A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study
BackgroundAcute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality. ObjectiveThis study aimed to establish and validate predictive models for AKI in hospitalized patients with...
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JMIR Publications
2024-12-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e51255 |
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| author | Mengqing Ma Caimei Chen Dawei Chen Hao Zhang Xia Du Qing Sun Li Fan Huiping Kong Xueting Chen Changchun Cao Xin Wan |
| author_facet | Mengqing Ma Caimei Chen Dawei Chen Hao Zhang Xia Du Qing Sun Li Fan Huiping Kong Xueting Chen Changchun Cao Xin Wan |
| author_sort | Mengqing Ma |
| collection | DOAJ |
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BackgroundAcute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality.
ObjectiveThis study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms.
MethodsWe trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). Feature selection was conducted using the sliding window forward feature selection technique. Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation.
ResultsA total of 6371 patients with CAP met the inclusion criteria. The development of CAP-associated AKI (CAP-AKI) was recognized in 1006 (15.8%) patients. The 11 selected indicators were sex, temperature, breathing rate, diastolic blood pressure, C-reactive protein, albumin, white blood cell, hemoglobin, platelet, blood urea nitrogen, and neutrophil count. The DF model achieved the best area under the receiver operating characteristic curve (AUC) and accuracy in the internal (AUC=0.89, accuracy=0.90) and external validation sets (AUC=0.87, accuracy=0.83). Furthermore, the DF model had the best calibration among all models. In addition, a web-based prediction platform was developed to predict CAP-AKI.
ConclusionsThe model described in this study is the first multicenter-validated AKI prediction model that accurately predicts CAP-AKI during hospitalization. The web-based prediction platform embedded with the DF model serves as a user-friendly tool for early identification of high-risk patients. |
| format | Article |
| id | doaj-art-1a92cd63f8244b0b92c1b1e6a751ae09 |
| institution | OA Journals |
| issn | 1438-8871 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-1a92cd63f8244b0b92c1b1e6a751ae092025-08-20T01:58:33ZengJMIR PublicationsJournal of Medical Internet Research1438-88712024-12-0126e5125510.2196/51255A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation StudyMengqing Mahttps://orcid.org/0000-0003-4402-7988Caimei Chenhttps://orcid.org/0000-0003-4954-954XDawei Chenhttps://orcid.org/0000-0001-5088-6565Hao Zhanghttps://orcid.org/0000-0001-8055-7789Xia Duhttps://orcid.org/0009-0005-9090-6264Qing Sunhttps://orcid.org/0009-0007-2878-1194Li Fanhttps://orcid.org/0000-0002-7853-3195Huiping Konghttps://orcid.org/0000-0002-4384-1251Xueting Chenhttps://orcid.org/0009-0007-5593-9110Changchun Caohttps://orcid.org/0000-0003-1992-4257Xin Wanhttps://orcid.org/0000-0001-5226-4444 BackgroundAcute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality. ObjectiveThis study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms. MethodsWe trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). Feature selection was conducted using the sliding window forward feature selection technique. Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation. ResultsA total of 6371 patients with CAP met the inclusion criteria. The development of CAP-associated AKI (CAP-AKI) was recognized in 1006 (15.8%) patients. The 11 selected indicators were sex, temperature, breathing rate, diastolic blood pressure, C-reactive protein, albumin, white blood cell, hemoglobin, platelet, blood urea nitrogen, and neutrophil count. The DF model achieved the best area under the receiver operating characteristic curve (AUC) and accuracy in the internal (AUC=0.89, accuracy=0.90) and external validation sets (AUC=0.87, accuracy=0.83). Furthermore, the DF model had the best calibration among all models. In addition, a web-based prediction platform was developed to predict CAP-AKI. ConclusionsThe model described in this study is the first multicenter-validated AKI prediction model that accurately predicts CAP-AKI during hospitalization. The web-based prediction platform embedded with the DF model serves as a user-friendly tool for early identification of high-risk patients.https://www.jmir.org/2024/1/e51255 |
| spellingShingle | Mengqing Ma Caimei Chen Dawei Chen Hao Zhang Xia Du Qing Sun Li Fan Huiping Kong Xueting Chen Changchun Cao Xin Wan A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study Journal of Medical Internet Research |
| title | A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study |
| title_full | A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study |
| title_fullStr | A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study |
| title_full_unstemmed | A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study |
| title_short | A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study |
| title_sort | machine learning based prediction model for acute kidney injury in patients with community acquired pneumonia multicenter validation study |
| url | https://www.jmir.org/2024/1/e51255 |
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