scholarly journals Assessing the accuracy of using diagnostic codes from administrative data to infer antidepressant treatment indications: a validation study

2018 ◽  
Vol 27 (10) ◽  
pp. 1101-1111 ◽  
Author(s):  
Jenna Wong ◽  
Michal Abrahamowicz ◽  
David L. Buckeridge ◽  
Robyn Tamblyn
2017 ◽  
Vol 33 (12) ◽  
pp. 1729-1732
Author(s):  
John A. Staples ◽  
Cristian Vadeanu ◽  
Bobby Gu ◽  
Shannon Erdelyi ◽  
Herbert Chan ◽  
...  

Author(s):  
Ruth Hall ◽  
Luke Mondor ◽  
Joan Porter ◽  
Jiming Fang ◽  
Moira K. Kapral

AbstractObjective: 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 [CI95%]=81.0, 83.3) and a PPV of 68.8% (CI95%=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% (CI95%=54.8, 58.7) and 59.1% (CI95%=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.


2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Lorraine L. Lipscombe ◽  
Jeremiah Hwee ◽  
Lauren Webster ◽  
Baiju R. Shah ◽  
Gillian L. Booth ◽  
...  

2012 ◽  
Vol 40 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Ruth Ann Marrie ◽  
Bo Nancy Yu ◽  
Stella Leung ◽  
Lawrence Elliott ◽  
Patricia Caetano ◽  
...  

Head & Neck ◽  
2019 ◽  
Vol 41 (7) ◽  
pp. 2291-2298
Author(s):  
Yuan Xu ◽  
Shiying Kong ◽  
Winson Y. Cheung ◽  
May Lynn Quan ◽  
Steven C. Nakoneshny ◽  
...  

2020 ◽  
Vol 29 (11) ◽  
pp. 1423-1431
Author(s):  
Katja Biering Leth‐Møller ◽  
Tea Skaaby ◽  
Flemming Madsen ◽  
Janne Petersen ◽  
Allan Linneberg

2019 ◽  
Vol 74 (7) ◽  
pp. 2091-2097 ◽  
Author(s):  
Kevin L Schwartz ◽  
Andrew S Wilton ◽  
Bradley J Langford ◽  
Kevin A Brown ◽  
Nick Daneman ◽  
...  

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Corinne M Hohl ◽  
Lisa Kuramoto ◽  
Eugenia Yu ◽  
Basia Rogula ◽  
Jürgen Stausberg ◽  
...  

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