Evaluation of Estimation of Physiologic Ability and Surgical Stress to predict in-hospital mortality in cardiac surgery

• Published in: Journal of anesthesia Vol. 25; no. 4; pp. 481 - 491
• Main Authors: Kotera, Atsushi, Haga, Yoshio, Kei, Junichi, Okamoto, Minoru, Seo, Katsuhiro
• Format: Journal Article
• Language: English
• Published: Japan Springer Japan 01.08.2011; Springer
• Subjects: Adult; Aged; Aged, 80 and over; Analysis; Anesthesiology; Cardiac patients; Cardiac Surgical Procedures - methods; Cardiac Surgical Procedures - mortality; Cohort Studies; Critical Care Medicine; Elective Surgical Procedures - methods; Elective Surgical Procedures - mortality; Emergency Medicine; Female; Hospital Mortality; Humans; Intensive; Japan; Male; Medicine; Medicine & Public Health; Middle Aged; Mortality; Original Article; Pain Medicine; Patient outcomes; Postoperative Complications - mortality; Predictive Value of Tests; Prognosis; Retrospective Studies; Risk assessment; Risk Assessment - methods; Risk Factors; Stress, Physiological - physiology; Surgery; Young Adult; Japan; Mortality; Prediction; Cardiac surgery
• ISSN: 0913-8668; 1438-8359
• DOI: 10.1007/s00540-011-1162-z

Purpose Prediction of postoperative risk in cardiac surgery is important for cardiac surgeons and anesthesiologists. We generated a prediction rule for elective digestive surgery, designated as Estimation of Physiologic Ability and Surgical Stress (E-PASS). This study was undertaken to evaluate the accuracy of E-PASS in predicting postoperative risk in cardiac surgery. Methods We retrospectively collected data from patients who underwent elective cardiac surgery at a low-volume center ( N  = 291) and at a high-volume center ( N  = 784). Data were collected based on the variables required by E-PASS, the European system for cardiac operative risk evaluation (EuroSCORE), and the Ontario Province Risk Score (OPRS). Calibration and discrimination were assessed by the Hosmer–Lemeshow test and the area under the receiver operating characteristic curve (AUC), respectively. The ratio of observed-to-estimated in-hospital mortality rates (OE ratio) was defined as a measure of quality. Results In-hospital mortality rates were 7.6% at the low-volume center and 1.3% at the high-volume center, accounting for an overall mortality rate of 3.0%. AUC values to detect in-hospital mortality were 0.88 for E-PASS, 0.77 for EuroSCORE, and 0.71 for OPRS. Hosmer–Lemeshow analysis showed a good calibration in all models ( P  = 0.81 for E-PASS, P  = 0.49 for EuroSCORE, and P  = 0.94 for OPRS). OE ratios for the low-volume center were 0.83 for E-PASS, 0.70 for EuroSCORE, and 0.83 for OPRS, whereas those for the high-volume center were 0.26 for E-PASS, 0.14 for EuroSCORE, and 0.27 for OPRS. Conclusions E-PASS may accurately predict postoperative risk in cardiac surgery. Because the variables are different between cardiac-specific models and E-PASS, patients’ risks can be double-checked by cardiac surgeons using cardiac-specific models and by anesthesiologists using E-PASS.