OBJECTIVE: To design and validate a statistical method for evaluating the performance of surgical units that adjusts for case volume and case mix. DESIGN: Validation study using routinely collected data on in-hospital mortality. DATA SOURCES: Two UK databases, the ASCOT prospective database and the risk scoring collaborative (RISC) database, covering 1042 patients undergoing surgery in 29 hospitals for gastro-oesophageal cancer between 1995 and 2000. STATISTICAL ANALYSIS: A two level hierarchical logistic regression model was used to adjust each unit's operative mortality for case mix. Crude or adjusted operative mortality was plotted on mortality control charts (a graphical representation of surgical performance) as a function of number of operations. Control limits defined as 90%, 95%, and 99% confidence intervals identified units whose performance diverged significantly from the mean. RESULTS: The mean in-hospital mortality was 12% (range 0% to 50%). The case volume of the units ranged from one to 55 cases a year. When crude figures were plotted on the mortality control chart, four units lay outside the 90% control limit, including two outside the 95% limit. When operative mortality was adjusted for risk, three units lay outside the 90% limit and one outside the 95% limit. The model fitted the data well and had adequate discrimination (area under the receiver operating characteristics curve 0.78). CONCLUSIONS: The mortality control chart is an accurate, risk adjusted means of identifying units whose surgical performance, in terms of operative mortality, diverges significantly from the population mean. It gives an early warning of divergent performance. It could be adapted to monitor performance across various specialties.
OBJECTIVE: To design and validate a statistical method for evaluating the performance of surgical units that adjusts for case volume and case mix. DESIGN: Validation study using routinely collected data on in-hospital mortality. DATA SOURCES: Two UK databases, the ASCOT prospective database and the risk scoring collaborative (RISC) database, covering 1042 patients undergoing surgery in 29 hospitals for gastro-oesophageal cancer between 1995 and 2000. STATISTICAL ANALYSIS: A two level hierarchical logistic regression model was used to adjust each unit's operative mortality for case mix. Crude or adjusted operative mortality was plotted on mortality control charts (a graphical representation of surgical performance) as a function of number of operations. Control limits defined as 90%, 95%, and 99% confidence intervals identified units whose performance diverged significantly from the mean. RESULTS: The mean in-hospital mortality was 12% (range 0% to 50%). The case volume of the units ranged from one to 55 cases a year. When crude figures were plotted on the mortality control chart, four units lay outside the 90% control limit, including two outside the 95% limit. When operative mortality was adjusted for risk, three units lay outside the 90% limit and one outside the 95% limit. The model fitted the data well and had adequate discrimination (area under the receiver operating characteristics curve 0.78). CONCLUSIONS: The mortality control chart is an accurate, risk adjusted means of identifying units whose surgical performance, in terms of operative mortality, diverges significantly from the population mean. It gives an early warning of divergent performance. It could be adapted to monitor performance across various specialties.
Authors: S G Swisher; L Deford; K W Merriman; G L Walsh; R Smythe; A Vaporicyan; J A Ajani; T Brown; R Komaki; J A Roth; J B Putnam Journal: J Thorac Cardiovasc Surg Date: 2000-06 Impact factor: 5.209
Authors: P Aylin; B Alves; N Best; A Cook; P Elliott; S J Evans; A E Lawrence; G D Murray; J Pollock; D Spiegelhalter Journal: Lancet Date: 2001-07-21 Impact factor: 79.321
Authors: Hemant M Kocher; Paris P Tekkis; Palepu Gopal; Ameet G Patel; Simon Cottam; Irving S Benjamin Journal: World J Gastroenterol Date: 2005-04-28 Impact factor: 5.742