Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study

Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

BACKGROUND

Prognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure.

METHODS

We identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library-CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty.

RESULTS

Ninety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death.

CONCLUSIONS

There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty.

SYSTEMATIC REVIEW REGISTRATION

This review has been registered on PROSPERO (CRD42022347295).

Prediction models used in the progression of chronic kidney disease: A scoping review

Objective

To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).

Design

Scoping review.

Data sources

Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.

Study selection

All 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 extraction

Eligible 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.

Results

From 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.

Conclusions

Researchers 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.

Low performance of prognostic tools for predicting death before dialysis in older patients with advanced CKD

INTRODUCTION

Chronic kidney disease (CKD) is a disease which is spreading worldwide, especially among older patients. Several prognostic scores have been developed to predict death in older CKD patients, but they have not been validated. We aimed to evaluate the existing risk scores for predicting death before dialysis start, identified via an in-depth review, in a cohort of elderly patients with advanced CKD.

METHODS

We performed a review to identify scores predicting death, developed in and applicable to CKD patients. Each score was evaluated with an absolute risk calculation from the patients' baseline characteristics. We used a French prospective multicentre cohort of elderly patients (> 75 years) with advanced CKD [estimated glomerular filtration rate (eGFR) < 20 mL/min/1.73 m²], recruited from nephrological centres, with a 5-year follow-up. The outcome considered was death before initiating dialysis. Discrimination [area under curve (AUC)], calibration and Brier score were calculated for each score at its time frame.

RESULTS

Our review found 6 equations predicting death before dialysis in CKD patients. Four of these (GOLDFARB, BANSAL, GRAMS 2 and 4 years) were evaluated. The validation cohort (Parcours de Soins des Personnes Âgées Parcours de Soins des Personnes Âgées, PSPA) included 573 patients, with a median age of 82 years and a median eGFR of 13 mL/min/1.73 m². At the end of follow-up, 287 (50%) patients had started dialysis and 238 (41%) patients had died before dialysis. The four equations evaluated showed average discrimination (AUC 0.61-0.70) and, concerning calibration, a global overestimation of the risk of death.

DISCUSSION

The available scores predicting death before dialysis showed low performance among older patients with advanced CKD in a French multicentre cohort, indicating the need to upgrade them or develop new scores for this population.

Towards the best kidney failure prediction tool: a systematic review and selection aid

Background

Prediction tools that identify chronic kidney disease (CKD) patients at a high risk of developing kidney failure have the potential for great clinical value, but limited uptake. The aim of the current study is to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the interpretation and uptake of these tools.

Methods

PubMed and EMBASE were searched for relevant articles. Titles, abstracts and full-text articles were sequentially screened for inclusion by two independent researchers. Data on study design, model development and performance were extracted. The risk of bias and clinical usefulness were assessed and combined in order to provide recommendations on which models to use.

Results

Of 2183 screened studies, a total of 42 studies were included in the current review. Most studies showed high discriminatory capacity and the included predictors had large overlap. Overall, the risk of bias was high. Slightly less than half the studies (48%) presented enough detail for the use of their prediction tool in practice and few models were externally validated.

Conclusions

The current systematic review may be used as a tool to select the most appropriate and robust prognostic model for various settings. Although some models showed great potential, many lacked clinical relevance due to being developed in a prevalent patient population with a wide range of disease severity. Future research efforts should focus on external validation and impact assessment in clinically relevant patient populations.

Low performance of prognostic tools for predicting dialysis in elderly people with advanced CKD

INTRODUCTION

Clinical decision-making about care plans can be difficult for very elderly people with advanced chronic kidney disease (CKD). Current guidelines propose the use of prognostic tools predicting end stage renal disease (ESRD) to assist in a patient-centered shared decision-making approach. Our objective was to evaluate the existing risk model scores predicting ESRD, from data collected for a French prospective multicenter cohort of mainly octogenarians with advanced CKD.

METHODS

We performed a rapid review to identify the risk model scores predicting ESRD developed from CKD patient cohorts and evaluated them with data from a prospective multicenter French cohort of elderly (> 75 years) patients with advanced CKD (estimated glomerular filtration rate [eGFR] < 20 mL/min/1.75m²), followed up for 5 years. We evaluated these scores (in absolute risk) for discrimination, calibration and the Brier score. For scores using the same time frame, we made a joint calibration curve and compared areas under the curve (AUCs).

RESULTS

The PSPA cohort included 573 patients; their mean age was 83 years and their median eGFR was 13 mL/min/1.73 m². At the end of follow-up, 414 had died and 287 had started renal replacement therapy (RRT). Our rapid review found 12 scores that predicted renal replacement therapy. Five were evaluated: the TANGRI 4-variable, DRAWZ, MARKS, GRAMS, and LANDRAY scores. No score performed well in the PSPA cohort: AUCs ranged from 0.57 to 0.65, and Briers scores from 0.18 to 0.25.

CONCLUSIONS

The low predictiveness for ESRD of the scores tested in a cohort of octogenarian patients with advanced CKD underlines the need to develop new tools for this population.

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

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.

Development of risk models for major adverse chronic renal outcomes among patients with type 2 diabetes mellitus using insurance claims: a retrospective observational study

Objective: To develop and validate models allowing the prediction of major adverse chronic renal outcomes (MACRO) in patients with type 2 diabetes mellitus (T2DM) using insurance claims data.Methods: The Optum Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006-09/30/2016) was used to identify T2DM patients ≥50 years old. Risk factors were assessed over a 12-month baseline period, and MACRO were subsequently assessed until the end of data availability, continuous enrollment, or death. Separate models were built for moderate-to-severe diabetic kidney disease (DKD), end-stage renal disease (ESRD), and renal death. A random split-sample approach was employed, where 70% of the sample served for model development (training set) and the remaining 30% served for validation (testing set). C-statistics were used to assess model performance.Results: A total of 160,031 patients were included. Risk factors associated with MACRO for all models included adapted diabetes complications severity index, heart failure, anemia, diabetic nephropathy, and CKD. C-statistics ranged between 0.70 (moderate-to-severe DKD) and 0.84 (renal death) in the testing set. A substantial proportion (e.g. 88.7% for moderate-to-severe DKD) of patients predicted to be at high-risk of MACRO did not have diabetic nephropathy, proteinuria, or CKD at baseline.Conclusions: The models developed using insurance claims data could reliably predict the risk of MACRO in patients with T2DM and enabled patients at higher-risk of DKD to be identified in the absence of baseline diabetic nephropathy, CKD, or proteinuria. These models could help establish strategies to reduce the risk of MACRO in T2DM patients.