Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database
Background Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine lea...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2303395 |
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| author | Shengwei Lin Wenbin Lu Ting Wang Ying Wang Xueqian Leng Lidan Chi Peipei Jin Jinjun Bian |
| author_facet | Shengwei Lin Wenbin Lu Ting Wang Ying Wang Xueqian Leng Lidan Chi Peipei Jin Jinjun Bian |
| author_sort | Shengwei Lin |
| collection | DOAJ |
| description | Background Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.Methods Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation.Results We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM’s performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903).Conclusions The GBM model’s precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care. |
| format | Article |
| id | doaj-art-6c99f1bec98141418863a1c87e28b8e2 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-6c99f1bec98141418863a1c87e28b8e22025-01-23T04:17:49ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2303395Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV databaseShengwei Lin0Wenbin Lu1Ting Wang2Ying Wang3Xueqian Leng4Lidan Chi5Peipei Jin6Jinjun Bian7Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaFaculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, ChinaBackground Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.Methods Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation.Results We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM’s performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903).Conclusions The GBM model’s precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2303395Acute kidney injuryacute pancreatitismachine learningprediction modelMIMIC- IV database |
| spellingShingle | Shengwei Lin Wenbin Lu Ting Wang Ying Wang Xueqian Leng Lidan Chi Peipei Jin Jinjun Bian Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database Renal Failure Acute kidney injury acute pancreatitis machine learning prediction model MIMIC- IV database |
| title | Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database |
| title_full | Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database |
| title_fullStr | Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database |
| title_full_unstemmed | Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database |
| title_short | Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database |
| title_sort | predictive model of acute kidney injury in critically ill patients with acute pancreatitis a machine learning approach using the mimic iv database |
| topic | Acute kidney injury acute pancreatitis machine learning prediction model MIMIC- IV database |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2303395 |
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