Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation
Abstract Aims Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real‐world data. This study aimed to develop a deep learn...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | ESC Heart Failure |
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| Online Access: | https://doi.org/10.1002/ehf2.14918 |
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| author | Mi‐Na Kim Yong Seok Lee Youngmin Park Ayoung Jung Hanjee So Joonwoong Park Jin‐Joo Park Dong‐Joo Choi So‐Ree Kim Seong‐Mi Park |
| author_facet | Mi‐Na Kim Yong Seok Lee Youngmin Park Ayoung Jung Hanjee So Joonwoong Park Jin‐Joo Park Dong‐Joo Choi So‐Ree Kim Seong‐Mi Park |
| author_sort | Mi‐Na Kim |
| collection | DOAJ |
| description | Abstract Aims Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real‐world data. This study aimed to develop a deep learning‐based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge. Methods and results We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning‐based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty‐two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time‐independent and 16 time‐dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow‐up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points. Conclusions Our deep learning‐based model using real‐world data could provide valid predictions of HF rehospitalization in 1 year follow‐up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization. |
| format | Article |
| id | doaj-art-b3f208eb489243c9aa5b6c64a1008bbb |
| institution | OA Journals |
| issn | 2055-5822 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | ESC Heart Failure |
| spelling | doaj-art-b3f208eb489243c9aa5b6c64a1008bbb2025-08-20T02:39:04ZengWileyESC Heart Failure2055-58222024-12-011163702371210.1002/ehf2.14918Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validationMi‐Na Kim0Yong Seok Lee1Youngmin Park2Ayoung Jung3Hanjee So4Joonwoong Park5Jin‐Joo Park6Dong‐Joo Choi7So‐Ree Kim8Seong‐Mi Park9Department of Internal Medicine, Division of Cardiology, Anam Hospital Korea University Medicine Seoul KoreaData Analytics Group, Samsung SDS Seoul KoreaData Analytics Group, Samsung SDS Seoul KoreaData Analytics Group, Samsung SDS Seoul KoreaData Analytics Group, Samsung SDS Seoul KoreaData Analytics Group, Samsung SDS Seoul KoreaDepartment of Internal Medicine, Division of Cardiology Seoul National University Bundang Hospital Seongnam KoreaDepartment of Internal Medicine, Division of Cardiology Seoul National University Bundang Hospital Seongnam KoreaDepartment of Internal Medicine, Division of Cardiology, Anam Hospital Korea University Medicine Seoul KoreaDepartment of Internal Medicine, Division of Cardiology, Anam Hospital Korea University Medicine Seoul KoreaAbstract Aims Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real‐world data. This study aimed to develop a deep learning‐based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge. Methods and results We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning‐based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty‐two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time‐independent and 16 time‐dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow‐up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points. Conclusions Our deep learning‐based model using real‐world data could provide valid predictions of HF rehospitalization in 1 year follow‐up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization.https://doi.org/10.1002/ehf2.14918Deep learningHeart failureRehospitalizationRisk assessment |
| spellingShingle | Mi‐Na Kim Yong Seok Lee Youngmin Park Ayoung Jung Hanjee So Joonwoong Park Jin‐Joo Park Dong‐Joo Choi So‐Ree Kim Seong‐Mi Park Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation ESC Heart Failure Deep learning Heart failure Rehospitalization Risk assessment |
| title | Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation |
| title_full | Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation |
| title_fullStr | Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation |
| title_full_unstemmed | Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation |
| title_short | Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation |
| title_sort | deep learning for predicting rehospitalization in acute heart failure model foundation and external validation |
| topic | Deep learning Heart failure Rehospitalization Risk assessment |
| url | https://doi.org/10.1002/ehf2.14918 |
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