Richard Hunger1, Barbara Seliger2, Shuji Ogino3, Rene Mantke4. 1. Faculty of Medicine, University Hospital Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg, Germany. 2. Martin Luther University Halle-Wittenberg, Institute of Medical Immunology, Halle, Germany; Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany. 3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA. 4. Faculty of Medicine, University Hospital Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg, Germany; Faculty of Health Sciences, Brandenburg Medical School Theodor Fontane, Brandenburg, Germany. Electronic address: mantke.mhb@klinikum-brandenburg.de.
Abstract
BACKGROUND: How the extent of confounding adjustment impact (hospital) volume-outcome relationships in published studies on pancreatic cancer surgery is unknown. METHODS: A systematic literature search was conducted for studies that investigated the relationship between volume and outcome using a risk adjustment procedure by querying the following databases: PubMed, Cochrane Central Register of Controlled Trials, Livivo, Medline and the International Clinical Trials Registry Platform (last query: 2020/09/16). Importance of risk-adjusting covariates were assessed by effect size (odds ratio, OR) and statistical significance. The impact of covariate adjustment on hospital (or surgeon) volume effects was analyzed by regression and meta-regression models. RESULTS: We identified 87 studies (75 based on administrative data) with nearly 1 million patients undergoing pancreatic surgery that included in total 71 covariates for risk adjustment. Of these, 33 (47%) had statistically significant effects on short-term mortality and 23 (32%) did not, while for 15 (21%) factors neither effect size nor statistical significance were reported. The most important covariates for short term mortality were patient-specific factors. Concerning the covariates, single comorbidities (OR: 4.6, 95% CI: 3.3 to 6.3) had the strongest impact on mortality followed by hospital volume (OR: 2.9, 95% CI: 2.5 to 3.3) and the procedure (OR: 2.2, 95% CI: 1.9 to 2.5). Among the single comorbidities, coagulopathy (OR: 4.5, 95% CI: 2.8 to 7.2) and dementia (OR: 4.2, 95% CI: 2.2 to 8.0) had the strongest influence on mortality. The regression analysis showed a significant decrease hospital volume effect with an increasing number of covariates considered (OR: 0.06, 95% CI: 0.10 to -0.03, P < 0.001), while such a relationship was not observed for surgeon volume (P = 0.35). CONCLUSIONS: This analysis demonstrated a significant inverse relationship between the extent of risk adjustment and the volume effect, suggesting the presence of unmeasured confounding and overestimation of volume effects. However, the conclusions are limited in that only the number of included covariates was considered, but not the effect size of the non-included covariates.
BACKGROUND: How the extent of confounding adjustment impact (hospital) volume-outcome relationships in published studies on pancreatic cancer surgery is unknown. METHODS: A systematic literature search was conducted for studies that investigated the relationship between volume and outcome using a risk adjustment procedure by querying the following databases: PubMed, Cochrane Central Register of Controlled Trials, Livivo, Medline and the International Clinical Trials Registry Platform (last query: 2020/09/16). Importance of risk-adjusting covariates were assessed by effect size (odds ratio, OR) and statistical significance. The impact of covariate adjustment on hospital (or surgeon) volume effects was analyzed by regression and meta-regression models. RESULTS: We identified 87 studies (75 based on administrative data) with nearly 1 million patients undergoing pancreatic surgery that included in total 71 covariates for risk adjustment. Of these, 33 (47%) had statistically significant effects on short-term mortality and 23 (32%) did not, while for 15 (21%) factors neither effect size nor statistical significance were reported. The most important covariates for short term mortality were patient-specific factors. Concerning the covariates, single comorbidities (OR: 4.6, 95% CI: 3.3 to 6.3) had the strongest impact on mortality followed by hospital volume (OR: 2.9, 95% CI: 2.5 to 3.3) and the procedure (OR: 2.2, 95% CI: 1.9 to 2.5). Among the single comorbidities, coagulopathy (OR: 4.5, 95% CI: 2.8 to 7.2) and dementia (OR: 4.2, 95% CI: 2.2 to 8.0) had the strongest influence on mortality. The regression analysis showed a significant decrease hospital volume effect with an increasing number of covariates considered (OR: 0.06, 95% CI: 0.10 to -0.03, P < 0.001), while such a relationship was not observed for surgeon volume (P = 0.35). CONCLUSIONS: This analysis demonstrated a significant inverse relationship between the extent of risk adjustment and the volume effect, suggesting the presence of unmeasured confounding and overestimation of volume effects. However, the conclusions are limited in that only the number of included covariates was considered, but not the effect size of the non-included covariates.
Authors: Brent K Hollenbeck; Rodney L Dunn; David C Miller; Stephanie Daignault; David A Taub; John T Wei Journal: J Clin Oncol Date: 2007-01-01 Impact factor: 44.544
Authors: Hemalkumar B Mehta; Abhishek D Parmar; Deepak Adhikari; Nina P Tamirisa; Francesca Dimou; Daniel Jupiter; Taylor S Riall Journal: J Surg Res Date: 2016-05-11 Impact factor: 2.192
Authors: Taylor S Riall; Karl A Eschbach; Courtney M Townsend; William H Nealon; Jean L Freeman; James S Goodwin Journal: J Gastrointest Surg Date: 2007-08-13 Impact factor: 3.452