Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

PLoS One. 2019 Jul 8;14(7):e0219302. doi: 10.1371/journal.pone.0219302. eCollection 2019.

Abstract

Aims: This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).

Methods and results: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines-Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876-0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720-0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001).

Conclusion: DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Area Under Curve
  • Artificial Intelligence
  • Chronic Disease
  • Deep Learning
  • Female
  • Forecasting / methods*
  • Heart Failure / mortality*
  • Hospital Mortality / trends
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Middle Aged
  • Prognosis
  • ROC Curve
  • Registries
  • Republic of Korea / epidemiology
  • Risk Assessment / methods
  • Risk Factors

Grants and funding

The KorAHF registry was supported by Research of Korea Centers for Disease Control and Prevention [2010-E63003-00, 2011-E63002-00, 2012-E63005-00, 2013-E63003-00, 2013-E63003-01, 2013-E63003-02, 2016-ER6303-00, 2016-ER6303-01]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.