BMC anesthesiology · 2024
Risk analysis of postoperative nausea and vomiting in patients after gynecologic laparoscopic surgery.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Luo Danzhi, Huang Zhenxing, Tang Simin, Cheng Jiurong, Deng Yingdong, Zhang Xiangsheng, Cao Yu, Zhou Chenxi, Zhou Jun
Аннотация
AIMS: This study is designed to identify risk factors of postoperative nausea and vomiting (PONV) in patients after gynecologic laparoscopic surgery and establish a nomogram model.
METHODS: In this retrospective and multicenter study, we collected and analyzed data from 1233 patients who underwent gynecologic laparoscopic surgery. The Lasso algorithm was used to optimize the selection of independent variables in the development group. Multivariate logistic regression analysis was used to explore the risk factors of PONV to develop the predictive nomogram model. Finally, we used an internal and external verification group and machine learning (ML) to evaluate the accuracy and clinical applicability of the model.
RESULTS: The dosage of sufentanil in patient-controlled intravenous analgesia (PCIA), the dosage of remifentanil, the use of neostigmine, duration of surgery and the maximum value of the PETCO2 were risk factors of PONV in patients after gynecologic laparoscopic surgery. In contrast, the dosage of propofol during the surgery and the use of steroids were protective factors. The nomogram was then established to predict the incidence of PONV in these patients based on the above eight indicators. The C-index of the development group, internal, and external verification groups are 0.802, 0.857, and 0.966, respectively.
CONCLUSION: A nomogram model was developed to predict the incidence of PONV in patients after gynecologic laparoscopic surgery. This model was found to be reliable and of high clinical value.
METHODS: In this retrospective and multicenter study, we collected and analyzed data from 1233 patients who underwent gynecologic laparoscopic surgery. The Lasso algorithm was used to optimize the selection of independent variables in the development group. Multivariate logistic regression analysis was used to explore the risk factors of PONV to develop the predictive nomogram model. Finally, we used an internal and external verification group and machine learning (ML) to evaluate the accuracy and clinical applicability of the model.
RESULTS: The dosage of sufentanil in patient-controlled intravenous analgesia (PCIA), the dosage of remifentanil, the use of neostigmine, duration of surgery and the maximum value of the PETCO2 were risk factors of PONV in patients after gynecologic laparoscopic surgery. In contrast, the dosage of propofol during the surgery and the use of steroids were protective factors. The nomogram was then established to predict the incidence of PONV in these patients based on the above eight indicators. The C-index of the development group, internal, and external verification groups are 0.802, 0.857, and 0.966, respectively.
CONCLUSION: A nomogram model was developed to predict the incidence of PONV in patients after gynecologic laparoscopic surgery. This model was found to be reliable and of high clinical value.