Late diagnosis of Heart failure (HF) is associated with worse outcomes. We aimed to develop a scalable tool to identify those at high risk of undiagnosed HF using routine electronic health records (EHR). We developed and internally validated a logistic regression (FIND-HF) model for incident HF diagnosis within one year in United Kingdom primary care EHRs (CPRD-Aurum, n=3 520 186), with good prediction performance (area under the receiver operating characteristic curve (AUC) 0.79), equal to more complex modelling techniques. We externally validated FIND-HF in United Kingdom (CPRD-GOLD, n=570 850, AUC 0.72), Japan (JMDC, n=6 820 694, AUC 0.73), United States of America (Epic Cosmos, n=7 710 398, AUC 0.78), and Taiwan (NTUH, n=170 518, AUC 0.85). In a cohort who had undergone HF diagnostics an optimised FIND-HF threshold had a positive predictive value of 21.4% and a negative predictive value of 96.9%. Amongst patients with HF who had undergone cardiac magnetic resonance imaging, high FIND-HF risk compared with low FIND-HF risk as reference, was associated with increased risk of a primary composite outcome of heart failure hospitalisation or cardiovascular death and more advanced adverse remodelling including lower left ventricular ejection fraction. FIND-HF is a scalable EHR-based model which has the potential to help rule out undiagnosed HF in low risk cases, whilst high risk cases are associated with more advanced cardiac dysfunction and worse prognosis.
Y. Nakao, R. Nadarajah, F. Shuweihdi et al.· Scientific Reports· 0 citations
Background Atrial fibrillation detected after stroke (AFDAS) is clinically important, but AFDAS-specific risk tools for patients without known atrial fibrillation (AF) remain limited. We developed and temporally validated the Prediction of AF in Ischemic Stroke (PAFIS) score. Methods We retrospectively analyzed ischemic-stroke patients from the National Taiwan University Hospital Integrative Medical Data Center. The development cohort included patients hospitalized in 2010–2020 (n = 3406), and the temporal validation cohort included those hospitalized in 2021–2023 (n = 1366). Known AF (KAF) was defined as AF documented before stroke or within 14 days after stroke; AFDAS was defined as newly documented AF beyond 14 days among patients without KAF. Multivariable logistic regression restricted to KAF-free patients was used for score derivation. Discrimination, calibration, and time-to-AFDAS risk stratification were assessed. Results Among KAF-free patients, AFDAS was detected during routine clinical follow-up in 176 of 2175 (8.1%) in the development cohort and 148 of 1366 (10.8%) in the validation cohort. The final PAFIS score included age ≥ 75 years, female sex, valvular heart disease, left atrial diameter ≥ 40 mm, and tricuspid regurgitation peak gradient ≥30 mmHg. AUCs were 0.72 (95% CI, 0.68–0.76) in development and 0.65 (95% CI, 0.60–0.70) in validation. PAFIS outperformed CHA₂DS₂-VASc, HAVOC, and AF-ESUS, but not Brown ESUS-AF. Observed AFDAS detection rates increased across risk groups in both cohorts. Conclusions PAFIS provides a simple AFDAS-specific tool for selective post-stroke rhythm monitoring. Because AF ascertainment was based on routine clinical care without standardized prolonged monitoring, PAFIS predicts AF detection under routine practice rather than true AFDAS incidence. Prospective multicenter validation with standardized monitoring is warranted.
J. Hsu, Ting-Chuan Wang, Yen-Yun Yang et al.· International Journal of Car...· 1 citation
A supervised machine learning algorithm for AF in a Western Pacific population was derived and demonstrated that higher risk was associated with hospitalisation for other cardio-renal diseases and death.
J. Hsu, C. Hayward, Tobin Joseph et al.· Heart, Lung and Circulation· 0 citations