Validation of the Diagnostic Algorithms for 5 Chronic Conditions in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN): A Kingston Practice-based Research Network (PBRN) Report

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Abstract
Objective: The objective of this study was to assess the validity of electronic medical records–based diagnostic algorithms for 5 chronic conditions. Methods: A retrospective validation study using primary chart abstraction. A standardized abstraction form was developed to ascertain diagnoses of diabetes, hypertension, osteoarthritis, chronic obstructive pulmonary disease, and depression. Information about billing, laboratory tests, notes, specialist and hospital reports, and physiologic data was collected. An age-stratified random sample of 350 patient charts was selected from Kingston, Ontario, Canada. Approximately 90% of those charts were allocated to people aged ≥60 years. Results: Three hundred thirteen patient records were included in the study. Patients' mean age was 68 years and 52% were women. High interrater reliability was indicated by 92% complete agreement and a κ statistic of 89.3%. The sensitivities of algorithms were 100% (diabetes), 83% (hypertension), 45% (osteoarthritis), 41% (chronic obstructive pulmonary disease), and 39% (depression). The lowest specificity was 97%, for depression. The positive predictive value ranged from 79% (depression) to 100%, and the negative predictive value ranged from 68% (osteoarthritis) to 100%. Conclusions: The diagnostic algorithms for diabetes and hypertension demonstrate adequate accuracy, thus allowing their use for research and policy-making purposes. The algorithms for the other 3 conditions require further refinement to attain better sensitivities.

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