ArticleMedicine2025
A risk factor prediction model for moderate-to-severe postoperative pain in patients undergoing laparoscopic sleeve gastrectomy.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
8 authors.
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Abstract
The primary goal of this study was to identify the risk factors contributing to moderate-to-severe postoperative pain in patients undergoing laparoscopic sleeve gastrectomy (LSG) and to create a predictive model for these risk factors. A retrospective analysis was performed on a cohort of 375 patients who underwent LSG at Jinan Central Hospital from January 2017 to June 2023. Data for this study was extracted using medical databases. Patients were classified into 2 groups based on their postoperative pain levels: those experiencing moderate-to-severe pain and those not experiencing moderate-to-severe pain. Univariate and multivariate logistic regression analyses were employed to determine which variables were significantly associated with moderate-to-severe pain. Receiver operating characteristic curves were utilized to assess the diagnostic efficacy of different indicators. Additionally, calibration curves and clinical decision curves were applied for model validation. Multifactorial logistic regression analysis identified age, body mass index (BMI), and the modified frailty index (mFI) as independent risk factors for moderate-to-severe postoperative pain in LSG patients. Based on the regression analysis, a predictive model was constructed. The receiver operating characteristic curve for this model demonstrated an area under the curve of 0.96 (95% CI: 0.94-0.97), indicating excellent discriminatory ability between patients likely and unlikely to experience moderate-to-severe pain post-surgery. A scoring system was developed from the predictive model, assigning points to each risk factor. BMI was the most significant predictor (100 points), followed by mFI (30 points) and age (15 points). Calibration analysis showed that the predicted values closely matched the actual values, with a mean error of 0.008, indicating high accuracy of the model. Clinical decision analysis demonstrated a positive net benefit when the threshold probability ranged from 0.001 to 0.999, suggesting broad applicability of the model in clinical decision-making. Age, BMI, and mFI are significant predictors of moderate-to-severe postoperative pain in patients undergoing LSG.
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