Prognostic models for chronic kidney disease: a systematic review and external validation

2020 ◽  
Author(s):  
Marieke H C van Rijn ◽  
Moniek van de Luijtgaarden ◽  
Arjan D van Zuilen ◽  
Peter J Blankestijn ◽  
Jack F M Wetzels ◽  
...  
Keyword(s):  
Systematic Review ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Nurse Practitioners ◽  
External Validation ◽  
Primary Objective ◽  
Prognostic Models ◽  
Full Model ◽  
Moderate Quality ◽  
Model Equations

Abstract Background Accurate risk prediction is needed in order to provide personalized healthcare for chronic kidney disease (CKD) patients. An overload of prognosis studies is being published, ranging from individual biomarker studies to full prediction studies. We aim to systematically appraise published prognosis studies investigating multiple biomarkers and their role in risk predictions. Our primary objective was to investigate if the prognostic models that are reported in the literature were of sufficient quality and to externally validate them. Methods We undertook a systematic review and appraised the quality of studies reporting multivariable prognosis models for end-stage renal disease (ESRD), cardiovascular (CV) events and mortality in CKD patients. We subsequently externally validated these models in a randomized trial that included patients from a broad CKD population. Results We identified 91 papers describing 36 multivariable models for prognosis of ESRD, 50 for CV events, 46 for mortality and 17 for a composite outcome. Most studies were deemed of moderate quality. Moreover, they often adopted different definitions for the primary outcome and rarely reported full model equations (21% of the included studies). External validation was performed in the Multifactorial Approach and Superior Treatment Efficacy in Renal Patients with the Aid of Nurse Practitioners trial (n = 788, with 160 events for ESRD, 79 for CV and 102 for mortality). The 24 models that reported full model equations showed a great variability in their performance, although calibration remained fairly adequate for most models, except when predicting mortality (calibration slope >1.5). Conclusions This review shows that there is an abundance of multivariable prognosis models for the CKD population. Most studies were considered of moderate quality, and they were reported and analysed in such a manner that their results cannot directly be used in follow-up research or in clinical practice.

The impact of advanced nurse practitioners on patient outcomes in chronic kidney disease: A systematic review

2018 ◽  
Vol 44 (4) ◽  
pp. 197-209 ◽  
Author(s):  
Geraldine McCrory ◽  
Declan Patton ◽  
Zena Moore ◽  
Tom O'Connor ◽  
Linda Nugent
Keyword(s):  
Systematic Review ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Patient Outcomes ◽  
Nurse Practitioners ◽  
The Impact

scholarly journals Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study

BMJ ◽  
2021 ◽  
pp. n2134
Author(s):  
Roderick C Slieker ◽  
Amber A W A van der Heijden ◽  
Moneeza K Siddiqui ◽  
Marlous Langendoen-Gort ◽  
Giel Nijpels ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
General Population ◽  
Diabetic Kidney Disease ◽  
Prediction Models ◽  
External Validation ◽  
Diabetic Kidney

Abstract Objectives To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. Design Systematic review and external validation. Data sources PubMed and Embase. Eligibility criteria Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. Methods Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell’s C statistic). Results 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. Conclusions This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. Systematic review registration PROSPERO CRD42020192831.

scholarly journals Chronic kidney disease in Low- and Middle- Income Countries: Protocol for a systematic review of diagnostic and prognostic models

2021 ◽  
Author(s):  
Edson J Ascencio ◽  
Diego J Aparcana-Granda ◽  
Rodrigo M Carrillo-Larco
Keyword(s):  
Systematic Review ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
General Population ◽  
Data Extraction ◽  
Screening Tests ◽  
Prognostic Models ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low And Middle Income

ABSTRACTBackgroundChronic Kidney Disease (CKD) is a highly prevalent condition with a large disease burden globally. In low- and middle-income countries (LMIC) the CKD screening challenges the health system. This systematic and comprehensive search of all CKD diagnostic and prognostic models in LMIC will inform screening strategies in LMIC following a risk-based approach.ObjectiveTo summarize all multivariate diagnostic and prognostic models for CKD in adults in LMIC.MethodsSystematic review. Without date or language restrictions we will search Embase, Medline, Global Health (these three through Ovid), SCOPUS and Web of Science. We seek multivariable diagnostic or prognostic models which included a random sample of the general population. We will screen titles and abstracts; we will then study the selected reports. Both phases will be done by two reviewers independently. Data extraction will be performed by two researchers independently using a pre-specified Excel form (CHARMS model). We will evaluate the risk of bias with the PROBAST tool.ConclusionThis systematic review will provide the most comprehensive list and critical appraisal of diagnostic and prognostic models for CKD available for the general population in LMIC. This evidence could inform policies and interventions to improve CKD screening in LMIC following a risk-based approach, maximizing limited resources and reaching populations with limited access to CKD screening tests. This systematic review will also reveal methodological limitations and research needs to improve CKD diagnostic and prognostic models in LMIC.

scholarly journals External validation of prognostic models for chronic kidney disease among type 2 diabetes

2022 ◽  
Author(s):  
Sigit Ari Saputro ◽  
Anuchate Pattanateepapon ◽  
Oraluck Pattanaprateep ◽  
Wichai Aekplakorn ◽  
Gareth J. McKay ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
External Validation ◽  
Model Performance ◽  
Nationwide Survey ◽  
Prognostic Models ◽  
Diabetic Patients ◽  
Clinical Factors

Abstract Background Various prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort. Methods A nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting. Results Six relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565–0.605) to 0.786 (0.765–0.806) for CKD and 0.657 (0.610–0.703) to 0.760 (0.705–0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975–1.024) to 1.009 (0.929–1.090). Hosmer–Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low’s of 0.114 for CKD and Elley’s of 0.025 for ESRD. Conclusions All models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low’s (developed in Singapore) and Elley’s model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand. Graphical abstract

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