Development and Validation of a Bariatric Surgery Mortality Risk Calculator

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• 作者：Bala Ramanan ; MBBS^a ; Prateek K. Gupta ; MD^a ; Himani Gupta ; MD^c ; Xiang Fang ; PhD^b ; R. Armour Forse ; MD ; PhD ; FACS^a ; ^{rarmourforse@creighton.edu}
• 关键词：BMI ; body mass index ; IQR ; interquartile range ; NSQIP ; National Surgical Quality Improvement Program ; OS-MRS ; obesity surgery mortality risk score ; PCI ; percutaneous coronary intervention ; PVD ; Peripheral vascular disease requiring revascularization or am
• 刊名：Journal of the American College of Surgeons
• 出版年：2012
• 期刊代码：175_10727515
• 类别：med
• 出版时间：June, 2012
• 卷：214
• 期：6
• 页码：892-900
• 文件大小：358 K

摘要

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Background

While the epidemic of obesity continues to plague America, bariatric surgery is underused due to concerns for surgical risk among patients and referring physicians. A risk score estimating postoperative mortality (OS-MRS) exists, however, is limited by consideration of only 12 preoperative variables, failure to separate open and laparoscopic cases, a lack of robust statistical analyses, risk factors not being weighted, and being applicable to only gastric bypass surgery. The objective of this study was to develop a validated risk calculator for 30-day postoperative mortality after bariatric surgery.

Study Design

The National Surgical Quality Improvement Program (NSQIP) dataset (2006 to 2008) was used. Patients undergoing bariatric surgery for morbid obesity (n = 32,889) were divided into training (n = 21,891) and validation (n = 10,998) datasets. Multiple logistic regression analysis was performed on the training dataset. The model fit from the training dataset was maintained and was used to estimate mortality probabilities for all patients in the validation dataset.

Results

Thirty-day mortality was 0.14%. Seven independent predictors of mortality were identified: peripheral vascular disease, dyspnea, previous percutaneous coronary intervention, age, body mass index, chronic corticosteroid use, and type of bariatric surgery. This risk model was subsequently validated. The model performance was very similar between the training and the validation datasets (c-statistics, 0.80 and 0.82, respectively). The high c-statistics indicate excellent predictive performance. The risk model was used to develop an interactive risk calculator.

Conclusions

This risk calculator has excellent predictive ability for mortality after bariatric procedures. It is anticipated that it will aid in surgical decision-making, informed patient consent, and in helping patients and referring physicians to assess the true bariatric surgical risk.