Dataset Information

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

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.

SUBMITTER: Kwon JM

PROVIDER: S-EPMC6613702 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Kwon Joon-Myoung JM Kim Kyung-Hee KH Jeon Ki-Hyun KH Lee Sang Eun SE Lee Hae-Young HY Cho Hyun-Jai HJ Choi Jin Oh JO Jeon Eun-Seok ES Kim Min-Seok MS Kim Jae-Joong JJ Hwang Kyung-Kuk KK Chae Shung Chull SC Baek Sang Hong SH Kang Seok-Min SM Choi Dong-Ju DJ Yoo Byung-Su BS Kim Kye Hun KH Park Hyun-Young HY Cho Myeong-Chan MC Oh Byung-Hee BH

PloS one 20190708 7

<h4>Aims</h4>This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).<h4>Methods and results</h4>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 ...[more]

PMID: 31283783

Similar Datasets

Project description:Background In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24?h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14?days. Results We used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24?h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P?<?0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P?<?0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.

Project description:Background Our aim was to calibrate and externally revalidate the ELAN-HF (European Collaboration on Acute Decompensated Heart Failure) score, to confirm and improve on a previous external validation of the risk score. Methods and Results The ELAN-HF score predicts 6-month all-cause mortality in patients hospitalized for acute decompensated heart failure using absolute and percentage change of NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels in addition to clinical variables. For the external validation, we used the PRIMA II (Can NT-proBNP-Guided Therapy During Hospital Admission for Acute Decompensated Heart Failure Reduce Mortality and Readmissions?) trial. For both data sets, observed versus predicted mortality was compared for the 4 risk categories; and the mean predicted mortality was plotted against the observed mortality with calculation of a correlation coefficient and SEE. The model discriminant ability was determined by comparing the C-statistics for both data sets. The predicted versus actual 6-month mortality values in the derivation cohort were 3.7% versus 3.6% for the low-risk category, 9.4% versus 9.2% for the intermediate-risk category, 24.2% versus 23.5% for the high-risk category, and 54.2% versus 51.1% for the very-high-risk category. The correlation between predicted and observed mortality by deciles was 0.92, with an SEE of ±4%. In the validation cohort, predicted versus actual 6-month mortality values were 3.0% versus 2.2% for the low-risk category, 9.4% versus 8.2% for the intermediate-risk category, 25.0% versus 22.9% for the high-risk category, and 56.8% versus 53.6% for the very-high-risk category. The correlation between predicted and actual mortality by quintiles was 0.99, with an SEE of ±2%. There was no significant difference in C-statistic between the derivation cohort (0.78; 95% CI, 0.74-0.82) and the validation cohort (0.77; 95% CI, 0.69-0.84; P=0.693). Conclusions Our study confirms that the ELAN-HF score predicts accurately 6-month mortality in patients hospitalized for acute decompensated heart failure with the use of easily obtained characteristics.

Dataset Information

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

Aims

Methods and results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets