A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations

Abstract

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care. Lay summary Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion. Graphical abstract

Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease.

“…The models developed obtained an S of 76 to 88% and an E of 85%, values similar to those of the models in our study. Recently, Morrill et al 30 reported diagnostic models of decompensated HF developed with ML techniques with an S of 100% and E of 73% but based on simulated clinical situations and not real patients.…”

Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease

“…The models developed obtained an S of 76 to 88% and an E of 85%, values similar to those of the models in our study. Recently, Morrill et al 33 reported diagnostic models of decompensated HF developed with ML techniques with an S of 100% and E of 73% but based on simulated clinical situations and not real patients.…”

A telehealth application for adequate hospital visit advice by physicians affected users’ subsequent hospital visit behavior: a historical cohort study with machine learning

“…Telehealth provides support for adequate hospital visiting (Hollander and Carr, 2020;Sivarajasingam, 2021). In fact, several studies have reported that an online strategy provided effective support for adequate hospital visiting for emergency patients and patients with burns, heart failure, and cervical cancer (Arrossi et al, 2019;Carmichael et al, 2020;Chai et al, 2021;Beyer et al, 2022;Morrill et al, 2022). A study that conducted telemedicine screening assessments on four diagnostic cohorts-patients with gastroenteritis, psychiatric conditions, burn, or fractures-reported that the median doorto-provider times were reduced through telemedicine screening (Friedman et al, 2021).…”