[0069] In this embodiment, the Parkinson's disease survival prediction nomogram is a constructed nomogram, and the scores corresponding to each target indicator are obtained on the single score line segment according to the value of each target indicator, and the total score on the total score line segment is found according to the sum of all the obtained scores. Finally, the probability value is read on the 10-year survival probability line segment according to the total score, and the probability value is read on the 20-year survival probability line segment according to the total score. Among them, the prediction of the survival probability of the Parkinson's disease survival prediction nomogram can be converted into a webpage formula for calculation, such as the webpage https: / / pdmdcnc-pd.shinyapps.io / PD-survival / , and only six target indicators need to be entered on the webpage to directly obtain the results of the 10-year survival probability and the 20-year survival probability.

[0070] The prediction system of the survival probability of Parkinson's disease provided in the embodiment of the present application can solve the unclear contribution of the risk factors of death to the death of Parkinson's patients at this stage. The nomogram, as a visual tool, shows the weights of different target indicators for the death of Parkinson's patients, and then obtains the results of 10-year survival probability and 20-year survival probability, which provides effective help in Parkinson's disease monitoring and risk stratification. The embodiment of the present application can be convenient for clinicians to use in the prediction of survival prognosis of Parkinson's patients, and compared with the conventional UPDRSⅢ score and HY stage to roughly infer the survival probability of PD patients, the clinical benefit of the embodiment of the present application is higher and the conclusion is more referenceable.

[0071] The present application is described in detail below in conjunction with a specific embodiment.

[0072] like Figure 2 As shown in the figure, it is a nomogram for survival prediction of Parkinson's disease, which has ten rows. In the nomogram, the first row is a single score segment, and its score range is 0-100; the second to seventh rows are target index lines, and the target indexes are age of onset, diabetes status, UPDRSⅢ score, HY stage, depression status and cognitive impairment status, respectively. Each value of the target index in the second to seventh rows has a corresponding score on the single score segment, and this variable is the independent variable; the eighth row is a total score segment, and its score range is 0-300; the ninth to tenth rows are 10-year survival probability segments and 20-year survival probability segments, respectively, and this variable is the dependent variable. The survival probability values ​​in the ninth and tenth rows are unequally divided on the survival probability segment, and each value in the survival probability variable in the ninth and tenth rows has a corresponding total score on the total score segment, and the sum of the scores of the six target indicators is equal to the total score on the total score segment.

[0073] In the Parkinson's disease survival prediction nomogram, the age of onset includes two variable values, namely, the age of onset is under 50 years old and the age of onset is over 50 years old. The single score of the age of onset under 50 years old is 0, and the single score of the age of onset over 50 years old is 100; the diabetes status includes two variable values, namely, the presence or absence of diabetes. The single score of no diabetes is 0, and the single score of diabetes is 45.8; the variable value range of UPDRSⅢ score is 0-108, UPDRSⅢ The maximum score of 108 corresponds to a single score of 63.7, and the minimum score of 0 corresponds to a single score of 0. Different single scores are determined according to different values, and the corresponding relationship between the UPDRSⅢ score and the single score is a linear relationship; the HY stage includes 6 variable values, namely HY1, HY2, HY2.5, HY3, HY4 and HY5, and the corresponding single scores are 0, 25.4, 36.6, 42.4, 50.3 and 68.0 respectively. ; Depression status includes two variables, namely, the presence or absence of depression. The single score of no depression is 0, and the single score of depression is 16.3; cognitive impairment status includes two variables, namely, the presence or absence of cognitive impairment. The single score of no cognitive impairment is 0, and the single score of cognitive impairment is 22.8; The value range of the 10-year survival probability line segment is 0.5-0.95, corresponding to 107-275 on the total score line segment, among which the values ​​of 10-year survival probability are 0.5, 0.7, 0.8 , 0.9, and 0.95 correspond to total scores of 275, 232, 202, 154, and 107 respectively; the value range of the 20-year survival probability segment is 0.1-0.95, corresponding to the total score segment of 14.7-259.1, among which the 20-year survival probability values ​​of 0.1, 0.3, 0.5, 0.7, 0.8, 0.9, and 0.95 correspond to total scores of 259.1, 217.5, 182.0, 139.3, 109.2, 60.9, and 14.7 respectively.

[0074] like Figure 3 As shown, it is a schematic diagram of the survival curves of high-risk patients and low-risk patients in the test population. In this embodiment, the optimal cutoff value of the Parkinson's disease survival prediction nomogram is 150. According to the optimal cutoff value 150, the test population is divided into two levels, one is high and the other is low, where the high value indicates that the test subject is a high-risk patient. The test population can be obtained from the Parkinson's disease registration grade system in my country, and the Kaplan-Meier curve shows the changing trend of the survival rate of different grades, such as Figure 3 As shown in , the 10-year cumulative survival rate and 20-year cumulative survival rate of each level can also be calculated, as Figure 4 The results showed that the 20-year cumulative survival rate of high-risk patients was much lower than that of low-risk patients, and there was a gap in the 10-year cumulative survival rate, but it was far less than the 20-year cumulative survival rate.

[0075] Confirmatively, the Parkinson's disease test population was divided into a first validation group and a second validation group in a ratio of 7:3 using the "createDataPartition" function in R language to ensure that the outcome events were randomly distributed between the two groups. Two different validation groups were used to validate and evaluate the accuracy of the above-mentioned Parkinson's disease survival prediction nomogram. The evaluation indicators included C-Index (concordance index) and time-dependent AUC (Area Under the Curve).

[0076] It should be noted that the value range of C-Index is 0.5 to 1.0. When C-Index is 0.5, it means that the prediction is completely random; when C-Index is 1, it means that the prediction is completely accurate. Generally speaking, the higher the value of C-Index, the higher the prediction accuracy. Usually, a C-Index value greater than 0.7 indicates that the prediction accuracy is reasonable. In addition, the time-dependent AUC refers to the area under the Receiver Operating Characteristic curve (ROC curve). The ROC curve is a curve drawn by continuously changing the diagnostic threshold, with the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa. The value of AUC is between 0.5 and 1. The closer the value is to 1, the better the prediction performance. AUC values ​​greater than 0.7 indicate reasonable prediction accuracy.

[0077] like Figure 5 and Figure 6 As shown, they are the calibration curves of the first and second validation groups, respectively. Figure 5 It can be seen that in the first validation group, the C-Index of the 10-year survival probability is 0.7353, and the C-Index of the 20-year survival probability is 0.8021, and both C-Index are above 0.7. Figure 6 It can be seen that in the second validation group, the C-Index (C-value) of the 10-year survival probability is 0.7253, and the C-Index of the 20-year survival probability is 0.7588, and the C-Index is also above 0.7. It can be seen from the two calibration curves that both the first validation group and the second validation group show the rationality of the estimation of the Parkinson's disease survival prediction nomogram.

[0078] like Figure 7 and Figure 8 As shown, they are the ROC curves of the first and second validation groups respectively. Figure 7It can be seen that in the first validation group, the AUC of the 10-year survival probability was 0.74, and the AUC of the 20-year survival probability was 0.80, both of which were above 0.7. Figure 8 It can be seen that in the second validation group, the AUC of the 10-year survival probability is 0.73, and the AUC of the 20-year survival probability is 0.76, and the AUC is also above 0.7. It can be inferred from the two ROC curves that both the first validation group and the second validation group can demonstrate the rationality of the estimation of the Parkinson's disease survival prediction nomogram.

[0079] The embodiment of the present application uses a plurality of different evaluation indicators to verify the Parkinson's disease survival prediction nomogram, which can reflect the accuracy of the nomogram in estimating the survival probability of the tested person.

[0080] like Fig. 9 As shown, the DCA (Decision Curve Analysis) curves of the nomogram, UPDRSⅢ score and HY stage corresponding to the first and second validation groups for 10-year survival time, and the DCA curves of the nomogram, UPDRSⅢ score and HY stage corresponding to the first and second validation groups for 20-year survival time, wherein Fig. 9 On the left are the nomogram, UPDRSⅢ score, and DCA curve of HY stage for the first validation group, and the nomogram, UPDRSⅢ score, and DCA curve of HY stage for the second validation group. Fig. 9 The two sets of DCA curves in the figure plotted the curves for the scenario with all deaths (representing the highest clinical cost) and the scenario with no deaths (representing no clinical benefit) as two references. DCA, or decision curve analysis, is one of the methods for evaluating clinical predictions, diagnostic tests, and treatment strategies. It is mainly used to compare the net benefits of different strategies under different threshold probabilities, helping clinicians and decision makers make more reasonable decisions in actual clinical settings. By drawing the DCA curve, you can intuitively see the pros and cons of different strategies in different situations. Fig. 9 It is not difficult to find that the net benefit corresponding to the nomogram is relatively the highest. Therefore, compared with the conventional UPDRSⅢ score and HY stage to roughly infer the survival probability of PD patients, the clinical benefit of the embodiment of the present application is higher and the conclusion is more referenceable.

[0081] Corresponding to the aforementioned application function implementation system embodiment, the present application also provides a method for determining the survival probability of Parkinson's patients, an electronic device and corresponding embodiments.

[0082] Fig.10 This is a flow chart of a method for predicting the survival probability of Parkinson's disease according to an embodiment of the present application.

[0083] like Fig.10 As shown, the embodiment of the present application provides a method for predicting the survival probability of Parkinson's disease, which is applied to the above prediction system for the survival probability of Parkinson's disease. The prediction method includes:

[0084] Step S1001: Acquire six target indicators of the subject, including age of onset, diabetes status, UPDRS III score, HY stage, depression status and cognitive impairment status;

[0085] Step S1002: Based on all target indicators and in combination with the Parkinson's disease survival prediction nomogram, the survival probability of the test subject within a set time in the future is predicted.

[0086] Regarding the method in the above embodiment, the specific operation mode of each process has been described in detail in the embodiment of the system, and will not be elaborated here.

[0087] The present application embodiment can be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium, which is loaded with a computer-readable program instruction for making a processor realize various aspects of the present application. The computer program product can be written in any combination of one or more programming languages ​​to perform the program code for performing the operation of the present application embodiment, and the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed completely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or completely on the remote computing device or server. In the case of a remote computer, the remote computer can be connected to the user computer through any type of network-including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). In some embodiments, by utilizing the state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present application.

[0088] Computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. Computer readable storage medium is a tangible device that can keep and store instructions used by an instruction execution device. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination of the above. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0089] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0090] Various aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0091] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0092] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0093] An embodiment of the present application also provides an electronic device. Fig.11 Shown is a schematic structural diagram of an electronic device provided in one embodiment of the present application.

[0094] like Fig.11 As shown, the electronic device 1100 includes:

[0095] Processor 1101;

[0096] The memory 1102 is used to store computer executable instructions. When the executable code is executed by the processor, the processor executes the above method.

[0097] The processor 1101 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0098] The memory 1102 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1101 may execute the program instructions to implement the steps in the text recognition method of each embodiment of the present application described above and / or other desired functions.

[0099] In one example, the electronic device 1100 may further include: an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown in the figure).

[0100] In addition, the input device may also include, for example, a keyboard, a mouse, a microphone, etc. The output device may output various information to the outside, and may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0101] Of course, to simplify, Fig.11 Only a part of the components related to the present application in the electronic device 1100 is shown, and components such as a bus, an input device / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device 1100 may also include any other appropriate components.

[0102] It should be noted that the system embodiment, method embodiment, medium embodiment and device embodiment for predicting the survival probability of Parkinson's disease provided in the embodiments of the present application belong to the same concept; the technical features in the technical solutions recorded in each embodiment can be arbitrarily combined without conflict.

[0103] It should be understood that the above embodiments are exemplary and are not intended to include all possible implementations included in the claims. Various modifications and changes may be made on the basis of the above embodiments without departing from the scope of the present disclosure. Similarly, the various technical features of the above embodiments may be arbitrarily combined to form other embodiments of the present invention that may not be explicitly described. Therefore, the above embodiments only express several implementations of the present invention and do not limit the scope of protection of the patent of the present invention.

Claims

1. A prediction system for the survival probability of Parkinson's disease, characterized in that: include: An acquisition module, which is configured to acquire six target indicators of the test subject, wherein the target indicators include age of onset, diabetes status, UPDRS III score, HY stage, depression status and cognitive impairment status; The prediction module is configured to predict the survival probability of the test subject within a set time in the future based on all the target indicators and in combination with the Parkinson's disease survival prediction nomogram.

2. The system for predicting the survival probability of Parkinson's disease according to claim 1, characterized in that: Among the target indicators: The age of onset is a categorical variable, and the value of the age of onset indicates whether the onset age of the subject is under 50 years old or over 50 years old; The diabetes status is a categorical variable, and the value of the diabetes status indicates whether the subject suffers from diabetes; The UPDRSⅢ score is a continuous variable, and the value of the UPDRSⅢ score is the score of the Unified Parkinson's Disease Rating Scale Ⅲ of the test subject; The HY stage is a categorical variable, and the value of the HY stage represents the HY stage content of the test subject; The depression state is a categorical variable, and the value of the depression state indicates whether the subject suffers from depression; The cognitive impairment state is a categorical variable, and the value of the cognitive impairment state indicates whether the subject suffers from cognitive impairment.

3. The prediction system for Parkinson's disease survival probability according to claim 2, characterized in that: The Parkinson's disease survival prediction nomogram includes a single score line segment, six target index line segments, a total score line segment, and a survival probability line segment within a future set time; The value of the target indicator on the corresponding target indicator line segment corresponds to the score on the single score line segment; The sum of the scores of all the target indicators is the score on the total score line segment; The value of the survival probability within the future set time on the corresponding survival probability line segment corresponds to the score on the total score line segment; Among them, the single score line segment, the target indicator line segment, the total score line segment, and the survival probability line segment within the future set time each occupy a row on the Parkinson's disease survival prediction nomogram.

4. The system for predicting the survival probability of Parkinson's disease according to claim 3, characterized in that: In the Parkinson's disease survival prediction nomogram, the score range of the single score line segment is 0 to 100; If the onset age of the tested person is under 50 years old, the value of the onset age corresponds to 0 on the single score line segment; if the onset age of the tested person is over 50 years old, the value of the onset age corresponds to 100 on the single score line segment; If the subject does not have diabetes, the value of the diabetes status corresponds to 0 on the single score line segment; if the subject has diabetes, the value of the diabetes status corresponds to 45.8 on the single score line segment; If the UPDRSⅢ score of the test subject ranges from 0 to 108, the maximum value of the UPDRSⅢ score corresponds to 63.7 on the single item score line segment, the minimum value corresponds to 0, and the corresponding relationship between the value and the score is a linear relationship; If the HY stage of the test subject is 1 stage, 2 stages, 2.5 stages, 3 stages, 4 stages or 5 stages, the value of HY stage corresponds to 0, 25.4, 36.6, 42.4, 50.3 or 68.0 on the single score line segment; If the subject is not depressed, the value of the depression state corresponds to 0 on the single score line segment; if the subject suffers from depression, the value of the depression state corresponds to 16.3 on the single score line segment; If the subject has no cognitive impairment, the value of the cognitive impairment state corresponds to 0 on the single score line segment; if the subject has cognitive impairment, the value of the cognitive impairment state corresponds to 22.8 on the single score line segment.

5. The prediction system for Parkinson's disease survival probability according to claim 3, characterized in that: In the Parkinson's disease survival prediction nomogram, the survival probability line segment within the future set time includes a 10-year survival probability line segment and a 20-year survival probability line segment, the values ​​on the same survival probability line segment are unequally divided continuous variables, and the score range of the total score line segment is 0 to 300; The value range of the 10-year survival probability line segment is 0.5-0.95, corresponding to 107-275 on the total score line segment, wherein the values ​​0.5, 0.7, 0.8, 0.9, and 0.95 correspond to 275, 232, 202, 154, and 107 on the total score line segment, respectively; The value range of the 20-year survival probability segment is 0.1-0.95, corresponding to 14.7-259.1 on the total score segment, among which the values ​​0.1, 0.3, 0.5, 0.7, 0.8, 0.9, and 0.95 correspond to 259.1, 217.5, 182.0, 139.3, 109.2, 60.9, and 14.7 on the total score segment respectively.

6. The system for predicting the survival probability of Parkinson's disease according to claim 5, characterized in that: The optimal cutoff value of the Parkinson's disease survival prediction nomogram was 150.

7. The system for predicting the survival probability of Parkinson's disease according to claim 1, characterized in that: Also includes: The display module is configured to display the predicted survival probability of the test subject within a set future time.

8. A method for predicting the survival probability of Parkinson's disease, characterized in that: The system for predicting the survival probability of Parkinson's disease according to any one of claims 1 to 7, wherein the prediction method comprises: Obtaining six target indicators of the test subject, including age of onset, diabetes status, UPDRSⅢ score, HY stage, depression status and cognitive impairment status; Based on all the target indicators and in combination with the Parkinson's disease survival prediction nomogram, the survival probability of the test subject within a set time in the future is predicted.

9. A computer-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in claim 8.

10. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored therein, which, when executed by the processor, causes the processor to execute the method according to claim 8.

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