Patient profile and outcomes associated with follow-up in specialty vs. primary care in heart failure

Abstract

Aims: Factors influencing follow-up referral decisions and their prognostic implications are poorly investigated in patients with heart failure (HF) with reduced (HFrEF), mildly reduced (HFmrEF), and preserved (HFpEF) ejection fraction (EF). We assessed (i) the proportion of, (ii) independent predictors of, and (iii) outcomes associated with follow-up in specialty vs. primary care across the EF spectrum.

Methods and results: We analysed 75 518 patients from the large and nationwide Swedish HF registry between 2000-2018. Multivariable logistic regression models were fitted to identify the independent predictors of planned follow-up in specialty vs. primary care, and multivariable Cox models to assess the association between follow-up type and outcomes. In this nationwide registry, 48 115 (64%) patients were planned for follow-up in specialty and 27 403 (36%) in primary care. The median age was 76 [interquartile range (IQR) 67-83] years and 27 546 (36.5%) patients were female. Key independent predictors of planned follow-up in specialty care included optimized HF care, that is follow-up in a nurse-led HF clinic [odds ratio (OR) 4.60, 95% confidence interval (95% CI) 4.41-4.79], use of HF devices (OR 3.99, 95% CI 3.62-4.40), beta-blockers (OR 1.39, 95% CI 1.32-1.47), renin-angiotensin system/angiotensin-receptor-neprilysin inhibitors (OR 1.21, 95% CI 1.15-1.27), and mineralocorticoid receptor antagonists (OR 1.31, 95% CI 1.26-1.37); and more severe HF, that is higher NT-proBNP (OR 1.13, 95% CI 1.06-1.20) and NYHA class (OR 1.13, 95% CI 1.08-1.19). Factors associated with lower likelihood of follow-up in specialty care included older age (OR 0.29, 95% CI 0.28-0.30), female sex (OR 0.89, 95% CI 0.86-0.93), lower income (OR 0.79, 95% CI 0.76-0.82) and educational level (OR 0.77, 95% CI 0.73-0.81), higher EF [HFmrEF (OR 0.65, 95% CI 0.62-0.68) and HFpEF (OR 0.56, 95% CI 0.53-0.58) vs. HFrEF], and higher comorbidity burden, such as presence of kidney disease (OR 0.91, 95% CI 0.87-0.95), atrial fibrillation (OR 0.85, 95% CI 0.81-0.89), and diabetes mellitus (OR 0.92, 95% CI 0.88-0.96). A planned follow-up in specialty care was independently associated with lower risk of all-cause [hazard ratio (HR) 0.78, 95% CI 0.76-0.80] and cardiovascular death (HR 0.76, 95% CI 0.73-0.78) across the EF spectrum, but not of HF hospitalization (HR 1.06, 95% CI 1.03-1.10).

Conclusions: In a large nationwide HF population, referral to specialty care was linked with male sex, younger age, lower EF, lower comorbidity burden, better socioeconomic environment and optimized HF care, and associated with better survival across the EF spectrum. Our findings highlight the need for greater and more equal access to HF specialty care and improved quality of primary care.

Keywords: Disparaties; Follow-up referrals; Heart failure; Quality and outcomes; Risk factors.

Figure 1

Independent odds ratios for follow‐up in specialty vs. primary care. Multivariable logistic regression model with follow‐up in specialty vs. primary care as dependent variable. Abbreviations: ARNi, angiotensin‐receptor‐neprilysin inhibitor; b.p.m, beats per minutes; CI, confidence interval; HF, heart failure; HF device, heart failure device (cardiac resynchronization therapy or implantable cardioverter‐defibrillator); HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; MRA, mineralocorticoid receptor antagonist; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide; NYHA, New York Heart Association functional class; OR, odds ratio; RASi, renin–angiotensin‐system inhibitor.

Figure 2

Kaplan–Meier curves for all‐cause mortality (A), cardiovascular mortality (B), and first HF hospitalization (C). Abbreviations: EF, ejection fraction; HF, heart failure; HFmrEF, HF with mildly reduced EF; HFpEF, HF with preserved EF; HFrEF, HF with reduced EF.

Figure 3

Association between follow‐up type and risk of outcomes. Cox proportional hazards regression models with step‐wise adjustments. Demographics include index year (2000–2011 vs. 2012–2018), age (<75 vs. ≥75), sex. Socioeconomics include income level (lowest tertile vs. upper two tertiles), education level (university vs. secondary school or less), living alone, children. Clinical characteristics include: ejection fraction phenotype, caregiver (in‐patient vs. out‐patient), heart failure duration ≥6 months, New York Heart Association functional class (I–II vs. III–IV), body mass index (<30 vs. ≥30), mean arterial pressure (<90 vs. ≥90), heart rate (<70 vs. ≥70), N‐terminal pro‐B‐type natriuretic peptide (

Figure 4

Association between follow‐up type and risk of death in clinically relevant subgroups. Cox proportional hazards regression models adjusted for variables labelled with a superscript a (^a) in Table 1 , including an interaction term between the subgroup variable and follow‐up type. Abbreviations: CI, confidence interval; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HR, hazard ratio; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide.