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Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

ABSTRACT: Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

SUBMITTER: Tripoliti EE

PROVIDER: S-EPMC5133661 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

Tripoliti Evanthia E EE Papadopoulos Theofilos G TG Karanasiou Georgia S GS Naka Katerina K KK Fotiadis Dimitrios I DI

Computational and structural biotechnology journal 20161117

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires ...[more]

PMID: 27942354

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Project description:BackgroundInitiation and up-titration of guideline-directed medical therapies (GDMTs) for heart failure with reduced ejection fraction (HFrEF) remains suboptimal, in part because of concerns regarding tolerability and adverse events (AEs).ObjectivesThe authors sought to compare rates of AE in patients randomized to GDMT medication vs placebo in a meta-analysis of landmark cardiovascular outcomes trials.MethodsThe authors assessed rates of reported AE in 17 landmark HFrEF clinical trials across each class of GDMT in the placebo and intervention arms. The overall rates of AE for each drug class, the absolute difference in frequency in AEs between the placebo and intervention arms, and the odds of each AE according based on randomization strata were calculated.ResultsAE were reported commonly in trials across each class of GDMT, with 75% to 85% of participants reporting at least 1 AE. There was no significant difference in the frequency of AE between the intervention and placebo arms, except for angiotensin-converting enzyme inhibitors (87.0% [95% CI: 85.0%-88.8%] vs 82.0% [95% CI: 79.8%-84.0%], absolute difference: +5% with intervention; P < 0.001). There was no significant difference in drug discontinuation because of AE between placebo and intervention arms in angiotensin-converting enzyme inhibitors, mineralocorticoid receptor antagonists, sodium glucose cotransporter 2 inhibitors, or angiotensin receptor neprilysin inhibitor/angiotensin II receptor blocker trials. Patients randomized to beta-blocker were significantly less likely to stop study drug because of AE than placebo (11.3% [95% CI: 10.3%-12.3%] vs 13.7% [95% CI: 12.5%-14.9%], absolute difference: -1.1%; P = 0.015). When individual types of AE were assessed, the initiation of an intervention vs placebo resulted in small differences in absolute frequency of AE that were largely not statistically significant.ConclusionsIn clinical trials of GDMT for HFrEF, AEs are observed frequently. However, rates of AE are similar between active medication and control, suggesting these may reflect the high risk nature of the heart failure disease state rather than be attributive to a specific therapy.

Project description:ObjectivesHeart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may slow progression in some patients. Preferably, it is desired to reuse already measured data for screening of all persons in an age group, such as general practitioner (GP) data. Furthermore, it is essential to evaluate the number of people needed to screen to find one patient using true incidence rates, as this indicates the generalisability in the true population. Therefore, we aim to create a machine learning model for the prediction of HF using GP data and evaluate the number needed to screen with true incidence rates.Design, settings and participantsGP data from 8543 patients (-2 to -1 year before diagnosis) and controls aged 70+ years were obtained retrospectively from 01 January 2012 to 31 December 2019 from the Nivel Primary Care Database. Codes about chronic illness, complaints, diagnostics and medication were obtained. Data were split in a train/test set. Datasets describing demographics, the presence of codes (non-sequential) and upon each other following codes (sequential) were created. Logistic regression, random forest and XGBoost models were trained. Predicted outcome was the presence of HF after 1 year. The ratio case:control in the test set matched true incidence rates (1:45).ResultsSole demographics performed average (area under the curve (AUC) 0.692, CI 0.677 to 0.706). Adding non-sequential information combined with a logistic regression model performed best and significantly improved performance (AUC 0.772, CI 0.759 to 0.785, p<0.001). Further adding sequential information did not alter performance significantly (AUC 0.767, CI 0.754 to 0.780, p=0.07). The number needed to screen dropped from 14.11 to 5.99 false positives per true positive.ConclusionThis study created a model able to identify patients with pending HF a year before diagnosis.

Dataset Information

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

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Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

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