OBJECTIVE: Administrative data validation is essential for identifying biases and misclassification in research. The objective of this study was to determine the accuracy of diagnostic codes for acute stroke and transient ischemic attack (TIA) using the Ontario Stroke Registry (OSR) as the reference standard. METHODS: We identified stroke and TIA events in inpatient and emergency department (ED) administrative data from eight regional stroke centres in Ontario, Canada, from April of 2006 through March of 2008 using ICD-10-CA codes for subarachnoid haemorrhage (I60, excluding I60.8), intracerebral haemorrhage (I61), ischemic (H34.1 and I63, excluding I63.6), unable to determine stroke (I64), and TIA (H34.0 and G45, excluding G45.4). We linked administrative data to the Ontario Stroke Registry and calculated sensitivity and positive predictive value (PPV). RESULTS: We identified 5,270 inpatient and 4,411 ED events from the administrative data. Inpatient administrative data had an overall sensitivity of 82.2% (95% confidence interval [CI 95%]=81.0, 83.3) and a PPV of 68.8% (CI 95%=67.5, 70.0) for the diagnosis of stroke, with notable differences observed by stroke type. Sensitivity for ischemic stroke increased from 66.5 to 79.6% with inclusion of I64. The sensitivity and PPV of ED administrative data for diagnosis of stroke were 56.8% (CI 95%=54.8, 58.7) and 59.1% (CI 95%=57.1, 61.1), respectively. For all stroke types, accuracy was greater in the inpatient data than in the ED data. CONCLUSION: The accuracy of stroke identification based on administrative data from stroke centres may be improved by including I64 in ischemic stroke type, and by considering only inpatient data.
OBJECTIVE: Administrative data validation is essential for identifying biases and misclassification in research. The objective of this study was to determine the accuracy of diagnostic codes for acute stroke and transient ischemic attack (TIA) using the Ontario Stroke Registry (OSR) as the reference standard. METHODS: We identified stroke and TIA events in inpatient and emergency department (ED) administrative data from eight regional stroke centres in Ontario, Canada, from April of 2006 through March of 2008 using ICD-10-CA codes for subarachnoid haemorrhage (I60, excluding I60.8), intracerebral haemorrhage (I61), ischemic (H34.1 and I63, excluding I63.6), unable to determine stroke (I64), and TIA (H34.0 and G45, excluding G45.4). We linked administrative data to the Ontario Stroke Registry and calculated sensitivity and positive predictive value (PPV). RESULTS: We identified 5,270 inpatient and 4,411 ED events from the administrative data. Inpatient administrative data had an overall sensitivity of 82.2% (95% confidence interval [CI 95%]=81.0, 83.3) and a PPV of 68.8% (CI 95%=67.5, 70.0) for the diagnosis of stroke, with notable differences observed by stroke type. Sensitivity for ischemic stroke increased from 66.5 to 79.6% with inclusion of I64. The sensitivity and PPV of ED administrative data for diagnosis of stroke were 56.8% (CI 95%=54.8, 58.7) and 59.1% (CI 95%=57.1, 61.1), respectively. For all stroke types, accuracy was greater in the inpatient data than in the ED data. CONCLUSION: The accuracy of stroke identification based on administrative data from stroke centres may be improved by including I64 in ischemic stroke type, and by considering only inpatient data.
Entities:
Keywords:
Diagnostic accuracy; Health administrative data; Health services research; Predictive value; Sensitivity; Stroke
Authors: Clare L Atzema; Peter C Austin; Bing Yu; Michael J Schull; Cynthia A Jackevicius; Noah M Ivers; Paula A Rochon; Douglas S Lee Journal: CMAJ Date: 2018-12-17 Impact factor: 8.262
Authors: Amol A Verma; Yishan Guo; Janice L Kwan; Lauren Lapointe-Shaw; Shail Rawal; Terence Tang; Adina Weinerman; Peter Cram; Irfan A Dhalla; Stephen W Hwang; Andreas Laupacis; Muhammad M Mamdani; Steven Shadowitz; Ross Upshur; Robert J Reid; Fahad Razak Journal: CMAJ Open Date: 2017-12-13
Authors: Jennifer A Watt; Tara Gomes; Susan E Bronskill; Anjie Huang; Peter C Austin; Joanne M Ho; Sharon E Straus Journal: CMAJ Date: 2018-11-26 Impact factor: 8.262
Authors: Clare L Atzema; Cynthia A Jackevicius; Alice Chong; Paul Dorian; Noah M Ivers; Ratika Parkash; Peter C Austin Journal: CMAJ Date: 2019-12-09 Impact factor: 8.262
Authors: Jennifer J Y Lee; Brian M Feldman; Brian W McCrindle; Ping Li; Rae Sm Yeung; Jessica Widdifield Journal: Pediatr Res Date: 2022-08-24 Impact factor: 3.953
Authors: Amy Y X Yu; Eric E Smith; Murray Krahn; Peter C Austin; Mohammed Rashid; Jiming Fang; Joan Porter; Manav V Vyas; Susan E Bronskill; Richard H Swartz; Moira K Kapral Journal: Neurology Date: 2021-08-18 Impact factor: 11.800
Authors: Kori S Zachrison; Sijia Li; Mathew J Reeves; Opeolu Adeoye; Carlos A Camargo; Lee H Schwamm; Renee Y Hsia Journal: Stroke Vasc Neurol Date: 2020-11-11