Danielle A Southern1, Christopher Doherty2, Michael A De Souza3, Hude Quan4, A Robertson Harrop5, Duncan Nickerson6, Doreen Rabi7. 1. Danielle A. Southern, MSc, is a research associate with the O'Brien Institute for Public Health and the Department of Community Health Sciences of the University of Calgary, in Calgary, Alberta, Canada. 2. Christopher Doherty, MD, MPH, FRCSC, is an assistant professor at the Roth | McFarlane Hand and Upper Limb Centre at the University of Western Ontario Schulich School of Medicine and Dentistry in London, Ontario, Canada. 3. Michael A. De Souza is a research assistant in the Department of Biological Sciences at the University of Calgary in Calgary, Alberta, Canada. 4. Hude Quan, PhD, is a professor at the O'Brien Institute for Public Health and the Department of Community Health Sciences of the University of Calgary in Calgary, Alberta, Canada. 5. A. Robertson Harrop, MD, MSc, is section chief of plastic surgery in the Department of Surgery at the University of Calgary in Calgary, Alberta, Canada. 6. Duncan Nickerson, MD, FRCSCS, FACS, is medical director at the Calgary Firefighters' Burn & Treatment Centre at Foothills Medical Centre and clinical associate professor in the Section of Plastic Surgery in the Department of Surgery at the University of Calgary in Calgary, Alberta, Canada. 7. Doreen Rabi, MD, is an assistant professor in the Departments of Medicine, Community Health Sciences, and Cardiac Sciences at the University of Calgary in Calgary, Alberta, Canada.
Abstract
BACKGROUND: Sternal wound infection (SWI) in patients undergoing coronary artery bypass grafting (CABG) can carry a significant risk of morbidity and mortality. The objective of this work is to describe the methods used to identify cases of SWI in an administrative database and to demonstrate the effectiveness of using an International Classification of Diseases, Tenth Revision (ICD-10) coding algorithm for this purpose. METHODS: ICD-10 codes were used to identify cases of SWI within one year of CABG between April 2002 and November 2009. We randomly chose 200 charts for detailed chart review (100 from each of the groups coded as having SWI and not having SWI) to determine the utility of the ICD-10 coding algorithm. RESULTS: There were 2,820 patients undergoing CABG. Of these, 264 (9.4 percent) were coded as having SWI. Thirty-eight cases of SWI were identified by chart review. The ICD-10 coding algorithm of T81.3 or T81.4 was able to identify incident SWI with a positive predictive value of 35 percent and a negative predictive value of 97 percent. The agreement between the ICD-10 coding algorithm and presence of SWI remained fair, with an overall kappa coefficient of 0.32 (95 percent confidence interval, 0.22-0.43). The effectiveness of identifying deep SWI cases is also presented. CONCLUSIONS: This article describes an effective algorithm for identifying a cohort of patients with SWI following open sternotomy in large databases using ICD-10 coding. In addition, alternative search strategies are presented to suit researchers' needs.
BACKGROUND: Sternal wound infection (SWI) in patients undergoing coronary artery bypass grafting (CABG) can carry a significant risk of morbidity and mortality. The objective of this work is to describe the methods used to identify cases of SWI in an administrative database and to demonstrate the effectiveness of using an International Classification of Diseases, Tenth Revision (ICD-10) coding algorithm for this purpose. METHODS: ICD-10 codes were used to identify cases of SWI within one year of CABG between April 2002 and November 2009. We randomly chose 200 charts for detailed chart review (100 from each of the groups coded as having SWI and not having SWI) to determine the utility of the ICD-10 coding algorithm. RESULTS: There were 2,820 patients undergoing CABG. Of these, 264 (9.4 percent) were coded as having SWI. Thirty-eight cases of SWI were identified by chart review. The ICD-10 coding algorithm of T81.3 or T81.4 was able to identify incident SWI with a positive predictive value of 35 percent and a negative predictive value of 97 percent. The agreement between the ICD-10 coding algorithm and presence of SWI remained fair, with an overall kappa coefficient of 0.32 (95 percent confidence interval, 0.22-0.43). The effectiveness of identifying deep SWI cases is also presented. CONCLUSIONS: This article describes an effective algorithm for identifying a cohort of patients with SWI following open sternotomy in large databases using ICD-10 coding. In addition, alternative search strategies are presented to suit researchers' needs.
Authors: J H Braxton; C A Marrin; P D McGrath; C S Ross; J R Morton; M Norotsky; D C Charlesworth; S J Lahey; R A Clough; G T O'Connor Journal: Ann Thorac Surg Date: 2000-12 Impact factor: 4.330
Authors: John H Braxton; Charles A S Marrin; Paul D McGrath; Jeremy R Morton; Mitchell Norotsky; David C Charlesworth; Stephen J Lahey; Robert Clough; Cathy S Ross; Elaine M Olmstead; Gerald T O'Connor Journal: Semin Thorac Cardiovasc Surg Date: 2004