The impact of operative complexity on patient risk factors

The VA National Surgical Quality Improvement Program (NSQIP) formula for risk factors was applied to the University of Texas Health Science Center at San Antonio (UTHSCSA)/University Hospital (UH) database. Its applicability to a civilian organization was established. Logistic regression analysis of the UH database revealed that operative complexity was significantly related to mortality only at high complexity levels. Patient risk factors were the major determinants of operative mortality for most civilian surgical cases.

Since 1994, the NSQIP has collected preoperative risk factors, intraoperative data, 30-day morbidity, and mortality within the VA health system. The VA formula to predict 30-day postoperative mortality was applied to our UH patients (N = 8593). The c-index of .907, a statistical measure of accuracy, compared favorably to the VA patient c-index of .89. The UH database did not include a surrogate for operative complexity. We were elated by the predictive accuracy but had concern that operative complexity needed further evaluation.

Operative complexity was ascribed to each of the 8593 UH cases, and logistic regression analyses were compared with and without operative complexity. Operative complexity was graded on a scale of 1 to 5; 5 was the most complex.

Without operative complexity, a c-index was .915. With operative complexity: an even higher c-index of .941 was reached. The large volume of level 2-3 operative cases obscured to a degree the effect of operative difficulty on mortality.

Operative complexity played a major role in risk estimation, but only at the extreme. The dominance of cases of midlevel complexity masked the effect of higher complexity cases on mortality. In any individual case, operative complexity must be added to estimate operative mortality accurately. Patient risk factors alone accounted for operative mortality for operations less than level 4 (95% of patients).