Dataset Information

Risk models to predict chronic kidney disease and its progression: a systematic review.

ABSTRACT:

Background

Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.

Methods and findings

We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.

Conclusions

The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored.

SUBMITTER: Echouffo-Tcheugui JB

PROVIDER: S-EPMC3502517 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Risk models to predict chronic kidney disease and its progression: a systematic review.

Echouffo-Tcheugui Justin B JB Kengne Andre P AP

PLoS medicine 20121120 11

<h4>Background</h4>Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluate ...[more]

PMID: 23185136

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Project description:The prevalence of chronic kidney disease (CKD) is increasing and dietary interventions may be a strategy to reduce this burden. In the general population, higher potassium intake is considered protective for cardiovascular health. Due to the risk of hyperkalemia in CKD, limiting potassium intake is often recommended. However, given that poor cardiovascular function can cause kidney damage, following a low-potassium diet may be deleterious for patients with CKD. The aim of this systematic review was to summarize the evidence on dietary potassium intake and CKD progression. Multiple databases were searched on 7 June 2019 and data were managed with Covidence. No intervention trials met the inclusion criteria. Eleven observational studies met the inclusion criteria (10 post hoc analyses, 1 retrospective cohort), representing 49,573 stage 1-5 predialysis patients with CKD from 41 different countries. Of the 11 studies, 6 studies reported exclusively on early CKD (stage 1-2), 4 studies separately reported analyses on both early and late (stage 3-5) CKD, and 2 studies reported exclusively on late CKD. A total of 9 studies reported risk of disease progression in early CKD; in 4 studies high potassium intake was associated with lower risk, while in 2 studies the low intake showed a higher progression of risk, and 3 studies reported no relation. In late CKD, results are mixed: 2 studies suggested benefit of higher potassium intake and 1 suggested benefit of lower potassium intake, whereas 3 studies were neutral. These results should be interpreted with caution, as considerations preventing firm conclusions include 1) the overall low range of dietary potassium intake, with all studies reporting an average intake below the 2004 Kidney Disease Outcomes Quality Initiatives guidelines, and 2) the method used to assess potassium intake in most studies (i.e., urine) in late stages of CKD. Ideally, well-controlled intervention studies are needed to understand how dietary potassium intake is linked to CKD progression.

Project description:The risk factors influencing the natural course of chronic kidney disease (CKD) are complex and heterogeneous, and few systematic reviews to date have focused on this issue. The aim of the study is to identify the risk factors for disease development and progression in each stage of CKD. We conducted electronic literature searches of PubMed, MEDLINE, Scopus, and the Cochrane Library up to October 15, 2012, for observational studies evaluating the risk factors on the development or progression of CKD. Eligible studies should have collected repeated information that could evaluate changes in renal function. Extracted information from all the included studies was synthesized narratively. Quality assessments were performed using the Newcastle-Ottawa Scale. An exploratory random-effects meta-analysis was performed where feasible to pool effect sizes across studies for a specific risk factor in a specific outcome. We identified 38 cohort studies and 2 case-control studies from 40 articles, with a total of 318,898 participants from 14 countries. The follow-up duration ranged from 1.5 to 16 years. The majority of the included studies were of high quality. The baseline CKD stages of the included studies ranged from normal to later stages, and only 19 studies could be classified into a specific range of CKD stages during follow-up. Three risk factors from studies of the same baseline and follow-up CKD stages were eligible for the exploratory meta-analysis, including male sex, substantial proteinuria, and diabetes. The hazard ratios for the progression from CKD stages 3-5 to end-stage renal disease (ESRD) were 1.37 (95% confidence interval 1.17-1.62), 1.64 (1.01-2.66), and 1.16 (0.98-1.38) for male sex, substantial proteinuria, and diabetes, respectively. In conclusion, our analyses comprehensively summarize the initiating and perpetuating factors for CKD. Male sex and substantial proteinuria are significant perpetuating factors for the progression from late stage CKD to ESRD, and diabetes may play a minor role for the outcome of ESRD among patients with later stages of CKD.

Project description:ObjectiveTo provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).DesignScoping review.Data sourcesMedline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.Study selectionAll English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.Data extractionEligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.ResultsFrom 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.ConclusionsResearchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

Project description:ObjectivesModifying lifestyle can prevent the progression of chronic kidney disease (CKD) but the specific elements which lead to favourable behaviour change are not well understood. We aimed to identify and evaluate behaviour change techniques and functions in lifestyle interventions for preventing the progression of CKD.DesignSystematic review.Data sourcesMEDLINE, EMBASE, CINAHL and PsycINFO.Eligibility criteriaTrials of lifestyle behaviour change interventions (including diet, physical activity, smoking and/or alcohol) published to September 2018 in adults with CKD stages 1-5.Data extraction and synthesisTrial characteristics including population, sample size, study setting, intervention, comparator, outcomes and study duration, were extracted. Study quality was independently assessed by two reviewers using the Cochrane risk of bias tool. The Behaviour Change Technique Taxonomy v1 was used to identify behaviour change techniques (eg, goal setting) and the Health Behaviour Change Wheel was used to identify intervention functions (eg, education). Both were independently assessed by three reviewers.ResultsIn total, 26 studies involving 4263 participants were included. Risk of bias was high or unclear in most studies. Interventions involved diet (11), physical activity (8) or general lifestyle (7). Education was the most frequently used function (21 interventions), followed by enablement (18), training (12), persuasion (4), environmental restructuring (4), modelling (2) and incentivisation (2). The most common behaviour change techniques were behavioural instruction (23 interventions), social support (16), behavioural demonstration (13), feedback on behaviour (12) and behavioural practice/rehearsal (12). Eighteen studies (69%) showed a significant improvement in at least one primary outcome, all of which included education, persuasion, modelling and incentivisation.ConclusionLifestyle behaviour change interventions for CKD patients frequently used education, goal setting, feedback, monitoring and social support. The most promising interventions included education and used a variety of intervention functions (persuasion, modelling and incentivisation).Prospero registration numberCRD42019106053.

Project description:ContextEarly detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals.ObjectiveThis review will identify and compare published models that predict the risk of developing kidney cancer in the general population.Evidence acquisitionA search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool.Evidence synthesisThe search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies.ConclusionsWe identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies.Patient summaryIn this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.

Dataset Information

Risk models to predict chronic kidney disease and its progression: a systematic review.

Background

Methods and findings

Conclusions

Publications

Risk models to predict chronic kidney disease and its progression: a systematic review.

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