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Published on: February 26, 2013
Accuracy and validation of an automated electronic algorithm to identify patients with atrial fibrillation at risk
Ann Marie Navar-Boggan1, Jennifer A Rymer1, Jonathan P Piccini1
1Duke University Medical Center, Durham, NC.
Insights
Identifying atrial fibrillation (AF) patients for stroke risk using electronic data is challenging. A 1-year algorithm based on CHA2DS2-VASC scores offers improved accuracy for performance measures.
Area of Science:
- Cardiology
- Health Informatics
- Public Health
Background:
- No universal algorithm exists for identifying atrial fibrillation (AF) patients and their stroke risk using electronic health data for performance measures.
- Accurate identification is crucial for quality assessment and patient management.
Purpose of the Study:
- To evaluate the accuracy of different algorithms for identifying AF patients and stratifying stroke risk using electronic health records.
- To compare a 1-year restrictive algorithm with a 10-year broad algorithm for AF patient identification and stroke risk assessment.
Main Methods:
- Patients with AF were identified using International Classification of Diseases (ICD-9) codes.
- CHADS2 and CHA2DS2-VASC scores were calculated using 1-year and 10-year algorithms.
- Algorithm accuracy was validated through chart reviews of 300 patients.
Main Results:
- A 1-year CHA2DS2-VASC algorithm (score ≥2) maximized positive predictive value (97.5%) for identifying high-risk patients.
- The 10-year CHADS2 algorithm had a positive predictive value of 88.0%, with 12% misclassified.
- Anticoagulation rates were similar across algorithms for eligible patients.
Conclusions:
- Automated methods can identify AF patients for anticoagulation, but misclassification (up to 12%) limits reliance on administrative data alone for quality assessment.
- Requiring recent comorbidity diagnoses (1-year window) and using CHA2DS2-VASC scores minimizes misclassification.
- Despite accuracy differences, system-wide anticoagulation rates were consistent across algorithms.
Background:
There is no universally accepted algorithm for identifying atrial fibrillation (AF) patients and stroke risk using electronic data for use in performance measures.
Methods:
Patients with AF seen in clinic were identified based on International Classification of Diseases, Ninth Revision(ICD-9) codes. CHADS(2) and CHA(2)DS(s)-Vasc scores were derived from a broad, 10-year algorithm using IICD-9 codes dating back 10 years and a restrictive, 1-year algorithm that required a diagnosis within the past year. Accuracy of claims-based AF diagnoses and of each stroke risk classification algorithm were evaluated using chart reviews for 300 patients. These algorithms were applied to assess system-wide anticoagulation rates.
Results:
Between 6/1/2011, and 5/31/2012, we identified 6,397 patients with AF. Chart reviews confirmed AF or atrial flutter in 95.7%. A 1-year algorithm using CHA(2)DS(2)-Vasc score ≥2 to identify patients at risk for stroke maximized positive predictive value (97.5% [negative predictive value 65.1%]). The PPV of the 10-year algorithm using CHADS(2) was 88.0%; 12% those identified as high-risk had CHADS(2) scores <2. Anticoagulation rates were identical using 1-year and 10-year algorithms for patients with CHADS(2) scores ≥2 (58.5% on anticoagulation) and CHA(2)DS(2)-Vasc scores ≥2 (56.0% on anticoagulation).
Conclusions:
Automated methods can be used to identify patients with prevalent AF indicated for anticoagulation but may have misclassification up to 12%, which limits the utility of relying on administrative data alone for quality assessment. Misclassification is minimized by requiring comorbidity diagnoses within the prior year and using a CHA(2)DS(2)-Vasc based algorithm. Despite differences in accuracy between algorithms, system-wide anticoagulation rates assessed were similar regardless of algorithm used.
