• Raghunandan Venkat, Milo A Puhan, Richard D Schulick, John L Cameron, Frederic E Eckhauser, Michael A Choti, Martin A Makary, Timothy M Pawlik, Nita Ahuja, Barish H Edil, and Christopher L Wolfgang.
• Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
• Arch Surg. 2011 Nov 1;146(11):1277-84.

ObjectiveTo develop and validate a risk score to predict the 30- and 90-day mortality after a pancreaticoduodenectomy or total pancreatectomy on the basis of preoperative risk factors in a high-volume program.DesignData from a prospectively maintained institutional database were collected. In a random subset of 70% of patients (training cohort), multivariate logistic regression was used to develop a simple integer score, which was then validated in the remaining 30% of patients (validation cohort). Discrimination and calibration of the score were evaluated using area under the receiver operating characteristic curve and Hosmer-Lemeshow test, respectively.SettingTertiary referral center.PatientsThe study comprised 1976 patients in a prospectively maintained institutional database who underwent pancreaticoduodenectomy or total pancreatectomy between 1998 and 2009.Main Outcome MeasuresThe 30- and 90-day mortality.ResultsIn the training cohort, age, male sex, preoperative serum albumin level, tumor size, total pancreatectomy, and a high Charlson index predicted 90-day mortality (area under the curve, 0.78; 95% CI, 0.71-0.85), whereas all these factors except Charlson index also predicted 30-day mortality (0.79; 0.68-0.89). On validation, the predicted and observed risks were not significantly different for 30-day (1.4% vs 1.0%; P = .62) and 90-day (3.8% vs 3.4%; P = .87) mortality. Both scores maintained good discrimination (for 30-day mortality, area under the curve, 0.74; 95% CI, 0.54-0.95; and for 90-day mortality, 0.73; 0.62-0.84).ConclusionsThe risk scores accurately predicted 30- and 90-day mortality after pancreatectomy. They may help identify and counsel high-risk patients, support and calculate net benefits of therapeutic decisions, and control for selection bias in observational studies as propensity scores.

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