Development and Validation of the GAASThyriC Model for Predicting Patients with Suboptimal Clinical Response After Laparoscopic Sleeve Gastrectomy and a Practical Calculator: A Retrospective Cohort Study

医学 减肥 体质指数 袖状胃切除术 外科 回顾性队列研究 队列 内科学 肥胖 胃分流术
作者
Muhammed Taha Demirpolat,Mehmet Muzaffer İSLAM
出处
期刊:Surgical laparoscopy, endoscopy & percutaneous techniques [Lippincott Williams & Wilkins]
标识
DOI:10.1097/sle.0000000000001300
摘要

Background: It might not be possible to achieve the desired outcome in every patient following bariatric surgery, even though every patient is thoroughly examined before surgery. This study aimed to develop a regression model based on parameters that affect weight loss success in patients scheduled for laparoscopic sleeve gastrectomy (LSG) and thus preoperatively predict whether the patients will have an optimal clinical response in terms of weight loss at the end of the first year. Materials and Methods: Between January 2018 and August 2022, patients who underwent LSG were analyzed retrospectively. Age, sex, comorbidities, smoking status, alcohol use status, preoperative weight, preoperative body mass index (BMI), preoperative laboratory data, weight, and total weight loss (TWL)% values at the end of the first year were recorded. At the end of the first year following LSG, patients with TWL% above 20% were defined as having an optimal clinical response in terms of weight loss. This study is designed, conducted, and reported regarding the “transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” (TRIPOD) statement. The final model was used to construct an Excel-based calculator. Results: Four hundred thirty-eight patients underwent the sleeve gastrectomy procedure, and 38 of them were excluded from the study because of a lack of 1-year follow-up information, resulting in 400 eligible patients for our study. Age, glucose, thyroid stimulating hormone (TSH), alcohol consumption, systemic immune inflammation index (SII), and tobacco were the independent predictors of optimal clinical response ( P <0.001, P <0.001, P <0.001, P =0.011, P =0.039, P =0.045, respectively). The model was called the GAASThyriC score. When the final model was tested in the validation cohort, the AUC was 0.875 (95% CI, 0.742-0.999), the sensitivity was 83.3% (95% CI, 51.6-97.9), specificity was 86.4% (95% CI, 77.4-92.8), negative likelihood ratio was 0.19 (95% CI, 0.05-0.68), and accuracy was 86% (95% CI, 77.6-92.1) when the cutoff value was set to the optimal threshold (logit = 0.8451). Conclusion: The GAASThyriC score can be used as an effective auxiliary tool to predict the patient population with suboptimal clinical response in terms of TWL% at the end of the first year after LSG.
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