Mortality after noncardiac surgery: prediction from administrative versus clinical data.

Gordon HS, Johnson ML, Wray NP, Petersen NJ, Henderson WG, Khuri SF, Geraci JM. Mortality after noncardiac surgery: prediction from administrative versus clinical data. Medical care. 2005 Feb 1; 43(2):159-67.

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Abstract:

BACKGROUND: Hospital profiles are increasingly constructed using risk-adjusted clinical data abstracted from patient records. OBJECTIVE: We sought to compare hospital profiles based on risk adjusted death within 30 days of surgery from administrative versus clinical data in a national cohort of surgical patients. DESIGN: This was a cohort study that included 78,546 major noncardiac operations performed between October 1, 1991 and December 31, 1993 in 44 Veterans Affairs hospitals. Administrative data were used to develop and validate multivariable logistic regression models of 30-day postoperative death for all surgery and 4 surgical specialties (general, orthopedic, thoracic, and vascular). Previously developed and validated clinical models were obtained and reproduced for matching operations using data from the VA National Surgical Quality Improvement Program. Observed-to-expected 30-day mortality ratios for administrative and clinical data were calculated and compared for each hospital. RESULTS: In multivariable logistic regression models using administrative data, characteristics such as patient age, race, marital status, admission from a nursing home, interhospital transfer, admission on the weekend, weekend surgery, and risk strata consisting of groups of principal and comorbidity diagnoses were predictive of postoperative mortality (P < 0.05). Correlations of the clinical and administrative observed-to-expected ratios were 0.75, 0.83, 0.64, 0.78, and 0.86 for all surgery, general, orthopedic, thoracic, and vascular surgery, respectively. When compared with clinical models, administrative models identified outlier hospitals with sensitivity of 73%, specificity of 89%, positive predictive value of 51%, and negative predictive value of 96%. CONCLUSIONS: Our data suggest that risk adjustment of mortality using administrative data may be useful for screening hospitals for potential quality problems.