Performance of an electronic health record-based predictive model to identify patients with atrial fibrillation across countries

TY - JOUR

T1 - Performance of an electronic health record-based predictive model to identify patients with atrial fibrillation across countries

AU - Mokgokong, Ruth

AU - Schnabel, Renate

AU - Witt, Henning

AU - Miller, Robert

AU - Lee, Theodore C

PY - 2022

Y1 - 2022

N2 - BACKGROUND: Atrial fibrillation (AF) burden on patients and healthcare systems warrants innovative strategies for screening asymptomatic individuals.OBJECTIVE: We sought to externally validate a predictive model originally developed in a German population to detect unidentified incident AF utilising real-world primary healthcare databases from countries in Europe and Australia.METHODS: This retrospective cohort study used anonymized, longitudinal patient data from 5 country-level primary care databases, including Australia, Belgium, France, Germany, and the UK. The study eligibility included adult patients (≥45 years) with either an AF diagnosis (cases) or no diagnosis (controls) who had continuous enrolment in the respective database prior to the study period. Logistic regression was fitted to a binary response (yes/no) for AF diagnosis using pre-determined risk factors.RESULTS: AF patients were from Germany (n = 63,562), the UK (n = 42,652), France (n = 7,213), Australia (n = 2,753), and Belgium (n = 1,371). Cases were more likely to have hypertension or other cardiac conditions than controls in all validation datasets compared to the model development data. The area under the receiver operating characteristic (ROC) curve in the validation datasets ranged from 0.79 (Belgium) to 0.84 (Germany), comparable to the German study model, which had an area under the curve of 0.83. Most validation sets reported similar specificity at approximately 80% sensitivity, ranging from 67% (France) to 71% (United Kingdom). The positive predictive value (PPV) ranged from 2% (Belgium) to 16% (Germany), and the number needed to be screened was 50 in Belgium and 6 in Germany. The prevalence of AF varied widely between these datasets, which may be related to different coding practices. Low prevalence affected PPV, but not sensitivity, specificity, and ROC curves.CONCLUSIONS: AF risk prediction algorithms offer targeted ways to identify patients using electronic health records, which could improve screening number and the cost-effectiveness of AF screening if implemented in clinical practice.

AB - BACKGROUND: Atrial fibrillation (AF) burden on patients and healthcare systems warrants innovative strategies for screening asymptomatic individuals.OBJECTIVE: We sought to externally validate a predictive model originally developed in a German population to detect unidentified incident AF utilising real-world primary healthcare databases from countries in Europe and Australia.METHODS: This retrospective cohort study used anonymized, longitudinal patient data from 5 country-level primary care databases, including Australia, Belgium, France, Germany, and the UK. The study eligibility included adult patients (≥45 years) with either an AF diagnosis (cases) or no diagnosis (controls) who had continuous enrolment in the respective database prior to the study period. Logistic regression was fitted to a binary response (yes/no) for AF diagnosis using pre-determined risk factors.RESULTS: AF patients were from Germany (n = 63,562), the UK (n = 42,652), France (n = 7,213), Australia (n = 2,753), and Belgium (n = 1,371). Cases were more likely to have hypertension or other cardiac conditions than controls in all validation datasets compared to the model development data. The area under the receiver operating characteristic (ROC) curve in the validation datasets ranged from 0.79 (Belgium) to 0.84 (Germany), comparable to the German study model, which had an area under the curve of 0.83. Most validation sets reported similar specificity at approximately 80% sensitivity, ranging from 67% (France) to 71% (United Kingdom). The positive predictive value (PPV) ranged from 2% (Belgium) to 16% (Germany), and the number needed to be screened was 50 in Belgium and 6 in Germany. The prevalence of AF varied widely between these datasets, which may be related to different coding practices. Low prevalence affected PPV, but not sensitivity, specificity, and ROC curves.CONCLUSIONS: AF risk prediction algorithms offer targeted ways to identify patients using electronic health records, which could improve screening number and the cost-effectiveness of AF screening if implemented in clinical practice.

KW - Adult

KW - Atrial Fibrillation/diagnosis

KW - Electronic Health Records

KW - Humans

KW - Predictive Value of Tests

KW - Retrospective Studies

KW - United Kingdom/epidemiology

U2 - 10.1371/journal.pone.0269867

DO - 10.1371/journal.pone.0269867

M3 - SCORING: Journal article

C2 - 35802569

VL - 17

JO - PLOS ONE

JF - PLOS ONE

SN - 1932-6203

IS - 7

M1 - e0269867

ER -