Discussion
In this nationwide cohort study, we developed prediction models for incident AF among patients with HF. Clinical experts independently preselected the predictor variables for inclusion in the models based on candidate predictors available in administrative data. The predicted risk was higher with increasing age and was substantially higher among patients with all predictors.
The reason for a declining risk in high-risk individuals with highest age is most likely a substantial increase in risk of death from approximately the age of 80 years. The respective predictive performance of the random forest and cause-specific Cox regression was modest and similar, with Brier scores of 7.0% and time-dependent AUCs of 64% and 66%, respectively. Furthermore, use of a single random split did not change the results substantially compared with the calendar split. Our models that included all predictor variables did not demonstrate substantially different predictive performance, and use of all clinical information did not seem to outperform the reduced models selected by clinical experts.
The clinical experts did not prioritise certain variables associated with incident AF, for example, elevated alcohol consumption (not selected) and valvular disease (selected by one expert). A reason for not selecting such variables may be that analyses of the Framingham Heart Study have shown that alcohol consumption has not contributed to the risk of AF in any of the epochs during the last 50 years.17 Furthermore, the population-attributable risk associated with significant murmur has decreased over time.17
We applied a calendar split approach and a single random split approach to examine the predictive performance of the models. The first approach is advantageous because it simulates a natural situation in which the model is built on data from past patients with incident HF, and the model is then applied to future patients with incident HF. However, this approach does not account for potential temporal trends, for instance by HF guideline updates or new approvals of therapy. We found that the crude 1-year risk of AF increased over years, but we did not examine calendar trends. A single random split approach may account for temporal changes but the approach also comes with disadvantages because splitting randomly makes the results depend on the random seed. As a random seed determines the split, the predictions depend on a potential lucky number of the analyst.15 However, we noted no substantial difference in the predictive performance between the approaches.
The number of prediction models for AF developed and/or validated among patients with HF is limited. To our knowledge, only one study has developed a prediction model for AF, namely among 623 AF-free HF patients with reduced LVEF<45%.9 The outcome was persistent AF and the patients were followed for at least 1 year, but no specific time horizon was chosen.9 In contrast to our study, the authors applied backward selection to identify significant predictors that were included to establish a risk score. Furthermore, 76 patients died with no AF during follow-up but the analysis did not include death as a competing risk, and the study reported no predictive performance of the model.9 The C2HEST score was originally developed in a general Chinese population and was based on a medical insurance database.18 Recently, Liang et al tested the C2HEST score in a HFpEF population (LVEF ≥45%) with 2202 AF-free patients from the TOPCAT trial.10 The fact that one of the inclusion criteria of the TOPCAT trial was history of HF hospitalisation within the previous 12 months or elevated brain natriuretic peptide within 60 days before randomisation questions the time origin and applicability of the C2HEST model.19 In comparison, the time origin of our models was on the day of the HF diagnosis, which may be simpler to implement for clinicians and patients. Liang et al reported a time-dependent AUC at 5 years of 0.69 (95% CI 0.64 to 0.74) but a measure of calibration was not reported.10 A direct comparison of predictive performance between our models and the C2HEST score would require the models to be applied in the same dataset, but we were unable to use the C2HEST in our data because the estimates of the competing risk of death were not reported.18
The COMMANDER HF trial did not demonstrate benefits of using anticoagulation for patients with HF and no AF.20 As far as we know, no study has identified patients with HF at high risk for AF and used a randomised control trial to examine efficacy and safety. Our model may have the potential to identify a high-risk group of patients with HF who may randomised to determine if they would benefit from anticoagulation or initiation of more aggressive control of AF risk factors to prevent or postpone the onset of AF.
Identifying HF patients at high risk for incident AF appears to be challenging with administrative data. Prospective studies are needed to quantify the clinical effect of implementing the prediction model in routine practice and to assess possibilities of improvement given the collection of more candidate predictor variables. Identification of a more robust model may form the basis of a clinical trial that aims to quantify the clinical effects of the prediction model. In the interim, patients with HF are expected to have frequent healthcare contact, and opportunistic screening of all HF patients should be considered at routine follow-ups or other contacts in primary and secondary care. The 2020 European Society of Cardiology (ESC) guideline for AF lists several screening methods, such as pulse palpation and Holter monitoring, but no specific recommendation is given for patients with HF.21 Confirmation of the AF diagnosis and appropriate characterisation of the arrhythmia is part the holistic or integrated care pathway approach to AF management.21 In addition to early detection of AF, clinical staff should therefore prioritise optimisation of HF-related care, such as patient education and medical therapy, and the management of comorbidities. Importantly, use of ACE-inhibitors, angiotensin receptor blockers, mineralocorticoid receptor antagonist, beta-blockers and SGLT-2 inhibitors may reduce the incidence of AF.22–24
Limitations
We may have missed patients with prior AF whose diagnosis was not recorded or not recognised in the registries. We were unable to clinically evaluate the patients for undiagnosed AF, and to subclassify the type of AF. Studies that validated the AF diagnosis coded in the registry have shown positive predictive values of 92% and 95%.14 However, non-differential misclassification of AF registration is possible. The time of registered AF may be wrong, as we only have information on the time at the diagnosis and not the time at the development. Data on death from the Civil Registration System are considered highly accurate.
We had no data on body mass index, natriuretic peptides, left atrial volume, left atrial fibrosis or atrial ectopic activity. However, information on predictors such as left atrial fibrosis and atrial ectopic may be costly and time-consuming to clinically obtain and therefore may not be resource effective to include in a prediction model applicable in routine practice.
The generalisability of our prediction models may be reduced by the inclusion and exclusion criteria of the DHFR and the fact that the population consisted mainly of European ancestry individuals. Data from the Framingham Heart Study has shown a higher burden of prevalent AF among HF patients with preserved LVEF,3 which may account for the lower proportion of patients with preserved LVEF observed in our study compared with most population-based studies. Hence, the generalisability of our findings to HF with preserved LVEF, which excessively affects women, is uncertain. Furthermore, the clinical performance of applying the model in routine clinical settings or external validation has not been determined.