The purpose of this study was to evaluate in-hospital outcomes among patients with a history of heart failure (HF) hospitalized with coronavirus disease-2019 (COVID-19).
Cardiometabolic comorbidities ...are common in patients with severe COVID-19. Patients with HF may be particularly susceptible to COVID-19 complications.
The Premier Healthcare Database was used to identify patients with at least 1 HF hospitalization or 2 HF outpatient visits between January 1, 2019, and March 31, 2020, who were subsequently hospitalized between April and September 2020. Baseline characteristics, health care resource utilization, and mortality rates were compared between those hospitalized with COVID-19 and those hospitalized with other causes. Predictors of in-hospital mortality were identified in HF patients hospitalized with COVID-19 by using multivariate logistic regression.
Among 1,212,153 patients with history of HF, 132,312 patients were hospitalized from April 1, 2020, to September 30, 2020. A total of 23,843 patients (18.0%) were hospitalized with acute HF, 8,383 patients (6.4%) were hospitalized with COVID-19, and 100,068 patients (75.6%) were hospitalized with alternative reasons. Hospitalization with COVID-19 was associated with greater odds of in-hospital mortality as compared with hospitalization with acute HF; 24.2% of patients hospitalized with COVID-19 died in-hospital compared to 2.6% of those hospitalized with acute HF. This association was strongest in April (adjusted odds ratio OR: 14.48; 95% confidence interval CI:12.25 to 17.12) than in subsequent months (adjusted OR: 10.11; 95% CI: 8.95 to 11.42; pinteraction <0.001). Among patients with HF hospitalized with COVID-19, male sex (adjusted OR: 1.26; 95% CI: 1.13 to 1.40) and morbid obesity (adjusted OR: 1.25; 95% CI: 1.07 to 1.46) were associated with greater odds of in-hospital mortality, along with age (adjusted OR: 1.35; 95% CI: 1.29 to 1.42 per 10 years) and admission earlier in the pandemic.
Patients with HF hospitalized with COVID-19 are at high risk for complications, with nearly 1 in 4 dying during hospitalization.
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Background
Solid‐organ transplant (SOT) recipients with coronavirus disease 2019 (COVID‐19) have higher risk of adverse outcomes compared to the general population. Whether hospitalized SOT ...recipients with COVID‐19 are at higher risk of mortality than SOT recipients hospitalized for other causes, including non‐COVID‐19 pneumonia, remains unclear.
Methods
We used logistic regression to compare outcomes of SOT recipients hospitalized with COVID‐19 to non‐COVID‐19 related admissions and with non‐COVID‐19 pneumonia.
Results
Of 17,012 hospitalized SOT recipients, 1682 had COVID‐19. Those with COVID‐19 had higher odds of ICU admission (adjusted odds ratio aOR 2.12 95%CI: 1.88–2.39) and mechanical ventilation (aOR 3.75 95%CI: 3.24–4.33). COVID‐19 was associated with higher odds of in‐hospital death, which was more pronounced earlier in the pandemic (aOR 9.74 95%CI: 7.08–13.39 for April/May vs. aOR 7.08 95%CI: 5.62–8.93 for June through November 2020; P‐interaction = .03). Compared to SOT recipients hospitalized with non‐COVID‐19 pneumonia, odds of in‐hospital death were higher in SOT recipients with COVID‐19 (aOR 2.44 95% CI: 1.90–3.13), regardless of time of hospitalization (P‐interaction > .40).
Conclusions
In this large cohort of SOT recipients, hospitalization with COVID‐19 was associated with higher odds of complications and in‐hospital mortality than non‐COVID‐19 related admissions, and 2.5‐fold higher odds of in‐hospital mortality, compared to SOT recipients with non‐COVID‐19 pneumonia.
Aims
Patients surviving an acute myocardial infarction (AMI) are at risk of developing symptomatic heart failure (HF) or premature death. We hypothesized that sacubitril/valsartan, effective in the ...treatment of chronic HF, prevents development of HF and reduces cardiovascular death following high‐risk AMI compared to a proven angiotensin‐converting enzyme (ACE) inhibitor. This paper describes the study design and baseline characteristics of patients enrolled in the Prospective ARNI vs. ACE inhibitor trial to DetermIne Superiority in reducing heart failure Events after Myocardial Infarction (PARADISE‐MI) trial.
Methods and results
PARADISE‐MI, a multinational (41 countries), double‐blind, active‐controlled trial, randomized patients within 0.5–7 days of presentation with index AMI to sacubitril/valsartan or ramipril. Transient pulmonary congestion and/or left ventricular ejection fraction (LVEF) ≤40% and at least one additional factor augmenting risk of HF or death (age ≥70 years, estimated glomerular filtration rate <60 mL/min/1.73 m2, diabetes, prior myocardial infarction, atrial fibrillation, LVEF <30%, Killip class ≥III, ST‐elevation myocardial infarction without reperfusion) were required for inclusion. PARADISE‐MI was event‐driven targeting 708 primary endpoints (cardiovascular death, HF hospitalization or outpatient development of HF). Randomization of 5669 patients occurred 4.3 ± 1.8 days from presentation with index AMI. The mean age was 64 ± 12 years, 24% were women. The majority (76%) qualified with ST‐segment elevation myocardial infarction; acute percutaneous coronary intervention was performed in 88% and thrombolysis in 6%. LVEF was 37 ± 9% and 58% were in Killip class ≥II.
Conclusions
Baseline therapies in PARADISE‐MI reflect advances in contemporary evidence‐based care. With enrollment complete PARADISE‐MI is poised to determine whether sacubitril/valsartan is more effective than a proven ACE inhibitor in preventing development of HF and cardiovascular death following AMI.
PARADISE‐MI study design. AMI, acute myocardial infarction; CV, cardiovascular; eGFR, estimated glomerular filtration rate; HF, heart failure; LVEF, left ventricular ejection fraction; MI, myocardial infarction; STEMI, ST‐elevation myocardial infarction.
Aims
Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at ...increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER‐HF), a previously validated risk score, could improve clinical trial efficiency.
Methods and results
Mortality rates and association of MARKER‐HF with all‐cause death by 1 year were evaluated in four community‐based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER‐HF risk and proposed treatment effect profiles. Patients from community‐based HF cohorts (n = 11 297) had higher observed mortality and MARKER‐HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER‐HF score was strongly associated with risk of 1‐year mortality both in the community (hazard ratio HR 1.48, 95% confidence interval CI 1.44–1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30–1.54), and HFpEF (HR 1.74, 95% CI 1.53–1.98), per 0.1 increase in MARKER‐HF. Using MARKER‐HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit.
Conclusion
Using a reliable predictor of mortality such as MARKER‐HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.
Estimated sample size based on the anticipated treatment effect and underlying risk profile of the study population. Shown is the estimated sample size required to provide a hypothetical clinical trial with 80% power to detect a significant reduction in all‐cause mortality with study treatment, according to the anticipated treatment effect (A–F) and the underlying MARKER‐HF risk profile of the study population (1–4). The underlying risk profile of the study population mirrors the distribution of MARKER‐HF scores in CHARM in (1) and of the UCSD HF cohort in (2). Row (3) represents a study population with a uniform distribution of MARKER‐HF scores ranging from −0.3 to 0.3. Row (4) shows a ‘high‐risk’ patient population with MARKER‐HF scores >0 within the UCSD cohort. Columns A–C illustrate a constant treatment effect of the investigational treatment across the entire risk spectrum with a 20% (A), 24% (B) and 16% (C) relative reduction in death, respectively. Columns D and E show heterogeneity in the treatment effect such that patients at lower risk (D) and those at higher risk (E), respectively, are expected to derive greater benefit from study treatment. Column F illustrates the scenario of diminished treatment efficacy at the extrema of risk.
Aims
Implementation of guideline‐directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) remains incomplete. Non‐cardiovascular hospitalization may present ...opportunities for GDMT optimization. We assessed the efficacy and durability of a virtual, multidisciplinary ‘GDMT Team’ on medical therapy prescription for HFrEF.
Methods and results
Consecutive hospitalizations in patients with HFrEF (ejection fraction ≤40%) were prospectively identified from 3 February to 1 March 2020 (usual care group) and 2 March to 28 August 2020 (intervention group). Patients with critical illness, de novo heart failure, and systolic blood pressure <90 mmHg in the preceeding 24 hs prior to enrollment were excluded. In the intervention group, a pharmacist–physician GDMT Team provided optimization suggestions to treating teams based on an evidence‐based algorithm. The primary outcome was a GDMT optimization score, the sum of positive (+1 for new initiations or up‐titrations) and negative therapeutic changes (−1 for discontinuations or down‐titrations) at hospital discharge. Serious in‐hospital safety events were assessed. Among 278 consecutive encounters with HFrEF, 118 met eligibility criteria; 29 (25%) received usual care and 89 (75%) received the GDMT Team intervention. Among usual care encounters, there were no changes in GDMT prescription during hospitalization. In the intervention group, β‐blocker (72% to 88%; P = 0.01), angiotensin receptor–neprilysin inhibitor (6% to 17%; P = 0.03), mineralocorticoid receptor antagonist (16% to 29%; P = 0.05), and triple therapy (9% to 26%; P < 0.01) prescriptions increased during hospitalization. After adjustment for clinically relevant covariates, the GDMT Team was associated with an increase in GDMT optimization score (+0.58; 95% confidence interval +0.09 to +1.07; P = 0.02). There were no serious in‐hospital adverse events.
Conclusions
Non‐cardiovascular hospitalizations are a potentially safe and effective setting for GDMT optimization. A virtual GDMT Team was associated with improved heart failure therapeutic optimization. This implementation strategy warrants testing in a prospective randomized controlled trial.
The IMPLEMENT‐HF pilot study. A physician–pharmacist led fully‐virtual, inpatient guideline‐directed medical therapy (GDMT) Team providing therapeutic optimization suggestions for hospitalized patients with heart failure with reduced ejection fraction (HFrEF) is associated with increases in β‐blocker (BB), mineralocorticoid receptor antagonist (MRA), angiotensin receptor–neprilysin inhibitor (ARNI), and triple therapy rates. HF, heart failure; RASi, renin–angiotensin system inhibitor.
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