A nomogram for predicting surgical complications in bariatric surgery patients.

BACKGROUND: To minimize morbidity and mortality associated with surgery risks in the obese patient, algorithms offer planning operative strategy. Because these algorithms often classify patients based on inadequate category granularity, outcomes may not be predicted accurately. We reviewed patient factors and patient outcomes for those who had undergone bariatric surgical procedures to determine relationships and developed a nomogram to calculate individualized patient risk.
METHODS: From the American College of Surgeons National Security Quality Improvement Program database, we identified 32,426 bariatric surgery patients meeting NIH criteria and treated between 2005 and 2008. We defined a composite binary outcome of 30-day postoperative morbidity and mortality. A predictive model based on preoperative variables was developed using multivariable logistic regression; a multiple imputation procedure allowed inclusions of observations with missing data. Model performance was assessed using the C-statistic. A calibration plot graphically assessed the agreement between predicted and observed probabilities in regard to 30-day morbidity/mortality.
RESULTS: The nomogram model was constructed for maximal predictive accuracy. The estimated C-statistic [95% confidence interval] for the predictive nomogram was 0.629 [0.614, 0.645], indicative of slight to moderate discriminative ability beyond that of chance alone, and the greatest impacts on the estimated probability of morbidity/mortality were determined to be age, body mass index, serum albumin, and functional status.
CONCLUSIONS: By accurately predicting 30-day morbidity and mortality, this nomogram may prove useful in patient preoperative counseling on postoperative complication risk. Our results additionally indicate that neither age nor presence of obesity-related comorbidities should exclude patients from bariatric surgery consideration.