Academic Journal
Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning
| العنوان: | Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning |
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| المؤلفون: | Tsai-Jung Wang, Chun-Te Huang, Chieh-Liang Wu, Cheng-Hsu Chen, Min-Shian Wang, Wen-Cheng Chao, Yi-Chia Huang, Kai-Chih Pai |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Acute kidney injury, Dialysis, Intensive care, Machine learning, Renal recovery, Medicine, Science |
| الوصف: | Abstract Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied area. This retrospective cohort study, conducted in a medical center in Taiwan from 2015 to 2020, enrolled patients with AKI-D during intensive care unit stays. We aimed to develop and temporally test models for predicting dialysis liberation before hospital discharge using machine learning algorithms and explore early predictors. The dataset comprised 90 routinely collected variables within the first three days of dialysis initiation. Out of 1,381 patients who received acute dialysis, 27.3% experienced renal recovery. The cohort was divided into the training group (N = 1135) and temporal testing group (N = 251). The models demonstrated good performance, with an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.81–0.88) and an area under the precision-recall curve of 0.69 (95% CI, 0.62–0.76) for the XGBoost model. Key predictors included urine volume, Charlson comorbidity index, vital sign derivatives (trend of respiratory rate and SpO2), and lactate levels. We successfully developed early prediction models for renal recovery by integrating early changes in vital signs and inputs/outputs, which have the potential to aid clinical decision-making in the ICU. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-63992-y |
| URL الوصول: | https://doaj.org/article/ec6cd84050a742bfa8459f5c5a83c2de |
| رقم الانضمام: | edsdoj.6cd84050a742bfa8459f5c5a83c2de |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-63992-y |