Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review

A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease

Introduction

This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD).

Methods

Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics.

Results

The findings showed that the least absolute shrinkage and selection operator regression model had the highest accuracy (C-index = 0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate, 24-h urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897-0.962). In addition, for the CVD risk prediction, the random survival forest model with the highest accuracy (C-index = 0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633-0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho.

Conclusion

We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.

One Heartbeat Away from a Prediction Model for Cardiovascular Diseases in Patients with Chronic Kidney Disease: A Systematic Review

INTRODUCTION

Patients with chronic kidney disease (CKD) have a high risk of cardiovascular disease (CVD). Prediction models, combining clinical and laboratory characteristics, are commonly used to estimate an individual's CVD risk. However, these models are not specifically developed for patients with CKD and may therefore be less accurate. In this review, we aim to give an overview of CVD prognostic studies available, and their methodological quality, specifically for patients with CKD.

METHODS

MEDLINE was searched for papers reporting CVD prognostic studies in patients with CKD published between 2012 and 2021. Characteristics regarding patients, study design, outcome measurement, and prediction models were compared between included studies. The risk of bias of studies reporting on prognostic factors or the development/validation of a prediction model was assessed with, respectively, the QUIPS and PROBAST tool.

RESULTS

In total, 134 studies were included, of which 123 studies tested the incremental value of one or more predictors to existing models or common risk factors, while only 11 studies reported on the development or validation of a prediction model. Substantial heterogeneity in cohort and study characteristics, such as sample size, event rate, and definition of outcome measurements, was observed across studies. The most common predictors were age (87%), sex (75%), diabetes (70%), and estimated glomerular filtration rate (69%). Most of the studies on prognostic factors have methodological shortcomings, mostly due to a lack of reporting on clinical and methodological information. Of the 11 studies on prediction models, six developed and internally validated a model and four externally validated existing or developed models. Only one study on prognostic models showed a low risk of bias and high applicability.

CONCLUSION

A large quantity of prognostic studies has been published, yet their usefulness remains unclear due to incomplete presentation, and lack of external validation of prognostic models. Our review can be used to select the most appropriate prognostic model depending on the patient population, outcome, and risk of bias. Future collaborative efforts should aim at improving existing models by externally validating them, evaluating the addition of new predictors, and assessment of the clinical impact.

REGISTRATION

We have registered the protocol of our systematic review on PROSPERO (CRD42021228043).

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

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An overview of systematic reviews on the application of AI including 129 studies.

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AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms.

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Half of the reviews did not evaluate validation procedures nor reporting guidelines.

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Risk of bias was only included un a third of the reviews.

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There is not sufficient evidence to transfer AI to actual healthcare delivery.

Background

Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges.

Objective

To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality.

Methods

A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist.

Results

The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews.

Conclusion

Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.

Artificial intelligence in peritoneal dialysis: general overview

OBJECTIVE

This article is a general overview about artificial intelligence/machine learning (AI/ML) algorithms in the domain of peritoneal dialysis (PD).

METHODS

We searched studies that used AI/ML in PD, which were classified according to the type of algorithm and PD issue.

RESULTS

Studies were divided into (a) predialytic stratification, (b) peritoneal technique issues, (c) infections, and (d) complications prediction. Most of the studies were observational and majority of them were reported after 2010.

CONCLUSIONS

There is a number of studies proved that AI/ML algorithms can predict better than conventional statistical method and even nephrologists. However, the soundness of AI/ML algorithms in PD still requires large databases and interpretation by clinical experts. In the future, we hope that AI will facilitate the management of PD patients, thus increasing the quality of life and survival.