Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids

Abstract

Rationale: To test whether novel biomarkers, such as microribonucleic acids (miRNAs), and nonstandard predictive models, such as decision tree learning, provide useful information for medical decision-making in patients on hemodialysis (HD). Methods: Samples from patients with end-stage renal disease receiving HD included in the AURORA trial were investigated (n=810). The study included two independent phases: phase I (matched cases and controls, n=410) and phase II (unmatched cases and controls, n=400). The composite endpoint was cardiovascular death, nonfatal myocardial infarction or nonfatal stroke. miRNA quantification was performed using miRNA sequencing and RT-qPCR. The CART algorithm was used to construct regression tree models. A bagging-based procedure was used for validation. Results: In phase I, miRNA sequencing in a subset of samples (n=20) revealed miR-632 as a candidate (fold change=2.9). miR-632 was associated with the endpoint, even after adjusting for confounding factors (HR from 1.43 to 1.53). These findings were not reproduced in phase II. Regression tree models identified eight patient subgroups with specific risk patterns. miR-186-5p and miR-632 entered the tree by redefining two risk groups: patients older than 64 years and with hsCRP<0.827 mg/L and diabetic patients younger than 64 years. miRNAs improved the discrimination accuracy at the beginning of the follow-up (24 months) compared to the models without miRNAs (integrated AUC [iAUC]=0.71). Conclusions: The circulating miRNA profile complements conventional risk factors to identify specific cardiovascular risk patterns among patients receiving maintenance HD.

Bronchial Aspirate-Based Profiling Identifies MicroRNA Signatures Associated With COVID-19 and Fatal Disease in Critically Ill Patients

“…Simultaneous assessment of miRNA profiles or miRNA ratios may hold promise to provide more comprehensive information about the clinical evolution of the patient. This hypothesis, which seems to be true for circulating miRNAs (34), can also be translated to respiratory specimens such as BAS. Using multivariable analysis for multimarker analysis, we constructed a miRNA ratio-based prediction model (miRNA ratio score) to optimize the best combination of ratios for risk prediction.…”

Circulating microRNA profiles predict the severity of COVID-19 in hospitalized patients

“…The ideal scenario for miRNA testing seems to be based on the concept of "several miRNAs-one disease", contrary to the traditional "one miRNA-one disease" concept. 12 Therefore, we explored whether COVID-19 severity could be associated with a specific miRNA signature. Multivariate predictive models were constructed using a variable selection process based on LASSO regression.…”

“…10 The results suggest that miRNAs are sensitive, robust and cost-effective biomarkers that offer additional information to clinical variables and already established clinical indicators. 11,12 Indeed, several miRNA-based diagnostic products are already available for clinical practice. 13 Here, we aimed to examine the circulating miRNA profile of hospitalized COVID-19 patients and explore the potential role and clinical significance as biomarkers of disease severity.…”

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning

“…For example, Alvin et al pointed out that the predictive model based on machine learning could reliably identify patients who have high-risk diseases and increase the utilization of healthcare services ( Rajkomar et al, 2019 ). David et al used machine learning algorithms to improve the cardiovascular risk prediction of patients with end-stage renal disease on hemodialysis ( de Gonzalo-Calvo et al, 2020 ). Michalis et al also found that, based on machine learning algorithms, using volatile organic compounds in exhaled gas as predictors distinguishes lung cancer from other lung diseases or healthy individuals well ( Zhang Z. et al, 2021 ).…”

“…However, the LG-based approach fails to consider the complex non-linear interactions between variables, which can be captured by more sophisticated model algorithms, thus improving the accuracy of risk prediction. Recently, machine learning has been widely applied to the development of clinical tools for disease diagnosis ( Rajkomar et al, 2019 ; de Gonzalo-Calvo et al, 2020 ; Zhang Z. et al, 2021 ). Unlike the traditional LG-based approach, machine learning can recognize hidden patterns and non-linear interactions in complex data, allowing for a better assessment of clinical outcomes ( Myszczynska et al, 2020 ).…”