Examining the continuum of resistance model in two population-based screening studies in Sweden

In studies recruited on a voluntary basis, lack of representativity may impair the ability to generalize findings to the target population. Previous studies, primarily based on surveys, have suggested that generalizability may be improved by exploiting data on individuals who agreed to participate only after receiving one or several reminders, as such individuals may be more similar to non-participants than what early participants are. Assessing this idea in the context of screenings, we compared sociodemographic characteristics and health across early, late, and non-participants in two large population-based screening studies in Sweden: STROKESTOP II (screening for atrial fibrillation; 6,867 participants) and SCREESCO (screening for colorectal cancer; 39,363 participants). We also explored the opportunities to reproduce the distributions of characteristics in the full invited populations, either by assuming that the non-participants were similar to the late participants, or by applying a linear extrapolation model based on both early and late participants. Findings showed that early and late participants exhibited similar characteristics along most dimensions, including civil status, education, income, and health examination results. Both these types of participants in turn differed from the non-participants, with fewer married, lower educational attainments, and lower incomes. Compared to early participants, late participants were more likely to be born outside of Sweden and to have comorbidities, with non-participants similar or even more so. The two empirical models improved representativity in some cases, but not always. Overall, we found mixed support that data on late participation may be useful for improving representativeness of screening studies

An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening.

AIMS: Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG. METHODS AND RESULTS: A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis. The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence-based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78-0.83], which had a wide age range of 65-90+ years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75-76 years), with AUCs of 0.62 (CI 0.61-0.64) and 0.62 (CI 0.58-0.65), respectively. CONCLUSION: An artificial intelligence-enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.The project was funded by Vinnova, Sweden’s innovation agency (grant to Zenicor Medical Systems AB). In addition, the project received funding by The Swedish Heart-Lung Foundation and CIMED. The study also received a research grant from The Swedish Research Council, Dnr 2022-01466. Emma Svennberg is supported by the Stockholm County Council (Clinical researcher appointment). The SAFER Study was funded by the National Institute for Health Research (NIHR), grant number RP-PG- 0217-20007 and by the NIHR School for Primary Care Research

Stepwise mass screening for atrial fibrillation using N-terminal B-type natriuretic peptide : the STROKESTOP II study

Aims: To study the prevalence of unknown atrial fibrillation (AF) in a high-risk, 75/76-year-old, population using N-terminal B-type natriuretic peptide (NT-proBNP) and handheld electrocardiogram (ECG) recordings in a stepwise screening procedure. Methods and results: The STROKESTOP II study is a population-based cohort study in which all 75/76-year-old in the Stockholm region (n = 28 712) were randomized 1:1 to be invited to an AF screening programme or to serve as the control group. Participants without known AF had NT-proBNP analysed and were stratified into low-risk (NT-proBNP <125 ng/L) and high-risk (NT-proBNP >= 125 ng/L) groups. The high-risk group was offered extended ECG-screening, whereas the low-risk group performed only one single-lead ECG recording. In total, 6868 individuals accepted the screening invitation of which 6315 (91.9%) did not have previously known AF. New AF was detected in 2.6% [95% confidence interval (CI) 2.2-3.0] of all participants without previous AF. In the high-risk group (n = 3766/6315, 59.6%), AF was diagnosed in 4.4% (95% CI 3.7-5.1) of the participants. Out of these, 18% had AF on their index-ECG. In the low-risk group, one participant was diagnosed with AF on index-ECG. The screening procedure resulted in an increase in known prevalence from 8.1% to 10.5% among participants. Oral anticoagulant treatment was initiated in 94.5% of the participants with newly diagnosed AF. Conclusion: N-terminal B-type natriuretic peptide-stratified systematic screening for AF identified 4.4% of the high-risk participants with new AF. Oral anticoagulant treatment initiation was well accepted in the group diagnosed with new AF