To our knowledge, this is the largest study of model-based clustering in HF published to date, using widely available clinical variables and a population sample which is representative of people living in the US. In doing so, we identified five distinct comorbidity clusters of patients with HF, namely the low-burden, metabolic-vascular, anemic, ischemic, and metabolic groups. Importantly, these comorbidity clusters had differential risks of hospital admission and death, indicating that comorbidity patterns reflect variable HF clinical trajectories and prognosis.
Previous studies have identified subgroups in HF: Tromp et al. [11] included registry patients from across Asia and identified five clusters, which had differential quality of life and rates of a combined outcome of death or HF hospitalization over 1 year follow-up. They similarly identified ischemic and metabolic subgroups, but with markedly different characteristics to the current cohort. Notably, the Asian metabolic group had lower rates of both diabetes (63.5% vs. 100%) and obesity (45.1% vs. 58%) and was on average 10 years younger than the US group. The Asian ischemic cluster had comparable prevalence of CAD; however, the US group had a higher frequency of non-CV comorbidities such as cancer and liver disease. The remaining three clusters identified by Tromp et al. [11], elderly/AF, young, and lean diabetic, did not have direct equivalence in the US, suggesting clustering of comorbidities may be specific to geographical region.
Another study, from the US, found four subgroups in a hospitalized HF population: a common disease group characterized by high prevalence of hypertension, a lifestyle group with high diabetes and obesity, a renal group, and a neurovascular group with increased levels of cerebrovascular disease [10]. The latter group was at most increased risk of inpatient mortality and had the highest medical cost. However, this cohort may reflect a more severe population as only hospitalized patients were included and was further limited by solely examining inpatient outcomes.
In our population-wide study, we found two new US-specific clusters: the anemic and metabolic-vascular groups. It is the first time a principally anemic group has been identified using model-based clustering techniques in HF. The second most frequently diagnosed comorbidity in this group was renal failure, with a prevalence second only to the metabolic-vascular group. Thus, it is not surprising that these two comorbidities clustered together, as the cardio-renal anemia syndrome is well-established in HF and has been linked to increased hospitalization and worse clinical prognosis as compared to patients without these comorbidities [25,26,27]. Compared to the low-burden cluster, the anemic group was at increased risk of both admission and mortality (49% and 46% increased risk, respectively). Surprisingly, the risk of death in this group was numerically higher than for patients in the ischemic group, suggesting this triad of comorbidities (HF, anemia, renal failure) incurs a higher clinical burden than that of patients fitting an older profile with more CV disease (such as the ischemic group).
Patients in the metabolic-vascular phenotype had the worst prognosis, denoted by the highest risk of admission and death compared to the low-burden group. The association with admission was significant after adjusting for HF medications, suggesting that therapies aimed at modifying mortality and morbidity risk and congestion relief do not necessarily decrease admission risk in this patient group. Although we did not assess compliance with medical or management of comorbidities, the particular combination of high-risk CV (PAD, CAD) and non-CV comorbidities (renal failure, diabetes) may increase the risk of admission independent of these factors.
The metabolic group had the lowest risk of admission or death, despite all patients being diagnosed with diabetes and over half with obesity. This group was, on average, the youngest among clusters, which may explain the comparatively favorable prognosis. Other studies [28, 29] have reported on the “obesity paradox” in HF where higher BMI appears to act as a protective factor against mortality or admission, though this has been described as either wrongly diagnosing HF in obese individuals, or lead time bias (earlier symptom onset attributable to added metabolic demands of obesity/diabetes), which may be plausible in a younger HF subgroup.
Nearly two thirds of our overall cohort had five or more comorbidities, similar to previous reports [30]. The total number of comorbidities varied across clusters and was highest in those with the poorest prognosis (i.e., metabolic-vascular, ischemic subgroups), confirming that increases in comorbidity burden worsen prognosis. Furthermore, there was a stepwise increase in risk of admission to hospital with each incremental rise in number of comorbidities, and those with over nine comorbidities were at tripled risk of being admitted to hospital, compared to those with two or less additional illnesses (Additional file 1: Table S11). However, individual comorbidity counts insufficiently describe the differences in clinical burden incurred by comorbid diseases (for example, anemia may be associated with a lower level of disability as compared to CAD, but the two diseases contribute equally when using a counting approach). Individual comorbidity counts may also fail to convey the severity of diseases or interactions between comorbidities that may give rise to distinct clinical trajectories. By contrast, identification of specific patterns or clusters of comorbidities, as performed in our study, may capture some of these interactions and provide more granular information that could identify priorities for clinical HF care.
Furthermore, among patients with EF data available, although we observed some preferential distribution of HFpEF to the metabolic-vascular or ischemic groups, and a greater predominance of HFrEF in the low-burden group, none of the clusters mapped perfectly to either EF group, highlighting the complexity and interrelatedness of comorbidity in HF (Additional file 1: Table S6) [31]. Importantly, differences in admission and survival persisted after adjusting for EF, which also did not act as an effect modifier (Additional file 1: Table S8, Table S13, Table S14), corroborating previous research showing that most comorbidities have a similar impact on both EF-defined HF groups [32]. Although EF has been the primary framework used to classify patients with HF, and the basis for recruitment into therapeutic trials, there are still no proven disease-modifying treatments for up to half of all patients with HF—i.e., those with preserved EF. Our findings suggest a potential for clinical trials to enroll patients and test therapies based on prognostic comorbidity patterns, not just limiting them to EF.
Healthcare resource utilization has not previously been reported in clustering studies of HF. Our data demonstrate a significant association of comorbidity patterns with healthcare utilization in HF. We found that patients with higher prevalence of CV comorbidities (metabolic-vascular, ischemic) were more often admitted to hospital, in contrast to the metabolic and anemic patients, who had comparatively more outpatient visits during follow-up. The lowest utilization rate was observed in the metabolic group. This may partly be explained by the younger age of patients in this group, and/or a low requirement for healthcare use for metabolic conditions in the absence of vascular complications (i.e., no CAD, PAD, and CVA). These data demonstrate a significant association of comorbidity patterns with healthcare utilization in HF and may reflect the different intensity of care and surveillance needed for the management of specific comorbidities or variable severity of associated HF across the clusters.
The anemic cluster experienced the highest adjusted rate of outpatient visits and high mortality. The main distinguishing features of this cluster (namely anemia-depression-cancer) have been independently linked to increased use of outpatient services, explained partly by care-seeking behaviors, poor medication adherence in depression [33], or undertreatment of HF due to deteriorating in health status in malignancy [34]. Indeed, the anemic cluster had among the lowest proportions of medication prescriptions across clusters, suggesting less than optimal management of HF.
Cost of care was primarily driven by inpatient and emergency room visits and was highest in the metabolic-vascular profile, intermediate in the anemic and ischemic groups, and lowest in the metabolic and low-burden groups, respectively. The identification of this “hierarchy” of cost, associated with common comorbidity patterns, calls for a targeted approach of resource allocation: thus, patients fitting profiles characterized by high inpatient use should be the focus of community interventions targeting lifestyle changes such as providing nutritional advice, encouraging exercise regimens, and compliance with HF medication that may help to prevent admissions to hospital.
Overall, it is challenging to manage patients with HF with co-occurring disease. Our results emphasize that the specific knowledge of how comorbidities cluster together and their association with clinical prognosis may assist clinicians who manage these complex patients to further refine and target their treatment. Arguably, patients within each cluster are more similar, on a group level, compared to those in other clusters—whether these subgroups may benefit from similar preventative and therapeutic plans needs to be evaluated in future prospective studies. Future characterizations of HF may benefit from integrating data on comorbidities ideally derived from large, real-world populations in relevant and local geographical settings, in order to derive a more nuanced taxonomy, enabling multidimensional and personalized HF care and resource allocation. Furthermore, our clustering analysis may serve as a hypothesis-generating paradigm in identifying comorbidity patterns, which may be improved upon in further studies. It would be interesting to assess whether membership to comorbidity cluster changes over time in patients with HF and to map their trajectories, similar to Vetrano and colleagues, who evaluated elderly individuals’ transitions among multimorbidity clusters over time [35]. A controlled setting such as a registry where data collection is standardized and collected at specific time points by trained healthcare staff may be more suitable for such an investigation.
