A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database
Objectives We aimed to develop an effective tool for predicting severe acute kidney injury (AKI) in patients admitted to the cardiac surgery recovery unit (CSRU).Design A retrospective cohort study.Setting Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database,...
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BMJ Publishing Group
2022-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e060258.full |
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| author | Qi Guo Jingjing Huang Yuewei Li Hongwei Li Jingfeng Wang Yong Xie Tucheng Huang Wanbing He Wenyu Lv Jieping Huang Yangxin Chen |
| author_facet | Qi Guo Jingjing Huang Yuewei Li Hongwei Li Jingfeng Wang Yong Xie Tucheng Huang Wanbing He Wenyu Lv Jieping Huang Yangxin Chen |
| author_sort | Qi Guo |
| collection | DOAJ |
| description | Objectives We aimed to develop an effective tool for predicting severe acute kidney injury (AKI) in patients admitted to the cardiac surgery recovery unit (CSRU).Design A retrospective cohort study.Setting Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database, consisting of critically ill participants between 2001 and 2012 in the USA.Participants A total of 6271 patients admitted to the CSRU were enrolled from the MIMIC-III database.Primary and secondary outcome Stages 2–3 AKI.Result As identified by least absolute shrinkage and selection operator (LASSO) and logistic regression, risk factors for AKI included age, sex, weight, respiratory rate, systolic blood pressure, diastolic blood pressure, central venous pressure, urine output, partial pressure of oxygen, sedative use, furosemide use, atrial fibrillation, congestive heart failure and left heart catheterisation, all of which were used to establish a clinical score. The areas under the receiver operating characteristic curve of the model were 0.779 (95% CI: 0.766 to 0.793) for the primary cohort and 0.778 (95% CI: 0.757 to 0.799) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Decision curve analysis demonstrated that the model could achieve a net benefit.Conclusion A clinical score built by using LASSO regression and logistic regression to screen multiple clinical risk factors was established to estimate the probability of severe AKI in CSRU patients. This may be an intuitive and practical tool for severe AKI prediction in the CSRU. |
| format | Article |
| id | doaj-art-08d101e56c3547c491db240de275e124 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-08d101e56c3547c491db240de275e1242025-01-27T16:35:13ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2021-060258A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC databaseQi Guo0Jingjing Huang1Yuewei Li2Hongwei Li3Jingfeng Wang4Yong Xie5Tucheng Huang6Wanbing He7Wenyu Lv8Jieping Huang9Yangxin Chen102 Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, ChinaDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Respiratory Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Cardiology, Sun Yat-Sen University Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, ChinaGuangdong Provincial Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China38 Department of Gastroenterology and Hepatology, First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, ChinaDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China3 Department of Cardiology, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, ChinaObjectives We aimed to develop an effective tool for predicting severe acute kidney injury (AKI) in patients admitted to the cardiac surgery recovery unit (CSRU).Design A retrospective cohort study.Setting Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database, consisting of critically ill participants between 2001 and 2012 in the USA.Participants A total of 6271 patients admitted to the CSRU were enrolled from the MIMIC-III database.Primary and secondary outcome Stages 2–3 AKI.Result As identified by least absolute shrinkage and selection operator (LASSO) and logistic regression, risk factors for AKI included age, sex, weight, respiratory rate, systolic blood pressure, diastolic blood pressure, central venous pressure, urine output, partial pressure of oxygen, sedative use, furosemide use, atrial fibrillation, congestive heart failure and left heart catheterisation, all of which were used to establish a clinical score. The areas under the receiver operating characteristic curve of the model were 0.779 (95% CI: 0.766 to 0.793) for the primary cohort and 0.778 (95% CI: 0.757 to 0.799) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Decision curve analysis demonstrated that the model could achieve a net benefit.Conclusion A clinical score built by using LASSO regression and logistic regression to screen multiple clinical risk factors was established to estimate the probability of severe AKI in CSRU patients. This may be an intuitive and practical tool for severe AKI prediction in the CSRU.https://bmjopen.bmj.com/content/12/6/e060258.full |
| spellingShingle | Qi Guo Jingjing Huang Yuewei Li Hongwei Li Jingfeng Wang Yong Xie Tucheng Huang Wanbing He Wenyu Lv Jieping Huang Yangxin Chen A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database BMJ Open |
| title | A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database |
| title_full | A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database |
| title_fullStr | A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database |
| title_full_unstemmed | A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database |
| title_short | A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database |
| title_sort | lasso derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit a large retrospective cohort study using the mimic database |
| url | https://bmjopen.bmj.com/content/12/6/e060258.full |
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