Heart failure with preserved ejection fraction management: a systematic review of clinical practice guidelines and recommendations.

Multiple guidelines exist for the diagnosis and management of heart failure with preserved ejection fraction (HFpEF). We systematically reviewed current guidelines and recommendations, developed by national and international medical organizations, on the management of HFpEF in adults to aid clinical decision-making. We searched MEDLINE and EMBASE on 28 February 2024 for publications over the last 10 years as well as websites of organizations relevant to guideline development. Of the ten guidelines and recommendations retrieved, seven showed considerable rigour of development and were subsequently retained for analysis. There was consensus on the definition of HFpEF and the diagnostic role of serum natriuretic peptides and resting transthoracic echocardiography. Discrepancies were identified in the thresholds of serum natriuretic peptides and transthoracic echocardiography parameters used to diagnose HFpEF. There was agreement on the general pharmacological and supportive management of acute and chronic HFpEF. However, differences exist in strategies to identify and address specific phenotypes. Contemporary guidelines for HFpEF management agree on measures to avoid its development and the consideration of cardiac transplantation in advanced disease. There were discrepancies in recommended frequency of surveillance for patients with HFpEF and sparse recommendations on screening for HFpEF in the general population, use of diagnostic scoring systems, and the role of newly emerging therapies.

The prognostic impact of specialist cardiology input in patients admitted for heart failure and normal ejection fraction.

AIMS: Specialist cardiology care is associated with a prognostic benefit in patients with heart failure (HF) with reduced ejection fraction (HFrEF) admitted with decompensated HF. However, up to one third of patients admitted with HF and normal ejection fraction (HFnEF) do not receive specialist cardiology input. Whether this has prognostic implications is unknown. METHODS AND RESULTS: Data on patients hospitalized with HFnEF from two tertiary centres were analysed. The primary outcome measure was all-cause mortality during follow-up. The secondary outcome was in-hospital mortality. A total of 1413 patients were included in the study. Of these, 23% (n = 322) did not receive in-hospital specialist cardiology input. Patients seen by a cardiologist were less likely to have hypertension (73% vs. 79%, P = 0.03) and respiratory co-morbidities (25% vs. 31%, P = 0.02) compared with those who did not receive specialist input. Similarly, clinical presentation was more severe for those who received specialist input (New York Heart Association III/IV 83% vs. 75% respectively, P = 0.003; moderate-to-severe peripheral oedema 65% vs. 54%, P < 0.001). Medical management was similar, except for a higher use of diuretics (90% vs. 86%, P = 0.04) and a longer length of stay for patients who received specialist input (9 vs. 4 days, P < 0.001). Long-term outcomes were comparable between patients who received specialist input and those who did not. However, specialist input was independently associated with lower in-hospital mortality (hazard ratio 0.19, confidence interval 0.09-0.43, P < 0.001). CONCLUSIONS: In-hospital cardiology specialist input has no long-term prognostic advantage in patients with HFnEF but is independently associated with reduced in-hospital mortality.

Long-term outcomes following catheter ablation versus medical therapy in patients with persistent atrial fibrillation and heart failure with reduced ejection fraction.

AIMS: The ARC-HF and CAMTAF trials randomized patients with persistent atrial fibrillation (AF) and heart failure (HF) to early routine catheter ablation (ER-CA) versus pharmacological rate control (RC). After trial completion, delayed selective catheter ablation (DS-CA) was performed where clinically indicated in the RC group. We hypothesized that ER-CA would result in a lower risk of cardiovascular hospitalization and death versus DS-CA in this population. METHODS AND RESULTS: Overall, 102 patients were randomized (age 60 ± 11 years, left ventricular ejection fraction [LVEF] 31 ± 11%): 52 to ER-CA and 50 to RC. After 12 months, patients undergoing ER-CA had improved self-reported symptom scores, lower New York Heart Association class (i.e. better functional capacity), and higher LVEF compared to patients receiving RC alone. During a median follow-up of 7.8 (interquartile range 3.9-9.9) years, 27 (54%) patients in the RC group underwent DS-CA and 34 (33.3%) patients died, including 17 (32.7%) randomized to ER-CA and 17 (34.0%) randomized to RC. Compared with DS-CA, a strategy of ER-CA exhibited similar risk of all-cause mortality (adjusted hazard ratio [aHR] 0.89, 95% confidence interval [CI] 0.44-1.77, p = 0.731) and combined all-cause mortality or cardiovascular hospitalization (aHR 0.80, 95% CI 0.43-1.47, p = 0.467). However, analyses according to treatment received suggested an association between CA and improved outcomes versus RC (all-cause mortality: aHR 0.43, 95% CI 0.20-0.91, p = 0.028; all-cause mortality/cardiovascular hospitalization: aHR 0.48, 95% CI 0.24-0.94, p = 0.031). CONCLUSIONS: In patients with persistent AF and HF, ER-CA produces similar long-term outcomes to a DS-CA strategy. The association between CA as a treatment received and improved outcomes means there is still a lack of clarity regarding the role of early CA in selected patients. Randomized trials are needed to clarify this question.

Diagnostic signature for heart failure with preserved ejection fraction (HFpEF): a machine learning approach using multi-modality electronic health record data.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is thought to be highly prevalent yet remains underdiagnosed. Evidence-based treatments are available that increase quality of life and decrease hospitalization. We sought to develop a data-driven diagnostic model to predict from electronic health records (EHR) the likelihood of HFpEF among patients with unexplained dyspnea and preserved left ventricular EF. METHODS AND RESULTS: The derivation cohort comprised patients with dyspnea and echocardiography results. Structured and unstructured data were extracted using an automated informatics pipeline. Patients were retrospectively diagnosed as HFpEF (cases), non-HF (control cohort I), or HF with reduced EF (HFrEF; control cohort II). The ability of clinical parameters and investigations to discriminate cases from controls was evaluated by extreme gradient boosting. A likelihood scoring system was developed and validated in a separate test cohort. The derivation cohort included 1585 consecutive patients: 133 cases of HFpEF (9%), 194 non-HF cases (Control cohort I) and 1258 HFrEF cases (Control cohort II). Two HFpEF diagnostic signatures were derived, comprising symptoms, diagnoses and investigation results. A final prediction model was generated based on the averaged likelihood scores from these two models. In a validation cohort consisting of 269 consecutive patients [with 66 HFpEF cases (24.5%)], the diagnostic power of detecting HFpEF had an AUROC of 90% (P < 0.001) and average precision of 74%. CONCLUSION: This diagnostic signature enables discrimination of HFpEF from non-cardiac dyspnea or HFrEF from EHR and can assist in the diagnostic evaluation in patients with unexplained dyspnea. This approach will enable identification of HFpEF patients who may then benefit from new evidence-based therapies.

Development of algorithms for determining heart failure with reduced and preserved ejection fraction using nationwide electronic healthcare records in the UK.

BACKGROUND: Determining heart failure (HF) phenotypes in routine electronic health records (EHR) is challenging. We aimed to develop and validate EHR algorithms for identification of specific HF phenotypes, using Read codes in combination with selected patient characteristics. METHODS: We used The Healthcare Improvement Network (THIN). The study population included a random sample of individuals with HF diagnostic codes (HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF) and non-specific HF) selected from all participants registered in the THIN database between 1 January 2015 and 30 September 2017. Confirmed diagnoses were determined in a randomly selected subgroup of 500 patients via GP questionnaires including a review of all available cardiovascular investigations. Confirmed diagnoses of HFrEF and HFpEF were based on four criteria. Based on these data, we calculated a positive predictive value (PPV) of predefined algorithms which consisted of a combination of Read codes and additional information such as echocardiogram results and HF medication records. RESULTS: The final cohort from which we drew the 500 patient random sample consisted of 10 275 patients. Response rate to the questionnaire was 77.2%. A small proportion (18%) of the overall HF patient population were coded with specific HF phenotype Read codes. For HFrEF, algorithms achieving over 80% PPV included definite, possible or non-specific HF HFrEF codes when combined with at least two of the drugs used to treat HFrEF. Only in non-specific HF coding did the use of three drugs (rather than two) contribute to an improvement of the PPV for HFrEF. HFpEF was only accurately defined with specific codes. In the absence of specific coding for HFpEF, the PPV was consistently below 50%. CONCLUSIONS: Prescription for HF medication can reliably be used to find HFrEF patients in the UK, even in the absence of a specific Read code for HFrEF. Algorithms using non-specific coding could not reliably find HFpEF patients.

Impact of COPD and asthma on in-hospital mortality and management of patients with heart failure in England and Wales: an observational analysis.

OBJECTIVE: To evaluate the association between having concomitant chronic obstructive pulmonary disease (COPD) or asthma, and in-patient mortality and post-discharge management among patients hospitalised for acute heart failure (HF). SETTING: Data were obtained from patients enrolled in the National Heart Failure Audit. PARTICIPANTS: 217 329 patients hospitalised for HF in England-Wales between March 2012 and 2018. OUTCOMES: In-hospital mortality, referrals to cardiology follow-up and prescriptions for HF medications were compared between patients with comorbid COPD (COPD-HF) or asthma (asthma-HF) versus HF-alone using mixed-effects logistic regression. RESULTS: Patients with COPD-HF were more likely to die during hospitalisation, and those with asthma-HF had a reduced likelihood of death, compared with patients who had HF-alone ((adjusted)ORadj, 95% CI: 1.10, 1.06 to 1.14 and ORadj, 95% CI: 0.84, 0.79 to 0.88). In patients who survived to discharge, referral to HF follow-up services differed between groups: patients with COPD-HF had reduced odds of cardiology follow-up (ORadj, 95% CI 0.79, 0.77 to 0.81), while cardiology referral odds for asthma-HF were similar to HF-alone. Overall, proportions of HF medication prescriptions at discharge were low for both COPD-HF and asthma-HF groups, particularly prescriptions for beta-blockers. CONCLUSIONS: In this nationwide analysis, we showed that COPD and asthma significantly impact the clinical course in patients hospitalised for HF. COPD is associated with higher in-patient mortality and lower cardiology referral odds, while COPD and asthma are both associated with lower use of prognostic HF therapies on discharge. These data highlight therapeutic gaps and a need for better integration of cardiopulmonary services to improve healthcare provision for patients with HF and coexisting respiratory disease.

Revascularization in ischaemic heart failure with preserved ejection fraction: a nationwide cohort study.

AIMS: Despite the common occurrence of coronary artery disease (CAD) and heart failure (HF) with preserved ejection fraction (HFpEF), there is limited evidence to guide revascularization. METHODS AND RESULTS: We investigated the long-term outcomes of coronary artery bypass grafting (CABG) in patients with HF and significant CAD across the spectrum of ejection fraction, using a large national cohort of patients from the Veteran Affairs (VA) Medical Centers in the US. Patients with HF were stratified into groups, HFpEF, HF with mid-range ejection fraction (HFmrEF), and HF with reduced ejection fraction (HFrEF) and compared to patients with no preoperative HF. We analysed 10 396 patients. Despite an increased hazard in the first year following revascularization, the long-term survival (median follow-up 6.6 years; interquartile range 3.7-10.1) of HFpEF post-CABG was similar to controls (hazard ratio 0.85, 95% confidence interval 0.68-1.06), but survival progressively declined with HFmrEF and HFrEF. Similar trends were seen with recurrent HF hospitalization with lower risk with baseline HFpEF (43.9 ± 6.9/100 patient-years) compared to HFmrEF (65.9 ± 3.8/100 patient-years) and HFrEF (93.4 ± 4.8/100 patient-years). Although HFpEF patients had lower mortality and HF hospitalization post-CABG compared to patients with a lower ejection fraction, they experienced the highest rates of future myocardial infarction. CONCLUSION: Although HFpEF patients with CAD have greater short-term risk post-CABG, their long-term survival is comparable to controls. However, they are at increased risk for HF hospitalizations and myocardial infarction. These data support the safety of CABG in HFpEF patients and suggest continuum of mortality risk for ischaemic HF when stratified by baseline ejection fraction before revascularization.

Hospitalization for Heart Failure in the United States, UK, Taiwan, and Japan: An International Comparison of Administrative Health Records on 413,385 Individual Patients.

BACKGROUND: Registries show international variations in the characteristics and outcome of patients with heart failure (HF), but national samples are rarely large, and case selection may be biased owing to enrolment in academic centers. National administrative datasets provide large samples with a low risk of bias. In this study, we compared the characteristics, health care resource use (HRU) and outcomes of patients with primary HF hospitalizations (HFH) using electronic health records (EHR) from 4 high-income countries (United States, UK, Taiwan, Japan) on 3 continents. METHODS AND RESULTS: We used electronic health record to identify unplanned HFH between 2012 and 2014. We identified 231,512, 10,991, 36,900, and 133,982 patients with a primary HFH from the United States, the UK, Taiwan, and Japan, respectively. HFH per 100,000 population was highest in the United States and lowest in Taiwan. Fewer patients in Taiwan and Japan were obese or had chronic kidney disease. The length of hospital stay was shortest in the United States (median 4 days) and longer in the UK, Taiwan, and Japan (medians of 7, 9, and 17 days, respectively). HRU during hospitalization was highest in Japan and lowest in UK. Crude and direct standardized in-hospital mortality was lowest in the United States (direct standardized rates 1.8, 95% confidence interval 1.7%-1.9%) and progressively higher in Taiwan (direct standardized rates 3.9, 95% CI 3.8%-4.1%), the UK (direct standardized rates 6.4, 95% CI 6.1%-6.7%), and Japan (direct standardized rates 6.7, 95% CI 6.6%-6.8%). The 30-day all-cause (25.8%) and HF (7.2%) readmissions were highest in the United States and lowest in Japan (11.9% and 5.1%, respectively). CONCLUSIONS: Marked international variations in patient characteristics, HRU, and clinical outcomes exist; understanding them might inform health care policy and international trial design.

Differences in Outcomes between Heart Failure Phenotypes in Patients with Coexistent Chronic Obstructive Pulmonary Disease: A Cohort Study.

Rationale: Differences in clinical presentation and outcomes between heart failure (HF) phenotypes in patients with chronic obstructive pulmonary disease (COPD) have not been assessed. Objectives: The aim of this study was to compare clinical outcomes and healthcare resource use between patients with COPD and HF with preserved ejection fraction (HFpEF), mildly reduced ejection fraction (HFmEF), and reduced ejection fraction (HFrEF). Methods: Patients with COPD and HF were identified in the U.S. administrative claims database OptumLabs DataWarehouse between 2008 and 2018. All-cause and cause-specific (HF) hospitalization, acute exacerbation of COPD (AECOPD, severe and moderate combined), mortality, and healthcare resource use were compared between HF phenotypes. Results: From 5,419 patients with COPD, 70% had HFpEF, 20% had HFrEF, and 10% had HFmEF. All-cause hospitalization did not differ across groups; however, patients with COPD and HFrEF had a greater risk of HF-specific hospitalization (hazard ratio [HR], 1.54; 95% confidence interval [CI], 1.29-1.84) and mortality (HR, 1.17; 95% CI, 1.03-1.33) than patients with COPD and HFpEF. Conversely, patients with COPD and HFrEF had a lower risk of AECOPD than those with COPD and HFpEF (HR, 0.75; 95% CI, 0.66-0.87). Rates of long-term stays (in skilled-nursing facilities) and emergency room visits were lower for those with COPD and HFrEF than for those with COPD and HFpEF. Conclusions: Outcomes in patients with comorbid COPD and HFpEF are largely driven by COPD. Given the paucity in treatments for HFpEF, better differentiation between cardiac and respiratory symptoms may provide an opportunity to reduce the risk of AECOPD. Risk of death and HF hospitalization were highest among patients with COPD and HFrEF, emphasizing the importance of optimizing guideline-recommended HFrEF therapies in this group.

Under-recognition of heart failure in patients with atrial fibrillation and the impact of gender: a UK population-based cohort study.

BACKGROUND: Patients with atrial fibrillation (AF) complicated by heart failure (HF) have a poor prognosis. We investigated whether long term loop-diuretic therapy in patients with AF and no known diagnosis of HF, as a potential surrogate marker of undiagnosed HF, is also associated with worse outcomes. METHODS: Adults with incident AF were identified from UK primary and secondary care records between 2004 and 2016. Repeat prescriptions for loop diuretics, without a diagnosis of HF or documented non-cardiac indication, were classified as 'isolated' loop diuretic use. RESULTS: Amongst 124,256 people with incident AF (median 76 years, 47% women), 22,001 (17.7%) had a diagnosis of HF, and 22,325 (18.0%) had isolated loop diuretic use. During 2.9 (LQ-UQ 1-6) years' follow-up, 12,182 patients were diagnosed with HF (incidence rate 3.2 [95% CI 3.1-3.3]/100 person-years). Of these, 3999 (32.8%) had prior isolated loop diuretic use, including 31% of patients diagnosed with HF following an emergency hospitalisation. The median time from AF to HF diagnosis was 3.6 (1.2-7.7) years in men versus 5.1 (1.8-9.9) years in women (p = 0.0001). In adjusted models, patients with isolated loop diuretic use had higher mortality (HR 1.42 [95% CI 1.37-1.47], p < 0.0005) and risk of HF hospitalisation (HR 1.60 [95% CI 1.42-1.80], p < 0.0005) than patients with no HF or loop diuretic use, and comparably poor survival to patients with diagnosed HF. CONCLUSIONS: Loop diuretics are commonly prescribed to patients with AF and may indicate increased cardiovascular risk. Targeted evaluation of these patients may allow earlier HF diagnosis, timely intervention, and better outcomes, particularly amongst women with AF, in whom HF appears to be under-recognised and diagnosed later than in men.

Pre-existing cardiovascular disease rather than cardiovascular risk factors drives mortality in COVID-19.

BACKGROUND: The relative association between cardiovascular (CV) risk factors, such as diabetes and hypertension, established CV disease (CVD), and susceptibility to CV complications or mortality in COVID-19 remains unclear. METHODS: We conducted a cohort study of consecutive adults hospitalised for severe COVID-19 between 1st March and 30th June 2020. Pre-existing CVD, CV risk factors and associations with mortality and CV complications were ascertained. RESULTS: Among 1721 patients (median age 71 years, 57% male), 349 (20.3%) had pre-existing CVD (CVD), 888 (51.6%) had CV risk factors without CVD (RF-CVD), 484 (28.1%) had neither. Patients with CVD were older with a higher burden of non-CV comorbidities. During follow-up, 438 (25.5%) patients died: 37% with CVD, 25.7% with RF-CVD and 16.5% with neither. CVD was independently associated with in-hospital mortality among patients < 70 years of age (adjusted HR 2.43 [95% CI 1.16-5.07]), but not in those ≥ 70 years (aHR 1.14 [95% CI 0.77-1.69]). RF-CVD were not independently associated with mortality in either age group (< 70 y aHR 1.21 [95% CI 0.72-2.01], ≥ 70 y aHR 1.07 [95% CI 0.76-1.52]). Most CV complications occurred in patients with CVD (66%) versus RF-CVD (17%) or neither (11%; p < 0.001). 213 [12.4%] patients developed venous thromboembolism (VTE). CVD was not an independent predictor of VTE. CONCLUSIONS: In patients hospitalised with COVID-19, pre-existing established CVD appears to be a more important contributor to mortality than CV risk factors in the absence of CVD. CVD-related hazard may be mediated, in part, by new CV complications. Optimal care and vigilance for destabilised CVD are essential in this patient group. Trial registration n/a.

Biological responses to COVID-19: Insights from physiological and blood biomarker profiles.

BACKGROUND: Understanding the spectrum and course of biological responses to coronavirus disease 2019 (COVID-19) may have important therapeutic implications. We sought to characterise biological responses among patients hospitalised with severe COVID-19 based on serial, routinely collected, physiological and blood biomarker values. METHODS AND FINDINGS: We performed a retrospective cohort study of 1335 patients hospitalised with laboratory-confirmed COVID-19 (median age 70 years, 56 % male), between 1st March and 30th April 2020. Latent profile analysis was performed on serial physiological and blood biomarkers. Patient characteristics, comorbidities and rates of death and admission to intensive care, were compared between the latent classes. A five class solution provided the best fit. Class 1 "Typical response" exhibited a moderately elevated and rising C-reactive protein (CRP), stable lymphopaenia, and the lowest rates of 14-day adverse outcomes. Class 2 "Rapid hyperinflammatory response" comprised older patients, with higher admission white cell and neutrophil counts, which declined over time, accompanied by a very high and rising CRP and platelet count, and exibited the highest mortality risk. Class 3 "Progressive inflammatory response" was similar to the typical response except for a higher and rising CRP, though similar mortality rate. Class 4 "Inflammatory response with kidney injury" had prominent lymphopaenia, moderately elevated (and rising) CRP, and severe renal failure. Class 5 "Hyperinflammatory response with kidney injury" comprised older patients, with a very high and rising CRP, and severe renal failure that attenuated over time. Physiological measures did not substantially vary between classes at baseline or early admission. CONCLUSIONS AND RELEVANCE: Our identification of five distinct classes of biomarker profiles provides empirical evidence for heterogeneous biological responses to COVID-19. Early hyperinflammatory responses and kidney injury may signify unique pathophysiology that requires targeted therapy.

Beta-blocker therapy in patients with COPD: a systematic literature review and meta-analysis with multiple treatment comparison.

BACKGROUND: Beta-blockers are associated with reduced mortality in patients with cardiovascular disease but are often under prescribed in those with concomitant COPD, due to concerns regarding respiratory side-effects. We investigated the effects of beta-blockers on outcomes in patients with COPD and explored within-class differences between different agents. METHODS: We searched the Cochrane Central Register of Controlled Trials, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Medline for observational studies and randomized controlled trials (RCTs) investigating the effects of beta-blocker exposure versus no exposure or placebo, in patients with COPD, with and without cardiovascular indications. A meta-analysis was performed to assess the association of beta-blocker therapy with acute exacerbations of COPD (AECOPD), and a network meta-analysis was conducted to investigate the effects of individual beta-blockers on FEV1. Mortality, all-cause hospitalization, and quality of life outcomes were narratively synthesized. RESULTS: We included 23 observational studies and 14 RCTs. In pooled observational data, beta-blocker therapy was associated with an overall reduced risk of AECOPD versus no therapy (HR 0.77, 95%CI 0.70 to 0.85). Among individual beta-blockers, only propranolol was associated with a relative reduction in FEV1 versus placebo, among 199 patients evaluated in RCTs. Narrative syntheses on mortality, all-cause hospitalization and quality of life outcomes indicated a high degree of heterogeneity in study design and patient characteristics but suggested no detrimental effects of beta-blocker therapy on these outcomes. CONCLUSION: The class effect of beta-blockers remains generally positive in patients with COPD. Reduced rates of AECOPD, mortality, and improved quality of life were identified in observational studies, while propranolol was the only agent associated with a deterioration of lung function in RCTs.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

BACKGROUND: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. METHODS: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. RESULTS: A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. CONCLUSIONS: NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization.

BACKGROUND: Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. The aim of this study is to characterize HF groups that account for higher-order interactions between comorbidities and to investigate the association between comorbidity groups and outcomes. METHODS: Latent class analysis (LCA) was performed on 12 comorbidities from patients with HF identified from administrative claims data in the USA (OptumLabs Data Warehouse®) between 2008 and 2018. Associations with admission to hospital and mortality were assessed with Cox regression. Negative binomial regression was used to examine rates of healthcare use. RESULTS: In a population of 318,384 individuals, we identified five comorbidity clusters, named according to their dominant features: low-burden, metabolic-vascular, anemic, ischemic, and metabolic. Compared to the low-burden group (minimal comorbidities), patients in the metabolic-vascular group (exhibiting a pattern of diabetes, obesity, and vascular disease) had the worst prognosis for admission (HR 2.21, 95% CI 2.17-2.25) and death (HR 1.87, 95% CI 1.74-2.01), followed by the ischemic, anemic, and metabolic groups. The anemic group experienced an intermediate risk of admission (HR 1.49, 95% CI 1.44-1.54) and death (HR 1.46, 95% CI 1.30-1.64). Healthcare use also varied: the anemic group had the highest rate of outpatient visits, compared to the low-burden group (IRR 2.11, 95% CI 2.06-2.16); the metabolic-vascular and ischemic groups had the highest rate of admissions (IRR 2.11, 95% CI 2.08-2.15, and 2.11, 95% CI 2.07-2.15) and healthcare costs. CONCLUSIONS: These data demonstrate the feasibility of using LCA to classify HF based on comorbidities alone and should encourage investigation of multidimensional approaches in comorbidity management to reduce admission and mortality risk among patients with HF.

A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19.

BACKGROUND: People of minority ethnic backgrounds may be disproportionately affected by severe COVID-19. Whether this relates to increased infection risk, more severe disease progression, or worse in-hospital survival is unknown. The contribution of comorbidities or socioeconomic deprivation to ethnic patterning of outcomes is also unclear. METHODS: We conducted a case-control and a cohort study in an inner city primary and secondary care setting to examine whether ethnic background affects the risk of hospital admission with severe COVID-19 and/or in-hospital mortality. Inner city adult residents admitted to hospital with confirmed COVID-19 (n = 872 cases) were compared with 3,488 matched controls randomly sampled from a primary healthcare database comprising 344,083 people residing in the same region. For the cohort study, we studied 1827 adults consecutively admitted with COVID-19. The primary exposure variable was self-defined ethnicity. Analyses were adjusted for socio-demographic and clinical variables. FINDINGS: The 872 cases comprised 48.1% Black, 33.7% White, 12.6% Mixed/Other and 5.6% Asian patients. In conditional logistic regression analyses, Black and Mixed/Other ethnicity were associated with higher admission risk than white (OR 3.12 [95% CI 2.63-3.71] and 2.97 [2.30-3.85] respectively). Adjustment for comorbidities and deprivation modestly attenuated the association (OR 2.24 [1.83-2.74] for Black, 2.70 [2.03-3.59] for Mixed/Other). Asian ethnicity was not associated with higher admission risk (adjusted OR 1.01 [0.70-1.46]). In the cohort study of 1827 patients, 455 (28.9%) died over a median (IQR) of 8 (4-16) days. Age and male sex, but not Black (adjusted HR 1.06 [0.82-1.37]) or Mixed/Other ethnicity (adjusted HR 0.72 [0.47-1.10]), were associated with in-hospital mortality. Asian ethnicity was associated with higher in-hospital mortality but with a large confidence interval (adjusted HR 1.71 [1.15-2.56]). INTERPRETATION: Black and Mixed ethnicity are independently associated with greater admission risk with COVID-19 and may be risk factors for development of severe disease, but do not affect in-hospital mortality risk. Comorbidities and socioeconomic factors only partly account for this and additional ethnicity-related factors may play a large role. The impact of COVID-19 may be different in Asians. FUNDING: British Heart Foundation; the National Institute for Health Research; Health Data Research UK.