A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery
Abstract Background Postoperative admission to the ICU for surgical patients is a significant burden in nursing care, and there is currently a lack of corresponding assessment tools. Methods Clinical information of patients was extracted from the VitalDB database. LASSO regression and random forest...
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BMC
2025-06-01
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| Series: | Perioperative Medicine |
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| Online Access: | https://doi.org/10.1186/s13741-025-00544-6 |
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| author | Li Li Hongye He Linjun Xiang Yongxiang Wang |
| author_facet | Li Li Hongye He Linjun Xiang Yongxiang Wang |
| author_sort | Li Li |
| collection | DOAJ |
| description | Abstract Background Postoperative admission to the ICU for surgical patients is a significant burden in nursing care, and there is currently a lack of corresponding assessment tools. Methods Clinical information of patients was extracted from the VitalDB database. LASSO regression and random forest algorithms were used to screen clinical variables related to postoperative ICU admission. Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. After selecting the best algorithm, postoperative ICU admission probability prediction nomogram was constructed. Results This study identified 18 clinical factors that influence postoperative ICU admission. The factors influencing patient outcomes include three physiological characteristics: age, weight, and gender; five preoperative laboratory tests:platelet count, prothrombin time(%),activated partial thromboplastin time, albumin, and blood urea nitrogen; and seven intraoperative anesthesia details: anesthesia duration, propofol dosing during surgery, midazolam dosing during surgery, phenylephrine dosing during surgery, calcium chloride dosing during surgery, American Society of Anesthesiologists (ASA) classification, and anesthesia method. Additionally, three other factors are considered: whether the surgery is classified as an emergency, the department category, and the type of surgery. The logistic regression model developed using these 18 variables was identified as the most effective predictive model for postoperative ICU admission, achieving an ROC AUC of 0.925. Conclusion The postoperative admission warning model constructed in this study can effectively predict the probability of patients being admitted to the ICU after surgery, providing a corresponding management tool for postoperative care in surgical patients. |
| format | Article |
| id | doaj-art-65dabf47ccd44a2fb8bcfa5c124974cd |
| institution | Kabale University |
| issn | 2047-0525 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Perioperative Medicine |
| spelling | doaj-art-65dabf47ccd44a2fb8bcfa5c124974cd2025-08-20T03:25:19ZengBMCPerioperative Medicine2047-05252025-06-0114111410.1186/s13741-025-00544-6A warning model for predicting patient admissions to the intensive care unit (ICU) following surgeryLi Li0Hongye He1Linjun Xiang2Yongxiang Wang3Nursing Department, The Third Xiangya Hospital, Central South UniversitySecond Xiangya Hospital, Central South UniversityNursing Department, The Third Xiangya Hospital, Central South UniversitySecond Xiangya Hospital, Central South UniversityAbstract Background Postoperative admission to the ICU for surgical patients is a significant burden in nursing care, and there is currently a lack of corresponding assessment tools. Methods Clinical information of patients was extracted from the VitalDB database. LASSO regression and random forest algorithms were used to screen clinical variables related to postoperative ICU admission. Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. After selecting the best algorithm, postoperative ICU admission probability prediction nomogram was constructed. Results This study identified 18 clinical factors that influence postoperative ICU admission. The factors influencing patient outcomes include three physiological characteristics: age, weight, and gender; five preoperative laboratory tests:platelet count, prothrombin time(%),activated partial thromboplastin time, albumin, and blood urea nitrogen; and seven intraoperative anesthesia details: anesthesia duration, propofol dosing during surgery, midazolam dosing during surgery, phenylephrine dosing during surgery, calcium chloride dosing during surgery, American Society of Anesthesiologists (ASA) classification, and anesthesia method. Additionally, three other factors are considered: whether the surgery is classified as an emergency, the department category, and the type of surgery. The logistic regression model developed using these 18 variables was identified as the most effective predictive model for postoperative ICU admission, achieving an ROC AUC of 0.925. Conclusion The postoperative admission warning model constructed in this study can effectively predict the probability of patients being admitted to the ICU after surgery, providing a corresponding management tool for postoperative care in surgical patients.https://doi.org/10.1186/s13741-025-00544-6ICU admissionMachine learningNomogramClinical Decision TreeNursing care |
| spellingShingle | Li Li Hongye He Linjun Xiang Yongxiang Wang A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery Perioperative Medicine ICU admission Machine learning Nomogram Clinical Decision Tree Nursing care |
| title | A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery |
| title_full | A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery |
| title_fullStr | A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery |
| title_full_unstemmed | A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery |
| title_short | A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery |
| title_sort | warning model for predicting patient admissions to the intensive care unit icu following surgery |
| topic | ICU admission Machine learning Nomogram Clinical Decision Tree Nursing care |
| url | https://doi.org/10.1186/s13741-025-00544-6 |
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