Comorbidity health pathways in heart failure patients: A sequences-of-regressions analysis using cross-sectional data from 10,575 patients in the Swedish Heart Failure Registry

Abstract

Background: Optimally treated heart failure (HF) patients often have persisting symptoms and poor health-related quality of life. Comorbidities are common, but little is known about their impact on these factors, and guideline-driven HF care remains focused on cardiovascular status. The following hypotheses were tested: (i) comorbidities are associated with more severe symptoms and functional limitations and subsequently worse patient-rated health in HF, and (ii) these patterns of association differ among selected comorbidities.

Methods and findings: The Swedish Heart Failure Registry (SHFR) is a national population-based register of HF patients admitted to >85% of hospitals in Sweden or attending outpatient clinics. This study included 10,575 HF patients with patient-rated health recorded during first registration in the SHFR (1 February 2008 to 1 November 2013). An a priori health model and sequences-of-regressions analysis were used to test associations among comorbidities and patient-reported symptoms, functional limitations, and patient-rated health. Patient-rated health measures included the EuroQol-5 dimension (EQ-5D) questionnaire and the EuroQol visual analogue scale (EQ-VAS). EQ-VAS score ranges from 0 (worst health) to 100 (best health). Patient-rated health declined progressively from patients with no comorbidities (mean EQ-VAS score, 66) to patients with cardiovascular comorbidities (mean EQ-VAS score, 62) to patients with non-cardiovascular comorbidities (mean EQ-VAS score, 59). The relationships among cardiovascular comorbidities and patient-rated health were explained by their associations with anxiety or depression (atrial fibrillation, odds ratio [OR] 1.16, 95% CI 1.06 to 1.27; ischemic heart disease [IHD], OR 1.20, 95% CI 1.09 to 1.32) and with pain (IHD, OR 1.25, 95% CI 1.14 to 1.38). Associations of non-cardiovascular comorbidities with patient-rated health were explained by their associations with shortness of breath (diabetes, OR 1.17, 95% CI 1.03 to 1.32; chronic kidney disease [CKD, OR 1.23, 95% CI 1.10 to 1.38; chronic obstructive pulmonary disease [COPD], OR 95% CI 1.84, 1.62 to 2.10) and with fatigue (diabetes, OR 1.27, 95% CI 1.13 to 1.42; CKD, OR 1.24, 95% CI 1.12 to 1.38; COPD, OR 1.69, 95% CI 1.50 to 1.91). There were direct associations between all symptoms and patient-rated health, and indirect associations via functional limitations. Anxiety or depression had the strongest association with functional limitations (OR 10.03, 95% CI 5.16 to 19.50) and patient-rated health (mean difference in EQ-VAS score, -18.68, 95% CI -23.22 to -14.14). HF optimizing therapies did not influence these associations. Key limitations of the study include the cross-sectional design and unclear generalisability to other populations. Further prospective HF studies are required to test the consistency of the relationships and their implications for health.

Conclusions: Identification of distinct comorbidity health pathways in HF could provide the evidence for individualised person-centred care that targets specific comorbidities and associated symptoms.

Fig 1. Wilson and Cleary’s health-related quality of life conceptual model.

Wilson and Cleary’s model for health-related quality of life [16], revised with permission from JAMA [17]. Wilson and Cleary’s general conceptual model of health links objective biological measurements to the subjective consequences of disease as perceived by patients. In this model, Wilson and Cleary propose causal linkages between 5 health domains: bio-physiological status, symptoms, functional status, general health perception, and quality of life. The arrows represent dominant causal relationships. Reciprocal relationships between the variables are recognised to exist but are not represented, and we did not consider these. The revised version extends the influence of individual and environmental characteristics to all health domains in the model.

Fig 2. Hypothetical HF health model.

Model based on a revised version of Wilson and Cleary’s health-related quality of life conceptual model [16,17]. The arrows represent direct relationships for patient and environmental factors as well as 4 of the 5 health domains: bio-physiological status (comorbidities), symptoms, functional status, and general health perception. Only arrows between adjacent domains are displayed, but it is postulated that each domain may have other direct relationships with any of the proceeding domains, and patient and environmental factors are related to every domain. ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin II receptor blocker; EQ-VAS, EuroQol–5 dimension visual analogue scale; HF, heart failure.

Fig 3. Prevalence of symptoms and functional limitations in heart failure by comorbidity.

Shortness of breath (SOB) and fatigue show patients with marked or severe symptoms. Pain, anxiety or depression, and functional limitations show patients with ‘any’. AF, atrial fibrillation; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; IHD, ischemic heart disease.

Fig 4. Patient-rated health (EQ-VAS) in heart failure by comorbidity.

EQ-VAS was based on patient-rated health ranging from 0 (worse imaginable health state), 100 (best imaginable health state). COPD, chronic obstructive pulmonary disease; EQ-VAS, EuroQol–5 dimension visual analogue scale; IHD, ischaemic heart disease.

Fig 5. Cardiovascular comorbidities in heart failure and patient health pathway.

In the regression graph an arrow is present between a response and an explanatory variable if there is a significant association (P < 0.01), controlling for all remaining regressors. The strength of this association is shown as OR (95% CI), if the response variable is binary, and mean difference (95% CI) in the response variable for a 1-unit increase in the explanatory variable, if the response variable is continuous. Significant interactions and non-linear relationships are also indicated. Reduced ejection fraction defined as <40%. Pain and anxiety or depression defined as ‘any problems’. Shortness of breath and fatigue defined as ‘marked or severe’, and functional limitation as ‘any’ limitation in usual activities. Patient-rated health was measured by EuroQol visual analogue scale, ranging from 0 (worst health imaginable) to 100 (best health imaginable). AF, atrial fibrillation; IHD, ischemic heart disease; OR, odds ratio.

Fig 6. Non-cardiovascular comorbidities in heart failure and patient health pathway.

In the regression graph an arrow is present between a response and an explanatory variable if there is a significant association (P < 0.01), controlling for all remaining regressors. The strength of this association is shown as OR (95% CI), if the response variable is binary, and mean difference (95% CI) in the response variable for a 1-unit increase in the explanatory variable, if the response variable is continuous. Significant interactions and non-linear relationships are also indicated. CKD defined as estimated glomerular filtration rate < 60 ml/min/1.73 m². Reduced ejection fraction defined as <40%. Pain and anxiety or depression defined as ‘any problems’. Shortness of breath and fatigue defined as ‘marked or severe’, and functional limitation as ‘any’ limitation in usual activities. Patient-rated health was measured by EuroQol visual analogue scale, ranging from 0 (worst health imaginable) to 100 (best health imaginable). CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; OR, odds ratio.