Why the study?
Accurate prediction of mortality and readmission risk guides decision making in HF, but traditional predictive models have reached only modest accuracy.
Do machine learning predictive models improve outcome prediction accuracy compared to traditional statistical scores in heart failure patients?
Do machine learning predictive models improve outcome prediction accuracy compared to traditional statistical scores in heart failure patients?
Machine learning approaches can outperform conventional risk scores in predicting outcomes for heart failure patients, though external validation is currently infrequent.
ML may improve HF risk stratification over statistical scores; extends comparative evidence but external validation remains limited.
Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.
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Błaziak et al. (2022) studied this question.
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