Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury
Background Although current guidelines didn’t support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an explainable machine learning (ML) model to differentiate between fu...
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Taylor & Francis Group
2023-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2022.2151468 |
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| author | Meng Jiang Chun-qiu Pan Jian Li Li-gang Xu Chang-li Li |
| author_facet | Meng Jiang Chun-qiu Pan Jian Li Li-gang Xu Chang-li Li |
| author_sort | Meng Jiang |
| collection | DOAJ |
| description | Background Although current guidelines didn’t support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an explainable machine learning (ML) model to differentiate between furosemide-responsive (FR) and furosemide-unresponsive (FU) oliguric AKI.Methods From Medical Information Mart for Intensive Care-IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD), oliguric AKI patients with urine output (UO) < 0.5 ml/kg/h for the first 6 h after ICU admission and furosemide infusion ≥ 40 mg in the following 6 h were retrospectively selected. The MIMIC-IV cohort was used in training a XGBoost model to predict UO > 0.65 ml/kg/h during 6–24 h succeeding the initial 6 h for assessing oliguria, and it was validated in the eICU-CRD cohort. We compared the predictive performance of the XGBoost model with the traditional logistic regression and other ML models.Results 6897 patients were included in the MIMIC-IV training cohort, with 2235 patients in the eICU-CRD validation cohort. The XGBoost model showed an AUC of 0.97 (95% CI: 0.96–0.98) for differentiating FR and FU oliguric AKI. It outperformed the logistic regression and other ML models in correctly predicting furosemide diuretic response, achieved 92.43% sensitivity (95% CI: 90.88–93.73%) and 95.12% specificity (95% CI: 93.51–96.3%).Conclusion A boosted ensemble algorithm can be used to accurately differentiate between patients who would and would not respond to furosemide in oliguric AKI. By making the model explainable, clinicians would be able to better understand the reasoning behind the prediction outcome and make individualized treatment. |
| format | Article |
| id | doaj-art-1c5b152e128843be80a2f7b520350515 |
| institution | DOAJ |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-1c5b152e128843be80a2f7b5203505152025-08-20T02:56:11ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492023-12-0145110.1080/0886022X.2022.2151468Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injuryMeng Jiang0Chun-qiu Pan1Jian Li2Li-gang Xu3Chang-li Li4Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Traumatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Critical Care Medicine, Wuhan Central Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of FSTC Clinic of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaBackground Although current guidelines didn’t support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an explainable machine learning (ML) model to differentiate between furosemide-responsive (FR) and furosemide-unresponsive (FU) oliguric AKI.Methods From Medical Information Mart for Intensive Care-IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD), oliguric AKI patients with urine output (UO) < 0.5 ml/kg/h for the first 6 h after ICU admission and furosemide infusion ≥ 40 mg in the following 6 h were retrospectively selected. The MIMIC-IV cohort was used in training a XGBoost model to predict UO > 0.65 ml/kg/h during 6–24 h succeeding the initial 6 h for assessing oliguria, and it was validated in the eICU-CRD cohort. We compared the predictive performance of the XGBoost model with the traditional logistic regression and other ML models.Results 6897 patients were included in the MIMIC-IV training cohort, with 2235 patients in the eICU-CRD validation cohort. The XGBoost model showed an AUC of 0.97 (95% CI: 0.96–0.98) for differentiating FR and FU oliguric AKI. It outperformed the logistic regression and other ML models in correctly predicting furosemide diuretic response, achieved 92.43% sensitivity (95% CI: 90.88–93.73%) and 95.12% specificity (95% CI: 93.51–96.3%).Conclusion A boosted ensemble algorithm can be used to accurately differentiate between patients who would and would not respond to furosemide in oliguric AKI. By making the model explainable, clinicians would be able to better understand the reasoning behind the prediction outcome and make individualized treatment.https://www.tandfonline.com/doi/10.1080/0886022X.2022.2151468Oliguric acute kidney injuryfurosemide responsivenessmachine learningXGBoost modeling |
| spellingShingle | Meng Jiang Chun-qiu Pan Jian Li Li-gang Xu Chang-li Li Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury Renal Failure Oliguric acute kidney injury furosemide responsiveness machine learning XGBoost modeling |
| title | Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury |
| title_full | Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury |
| title_fullStr | Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury |
| title_full_unstemmed | Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury |
| title_short | Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury |
| title_sort | explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury |
| topic | Oliguric acute kidney injury furosemide responsiveness machine learning XGBoost modeling |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2022.2151468 |
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