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

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

Hypothetical HF health model.

<p>Model based on a revised version of Wilson and Cleary’s health-related quality of life conceptual model [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002540#pmed.1002540.ref016" target="_blank">16</a>,<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002540#pmed.1002540.ref017" target="_blank">17</a>]. 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.</p

Selected regression models describing direct and indirect associations between health factors.

<p>Selected regression models describing direct and indirect associations between health factors.</p

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

<p>In the regression graph an arrow is present between a response and an explanatory variable if there is a significant association (<i>P</i> < 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.</p

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

<p>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.</p

Characteristics by patient-rated health.

<p>Characteristics by patient-rated health.</p

Conceptualizing multiple drug use in patients with comorbidity and multimorbidity: proposal for standard definitions beyond the term polypharmacy

With older and ageing populations, patients experience multiple chronic diseases at the same time. Individual chronic disease guidelines often recommend pharmacological therapies as a key intervention, resulting in patients being prescribed multiple regular medications for their different diseases. Whilst the term 'polypharmacy' has been applied to the use of multiple medications, there is no consistent definition and this term is now being used all inclusively. To improve both scientific rigor and optimal patient care, it is crucial that a standard terminology is used which reclassifies the term 'polypharmacy' into distinct phenotypes relating to the index chronic disease, additional conditions to the index ('comorbidity') or the experience of multiple chronic conditions at the same time (multimorbidity). Using three exemplar index conditions; heart failure, type 2 diabetes and breast cancer, we propose the reclassification of the term 'polypharmacy' into three distinct phenotypes. First, index drug or multi-index drug therapy, where each index condition creates multiple drug use for that condition; second, co-drug therapy, where addition of other comorbid conditions increases the multiple drug use and may influence the management of the index disease and third, multi drug therapy, where adult population with multimorbidity may be on many drugs. This paper reviews guidelines for the individual exemplars to develop the basis for the new terms and then develops the pharmaco-epidemiology of multiple drug use further by reviewing the evidence on the relationship between the phenotypic classification and important outcomes. The importance of standardising 'polypharmacy' terminology for the scientific agenda and clinical practice is that it relates to an index condition or disease safety outcomes including drug interactions, adverse side effects in hospital admissions and related 'polypill' concept

Chronic kidney disease, worsening renal function and outcomes in a heart failure community setting: A UK national study

Background Routine heart failure (HF) monitoring and management is in the community but the natural course of worsening renal function (WRF) and its influence on HF prognosis is unknown. We investigated the influence of routinely monitored renal decline and related comorbidities on imminent hospitalisation and death in the HF community population. Methods A nested case-control study within an incident HF cohort (N = 50,114) with 12-years follow-up. WRF over 6-months before first hospitalisation and 12-months before death was defined by >20% reduction in estimated glomerular filtration rate (eGFR). Additive interactions between chronic kidney disease (CKD) and comorbidities were investigated. Results Prevalence of CKD (eGFR<60 ml/min/1.73m2) in the HF community was 63%, which was associated with an 11% increase in hospitalisation and 17% in mortality. Both risk associations were significantly worse in the presence of diabetes. Compared to HF patients with eGFR,60–89, there was no or minimal increase in risk for mild to moderate CKD (eGFR,30–59) for both outcomes. Adjusted risk estimates for hospitalisation were increased only for severe CKD(eGFR,15–29); Odds Ratio 1.49 (95%CI;1.36,1.62) and renal failure(eGFR,<15); 3.38(2.67,4.29). The relationship between eGFR and mortality was U-shaped; eGFR, ≥90; 1.32(1.17,1.48), eGFR,15–29; 1.68(1.58,1.79) and eGFR,<15; 3.04(2.71,3.41). WRF is common and associated with imminent hospitalisation (1.50;1.37,1.64) and mortality (1.92;1.79,2.06). Conclusions In HF, the risk associated with CKD differs between the community and the acute HF setting. In the community setting, moderate CKD confers no risk but severe CKD, WRF or CKD with other comorbidities identifies patients at high risk of imminent hospitalisation and death

Consumer segmentation and time interval between types of hospital admission: a clinical linkage database study.

Background: Healthcare policies target unplanned hospital admissions and 30-day re-admission as key measures of efficiency, but do not focus on factors that influence trajectories of different types of admissions in the same patient over time. Objectives: To investigate the influence of consumer segmentation and patient factors on the time intervals between different types of hospital admission. Research design, subjects and measures: A cohort design was applied to an anonymised linkage database for adults aged 40 years and over (N = 58 857). Measures included Mosaic segmentation, multimorbidity defined on six chronic condition registers and hospital admissions over a 27-month time period. Results: The shortest mean time intervals between two consecutive planned admissions were: 90 years and over (160 days (95% confidence interval (CI): 146-175)), Mosaic groups 'Twilight subsistence' (171 days (164-179)) or 'Welfare borderline' and 'Municipal dependency' (177 days (172-182)) compared to the reference Mosaic groups (186 days (180-193)), and multimorbidity count of four or more (137 days (130-145)). Mosaic group 'Twilight subsistence' (rate ratio (RR) 1.22 (95% CI: 1.08-1.36)) or 'Welfare borderline' and 'Municipal dependency' RR 1.20 (1.10-1.31) were significantly associated with higher rate to an unplanned admission following a planned event. However, associations between patient factors and unplanned admissions were diminished by adjustment for planned admissions. Conclusion: Specific consumer segmentation and patient factors were associated with shorter time intervals between different types of admissions. The findings support innovation in public health approaches to prevent by a focus on long-term trajectories of hospital admissions, which include planned activity

Cardiovascular comorbidities in heart failure and patient health pathway.

<p>In the regression graph an arrow is present between a response and an explanatory variable if there is a significant association (<i>P</i> < 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.</p