Keith A Lawson1, Olli Saarela2, Zhihui Liu2, Luke T Lavallée3, Rodney H Breau3, Lori Wood4, Michael A S Jewett1, Anil Kapoor5, Simon Tanguay6, Ronald B Moore7, Ricardo Rendon8, Frederic Pouliot9, Peter C Black10, Jun Kawakami11, Darrel Drachenberg12, Antonio Finelli1. 1. Division of Urology, Princess Margaret Hospital, University of Toronto, Toronto, ON; Canada. 2. Dalla Lana School of Public Health, University of Toronto, Toronto, ON; Canada. 3. Division of Urology, University of Ottawa, Ottawa, ON; Canada. 4. Division of Medical Oncology, Dalhousie University, Halifax, NS; Canada. 5. Division of Urology, McMaster University, Hamilton, ON; Canada. 6. Division of Urology, McGill University, Montreal, QC; Canada. 7. Division of Urology, University of Alberta, Edmonton, AB; Canada. 8. Department of Urology, Dalhousie University, Halifax, NS; Canada. 9. Division of Urology, Université Laval, Quebec City, QC; Canada. 10. Department of Urologic Sciences, University of British Columbia, Vancouver, BC; Canada. 11. Division of Urology, University of Calgary, Calgary, AB; Canada. 12. Division of Urology, University of Manitoba, Winnipeg, MB; Canada.
Abstract
INTRODUCTION: There is a lack of validated quality metrics to evaluate the care of patients receiving surgery for renal cell carcinoma (RCC). To address this, the Kidney Cancer Research Network of Canada defined a list of quality indicators (QI) to assess hospital-level performance. We have case-mix adjusted these QIs to benchmark RCC surgical care at Canadian academic centres. METHODS: The Canadian Kidney Cancer information system (CKCis) was used to measure six QIs: laparoscopic approach proportion (LA), partial nephrectomy proportion (PN), partial nephrectomy in patients with chronic kidney disease (CKDPN), positive margin rate (PMR), partial nephrectomy complication rate (PNCx), and warm ischemia time (WIT). To benchmark performance, indirect standardization (observed-to-expected ratio) methodology was employed using multivariate regression models. RESULTS: Multivariate models for LA, PN, and CKDPN demonstrated good discrimination and were used for benchmarking. National averages of 74% (70-78%), 73% (70-75%), and 70% (67-74%) for the LA, PN, and CKDPN QIs, respectively, were determined and used to benchmark individual hospital performance. Overall, three (23%), two (15%), and two (15%) hospitals performed below expected for LA, PN, and CKDPN, respectively. Hospital identity was an independent predictor of LA, PN, and CKDPN (p<0.001). CONCLUSIONS: Significant variability between CKCis hospitals for three RCC surgical QIs exists. Using the CKCis infrastructure may provide a framework for institution-level audit feedback for quality improvement. Greater CKCis capture rates and further data supporting the construct validity of these QIs are required to extend the use of this dataset to real-world quality initiatives.
INTRODUCTION: There is a lack of validated quality metrics to evaluate the care of patients receiving surgery for renal cell carcinoma (RCC). To address this, the Kidney Cancer Research Network of Canada defined a list of quality indicators (QI) to assess hospital-level performance. We have case-mix adjusted these QIs to benchmark RCC surgical care at Canadian academic centres. METHODS: The Canadian Kidney Cancer information system (CKCis) was used to measure six QIs: laparoscopic approach proportion (LA), partial nephrectomy proportion (PN), partial nephrectomy in patients with chronic kidney disease (CKDPN), positive margin rate (PMR), partial nephrectomy complication rate (PNCx), and warm ischemia time (WIT). To benchmark performance, indirect standardization (observed-to-expected ratio) methodology was employed using multivariate regression models. RESULTS: Multivariate models for LA, PN, and CKDPN demonstrated good discrimination and were used for benchmarking. National averages of 74% (70-78%), 73% (70-75%), and 70% (67-74%) for the LA, PN, and CKDPN QIs, respectively, were determined and used to benchmark individual hospital performance. Overall, three (23%), two (15%), and two (15%) hospitals performed below expected for LA, PN, and CKDPN, respectively. Hospital identity was an independent predictor of LA, PN, and CKDPN (p<0.001). CONCLUSIONS: Significant variability between CKCis hospitals for three RCC surgical QIs exists. Using the CKCis infrastructure may provide a framework for institution-level audit feedback for quality improvement. Greater CKCis capture rates and further data supporting the construct validity of these QIs are required to extend the use of this dataset to real-world quality initiatives.
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