A systematic review of validated methods for identifying atrial fibrillation using administrative data

Abstract

Purpose To characterize the validity of algorithms to identify AF from electronic health data through a systematic review of the literature, and to identify gaps needing further research. Methods Two reviewers examined publications during 1997–2008 that identified patients with AF from electronic health data and provided validation information. We abstracted information including algorithm sensitivity, specificity, and positive predictive value (PPV). Results We reviewed 544 abstracts and 281 full-text articles, of which 18 provided validation information from 16 unique studies. Most used data from before 2000, and 10 of 16 used only inpatient data. Three studies incorporated electronic ECG data for case identification or validation. A large proportion of prevalent AF cases identified by ICD-9 code 427.31 were valid (PPV 70–96%, median 89%). Seven studies reported algorithm sensitivity (range, 57–95%; median 79%). One study validated an algorithm for incident AF and reported a PPV of 77%. Conclusions The ICD-9 code 427.31 performed relatively well, but conclusions about algorithm validity are hindered by few recent data, use of nonrepresentative populations, and a disproportionate focus on inpatient data. An optimal contemporary algorithm would likely draw on inpatient and outpatient codes and electronic ECG data. Additional research is needed in representative, contemporary populations regarding algorithms that identify incident AF and incorporate electronic ECG data.

Prediction of Atrial Fibrillation in a Racially Diverse Cohort: The Multi‐Ethnic Study of Atherosclerosis (MESA)

“…The AF cases included in this analysis therefore may not be representative of the average AF patient. Previous studies, however, have demonstrated adequate validity of this method of AF ascertainment in large epidemiological studies 20, 21. Moreover, the good calibration of the Framingham model among MESA whites indicates that the observed risk of AF in the MESA cohort is not different from the predicted risk calculated from the Framingham model, derived using a more detailed ascertainment of AF (including outpatient diagnosis) 4.…”

“…AF hospitalizations associated with open cardiac surgery were ignored in the definition. Previous studies have demonstrated the adequate validity of hospital discharge codes for ascertainment of AF in large cohort studies 20, 21. For this analysis, cases identified through the end of 2012 were considered.…”

Effectiveness and Safety of Apixaban, Dabigatran, and Rivaroxaban Versus Warfarin in Frail Patients With Nonvalvular Atrial Fibrillation

“…The Frailty Index offers a precise measurement of frailty, but the level of information required for calculation make it complicated to utilize in clinical situations. The Frailty Phenotype developed by Fried et al12 identifies frailty by the presence of ≥3 of the following components: unintentional weight loss of 10 pounds in the past year, self‐reported exhaustion, weakness as measured with grip strength, slow walking speed and low physical activity (patients can be consider pre‐frail if 1 or 2 criteria are met, and are considered non‐frail if none of the criteria are met) 10. Fried's Frailty Phenotype12 has shown to have the ability to predict poor health outcomes including incident of falls, worsening mobility, hospitalization, and death.…”

“…We included patients who were OAC‐naïve during the 12 months before the day of the first qualifying OAC dispensing (index date), had ≥2 ICD‐9 or ‐10 (427.31; I48) inpatient or outpatient diagnosis code in any position for atrial fibrillation (AF) without codes suggesting valvular disease and ≥12 months of continuous medical and prescription coverage before OAC initiation (baseline period) 10. Individuals were excluded if they had a history of venous thromboembolism or orthopedic arthroplasty, were pregnant, had a transient cause of NVAF, or were prescribed >1 OAC.…”

Methodologic Differences Across Studies of Patients With Atrial Fibrillation Lead to Varying Estimates of Stroke Risk

“…Friberg et al described the strong dependence of observed AF stroke rates on such analytic choices as ICD‐10 discharge diagnosis position, length of blanking period, and inclusion of transient ischemic attack as an outcome 11. Nielsen et al described the impact of excluding AF patients who later in follow‐up started OAC30 and others have assessed the validity of ICD codes relevant to identifying AF and ischemic stroke 31, 32. In the current study, we provide a more comprehensive perspective, exploring differences in stroke rates according to the full range of core features of study design (ie, characteristics of the cohort, assembly of AF patients, determination of anticoagulant status, assessment of comorbidities, ascertainment of outcome events, and analytic approach).…”