A nationwide, population-based study on specialized care for acute heart failure throughout the COVID-19 pandemic.

AIMS: The COVID-19 pandemic disrupted the delivery of care for patients with heart failure (HF), leading to fewer HF hospitalizations and increased mortality. However, nationwide data on quality of care and long-term outcomes across the pandemic are scarce. METHODS AND RESULTS: We used data from the National Heart Failure Audit (NHFA) linked to national records for hospitalization and deaths. We compared pre-COVID (2018-2019), COVID (2020), and late/post-COVID (2021-2022) periods. Data for 227 250 patients admitted to hospital with HF were analysed and grouped according to the admission year and the presence of HF with (HFrEF) or without reduced ejection fraction (non-HFrEF). The median age at admission was 81 years (interquartile range 72-88), 55% were men (n = 125 975), 87% were of white ethnicity (n = 102 805), and 51% had HFrEF (n = 116 990). In-hospital management and specialized cardiology care were maintained throughout the pandemic with an increasing percentage of patients discharged on disease-modifying medications over time (p < 0.001). Long-term outcomes improved over time (hazard ratio [HR] 0.92, 95% confidence interval [CI] 0.90-0.95, p < 0.001), mainly driven by a reduction in cardiovascular death. Receiving specialized cardiology care was associated with better long-term outcomes both for those who had HFrEF (HR 0.79, 95% CI 0.77-0.82, p < 0.001) and for those who had non-HFrEF (HR 0.87, 95% CI 0.85-0.90, p < 0.001). CONCLUSIONS: Despite the disruption of healthcare systems, the clinical characteristics of patients admitted with HF were similar and the overall standard of care was maintained throughout the pandemic. Long-term survival of patients hospitalized with HF continued to improve after COVID-19, especially for HFrEF.

Heart Failure Specialist Care and Long-Term Outcomes for Patients Admitted With Acute Heart Failure.

BACKGROUND: For patients with acute heart failure (HF), specialist HF care during admission improves diagnosis and treatments. OBJECTIVES: The authors aimed to investigate the association of HF specialist care with in-hospital and longer term prognosis. METHODS: The authors used data from the National Heart Failure Audit from January 1, 2018, to December 31, 2022, linked to electronic records for hospitalization and deaths. All-cause mortality was the primary outcome measure and in-hospital mortality the secondary outcome measure. RESULTS: Data for 227,170 patients admitted to hospital with HF (median age: 81 years; IQR: 72-88 years), were analyzed. Approximately 80% of acute HF admissions received support from HF specialists. Thirty-nine percent of patients (n = 70,720) were seen by a multidisciplinary team (HF physicians and HF specialist nurses [HFSNd]), 22% (n = 40,330) were seen by HFSNs alone, and the remaining 39% (n = 71,700) were seen exclusively by specialist HF physicians. At discharge, more patients who received HF specialist care were prescribed medical therapy for HF and had specialized follow-up. Conversely, diuretic agents were prescribed to fewer patients. HF specialist care was independently associated with a higher rate of prescribing HF therapies at discharge and a lower likelihood of receiving diuretic therapy (OR: 0.90 [95% CI: 0.86-0.95]; P < 0.001). HF specialist care was associated with better long-term survival (HR: 0.89 [95% CI: 0.87-0.90]; P < 0.001) and lower in-hospital mortality (OR: 0.92 [95% CI: 0.0.88-0.97]; P < 0.001). CONCLUSIONS: Receiving HF specialist care during admission for HF is associated with a higher rate of implementation of medical therapy, fewer discharges on diuretic therapy, and lower in-hospital and long-term mortality across the left ventricular ejection fraction spectrum, especially for patients with heart failure with reduced ejection fraction.

Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction.

AIM: Heart failure with preserved ejection fraction (HFpEF) remains under-diagnosed in clinical practice despite accounting for nearly half of all heart failure (HF) cases. Accurate and timely diagnosis of HFpEF is crucial for proper patient management and treatment. In this study, we explored the potential of natural language processing (NLP) to improve the detection and diagnosis of HFpEF according to the European Society of Cardiology (ESC) diagnostic criteria. METHODS AND RESULTS: In a retrospective cohort study, we used an NLP pipeline applied to the electronic health record (EHR) to identify patients with a clinical diagnosis of HF between 2010 and 2022. We collected demographic, clinical, echocardiographic and outcome data from the EHR. Patients were categorized according to the left ventricular ejection fraction (LVEF). Those with LVEF ≥50% were further categorized based on whether they had a clinician-assigned diagnosis of HFpEF and if not, whether they met the ESC diagnostic criteria. Results were validated in a second, independent centre. We identified 8606 patients with HF. Of 3727 consecutive patients with HF and LVEF ≥50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. Patients with confirmed HFpEF were hospitalized more frequently; however the ESC criteria group had a higher 5-year mortality, despite being less comorbid and experiencing fewer acute cardiovascular events. CONCLUSIONS: This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms.

Race- and Ethnicity-Related Differences in Heart Failure With Preserved Ejection Fraction Using Natural Language Processing.

Background: Heart failure with preserved ejection fraction (HFpEF) is the predominant form of HF in older adults. It represents a heterogenous clinical syndrome that is less well understood across different ethnicities. Objectives: This study aimed to compare the clinical presentation and assess the diagnostic performance of existing HFpEF diagnostic tools between ethnic groups. Methods: A validated Natural Language Processing (NLP) algorithm was applied to the electronic health records of a large London hospital to identify patients meeting the European Society of Cardiology criteria for a diagnosis of HFpEF. NLP extracted patient demographics (including self-reported ethnicity and socioeconomic status), comorbidities, investigation results (N-terminal pro-B-type natriuretic peptide, H2FPEF scores, and echocardiogram reports), and mortality. Analyses were stratified by ethnicity and adjusted for socioeconomic status. Results: Our cohort consisted of 1,261 (64%) White, 578 (29%) Black, and 134 (7%) Asian patients meeting the European Society of Cardiology HFpEF diagnostic criteria. Compared to White patients, Black patients were younger at diagnosis and more likely to have metabolic comorbidities (obesity, diabetes, and hypertension) but less likely to have atrial fibrillation (30% vs 13%; P < 0.001). Black patients had lower N-terminal pro-B-type natriuretic peptide levels and a lower frequency of H2FPEF scores ≥6, indicative of likely HFpEF (26% vs 44%; P < 0.0001). Conclusions: Leveraging an NLP-based artificial intelligence approach to quantify health inequities in HFpEF diagnosis, we discovered that established markers systematically underdiagnose HFpEF in Black patients, possibly due to differences in the underlying comorbidity patterns. Clinicians should be aware of these limitations and its implications for treatment and trial recruitment.