What is the clinical evidence from this study?

Study design: Cohort. Population: Acute ischemic stroke without known atrial fibrillation (n=5412). Intervention: CHASE-LESS score vs. Other risk scores (e.g., CHA2DS2-VASc, CHADS2). Primary outcome: Atrial fibrillation detected after stroke (AFDAS) (HR 1.44, 95% CI 1.38-1.51, p=<0.001).

April 29, 2022Open Access

Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study

Q: What are the key findings of this study?

The CHASE-LESS score significantly predicted atrial fibrillation detected after stroke (HR 1.44) and demonstrated adequate discrimination with a C-index of 0.741, performing better than other common risk scores.

CHCheng‐Yang HsiehCross-Cutting Cardiology HKHsuan-Min KaoChia-Yi Christian Hospital KSKuan‐Lin SungNational Taiwan University

Key Result

The CHASE-LESS score significantly predicted atrial fibrillation detected after stroke (HR 1.44) and demonstrated adequate discrimination with a C-index of 0.741, performing better than other common risk scores.

Study Design

Type

Cohort (n=5,412)

Multicenter

Structured PICO

Population

5,412 patients with acute ischemic stroke and no known atrial fibrillation, followed for a median of 10.6 months to validate risk scores for predicting post-stroke atrial fibrillation.

Exposure

8 risk scores for predicting atrial fibrillation detected after stroke (AS5F, C2HEST, CHADS2, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF)

Outcome

Atrial fibrillation detected after stroke (AFDAS)hard clinical

The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting atrial fibrillation detected after stroke, which may help select patients for intensive monitoring.

Main Result

Hazard Ratio: 1.44 (95% CI 1.38–1.51)

p-value: p=<0.001

Limitations

Single-site study, limiting generalizability to other settings.
Potential outcome misclassification if patients were diagnosed with AF outside the study hospital.
Methods used to identify poststroke AF were not standardized in all patients.
The CHASE-LESS score may have performed better because it was developed in a similar Taiwanese population.

Abstract

Background Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS). Methods We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C 2 HEST, CHADS 2 , CHA 2 DS 2 -VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis. Results The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, p = 0.223), which were significantly higher than the other risk scores. Conclusion The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring.

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