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| Date | August 31, 2013 |
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Abstract
Objective: Population-based identification of patients with a myocardial infarction is limited to patients presenting to hospital with an acute event. We set out to determine if adding physician billing data to hospital discharge data would result in an accurate capture of patients who have had a myocardial infarction.
Methods: We performed a retrospective chart abstraction of 969 randomly selected adult patients using data abstracted from primary care physicians on an electronic medical record in Ontario, Canada, as the reference standard.
Results: An algorithm of 3 physician billings in a one-year period with at least one being by a specialist or within a hospital or emergency room plus one hospital discharge abstract performed with a sensitivity of 80.4% (95% CI: 69.5-91.3), specificity of 98.0% (95% CI: 97.1-98.9), positive predictive value of 69.5% (95% CI: 57.7-81.2), negative predictive value of 98.9% (95% CI: 98.2% to 99.6%) and kappa statistic of 0.73 (95% CI: 0.63-0.83).
Conclusion: Using a combination of hospital discharge abstracts and physician bill- ing data may be the best way of assessing trends of MI occurrence over time since it increases the capture of MI beyond those patients who have been hospitalized.
Objective: Population-based identification of patients with a myocardial infarction is limited to patients presenting to hospital with an acute event. We set out to determine if adding physician billing data to hospital discharge data would result in an accurate capture of patients who have had a myocardial infarction.
Methods: We performed a retrospective chart abstraction of 969 randomly selected adult patients using data abstracted from primary care physicians on an electronic medical record in Ontario, Canada, as the reference standard.
Results: An algorithm of 3 physician billings in a one-year period with at least one being by a specialist or within a hospital or emergency room plus one hospital discharge abstract performed with a sensitivity of 80.4% (95% CI: 69.5-91.3), specificity of 98.0% (95% CI: 97.1-98.9), positive predictive value of 69.5% (95% CI: 57.7-81.2), negative predictive value of 98.9% (95% CI: 98.2% to 99.6%) and kappa statistic of 0.73 (95% CI: 0.63-0.83).
Conclusion: Using a combination of hospital discharge abstracts and physician bill- ing data may be the best way of assessing trends of MI occurrence over time since it increases the capture of MI beyond those patients who have been hospitalized.