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

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

Joon-Myoung Kwon  1   2 , Kyung-Hee Kim  3 , Ki-Hyun Jeon  1   3 , Sang Eun Lee  4 , Hae-Young Lee  5 , Hyun-Jai Cho  5 , Jin Oh Choi  6 , Eun-Seok Jeon  6 , Min-Seok Kim  4 , Jae-Joong Kim  4 , Kyung-Kuk Hwang  7 , Shung Chull Chae  8 , Sang Hong Baek  9 , Seok-Min Kang  10 , Dong-Ju Choi  11 , Byung-Su Yoo  12 , Kye Hun Kim  13 , Hyun-Young Park  14 , Myeong-Chan Cho  7 , Byung-Hee Oh  3
Affiliations

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

Joon-Myoung Kwon et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study flowchart.
KorAHF denotes Korean Acute Heart Failure registry.
Fig 2
Fig 2. Train and validation of deep-learning prediction model.
DAHF denotes deep-learning-based artificial intelligence algorithm for predicting mortality of patients with acute heart failure. Abbreviations: DBP, diastolic blood pressure; DNN, deep neural network; ECHO, echocardiography; ECG, electrocardiography; Hb, hemoglobin; LAD, left atrium dimension; LVDd, left ventricle end-diastolic dimension; QRS, QRS duration; QTc, corrected QT duration.
Fig 3
Fig 3. Receiver operating characteristic curve for predicting in-hospital mortality.
AUC, area under the receiver operating characteristic curve; CI, confidence interval; GWTG-HF, Get with the Guideline–Heart Failure.
Fig 4
Fig 4. Receiver operating characteristic curves for predicting long-term mortalities.
AUC, area under the receiver operating characteristic curve; CI, confidence interval; MAGGIC, Meta-Analysis Global Group in Chronic Heart Failure.
Fig 5
Fig 5. Cumulative hazard of 36-month mortality by deep-learning-based algorithm risk group.
See this image and copyright information in PMC

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