CN110232975A - A kind of pair of method that renal replacement therapies risk profile is entered in Diabetic Nephropathy patients 3 years - Google Patents
A kind of pair of method that renal replacement therapies risk profile is entered in Diabetic Nephropathy patients 3 years Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The method that the present invention relates to a kind of pair to enter renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years, the following steps are included: collect a large amount of diabetic nephropathies and do not enter the clinical data of renal replacement therapy patients, and tracks follow-up whether it entered renal replacement therapies after 3 years；Single factor test logistics recurrence is carried out by the data of " whether entering renal replacement therapies " to collection, filters out risk factors；By variable that single factor analysis filters out and clinically consider may significant variable be included in initial multifactor logistics model, obtain comprising the prediction model in relation to risk factors；The accuracy of prediction model is verified, and to each risk factors assignment in model, draws corresponding nomogram；Application program is converted by nomogram, inputs the risk facior data of different patients, obtains the forecasting risk of renal replacement therapies.The present invention helps to filter out the people at highest risk in Diabetic Nephropathy patients, is easily and effectively that Diabetic Nephropathy patients provide clinical guidance.
Description
Technical field
The present invention relates to renal replacement therapies risk profile fields more particularly to a kind of pair in Diabetic Nephropathy patients 3 years
The method for entering renal replacement therapies risk profile.
Background technique
Diabetic nephropathy is the one of the major reasons for leading to End-stage renal disease, brings heavy warp for society and family
Ji burden.Diabetic nephropathy insidious onset, is slowly in progress, and the related symptom of the kidney trouble of early stage is few.Nephrosis initial stage kidney increases,
Detection of glomeruli filtration function is hyperfunction and trace protein sustainable many years, is also not easy to be noted, most of Diabetic Nephropathy patients
It is to be perceived as in the obvious albuminuria of appearance or significant oedema when side.Therefore is entered for diabetic nephropathy End-stage renal disease morning
Phase is predicted into critical issue.
Applying wider prediction model at present is the KFRE model that Tangri et al. was delivered in 2011, and the model is for slow
Property kidney trouble 3-4 phase patient, for assess its enter renal replacement therapies risk.The model does not need to distinguish chronic renal in detail
The specific cause of disease of popular name for, it is only necessary to which age, gender, glomerular filtration rate and urinary albumin/urine creatinine value can make prediction.
But the model is mainly in all chronic kidney disease 3-4 phase patients, and Diabetic Nephropathy patients are relative to other chronic renals
Popular name for patient has the higher risk into End-stage renal disease, and whether KFRE model has Diabetic Nephropathy patients at present
Have preferable predictive still uncertain.And other the risk model for being directed to diabetogenous nephrosis disease forecasting more refer in the literature,
It is not further converted to the forms such as webpage calculator or APP, further clinic cannot be provided to Diabetic Nephropathy patients and referred to
It leads.
Summary of the invention
Against the above technical problems, the present invention provide entered in a kind of pair of Diabetic Nephropathy patients 3 years kidney substitution control
The method for treating risk profile, it is convenient to provide further dlinial prediction and guidance for more Diabetic Nephropathy patients.
The invention adopts the following technical scheme:
A kind of pair of method that renal replacement therapies risk profile is entered in Diabetic Nephropathy patients 3 years, including following step
It is rapid:
(1) a large amount of diabetic nephropathies are collected and do not enter the clinical data of renal replacement therapy patients, and track follow-up its 3
Whether renal replacement therapies are entered after year；
(2) single factor test is carried out to the data of collection by " whether entering renal replacement therapies " in result of study
Logistics is returned, and is calculated OR value and p value between each variable and final result by single factor analysis, is filtered out and significantly affect
The risk factors of result event；
(3) consider by variable that single factor analysis filters out and clinically may significant variable be included in it is initial mostly because
Plain logistics model, and screen out Confounding Factor for initial model using method of gradual regression, obtain comprising related risk because
The prediction model of element；
(4) cross validation is used, the accuracy of prediction model is verified, and to each risk factors assignment in model, is drawn
Nomogram is corresponded to out；
(5) application program is converted by nomogram, inputs the risk facior data of different patients, obtained and entered in 3 years
The forecasting risk of renal replacement therapies.
Further, step (3) risk factor includes age, gender, hemoglobin, neutrophil leucocyte absolute value/leaching
Bar cell absolute value, blood uric acid, cystatin C, twenty-four-hour urine Tot Prot, eGFR.
Further, further include establishing different prediction models for risk factors in step (3), calculate C value, AIC value,
Hosmer-Lemeshow detection calculates the discrimination and degree of correction of different models, and the reclassification between more different models improves
Index and comprehensive distinguishing improve index further assessment models efficiency, obtain optimum prediction model.
Further, the risk factors in optimum prediction model include neutrophil leucocyte absolute value/lymphocyte absolute value,
Cystatin C, twenty-four-hour urine Tot Prot, eGFR.
Further, the forecasting risk equation of optimum prediction model are as follows:
Further, the cutoff value of risk score value is 0.25 and 0.75, when the risk score value of patient is less than or equal to 0.25
When, it is low danger patient；It is middle danger patient when the risk score value of patient is between 0.25 and 0.75；When the risk point of patient
It is high-risk patient when value is greater than or equal to 0.75.
Further, the clinical data data for the Diabetic Nephropathy patients being collected into step (1) are 641.
It is of the invention to the method for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years, pass through receipts
The clinical data for collecting a large amount of Diabetic Nephropathy patients carries out single factor test logistics recurrence and initial multifactor to data
Logistics model obtains the prediction model comprising risk factors, after verifying model accuracy, draws to each risk factors assignment
Nomogram out, and be converted into application program, you can get it by inputting the risk facior data of patient enters kidney in its 3 years
The forecasting risk of replacement therapy helps to filter out the people at highest risk in Diabetic Nephropathy patients, gives early intervention, mitigates society
Meeting and individual burden can easily and effectively be that more Diabetic Nephropathy patients provide further dlinial prediction and guidance.
Detailed description of the invention
Illustrate the embodiment of the present invention or technical solution in the prior art in order to clearer, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it is clear that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is of the invention to entering the of renal replacement therapies Risk Forecast Method in Diabetic Nephropathy patients 3 years
A kind of flow chart of embodiment；
Fig. 2 is of the invention to entering the of renal replacement therapies Risk Forecast Method in Diabetic Nephropathy patients 3 years
The flow chart of two kinds of embodiments；
Fig. 3 is the nomogram that renal replacement therapies risk is entered in of the invention prediction Diabetic Nephropathy patients 3 years.
Specific embodiment
Below in conjunction with the attached drawing in the present invention, technical solution in the embodiment of the present invention carry out it is clear, completely retouch
It states, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
In embodiment, those skilled in the art's all other reality obtained without making creative work
Example is applied, protection scope of the present invention is belonged to.
As the first embodiment of the invention, a kind of pair enters kidney substitution in Diabetic Nephropathy patients 3 years and controls
The method for treating risk profile, as shown in Figure 1, comprising the following steps:
(1) a large amount of diabetic nephropathies are collected and do not enter the clinical data of renal replacement therapy patients, and track follow-up its 3
Whether renal replacement therapies are entered after year；
(2) single factor test is carried out to the data of collection by " whether entering renal replacement therapies " in result of study
Logistics is returned, and is calculated OR value and p value between each variable and final result by single factor analysis, is filtered out and significantly affect
The risk factors of result event；
(3) consider by variable that single factor analysis filters out and clinically may significant variable be included in it is initial mostly because
Plain logistics model, and screen out Confounding Factor for initial model using method of gradual regression, obtain comprising related risk because
Element prediction model, risk factors include the age, gender, hemoglobin, neutrophil leucocyte absolute value/lymphocyte absolute value,
Blood uric acid, cystatin C, twenty-four-hour urine Tot Prot, eGFR；
(4) cross validation is used, the accuracy of prediction model is verified, and to each risk factors assignment in model, is drawn
Nomogram is corresponded to out；
(5) application program is converted by nomogram, inputs the risk facior data of different patients, obtained and entered in 3 years
The forecasting risk of renal replacement therapies.
In the present embodiment, the clinical data data for the Diabetic Nephropathy patients being collected into step (1) are 641.
It is of the invention to the method for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years, pass through receipts
The clinical data for collecting a large amount of Diabetic Nephropathy patients carries out single factor test logistics recurrence and initial multifactor to data
Logistics model obtains the prediction model comprising risk factors, after verifying model accuracy, draws to each risk factors assignment
Nomogram out, and be converted into application program, you can get it by inputting the risk facior data of patient enters kidney in its 3 years
The forecasting risk of replacement therapy helps to filter out the people at highest risk in Diabetic Nephropathy patients, gives early intervention, to mitigate
Society and individual burden, can easily and effectively be that more Diabetic Nephropathy patients provide further dlinial prediction and guidance.
As second of embodiment of the invention, a kind of pair enters kidney substitution in Diabetic Nephropathy patients 3 years and controls
The method for treating risk profile, as shown in Figure 2, comprising the following steps:
(1) a large amount of diabetic nephropathies are collected and do not enter the clinical data of renal replacement therapy patients, and track follow-up its 3
Whether renal replacement therapies are entered after year；
(2) single factor test is carried out to the data of collection by " whether entering renal replacement therapies " in result of study
Logistics is returned, and is calculated OR value and p value between each variable and final result by single factor analysis, is filtered out and significantly affect
The risk factors of result event；
(3) consider by variable that single factor analysis filters out and clinically may significant variable be included in it is initial mostly because
Plain logistics model, and screen out Confounding Factor for initial model using method of gradual regression, obtain comprising related risk because
Element prediction model, risk factors include the age, gender, hemoglobin, neutrophil leucocyte absolute value/lymphocyte absolute value,
Blood uric acid, cystatin C, twenty-four-hour urine Tot Prot, eGFR, establish different prediction models for risk factors, calculate C value,
AIC value, Hosmer-Lemeshow detection calculate the discrimination and degree of correction of different models, and dividing between more different models again
Class, which improves index and comprehensive distinguishing, improves index further assessment models efficiency, obtains optimum prediction model, optimum prediction mould
Risk factors in type include neutrophil leucocyte absolute value/lymphocyte absolute value, cystatin C, twenty-four-hour urine Tot Prot,
eGFR；
(4) cross validation is used, verifies the accuracy of optimum prediction model, and assign to each risk factors in model
Value, draws corresponding nomogram；
(5) convert application program for nomogram, input the risk facior data of different patients, obtain in patient 3 years into
Enter the forecasting risk to renal replacement therapies.
Specifically, the forecasting risk equation of optimum prediction model are as follows:
More specifically, in some embodiments of the invention, using 5 folding cross validations, the essence of optimum prediction model is verified
Exactness gives assignment to each risk factors foundation in model using software R3.5.0, and draws corresponding nomogram.
Wherein: as shown in figure 3, nomogram includes the score value scale of the first row, wherein score range is 0-100；Second row
For patient's twenty-four-hour urine Tot Prot (HTP), HTP data area is 0-35g/d, and different HTP values corresponds to one phase of the first row
The score answered, specific corresponding situation are as shown in table 1；Third behavior patient neutrophil leucocyte absolute value/lymphocyte absolute value
(NLY), NLY data area is 0-70, and different NLY values corresponds to one corresponding score of the first row, specific corresponding situation such as table 1
It is shown；Fourth line is patient's cystatin C value (CYS), and CYS data area is 0-12mg/L, and different CYS values corresponds to the first row one
A corresponding score, specific corresponding situation are as shown in table 1；Fifth line is eGFR value (EGFR), and EGFR data area is 0-
160mL/min/1.73m2, different EGFR values corresponds to one corresponding score of the first row, and specific corresponding situation is as shown in table 1；
4 indexs of the second row to fifth line are added in the corresponding score of the first row, it is total to obtain patient by the 6th behavior patient's total score
Score value；The value-at-risk that renal replacement therapies are entered in 7th behavior Diabetic Nephropathy patients 3 years, by patient's total score of the 6th row
Value correspondence is projected to the value-at-risk for obtaining on the 7th row and entering renal replacement therapies in Diabetic Nephropathy patients 3 years.Certainly,
Two rows to the position of fifth line can replace between each other, as long as patient's total score can be calculated based on the score value scale of the first row
?.
The different risk factors assignment situations of table 1
For example, twenty-four-hour urine Tot Prot is 0.6g/d (32 points) as shown in figure 3, a Diabetic Nephropathy patients,
Neutrophil leucocyte absolute value/lymphocyte absolute value is 2 (35 points), and cystatin C is 0.8mg/L (28 points), and eGFR value is
120mL/min/1.73m2(15 points)；The patient's must be divided into 110 points, and the value-at-risk for being projected to renal replacement therapies is
0.00149, guidance is provided by the value-at-risk for next step treatment.
Specifically, the cutoff value of risk score value is 0.25 and 0.75, when the risk score value of patient is less than or equal to 0.25,
For low danger patient；It is middle danger patient when the risk score value of patient is between 0.25 and 0.75；When the risk score value of patient is high
It is high-risk patient when 0.75.The selection of cutoff value is to improve index according to reclassification as a result, under this cutoff value
Reclassification improve index it is best, the discrimination of hints model is best, can preferably filter out high-risk patient.Pass through patient's wind
Dangerous score value can provide guidance foundation compared with cutoff value for the treatment of next step.The kidney of the Diabetic Nephropathy patients in upper example
Replacement therapy value-at-risk is 0.00149, and less than 0.25, then the patient is low danger patient, and kidney substitution is entered in 3 years and is controlled
The risk for the treatment of is lower, and the treatment plan of next step can be instructed according to this result.
In order to more easily provide clinical guidance for patient, application program is converted by above-mentioned nomogram, is specifically micro-
Believe that small routine, APP form or webpage calculator etc., neutrophil leucocyte absolute value, the lymph that patient need to be only inputted on interface are thin
Born of the same parents' absolute value, cystatin C, twenty-four-hour urine Tot Prot, eGFR, you can get it enters the pre- of renal replacement therapies in its 3 years
Survey risk.More specifically, the present invention is not especially limited the method for converting application program for nomogram, any to can be achieved to turn
The operating method of change.
The present invention is analyzed by the Clinical Follow-up data to 600 many cases Diabetic Nephropathy patients, establishes diabetes
Nephrotic enters the risk forecast model of renal replacement therapies in 3 years, and by way of application program, only inputs patient
Value-at-risk can be obtained for indices data as a result, convenient provide further dlinial prediction for more Diabetic Nephropathy patients
And guidance, help to filter out the people at highest risk in Diabetic Nephropathy patients, gives early intervention.
The present invention is further described by specific embodiment above, it should be understood that, here specifically
Description, should not be construed as the restriction for the essence of the present invention with range, and one of ordinary skilled in the art is reading this explanation
The various modifications made after book to above-described embodiment belong to the range that the present invention is protected.
Claims (7)
1. a kind of pair of method for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years, which is characterized in that packet
Include following steps:
(1) a large amount of diabetic nephropathies are collected and do not enter the clinical data of renal replacement therapy patients, and track follow-up its after 3 years
Whether renal replacement therapies are entered；
(2) single factor test logistics is carried out to the data of collection by " whether entering renal replacement therapies " in result of study
It returns, OR value and p value between each variable and final result is calculated by single factor analysis, filters out and significantly affects result event
Risk factors；
(3) consider by variable that single factor analysis filters out and clinically may significant variable be included in it is initial multifactor
Logistics model, and Confounding Factor is screened out for initial model using method of gradual regression, it obtains comprising related risk factors
Prediction model；
(4) cross validation is used, the accuracy of the prediction model is verified, and to each risk factors assignment in model, is drawn
Nomogram is corresponded to out；
(5) application program is converted by nomogram, inputs the risk facior data of different patients, obtained and entered in patient 3 years
The forecasting risk of renal replacement therapies.
2. according to claim 1 to the side for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years
Method, which is characterized in that risk factors described in step (3) include age, gender, hemoglobin, neutrophil leucocyte absolute value/leaching
Bar cell absolute value, blood uric acid, cystatin C, twenty-four-hour urine Tot Prot, eGFR.
3. according to claim 2 to the side for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years
Method, which is characterized in that further include establishing different prediction models for the risk factors in step (3), calculate C value, AIC
Value, Hosmer-Lemeshow detection calculate the discrimination and degree of correction of different models, and the reclassification between more different models changes
Kind index and comprehensive distinguishing improve index and carry out assessment models efficiency, obtain optimum prediction model.
4. according to claim 3 to the side for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years
Method, which is characterized in that the risk factors in the optimum prediction model include that neutrophil leucocyte absolute value/lymphocyte is absolute
Value, cystatin C, twenty-four-hour urine Tot Prot, eGFR.
5. according to claim 4 to the side for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years
Method, which is characterized in that the forecasting risk equation of the optimum prediction model are as follows:
6. according to claim 5 to the side for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years
Method, which is characterized in that the cutoff value of risk score value is 0.25 and 0.75, when the risk score value of patient is less than or equal to 0.25,
For low danger patient；It is middle danger patient when the risk score value of patient is between 0.25 and 0.75；When the risk score value of patient is high
It is high-risk patient when 0.75.
7. according to claim 1 to the side for entering renal replacement therapies risk profile in Diabetic Nephropathy patients 3 years
Method, which is characterized in that the clinical data data for the Diabetic Nephropathy patients being collected into step (1) are 641.
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| CN111768862A (en) * | 2020-06-16 | 2020-10-13 | 上海市第六人民医院 | Autoimmune hepatitis risk prediction model and construction method thereof |
| CN111951953A (en) * | 2020-07-20 | 2020-11-17 | 肾泰网健康科技(南京)有限公司 | Method, model and system for screening diabetic kidney disease based on AI technology |
| CN112216384A (en) * | 2020-10-29 | 2021-01-12 | 北京大学第一医院 | Diabetic nephropathy risk assessment system |
| CN112635056A (en) * | 2020-12-17 | 2021-04-09 | 郑州轻工业大学 | Lasso-based esophageal squamous carcinoma patient risk prediction nomogram model establishing method |
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| CN117649936A (en) * | 2023-12-11 | 2024-03-05 | 中国人民解放军陆军军医大学第二附属医院 | Quantitative prediction model for response effect of multiple sclerosis patient to hormone impact treatment |
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Application publication date: 20190913 |