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A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

Abstract Background Chronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undia... Full description

Journal Title: Journal of clinical epidemiology 2013, Vol.66 (3), p.268-277
Main Author: Collins, Gary S
Other Authors: Omar, Omar , Shanyinde, Milensu , Yu, Ly-Mee
Format: Electronic Article Electronic Article
Language: English
Subjects:
Age
Quelle: Alma/SFX Local Collection
Publisher: New York, NY: Elsevier Inc
ID: ISSN: 0895-4356
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title: A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods
format: Article
creator:
  • Collins, Gary S
  • Omar, Omar
  • Shanyinde, Milensu
  • Yu, Ly-Mee
subjects:
  • Age
  • Analysis
  • Biological and medical sciences
  • Blood pressure
  • Brain damage
  • Calibration
  • Cancer
  • Cardiovascular disease
  • Chronic kidney failure
  • Data Interpretation, Statistical
  • Diabetes
  • Ethnicity
  • Hospitals
  • Humans
  • Hypertension
  • Internal Medicine
  • Kidney disease
  • Kidney diseases
  • Kidney stones
  • Kidneys
  • Medical sciences
  • Medical treatment
  • Mens health
  • Methodological conduct
  • Model development
  • Model validation
  • Models
  • Models, Statistical
  • Nephrology. Urinary tract diseases
  • Nephropathies. Renovascular diseases. Renal failure
  • Older people
  • Oncology, Experimental
  • Renal failure
  • Renal Insufficiency, Chronic - diagnosis
  • Renal Insufficiency, Chronic - etiology
  • Reporting
  • Reproducibility of Results
  • Risk Assessment
  • Risk Factors
  • Risk prediction models
  • Statistical methods
  • Studies
  • Urinary system involvement in other diseases. Miscellaneous
  • Womens health
ispartof: Journal of clinical epidemiology, 2013, Vol.66 (3), p.268-277
description: Abstract Background Chronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults. Methods We conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance. Results Eleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values. Conclusion We found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0895-4356
fulltext: fulltext
issn:
  • 0895-4356
  • 1878-5921
url: Link


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titleA systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods
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creatorcontribCollins, Gary S ; Omar, Omar ; Shanyinde, Milensu ; Yu, Ly-Mee
descriptionAbstract Background Chronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults. Methods We conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance. Results Eleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values. Conclusion We found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.
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subjectAge ; Analysis ; Biological and medical sciences ; Blood pressure ; Brain damage ; Calibration ; Cancer ; Cardiovascular disease ; Chronic kidney failure ; Data Interpretation, Statistical ; Diabetes ; Ethnicity ; Hospitals ; Humans ; Hypertension ; Internal Medicine ; Kidney disease ; Kidney diseases ; Kidney stones ; Kidneys ; Medical sciences ; Medical treatment ; Mens health ; Methodological conduct ; Model development ; Model validation ; Models ; Models, Statistical ; Nephrology. Urinary tract diseases ; Nephropathies. Renovascular diseases. Renal failure ; Older people ; Oncology, Experimental ; Renal failure ; Renal Insufficiency, Chronic - diagnosis ; Renal Insufficiency, Chronic - etiology ; Reporting ; Reproducibility of Results ; Risk Assessment ; Risk Factors ; Risk prediction models ; Statistical methods ; Studies ; Urinary system involvement in other diseases. Miscellaneous ; Womens health
ispartofJournal of clinical epidemiology, 2013, Vol.66 (3), p.268-277
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descriptionAbstract Background Chronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults. Methods We conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance. Results Eleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values. Conclusion We found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.
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0Age
1Analysis
2Biological and medical sciences
3Blood pressure
4Brain damage
5Calibration
6Cancer
7Cardiovascular disease
8Chronic kidney failure
9Data Interpretation, Statistical
10Diabetes
11Ethnicity
12Hospitals
13Humans
14Hypertension
15Internal Medicine
16Kidney disease
17Kidney diseases
18Kidney stones
19Kidneys
20Medical sciences
21Medical treatment
22Mens health
23Methodological conduct
24Model development
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26Models
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36Reproducibility of Results
37Risk Assessment
38Risk Factors
39Risk prediction models
40Statistical methods
41Studies
42Urinary system involvement in other diseases. Miscellaneous
43Womens health
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abstractAbstract Background Chronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults. Methods We conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance. Results Eleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values. Conclusion We found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.
copNew York, NY
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pmid23116690
doi10.1016/j.jclinepi.2012.06.020