Construction and verification of hypoxemia prediction model for gynecological operation patients after entering postanesthesia care unit
Journal Title: Chinese Journal of Clinical Research - Year 2024, Vol 37, Issue 12
Abstract
Objective To establish and verify the prediction model of hypoxemia in gynecological surgery patients after entering postanesthesia care unit (PACU). Methods Ninety patients who underwent gynecological surgery in Urumqi Hospital of Traditiond Chinese Medicine from June 2019 to June 2023 were selected as the study objects. According to the observation results of the PACU, the patients were divided into the hypoxemia group and the non-hypoxemia group. Clinical data of patients were collected and analyzed by univariate and multivariate logistic regression, in order to screen out the risk factors affecting postoperative hypoxemia in gynecological surgery patients and build a prediction model and verify it. Results Gynecological operation patients admitted to the PACU after surgery, 71 patients did not experience hypoxemia, 19 patients (21.11%) did. Multivariate logistic regression analysis showed that elder age, high BMI, history of hypertension, ASA Ⅲ grade and laparoscopic surgery were the risk factors for postoperative hypoxemia in gynecological surgery patients after entering PACU (OR=2.406, 2.948, 1.931, 3.180, 1.772, P<0.05). Multivariate logistic regression analysis was used to establish a risk early warning model for postoperative hypoxemia in gynecological operation patients after entering PACU. The internal verification results of Bootstrap method showed that the C-index was 0.797 (95%CI: 0.752-0.874), the area under receiver operating characteristic (ROC) curve, sensitivity and specificity were 0.825, 80.97% and 82.43%, respectively. Conclusion Age, BMI, history of hypertension, ASA grade and surgical method are all influencing factors for postoperative hypoxemia in gynecological surgery patients. The prediction model based on the above factors has a certain degree of differentiation and accuracy.
Authors and Affiliations
HUANG Yiyong, LI Jing, LI Min
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