Abstract
AimsThis study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).Methods and results12,654 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).ConclusionDAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.
Highlights
We developed and validated a deep-learning-based artificial intelligence algorithm for predicting mortality of patients with acute heart failure (DAHF) by using a large data from 12 hospitals
We developed four machine-learning models, random forest (RF), logistic regression (LR), supportive vector machine (SVM), and Bayesian network (BN), for the performance comparison with DAHF.[17]
After we developed the DAHF and machine-learning models, we compared the performance of these models with the conventional prediction scoring
Summary
26 million adults worldwide have heart failure, and acute heart failure (AHF) is the leading cause of hospitalization in Europe and the United States, resulting in more than 1 million admissions, and representing 1%–2% of all hospitalizations.[1,2] In the past decades, the mortality rate of AHF has improved with advances in treatment, but AHF is still a leading cause of mortality worldwide.[1,2,3] Risk stratification and prognosis prediction are critical in identifying high-risk patients and decision making for the treatment of patients with AHF.There are several mortality prediction models for heart failure, such as Get with the Guidelines-Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score.[4,5] these prognostic models have limitations for the current daily practice. GWTG-HF and MAGGIC were developed only for in-hospital and long-term mortality, respectively.[4,5] Second, because the accuracies of these methods are unsatisfactory, these methods cannot be used to decide the treatment of the patient. These models use only limited information that is based on a conventional statistical approach, such as multivariate analysis by the logistic regression model that has a potential limitation of information loss.[6,7,8]
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