Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients

Dovgan, Erik; Gradišek, Anton; Luštrek, Mitja; Uddin, Mohy; Nursetyo, Aldilas Achmad; Annavarajula, Sashi Kiran; Li, Yu-Chuan; Syed-Abdul, Shabbir

doi:10.1371/journal.pone.0233976

Cited by 38 publications

(30 citation statements)

References 40 publications

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“…The prediction articles focused on which PLWMCCs are at‐risk for death, hospitalizations, exacerbations of illness, disability, frailty, and other poor outcomes. Researchers found success in predicting mortality, hospitalizations, disability/frailty, and illness exacerbations or complications but rarely applied these algorithms to change care or reallocate resources 96,102,103,105–108,110,112,113 . For PLWMCCs, however, adding data beyond conditions was helpful—from personal emergency response to drug‐derived indices to measures of adverse childhood experiences, additional data sources helped increase the accuracy and potential actionability of the metrics 106,111 .…”

Section: Resultsmentioning

confidence: 99%

Health information technology to improve care for people with multiple chronic conditions

Samal

Camara

et al. 2021

Health Services Research

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Objective To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. Data Sources We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. Study Design We identified studies of health IT interventions for PLWMCC across three domains as follows: self‐management support, care coordination, and algorithms to support clinical decision making. Data Collection/Extraction Methods Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. Principal Findings The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi‐experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient‐centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. Conclusions Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.

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Section: Resultsmentioning

confidence: 99%

Health information technology to improve care for people with multiple chronic conditions

Samal

Camara

et al. 2021

Health Services Research

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“…Finally, as an advanced statistical approach, machine learning is being adopted in clinical CKD research. It has been used in several studies, including used with comorbidity data to predict kidney replacement therapy within 12 months of CKD diagnosis [ 59 ], and creation of biomarker panels using kidney measurements, dyslipidaemia biomarkers, serum sodium, and c-reactive protein to determine progressive CKD [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease

et al. 2020

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Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).

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“…Therefore, it can minimize errors, maximize models' performance, and reduce tree construction time. The central idea in XGBoost is to make a new model to correct the errors in the previous training model, then make the prediction [20].…”

Section: Multi-variate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Huang

Chen

et al. 2021

Healthcare

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Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients

Cited by 38 publications

References 40 publications

Health information technology to improve care for people with multiple chronic conditions

Health information technology to improve care for people with multiple chronic conditions

Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

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