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Malignant and Benign Phenotypes of Multimorbidity in Heart Failure

Implications for Clinical Practice

Chen, Lei, PhD; Chan, Yih-Kai, PhD; Busija, Lucy, PhD; Norekval, Tone M., PhD; Riegel, Barbara, PhD; Stewart, Simon, PhD

Journal of Cardiovascular Nursing: May/June 2019 - Volume 34 - Issue 3 - p 258–266
doi: 10.1097/JCN.0000000000000557
ARTICLES: Heart Failure

Background: The impact of different patterns of multimorbidity in heart failure (HF) on health outcomes is unknown.

Objectives: The aim of this study was to test the hypothesis that, independent of the extent of comorbidity, there are distinctive phenotypes of multimorbidity that convey an increased risk for premature mortality in patients hospitalized with HF.

Methods: We analyzed the clinical profile and health outcomes of 787 patients hospitalized with HF participating in a multidisciplinary HF management program with a minimum 12-month follow-up. A Classification and Regression Tree model was applied to explore the distinctive combinations of 10 most prevalent concurrent conditions (other than coronary artery disease and hypertension) associated with 12-month all-cause mortality.

Results: Mean (SD) age was 74 (12) years (59% men), and 65% had left ventricular systolic dysfunction. Most patients (88%) had 3 or more comorbid conditions, with a mean of 4.3 concurrent conditions in addition to HF. A total of 248 patients (32%) died (median, 663 [IQR, 492–910] days), including 142 deaths (18%) within 12 months. Patients with concurrent dysrhythmia, anemia, and respiratory disease experienced significantly higher 12-month all-cause mortality than those without these conditions (36.1% vs 3.6%, respectively; hazard ratio, 6.1 [95% confidence interval, 2.0–19.1]). Overall, this “malignant” phenotype of multimorbidity was associated with not only a markedly increased risk of all-cause mortality but also more unplanned readmissions, longer inpatient stays, and highest costs in the short (30-day) and longer terms when compared with more “benign” phenotypes of multimorbidity.

Conclusions: We found a differential pattern of health outcomes according to pattern of comorbidity present in older patients hospitalized with HF and exposed to postdischarge, multidisciplinary management.

Lei Chen, PhD Research Fellow, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne.

Yih-Kai Chan, PhD Research Fellow, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne.

Lucy Busija, PhD Senior Research Fellow, Institute for Health and Ageing, Australian Catholic University, Melbourne.

Tone M. Norekval, PhD Professor, Department of Heart Disease, Haukeland University Hospital; and Department of Clinical Science, University of Bergen, Bergen, Norway.

Barbara Riegel, PhD Professor, Biobehavioral Health Sciences Department, School of Nursing, University of Pennsylvania, Philadelphia.

Simon Stewart, PhD Professor, Cardiology Unit, The Queen Elizabeth Hospital, Adelaide, Australia.

This work was supported by the National Health and Medical Research Council of Australia (1049133 and 1041796 to S.S.).

The authors have no conflicts of interest to disclose.

Correspondence Simon Stewart, PhD, Cardiology Unit, The Queen Elizabeth Hospital, 28 Woodville Rd, South Australia 5011, Australia (

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (

Despite significant therapeutic advancements to improve survival after the onset of heart failure (HF), its burden, characterized by costly (predominantly unplanned) hospitalizations and premature deaths, continues to rise.1,2 Multimorbidity is increasingly prevalent in patients with HF, particularly among older individuals. This complicates their clinical care and increases their risks for poor health outcomes,3,4 yet the precise impact of multimorbidity in HF is not well studied. Our previous composite analysis of health outcomes in more than 1200 inpatients with a broad spectrum of chronic heart diseases, including HF, suggests that there is potential for worse outcomes despite evidence-based multidisciplinary care being applied to older patients with multimorbidity.5

It was on this basis that the clinical framework “Acknowledge, Routinely profile, Identify, Support, and Evaluate Heart Failure” (ARISE-HF) has been proposed to improve health outcomes in patients with HF who are affected by multimorbidity. In addition to well-known precursor cardiac conditions such as coronary artery disease (CAD) and hypertension, the 10 most common cardiac and noncardiac comorbidities (anemia, dysrhythmias, cognitive dysfunction, depression, diabetes, musculoskeletal disorders, renal dysfunction, respiratory disease, sleep disorders, and thyroid disease) were identified to reflect residually high levels of morbidity and mortality even with criterion standard care.3 Using this framework, we recently established that greater multimorbidity per se is associated with a higher risk of 30-day all-cause readmission despite application of high-quality care in patients hospitalized with chronic HF.6

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Hypothesis and Purpose

Although it is becoming increasingly clear that multimorbidity in HF is associated with a worse prognosis, it is unknown whether certain phenotypes/patterns of multimorbidity present within patients with HF will differentially influence overall health outcomes. Within this context, we hypothesized that, independent of the extent (number) of comorbidity, there are distinctive phenotypes of multimorbidity that convey an increased risk for recurrent hospitalization, increased days of hospital stays, increased healthcare cost, and mortality in the short and longer terms, especially among older patients hospitalized for HF.

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Study Design and Patients

Study data were derived from 787 hospitalized patients with chronic HF who were enrolled in the Which Heart failure Intervention is most Cost-effective in reducing Hospital stay (WHICH? II) Trial (ANZCTR #12613000921785).7 The investigation conformed to the principles outlined in the Declaration of Helsinki with appropriate administrative ethics approval.

The WHICH? II trial was a multicenter, randomized controlled trial comparing an intensified, multidisciplinary HF management program incorporating an outreach home-based intervention enhanced by structured telephone support (STS) to a standard HF postdischarge care (home-based intervention or STS for metropolitan- and regional-dwelling patients as per local guidelines). Adult inpatients (aged ≥18 years) admitted to the 4 tertiary hospitals in Adelaide, Melbourne, and Sydney, Australia, were eligible if they had a cardiologist-confirmed chronic HF diagnosis based on New York Heart Association (NYHA) function class and by echocardiography, had at least 1 admission for acute decompensated HF at any point in time, and were being discharged home. Patients in the intervention group were initially profiled before hospital discharge according to the Green, Amber, Red Delineation of risk And Need in Heart Failure (GARDIAN-HF) instrument to determine their level of risk of premature mortality or recurrent hospitalization. All metropolitan-dwelling (ie, residing within the treating hospitals' catchment area for face-to-face management) patients plus those remote-dwelling (ie, residing beyond routine visits to the treating hospital) patients initially categorized as GARDIAN-HF Red (high risk) received a home visit to reassess management (revision of GARDIAN-HF status). A combination of repeat home visits and STS calls were then applied accordingly, with brain natriuretic peptide levels monitored and treatment titrated where appropriate. The primary end-point analyses show that there were no significant differences in all-cause mortality, recurrent hospital stay, and total healthcare costs between the 2 management groups within the 12-month follow-up. The pattern of survival and hospitalization according to group assignment was similar for all 4 study sites.7 Therefore, patients in both management groups were pooled together for the present analysis.

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Assessment of Comorbidities at Baseline

Baseline demographic and clinical profiling data were collected via standardized case report forms. As per the ARISE-HF framework,3 we identified the 10 prespecified comorbid conditions most commonly associated with HF (anemia, dysrhythmias, mild cognitive impairment, depression or anxiety, diabetes or obesity, musculoskeletal disorders, renal impairment, respiratory disease, thyroid disease, and sleep disorders) via medical records, biochemical measurements, and prescribed pharmacotherapies during index admission. Validated instruments were used to assess cognition (Montreal Cognitive Assessment instrument)8 and depressive symptoms (2-item Arroll instrument).9 Detailed definitions for these comorbidities are presented in Table 1, Supplemental Digital Content, The overall burden of comorbidities was also measured by the Charlson Comorbidity Index score.10

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Health Outcomes

All surviving patients received a final 12-month clinical follow-up (prescheduled home/clinic visit). An extended census date for survival status data on January 31, 2017, was then applied or at last known contact date for those who were lost to follow-up. All health outcomes, including recurrent hospitalizations and nonadmitted emergency department (ED) visits within 12 months from study enrollment, were adjudicated by study investigators masked to group allocation. All hospital separations were categorized based on the Australian Refined Diagnosis Related Groups version 8.0.11

The primary outcome was 12-month all-cause mortality accompanied by the hypothesis that certain phenotypes are more malignant than other combinations. Other outcomes of interest at (1) 30 days were unplanned hospitalizations and nonadmitted ED visits or death; (2) 12 months were days of all-cause unplanned hospitalizations, days of all-cause hospitalizations (unplanned and elective), frequency of nonadmitted ED visits, and direct healthcare costs (including total, hospital-based, community-based, and HF-specific management)7; and (3) extended follow-up was all-cause mortality (during a mean [SD] of 23 [8] months). Details of healthcare costs are provided in the Online Appendix (Table 2, Supplemental Digital Content, In brief, total healthcare costs comprised 3 key cost components: (1) hospital care (including nonadmitted ED visits, unplanned and elective hospital admissions, rehabilitation, palliative care, and outpatient reviews/procedures); (2) community care (including primary care visits, allied healthcare, and nursing home stays); and (3) HF-specific management that, in the intervention group, reflected the additional costs of applying extra home visits and STS to remote- and metropolitan-dwelling patients, respectively, in addition to monitoring of serial level of brain natriuretic peptide among patients younger than 75 years.

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Statistical Analysis

Discrete variables were summarized by frequencies and percentages; and continuous variables, by means (standard deviation) or medians (with interquartile range) where appropriate. Baseline characteristics between survivors and deceased patients (within 12 months) were compared using the multiple logistic regression model. There were 25 subjects who withdrew consent and were censored alive at 12 months. The interactions between sex and each of the 10 comorbidities on 12-month all-cause mortality were examined using the likelihood ratio tests.

A Classification and Regression Tree analysis was conducted on the 10 prespecified comorbidities (ARISE-HF) to identify combinations of conditions associated with distinct probabilities of 12-month all-cause mortality. We applied 10-fold cross-validation, having a maximum number of tree depth of 5, a minimum node size of 50 respondents, and a minimum increase in R2 of 0.001 at each node to build the CART model.

Predictions of different combinations of comorbidities on the following end points were tested by logistic regression model for 30-day unplanned readmission or nonadmitted ED visit or death, Cox proportional hazards regression model for all-cause mortality within 12 months and full study follow-up, and linear regression model for days of hospital stay and healthcare costs within 12 months. Age, sex, treatment allocation, NYHA function class, and number of comorbidities had been adjusted in the multivariate analysis. All skewed variables were log transformed. All data were analyzed using Stata statistical software version 12.0, except for the CART analysis (SPSS Statistics version 24.0). The Venn diagram illustrating the relationship of different combinations of comorbidities was programmed in Python with a Matplotlib 2D plotting library.

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Cohort Characteristics

Of this typically older cohort (mean [SD] age, 74 [12] years; 59% men), 28% were NYHA functional class III/IV at their index discharge, and 65% had left ventricular systolic dysfunction (mean [SD] left ventricular ejection fraction, 31.4% [8.9%]; see Table 1).



As anticipated, multimorbidity was common, and the 6 most common concurrent conditions (other than CAD and hypertension) were depression or anxiety (70%), dysrhythmias (64%; primarily atrial fibrillation [AF], 54%), diabetes (type 2 or 1) or obesity (63%), renal impairment (60%), mild cognitive impairment (57%), and anemia (53%). Of the 10 comorbidities proposed in the ARISE-HF framework, 88% of men and 86% of women had 3 or more comorbid (mean [SD], 4.3 [1.6]) conditions in addition to HF. The mean (SD) Charlson Comorbidity Index score was 6.9 (2.4).

Overall, 142 deaths occurred within 12 months post–index discharge. Deceased patients were older and predominantly men, less likely to live independently, and more likely to be classified as NYHA III/IV. They also had a longer duration of HF; higher prevalence of anemia, renal impairment, respiratory disease (primarily chronic obstructive pulmonary disease [COPD], 46% vs 28%), and thyroid disease; and more concurrent comorbidities compared with survivors (Table 1). There were no statistically significant interactions between sex and any of the 10 comorbidities on 12-month all-cause mortality.

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Multimorbidity and 12-Month All-cause Mortality

Overall, there was a right-side shift in comorbidity frequency distribution among the deceased patients (63% had ≥5 comorbid conditions) compared with survivors (40% had ≥5 comorbid conditions). However, as shown in Figure 1 (CART model in a flowchart-like linkage), 7 prominent groups of comorbidities with distinct risks of 12-month all-cause mortality were identified. These 7 groups were based on the presence or absence of 4 key comorbidities including anemia, respiratory disease, renal impairment, and dysrhythmias. The remaining 6 comorbidities did not significantly influence mortality and thus were not included in the final CART model. Figure 2 illustrates the logical cross-links between the 7 groups.





In this cohort (Figure 1), the lowest rate of 12-month all-cause mortality (3.6%) was observed for 110 patients with HF (14%) who did not have anemia, renal impairment, or respiratory disease (labeled as “group A”); conversely, the highest was recorded for 108 patients with HF (36.1%) who had a combination of anemia, respiratory disease, and dysrhythmias (group G). Compared with group A, patients in the following 4 groups had significantly higher risks of 12-month all-cause mortality after adjustment for age, sex, treatment allocation, NYHA function class, and number of comorbidities: group D (with renal impairment and dysrhythmias but without anemia; hazard ratio [HR], 4.2 [95% confidence interval (CI), 1.4–12.7]), group E (with anemia but without respiratory disease; HR, 4.0 [95% CI, 1.4–11.4]), group F (with anemia and respiratory disease but without dysrhythmias; HR, 4.4 [95% CI, 1.3–14.1]), and group G (with anemia, respiratory disease, and dysrhythmias; HR, 6.1 [95% CI, 2.0–19.1]) (Table 2). However, patients in group B (with respiratory disease but without anemia or renal impairment) and in group C (with renal impairment but without anemia or dysrhythmias) had comparable 12-month mortality risk as group A.



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Healthcare Utilization and Patterns of Multimorbidity

Within 30 days of post–index discharge, 205 patients (26%) were readmitted for all-cause unplanned hospitalization or nonadmitted ED visit, including 8 deaths. Within 12 months, this cohort accumulated 1552 all-cause readmissions (10 853 days of hospital stay), including 1178 unplanned admissions (9909 days) and 485 nonadmitted ED visits.

Table 2 compares the pattern of rehospitalization and direct healthcare costs according to the 7 distinct groups. Patients in group G had a 2-fold increased risk of 30-day unplanned readmission/death as compared with those in group A (odds ratio, 2.0 [95% CI, 1.1–3.7]) after adjustment for age and sex, but the risk difference disappeared when further adjusted for treatment allocation, NYHA functional class, and number of comorbidities. Consequently, patients in group G had more days of unplanned and all-cause readmissions per patient compared with those in group A (both Ps < .05). There were no significant differences across the 7 groups with respect to frequency of nonadmitted ED visits per patient within 12-month follow-up.

Patients in groups E and G had higher total direct healthcare costs and hospital-based costs per patient per month than those in group A (P <.05), whereas patients in group F had higher community-based costs (P < .01). There were no significant differences in HF-specific management costs between the groups.

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Long-term, All-cause Survival and Patterns of Multimorbidity

During the full study follow-up (median, 663 [interquartile range, 492–910] days), there were 248 deaths comprising 10 of 110 (9.1%), 11 of 68 (16.2%), 10 of 62 (16.1%), 51 of 130 (39.2%), 86 of 256 (33.6%), 27 of 53 (50.9%), and 53 of 108 (49.1%) patients categorized from groups A to G, respectively. After adjusting for age, sex, treatment allocation, NYHA function class, and number of comorbidities, groups D to G had a significantly higher risk of all-cause death during the extended period than group A (all Ps < .01), with the highest risk observed in group F (HR, 3.8 [95% CI, 1.8–8.2] and group G (HR, 3.4 [95% CI, 1.6–7.4]) (Figure 3).



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To our knowledge, this is the first study to specifically explore the distinctive combinations of comorbidities associated with short- and long-term health outcomes in typically older patients with HF.6 As anticipated, there is not a simple, linear relationship between extent of multimorbidity and worse outcomes in patients with HF. Rather, beyond the typical antecedents of CAD and hypertension, we identified different phenotypes of multimorbidity that varyingly affect health outcomes. Significantly, we identified a particularly “malignant” cluster of comorbidities (HF in the co-presence of dysrhythmias, anemia, and respiratory disease), which is associated not only with a marked increased risk of 30-day unplanned rehospitalization or death but also with the highest risk of all-cause mortality, longer length of hospital stay, and greatest total healthcare costs within 12 months post–index discharge. At the other end of the spectrum, there are patterns of multimorbidity in HF that seem to be relatively “benign” (patients hospitalized with HF with only renal impairment or respiratory disease) when compared with more “malignant” phenotypes.

Our findings are particularly relevant in the context of increasing multimorbidity in individuals affected by HF, where around 50% have concurrent AF,12 anemia,13 COPD,14 or renal impairment.15 Authors of previous studies have typically focused only on a single condition16,17 or a limited number of conditions17 likely to influence health outcomes. Although it is well established that the overall number of comorbidities in patients with HF is strongly associated with rehospitalization in both short and long terms4,6 and with a greater risk of death,17,18 the prognostic importance of differential patterns of multimorbidity is far less understood. The ARISE-HF framework3 indicates that there are physiological reasons one condition may interact with another to provoke worse outcomes.

Authors of a community perspective study used heat maps to display the percentage of patients with 2 or more concurrent conditions (86%) according to the type of HF and gender but did not show the association with health outcomes.19 Authors of a few previous reports have attempted to differentiate unique patient profiles using the latent class analysis approach. Yet, such profiling only showed the proportion of patients with different combinations of comorbidities without elucidation of how specific combinations can influence outcomes.6,20 In the current study, patients who had anemia with or without respiratory disease (groups E and F), anemia with respiratory disease and dysrhythmias (group G), and those who had concurrent dysrhythmias and renal impairment but without anemia (group D) identified through the CART model were clearly demonstrated to have worse short- and long-term health outcomes.

Anemia, COPD (the most common type of chronic respiratory disease), dysrhythmias (with AF being the most common rhythm disturbance), and renal impairment share many common risk factors and direct pathophysiological links with HF and interact with and aggravate other comorbidities, leading to poorer prognoses for the patients.13,21–26 Consistent with many past studies reporting anemia as a strong independent predictor for mortality and hospitalizations in HF, we also identified anemia as a key determinant for defining specific clusters in our CART model, and patients hospitalized with HF with anemia irrespective of coexisting respiratory disease or dysrhythmias (groups E–G) had worse outcomes than those without this blood disorder. Moreover, HF and COPD exhibit a high symptom burden, and concurrent COPD independently predicts mortality in patients hospitalized with HF with either reduced or preserved ejection fraction.21 The most common causes of death in patients with HF are worsening of HF and sudden cardiac death,22 which are closely associated with dysrhythmias.23 In particular, AF is a strong predictor of in-hospital outcomes24 and long-term mortality in patients with HF.25 The worst health outcomes shown in patients with concurrent anemia, respiratory disease, and dysrhythmias (group G) may reflect the combined adverse effect of these 3 comorbidities in HF.

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This study has several limitations. First, this was a secondary analysis of existing data from a clinical trial; the present investigation represents an observational retrospective study in this circumstance. There was no a priori power calculation performed for this study. However, the “malignant” phenotype (classified as having concomitant anemia, respiratory disease, and dysrhythmias) was consistently linked with poorer short- and longer-term outcomes. Second, the WHICH? II trial was designed to compare the cost-effectiveness of standard HF care versus an intensified form of HF management based on individual risk profiling, and it did not include reassessment of multimorbidity at 6- and 12-month follow-up in the trial protocol; thus, we were unable to incorporate the change in severity of each comorbidity in the current analysis. However, these 10 comorbidities that were considered in this study all represented chronic conditions (with definitive in-hospital diagnoses and biochemical measures). Therefore, the identification and persistence of each comorbid condition were well established. Third, the WHICH? II trial was Australia based, and the CART is an exploratory analysis; the validity of our findings needs to be assessed in other cohorts of patients with HF. However, as it was a pragmatic health service intervention trial in a real-world health setting, and the baseline characteristics of the cohort were comparable with those in other clinical trials testing the efficacy of transitional care services in patients discharged from hospital with HF, our study findings are likely to be applicable in other settings. Finally, it will be important to undertake a pathophysiological investigation to understand the complex mechanism and connections involved in the matrix of multimorbidity and health outcomes in patients with HF.

In conclusion, this is the first study to demonstrate the impact of different combinations of comorbidities in patients hospitalized with HF with respect to risk of recurrent hospitalization, premature mortality, and healthcare expenditure. The authors of this study provide the first real evidence that the ARISE-HF model is valid from a clinical perspective. Our data may provide crucial insights into the identification and management of malignant phenotypes of multimorbidity to improve health outcomes in this increasingly common and challenging patient population.

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What’s New and Important

  • Different combinations of comorbidities can differentially influence overall health outcomes in patients with HF affected by multimorbidity.
  • Independent of the extent of comorbidity, there are distinctive phenotypes of multimorbidity that convey an increased risk for premature mortality or recurrent hospitalization.
  • We have identified a “malignant” cluster of comorbidities (HF in the co-presence of dysrhythmias, anemia, and respiratory disease) that is associated with worse outcomes despite evidence-based multidisciplinary care.
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We gratefully acknowledge our study participants, the research teams who conducted and supported the study, and the clinical teams who delivered the HF management.

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comorbidity; heart failure; hospitalization; mortality; multimorbidity

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