title: Surgical mortality score: risk management tool for auditing surgical performance.
abstract: Existing methods of risk adjustment in surgical audit are complex and costly. The present study aimed to develop a simple risk stratification score for mortality and a robust audit tool using the existing resources of the hospital Patient Administration System (PAS) database. This was an observational study for all patients undergoing surgical procedures over a two-year period, at a London university hospital. Logistic regression analysis was used to determine predictive factors of in-hospital mortality, the study outcome. Odds ratios were used as weights in the derivation of a simple risk-stratification model-the Surgical Mortality Score (SMS). Observed-to-expected mortality risk ratios were calculated for application of the SMS model in surgical audit. There were 11,089 eligible cases, under five surgical specialties (maxillofacial, orthopedic, renal transplant/dialysis, general, and neurosurgery). Incomplete data were 3.7% of the total, with no evidence of systematic underreporting. The SMS model was well calibrated [Hosmer-Lemeshow C-statistic: development set (3.432, p = 0.33), validation set (6.359, p = 0.10) with a high discriminant ability (ROC areas: development set [0.837, S.E.=0.013] validation set [0.816, S.E. = 0.016]). Subgroup analyses confirmed that the model can be used by the individual specialties for both elective and emergency cases. The SMS is an accurate risk- stratification model derived from existing database resources. It is simple to apply as a risk-management, screening tool to detect aberrations from expected surgical outcomes and to assist in surgical audit.
authors.affiliation: Department of Vascular Surgery, University Hospital Lewisham, London SE13 6LH, UK. vassilis@doctors.org.uk
authors.forename: Vassilis G
authors.initials: VG
authors.lastname: Hadjianastassiou
authors.forename: Paris P
authors.initials: PP
authors.lastname: Tekkis
authors.forename: Jan D
authors.initials: JD
authors.lastname: Poloniecki
authors.forename: Manolis C
authors.initials: MC
authors.lastname: Gavalas
authors.forename: David R
authors.initials: DR
authors.lastname: Goldhill
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006751:Hospital Information Systems / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006785:Hospitals, University / Q000706:statistics & numerical data; D006786:Hospitals, Urban / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008131:London; D008297:Male; D008485:Medical Audit / Q000706:statistics & numerical data*; D008875:Middle Aged; D016017:Odds Ratio; D011183:Postoperative Complications / Q000401:mortality*; D012372:ROC Curve; D012308:Risk Management / Q000706:statistics & numerical data*; D013043:Specialties, Surgical / Q000706:statistics & numerical data; D013514:Surgical Procedures, Operative / Q000401:mortality*