Patterns of comorbidities in patients with atrial fibrillation and impact on management and long-term prognosis: an analysis from the Prospective Global GLORIA-AF Registry

For this exploratory analysis, we considered 18 diseases and conditions, among those recorded at baseline. Cardiovascular conditions included arterial hypertension, coronary artery disease (CAD), heart failure (HF), peripheral artery disease (PAD), history of previous stroke/transient ischemic attack (TIA), history of venous thromboembolism and previous bleeding events. We also included non-cardiovascular conditions, i.e. diabetes mellitus, hyperlipidemia, obesity, history of cancer, abnormal kidney function (defined as chronic dialysis, renal transplantation, or serum creatinine ≥200 μmol/L), chronic obstructive pulmonary disease (COPD), emphysema, hyperthyroidism, liver disease, gastrointestinal disease (including peptic disease, heartburn/pyrosis and other abdominal conditions) and the presence of neurologic conditions (as recorded by investigator in the CRF). Investigators were able to record whether patients included had one of more of each condition, along with information regarding treatment prescribed. For this analysis, we considered use of antithrombotics, as well as concomitant treatment with cardiovascular drugs (i.e. angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers, diuretics, beta-blockers, digoxin, verapamil/diltiazem, class IC antiarrhythmic drugs (which included propafenone and flecainide), amiodarone/dronedarone, other antiarrhythmics) and non-cardiovascular drugs (i.e. oral hypoglycaemic agents, insulin, proton pump inhibitors and statins).

During phase II, a 2-year follow-up was performed only for patients prescribed dabigatran at baseline. During phase III, all patients (regardless of antithrombotic therapy received) were followed-up for 3 years. OAC discontinuation and major clinical outcomes were recorded during follow-up. We analysed treatment discontinuation at 24 months only for those patients who received OAC at baseline. As per previous analyses [20], discontinuation was defined as switching to another antithrombotic regimen (including switching to a different OAC) or interruption ≥30 days of treatment received at baseline. Non-persistence was defined as OAC discontinuation or study termination.

A graphical representation of the workflow of this analysis is reported in Additional file 1: Figure S1. We performed an exploratory latent class analysis based on the 18 conditions described above, using the ‘poLCA’ package in R [21]. The optimal number of classes was selected according to the Bayesian Information Criterion (BIC) and the consistent Akaike Information Criterion (cAIC), with lower values indicating better fit [22], and also according to clinical judgement. Posterior probability of membership was calculated for each patient, and for further analyses, each subject was then assigned to one of the latent classes, according to the modal posterior probability of membership. The classes identified were then named, considering the most relevant clinical characteristics, and the prevalence of comorbidities. Baseline characteristics were then computed and reported according to the groups identified.

Continuous variables were reported as mean and standard deviation (SD) or median and interquartile range (IQR); normally distributed variables were compared using parametric test, while non-normally distributed variables were compared using non-parametric tests. Binary and categorical variables were reported as frequencies and percentages, and Chi-square test was used for comparison.

The association between latent classes and drugs prescriptions was evaluated using a multiple logistic regression model, with components of CHA₂DS₂-VASc score (age <65, 65–75 or ≥75 years, sex, hypertension, diabetes, HF, CAD, history of stroke/TIA and PAD), phase of recruitment, type of AF (paroxysmal, persistent or permanent), BMI, and history of previous bleeding as covariates. Results were reported as odds ratios (OR) and 95% confidence intervals (CI).

Baseline characteristics according to latent class allocation are reported in Table 1; a synoptic view of comorbidities’ prevalence, and proportion of patients prescribed with each drug class is shown in Fig. 1.

The largest group was represented by the ‘low complexity’ phenotype (n = 12,774, 39.2%), defined by a low prevalence of most comorbid conditions, except for hypertension (52.9%) and HF (24.7%), and the lowest median number of comorbidities (1 [IQR 1–2]). Patients included in the ‘cardiovascular (CV) risk factors’ group (n = 9195, 28.2%; median number of comorbidities: 3 [IQR: 2–4]) were the youngest and had high prevalence of hypertension (95.9%), obesity and hyperlipidaemia (58.9% each), with also a considerable prevalence of diabetes mellitus (35.6%). The ‘atherosclerotic’ class (n = 3328, 10.2%; median number of comorbidities: 4 [IQR: 3–5]), conversely, had a high prevalence of CAD, HF and hyperlipidaemia and also showed the highest prevalence of PAD (11.7%) and the lowest female representation (33.9%). We also identified a ‘thromboembolic’ class (n = 2647, 8.1%, median number of comorbidities: 3 [IQR 2–3]), with 90% of patients with history of previous stroke/TIA, and a ‘cardiometabolic’ class (n = 2462, 7.6%, median number of comorbidities 5 [IQR 5–6]), mostly composed of obese subjects with high prevalence of both cardiovascular and metabolic conditions. Finally, the ‘high complexity’ class (n = 2154, 6.6%, median number of comorbidities 5 [IQR: 4–6]) had a significant burden of both cardiovascular and non-cardiovascular conditions, including gastrointestinal diseases (73.8%), history of bleeding (41.6%), cancer (32.4%) and COPD (18.2%).

Treatments received according to comorbidities phenotypes are reported in Additional file 1: Table S2. Additionally, antithrombotic treatment according to phenotypes are shown in Additional file 1: Figure S2.

OAC were largely used in all groups, with the ‘CV risk factors’ and the cardiometabolic groups showing the highest rates of OAC use (86.3% and 87.3%, respectively); use of OAC was lowest among patients in the atherosclerotic class (75.4%). The highest rate of non-vitamin K antagonist oral anticoagulant (NOAC) use was observed in the CV risk factor class (61.1%).

Regarding other treatments, the ‘CV risk factors’, atherosclerotic, cardiometabolic and high complexity phenotypes were more often treated with cardiovascular drugs (including ACE inhibitors, diuretics beta-blockers and antiarrhythmics). The cardiometabolic class had also more use of oral hypoglycaemic agents (43.0%), insulin (20.0%) and statins (77.7%). Higher median number of drugs received was observed in the atherosclerotic and cardiometabolic classes (5 [IQR 4–6] in both groups).

Multiple logistic regression models for treatments are reported in Table 2. Compared to the low complexity phenotype, all other groups were associated with higher OAC use, with highest figures in the high complexity (OR [95%CI]: 1.57 [1.35–1.81], p < 0.001) and cardiometabolic class (OR [95%CI]: 1.76 [1.49–2.09], p < 0.001). Similar results were observed for NOACs, which were more likely used in the high complexity, thromboembolic and ‘CV risk factors’ classes.

Regarding other treatments, all phenotypes showed higher odds of beta-blockers use, compared to the low complexity group. Similar results were observed for verapamil/diltiazem and particularly for the cardiometabolic (OR [95%CI]: 1.84 [1.50–2.26], p < 0.001) and high complexity groups (OR [95%CI]: 2.43 [2.02–2.93], p < 0.001). Finally, we also found an association between use of non-cardiovascular drugs (oral hypoglycaemic agents, PPI and statins) and more complex phenotypes (Table 2).

Of the 26,393 patients prescribed OACs at baseline, 19,980 (75.7%) had available follow-up data and were included in the analysis for OAC discontinuation. The proportion of patients who discontinued OAC at 6, 12 and 24 months after enrolment is shown in Additional file 1: Figure S3. Compared to the low complexity phenotype, no statistically significant differences regarding hazard of OAC discontinuation at 2 years were found for the other groups (Fig. 2).

23,375 patients (71.8%) had follow-up data for the primary composite outcome and were included in the survival analysis. Median follow-up was 3.0 [IQR: 2.2–3.1] years. No statistically significant differences were observed between patients included and excluded regarding age, sex and mean CHA₂DS₂-VASc score.

Kaplan–Meier curves for the primary composite outcome according to latent classes are reported in Fig. 3. The ‘CV risk factor’ and low complexity classes had the highest survival probabilities, while the atherosclerotic and high complexity phenotypes had the highest incidence of the primary composite outcome during follow-up.

Multiple Cox regression models for the primary and the exploratory secondary outcomes are reported in Table 3. Compared to the low complexity phenotype, all other groups were associated with a higher hazard of the primary outcome, except for the ‘CV risk factor’ class (hazard ratio [HR]: 0.87, 95%CI: 0.76–1.00, p = 0.042). The greatest association was observed for the cardiometabolic (HR [95%CI]: 1.37 [1.13–1.67], p = 0.001) and the high complexity phenotypes (HR [95%CI]: 1.47 [1.24–1.75], p < 0.001). Similar findings were observed for all-cause death. The atherosclerotic (aHR [95%CI]: 1.72 [1.24–2.38], p = 0.001), cardiometabolic (aHR [95%CI]: 1.77 [1.24–2.51], p = 0.001) and high complexity classes (aHR: [95%CI]: 2.19 [1.61–2.97], p < 0.001) were also associated with a higher risk of major bleeding, with similar results observed for MACE. Finally, the risk of thromboembolism was higher in the atherosclerotic class (aHR [95%CI]: 1.46 [1.06–2.02], p = 0.022) compared to the low-complexity group, while non-statistically significant results were observed for the other groups.

Our manuscript provides a first application of the LCA approach to analyse comorbidity patterns on a large, contemporary and global real-world cohort of AF patients. These findings inform clinicians on phenotypes of multimorbidity, and implications for management and prognosis. Nonetheless, we acknowledge some limitations. First, the current study is an exploratory post hoc analysis of a prospective observational study; therefore, we may have limited power to find differences between groups. Second, the analysis was based on a set of comorbidities which were available and defined according to the CRF of the GLORIA-AF registry; other diseases, which may be relevant in the natural history of AF, were not included, and we did not analyse the contribution of conditions that were diagnosed after the inclusion. Further studies, appropriately designed, will be needed to analyse the longitudinal trajectories of comorbidities in patients with AF. Moreover, although the median follow-up time in our study was considerable, longer follow-up may be needed to fully capture the trajectories and natural history of patients with AF according to their comorbidity patterns. The GLORIA-AF registry was conducted before the latest international guidelines for the management of AF [31, 39] recommended the implementation of a holistic and integrated approach (such as the ABC pathway) for the management of patients with AF. Therefore, whether more intensive treatment of such comorbidities could have altered our results remains unclear. Although we have adjusted for several covariates, we cannot exclude the contribution of other unaccounted confounders, particularly on the association between treatments and the risk of major outcomes. Finally, our results were not adjusted for multiple comparisons, and as such should be regarded as exploratory and interpreted with caution, particularly regarding secondary outcomes.