Dynamic SOFA component scores-based deep learning for short to long-term mortality prediction in sepsis survivors
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| Publicado no: | Journal of Big Data vol. 12, no. 1 (Jul 2025), p. 165 |
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| Autor principal: | |
| Outros Autores: | , , , , , , |
| Publicado em: |
Springer Nature B.V.
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| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 3229357675 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2196-1115 | ||
| 024 | 7 | |a 10.1186/s40537-025-01234-2 |2 doi | |
| 035 | |a 3229357675 | ||
| 045 | 2 | |b d20250701 |b d20250731 | |
| 100 | 1 | |a Wei, Juan |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 245 | 1 | |a Dynamic SOFA component scores-based deep learning for short to long-term mortality prediction in sepsis survivors | |
| 260 | |b Springer Nature B.V. |c Jul 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundSepsis survivors face substantial risks of late mortality following discharge, underscoring the critical need for early prediction and targeted interventions for this vulnerable population. Early identification of those at high risk of mortality following discharge may optimize healthcare resource allocation. We sought to feed common clinical available data to a deep learning algorithm for predicting short to long-term mortality in sepsis survivors.MethodsThis retrospective study, using a real-world database (MIMIC-IV database), screened adult critically ill patients with sepsis (as defined by Sepsis-3) admitted to the ICU with stays exceeding four days and who were discharged alive. Static features including patient characteristics, comorbidities, laboratory tests at ICU admission, and each dynamic SOFA component score over the first four days post-ICU admission were collected. We developed a deep learning-based combined model (DL-CMT) for post-discharge mortality prediction using multidimensional and time-series data. Comparisons were made with a multilayer perceptron and two machine learning models of random forest and eXtreme Gradient Boosting (XGBoost).Results7532 patients fulfilled the inclusion criteria, and the observed mortality rates were 30.7% at 28 days, 33.6% at 90 days, and 39.4% at one year post-ICU discharge. The proposed DL-CMT model achieved the best performance for mortality prediction at all the intervals, with area under the receiver operating characteristic curve of 0.95 (95% confidence interval [CI] 0.93–0.96), 0.92 (95% CI 0.90–0.94), and 0.90 (95% CI 0.87–0.92), respectively. Our model outperformed the multilayer perceptron, random forest, and XGBoost in all endpoints. Ablation experiments confirmed the model’s robustness, maintaining performance despite the absence of a specific physiological component.ConclusionsSepsis survivors have persistently high mortality risks post-discharge. The DL-CMT model, leveraging dynamic SOFA component scores and static features, demonstrated superior predictive performance for short to long-term mortality. This model has the potential to assist clinicians in optimizing post-discharge management and improving follow-up care. | |
| 653 | |a Patients | ||
| 653 | |a Electronic health records | ||
| 653 | |a Deep learning | ||
| 653 | |a Sepsis | ||
| 653 | |a Mortality | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Neural networks | ||
| 653 | |a Survival | ||
| 653 | |a Ablation | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Hospitals | ||
| 653 | |a Data processing | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Intensive care | ||
| 653 | |a Global health | ||
| 653 | |a Survival analysis | ||
| 653 | |a Critical care | ||
| 653 | |a Hematology | ||
| 653 | |a Big Data | ||
| 653 | |a Databases | ||
| 653 | |a Experiments | ||
| 653 | |a Long term | ||
| 653 | |a Robustness | ||
| 653 | |a Survivor | ||
| 653 | |a Mortality rates | ||
| 653 | |a Anatomical systems | ||
| 653 | |a Discharge | ||
| 653 | |a Predictions | ||
| 653 | |a Vulnerability | ||
| 653 | |a High risk | ||
| 653 | |a Health services | ||
| 653 | |a Health care | ||
| 700 | 1 | |a Lin, Feihong |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 700 | 1 | |a Jin, Tian |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 700 | 1 | |a Yao, Qian |u Tongji University, Clinical Research Center, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 700 | 1 | |a Wang, Sheng |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 700 | 1 | |a Feng, Di |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 700 | 1 | |a Lv, Xin |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535) | |
| 700 | 1 | |a He, Wen |u Tongji University, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Shanghai, China (GRID:grid.24516.34) (ISNI:0000000123704535); The Quzhou Affiliated Hospital of Wenzhou Medical University, Reproductive Medicine Center, Quzhou People’s Hospital, Quzhou, China (GRID:grid.268099.c) (ISNI:0000 0001 0348 3990) | |
| 773 | 0 | |t Journal of Big Data |g vol. 12, no. 1 (Jul 2025), p. 165 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
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