C Fischer1, H F Lingsma2, N van Leersum3, R A E M Tollenaar4, M W Wouters5, E W Steyerberg6. 1. Department of Public Health, Centre for Medical Decision Making, Erasmus MC, Rotterdam, The Netherlands. Electronic address: c.fischer@erasmusmc.nl. 2. Department of Public Health, Centre for Medical Decision Making, Erasmus MC, Rotterdam, The Netherlands. Electronic address: h.lingsma@erasmusmc.nl. 3. Department of Surgery, Leiden University Medical Centre, Leiden, The Netherlands; Dutch Institute for Clinical Auditing, Leiden, The Netherlands. Electronic address: n.vanleersum@clinicalaudit.nl. 4. Department of Surgery, Leiden University Medical Centre, Leiden, The Netherlands; Dutch Institute for Clinical Auditing, Leiden, The Netherlands. Electronic address: R.A.E.M.Tollenaar@lumc.nl. 5. Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands; Dutch Institute for Clinical Auditing, Leiden, The Netherlands. Electronic address: m.wouters@nki.nl. 6. Department of Public Health, Centre for Medical Decision Making, Erasmus MC, Rotterdam, The Netherlands. Electronic address: e.steyerberg@erasmusmc.nl.
Abstract
OBJECTIVE: When comparing performance across hospitals it is essential to consider the noise caused by low hospital case volume and to perform adequate case-mix adjustment. We aimed to quantify the role of noise and case-mix adjustment on standardized postoperative mortality and anastomotic leakage (AL) rates. METHODS: We studied 13,120 patients who underwent colon cancer resection in 85 Dutch hospitals. We addressed differences between hospitals in postoperative mortality and AL, using fixed (ignoring noise) and random effects (incorporating noise) logistic regression models with general and additional, disease specific, case-mix adjustment. RESULTS: Adding disease specific variables improved the performance of the case-mix adjustment models for postoperative mortality (c-statistic increased from 0.77 to 0.81). The overall variation in standardized mortality ratios was similar, but some individual hospitals changed considerably. For the standardized AL rates the performance of the adjustment models was poor (c-statistic 0.59 and 0.60) and overall variation was small. Most of the observed variation between hospitals was actually noise. CONCLUSION: Noise had a larger effect on hospital performance than extended case-mix adjustment, although some individual hospital outcome rates were affected by more detailed case-mix adjustment. To compare outcomes between hospitals it is crucial to consider noise due to low hospital case volume with a random effects model.
OBJECTIVE: When comparing performance across hospitals it is essential to consider the noise caused by low hospital case volume and to perform adequate case-mix adjustment. We aimed to quantify the role of noise and case-mix adjustment on standardized postoperative mortality and anastomotic leakage (AL) rates. METHODS: We studied 13,120 patients who underwent colon cancer resection in 85 Dutch hospitals. We addressed differences between hospitals in postoperative mortality and AL, using fixed (ignoring noise) and random effects (incorporating noise) logistic regression models with general and additional, disease specific, case-mix adjustment. RESULTS: Adding disease specific variables improved the performance of the case-mix adjustment models for postoperative mortality (c-statistic increased from 0.77 to 0.81). The overall variation in standardized mortality ratios was similar, but some individual hospitals changed considerably. For the standardized AL rates the performance of the adjustment models was poor (c-statistic 0.59 and 0.60) and overall variation was small. Most of the observed variation between hospitals was actually noise. CONCLUSION: Noise had a larger effect on hospital performance than extended case-mix adjustment, although some individual hospital outcome rates were affected by more detailed case-mix adjustment. To compare outcomes between hospitals it is crucial to consider noise due to low hospital case volume with a random effects model.
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