Probability of Accurate Heart Failure Diagnosis and the Implications for Hospital Readmissions

Differences between patients in whom physicians agree versus disagree about the preoperative diagnosis of heart failure

STUDY OBJECTIVE

To quantify preoperative heart failure (HF) diagnostic agreement and identify characteristics of patients in whom physicians agreed versus disagreed about the diagnosis.

DESIGN

Observational cohort study.

SETTING

Patients undergoing major non-cardiac surgery at an academic center between 2015 and 2019.

PATIENTS

40,659 patients undergoing major non-cardiac surgery, among which a stratified subsample of 1018 patients with and without documented HF was reviewed.

INTERVENTIONS

Via a panel of physicians frequently managing patients with HF (cardiologists, cardiac anesthesiologists, intensivists), detailed chart reviews were performed (two per patient; median review time 32 min per reviewer per patient) to render adjudicated HF diagnoses.

MEASUREMENTS

Adjudicated diagnostic agreement measures (percent agreement, Krippendorf's alpha) and univariate comparisons (standardized differences) between patients in whom physicians agreed versus disagreed about the preoperative HF diagnosis.

MAIN RESULTS

Among patients with documented HF, physicians agreed about the diagnosis in 80.0% of cases (consensus positive), disagreed in 13.8% (disagreement), and refuted the diagnosis in 6.3% (consensus negative). Conversely, among patients without documented HF, physicians agreed about the diagnosis in 88.0% (consensus negative), disagreed in 8.4% (disagreement), and refuted the diagnosis in 3.6% (consensus positive). The estimated agreement for the 40,659 cases was 91.1% (95% CI 88.3%-93.9%); Krippendorff's alpha was 0.77 (0.75-0.80). Compared to patients in whom physicians agreed about a HF diagnosis, patients in whom physicians disagreed exhibited fewer guideline-defined HF diagnostic criteria.

CONCLUSIONS

Physicians usually agree about HF diagnoses adjudicated via chart review, although disagreement is not uncommon and may be partly explained by heterogeneous clinical presentations. Our findings inform preoperative screening processes by identifying patients whose characteristics contribute to physician disagreement via chart review. Clinical Trial Number / Registry URL: Not applicable.

Prognostic Value of HFA-PEFF Score in Patients Undergoing Transcatheter Aortic Valve Implantation

Background

The HFA-PEFF score may help in predicting long-term outcomes in patients undergoing transcatheter aortic valve implantation (TAVI) for severe aortic stenosis and preserved left ventricular ejection fraction (EF).

Methods

We retrieved data from 1,332 patients undergoing TAVI between 2010 and 2019 from the Prospective Segeberg TAVI Registry (ClinicalTrials.gov Identifier: NCT03192774). We calculated the HFA-PEFF score for 1,022 patients who had preserved EF (≥50%). To assess the prognostic value of the HFA-PEFF score in predicting adverse events, we dichotomised the patients according to a cut-off score of five (score <5 group: n=528 (51.6%), score ≥5 group: n=494 (48.3%)).

Results

The HFA-PEFF score ≥5 groups were older (81.9±6.3 years vs. 80.3±6.9 years; p<0.001) and had a higher prevalence of atrial fibrillation (35.1% vs 20.8%; p<0.001) and chronic kidney disease (30.1% vs 26.1%; p<0.001). Kaplan-Meier survival analyses over 24 months showed increased cardiovascular (CV) mortality (12.5% vs. 7.7%, log-rank; p=0.028) and first heart failure-related rehospitalisation (7.7% vs. 4.0%, log-rank p=0.014) in the HFA-PEFF score ≥5 groups compared with those of lower scores. No significant difference in all-cause mortality between both groups was observed (22.0% vs. 17.9%, log-rank p=0.127). In multivariate analysis, HFA-PEFF score ≥5 failed to predict CV mortality (aHR 1.37, 95% CI: 0.90-2.08, p=0.140) and time to first heart failure-related rehospitalisation (aHR 1.49, 95% CI: 0.83-2.65, p=0.181).

Conclusion

The HFA-PEFF score showed limited value in predicting long-term mortality and adverse heart failure-related events in patients with preserved EF undergoing TAVI. Clinical variables specific to this population could complement the HFA-PEFF score for better risk prediction.

Clinical Phenotypes of Heart Failure across the spectrum of Ejection Fraction: A Cluster Analysis

INTRODUCTION

Heart failure (HF), and especially HF with preserved ejection fraction (HFpEF), remains a challenging condition to define. The heterogenous nature of this population may be related to a variety of underlying etiologies interacting myocardial dysfunction.

METHOD

Alberta HEART study was a prospective, observational cohort that enrolled participants along the spectrum of heart failure including: healthy controls, people at risk of HF, and patients with HF and preserved (HFpEF) or reduced ejection fraction (HFrEF). We aimed to explore phenotypes of patients with HF and at-risk of developing HF. Utilising 27 detailed clinical, echocardiographic and biomarker variables, latent class analysis with and without multiple imputation was undertaken to identify distinct clinical phenotypes.

RESULTS

Of 621 participants, 191 (30.8%) and 169 (27.2%) were adjudicated by cardiologists to have HFpEF and HFrEF respectively. In the overall cohort, latent class analysis identified four distinct phenotypes. Phenotype A (n=152, 24.5%) was a healthy and low risk group. Phenotype B (n=129, 20.8%) demonstrated increased left ventricular mass and end-diastolic volumes, with elevated natriuretic peptides and clinical features of congestion. Phenotype C (n=128, 20.6%) was primarily characterised by obesity (80%) and normal indexed cardiac chamber sizes, low natriuretic peptide levels and minimal features of congestion. Phenotype D (n=212, 34.1%) consisted of elderly patients with clinical features of congestions. Phenotypes B and D demonstrated the highest risk of mortality and hospitalization over a median follow-up of 3.7 years.

CONCLUSION

Phenotypes with congestive features demonstrated increased risk profiles. Heart failure is a heterogenous classification which requires further work to appropriately categorise patients based on the underlying etiology or mechanism of impairment.

Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure

OBJECTIVE

When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients.

MATERIALS AND METHODS

Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort.

RESULTS

The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77-0.79), image model AUROC was 0.74 (0.72-0.75), and EHR model AUROC was 0.74 (0.70-0.76). For heart failure, combined: 0.83 (0.77-0.84), image: 0.80 (0.71-0.81), and EHR: 0.79 (0.75-0.82). For COPD, combined: AUROC = 0.88 (0.83-0.91), image: 0.83 (0.77-0.89), and EHR: 0.80 (0.76-0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia.

CONCLUSIONS

Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.

Do we need a definition of acute heart failure with preserved ejection fraction?

Heart failure with preserved ejection fraction (HFpEF) might soon become the most prevalent type of acute heart failure. Still, despite more than 30 years of research on HFpEF, not only do we lack specific treatment, but also a generally accepted definition of HFpEF. Since 2016, several definitions and algorithms have been proposed for diagnosing both diastolic dysfunction and overt HFpEF. However, all of them focus exclusively on chronic (and not acute) HFpEF. Recent studies showed that acute HFpEF may be overdiagnosed in patients presenting with acute dyspnoea. The aim of our article was to address two questions: (1) why there is a need for specific diagnostic criteria for acute HFpEF, and (2) what such definition of acute HFpEF should encompass.KEY MESSAGES:Several scores and algorithms have been proposed for diagnosing chronic heart failure with preserved ejection fraction (HFpEF), however, so far, there is no definition of acute HFpEF.Acute HFpEF seems to be overdiagnosed in patients presenting with acute dyspnoea.Definition of acute HFpEF should comprise both (1) features of chronic HFpEF and (2) markers of increased left ventricular filling pressures and/or of pulmonary congestion.

Evaluating Recruitment Strategies for a Randomized Clinical Trial with Heart Failure Patients

Recruiting participants with chronic medical conditions is time-consuming and expensive. Electronic medical record databases and patient portals may enable outreach to larger numbers of patients in comparison to face-to-face methods. We aimed to describe the yields, benefits, and limitations of recruitment strategies used for a randomized clinical trial of cognitive behavioral therapy for insomnia among patients with chronic stable heart failure (NCT02660385). We used multiple recruitment strategies including clinic-based recruitment, letters to patients identified from electronic databases, the patient portal, brochures and posters placed in clinics, presentations to heart failure support groups, and online advertising. We screened 10,291 medical records, enrolled 231 participants, and 195 participants completed baseline data collection. We enrolled 92 (23%) participants using clinic-based recruitment, 24 and 29 (6% and 10%) using letters to patients from two electronic databases, and 42 (55%) via the patient portal. Multiple recruitment strategies and flexibility are needed to achieve recruitment goals.

On the search for the right definition of heart failure with preserved ejection fraction

The definition of heart failure with preserved ejection fraction (HFpEF) has evolved from a clinically based "diagnosis of exclusion" to definitions focused on objective evidence of diastolic dysfunction and/or elevated left ventricular filling pressures. Despite advances in our understanding of HFpEF pathophysiology and the development of more sophisticated imaging modalities, the diagnosis of HFpEF remains challenging, especially in the chronic setting, given that symptoms are provoked by exertion and diagnostic evaluation is largely conducted at rest. Invasive hemodynamic study, and in particular - invasive exercise testing, is considered the reference method for HFpEF diagnosis. However, its use is limited as opposed to the high number of patients with suspected HFpEF. Thus, diagnostic criteria for HFpEF should be principally based on non-invasive measurements. As no single non-invasive variable can adequately corroborate or refute the diagnosis, different combinations of clinical, echocardiographic, and/or biochemical parameters have been introduced. Recent years have brought an abundance of HFpEF definitions. Here, we present and compare four of them: 1) the 2016 European Society of Cardiology criteria for HFpEF; 2) the 2016 echocardiographic algorithm for diagnosing diastolic dysfunction; 3) the 2018 evidence-based H2FPEF score; and 4) the most recent, 2019 Heart Failure Association HFA-PEFF algorithm. These definitions vary in their approach to diagnosis, as well as sensitivity and specificity. Further studies to validate and compare the diagnostic accuracy of HFpEF definitions are warranted. Nevertheless, it seems that the best HFpEF definition would originate from a randomized clinical trial showing a favorable effect of an intervention on prognosis in HFpEF.

Is heart failure misdiagnosed in hospitalized patients with preserved ejection fraction? From the European Society of Cardiology - Heart Failure Association EURObservational Research Programme Heart Failure Long-Term Registry

Aims

In hospitalized patients with a clinical diagnosis of acute heart failure (HF) with preserved ejection fraction (HFpEF), the aims of this study were (i) to assess the proportion meeting the 2016 European Society of Cardiology (ESC) HFpEF criteria and (ii) to compare patients with restrictive/pseudonormal mitral inflow pattern (MIP) vs. patients with MIP other than restrictive/pseudonormal.

Methods and results

We included hospitalized participants of the ESC‐Heart Failure Association (HFA) EURObservational Research Programme (EORP) HF Long‐Term Registry who had echocardiogram with ejection fraction (EF) ≥ 50% during index hospitalization. As no data on e', E/e' and left ventricular (LV) mass index were gathered in the registry, the 2016 ESC HFpEF definition was modified as follows: elevated B‐type natriuretic peptide (BNP) (≥100 pg/mL for acute HF) and/or N‐terminal pro‐BNP (≥300 pg/mL) and at least one of the echocardiographic criteria: (i) presence of LV hypertrophy (yes/no), (ii) left atrial volume index (LAVI) of >34 mL/m²), or (iii) restrictive/pseudonormal MIP. Next, all patients were divided into four groups: (i) patients with restrictive/pseudonormal MIP on echocardiography [i.e. with presumably elevated left atrial (LA) pressure], (ii) patients with MIP other than restrictive/pseudonormal (i.e. with presumably normal LA pressure), (iii) atrial fibrillation (AF) group, and (iv) ‘grey area’ (no consistent description of MIP despite no report of AF). Of 6365 hospitalized patients, 1848 (29%) had EF ≥ 50%. Natriuretic peptides were assessed in 28%, LV hypertrophy in 92%, LAVI in 13%, and MIP in 67%. The 2016 ESC HFpEF criteria could be assessed in 27% of the 1848 patients and, if assessed, were met in 52%. Of the 1848 patients, 19% had restrictive/pseudonormal MIP, 43% had MIP other than restrictive/pseudonormal, 18% had AF and 20% were grey area. There were no differences in long‐term all‐cause or cardiovascular mortality, or all‐cause hospitalizations or HF rehospitalizations between the four groups. Despite fewer non‐cardiac comorbidities reported at baseline, patients with MIP other than restrictive/pseudonormal (i.e. with presumably normal LA pressure) had more non‐cardiovascular (14.0 vs. 6.7 per 100 patient‐years, P < 0.001) and cardiovascular non‐HF (13.2 vs. 8.0 per 100 patient‐years, P = 0.016) hospitalizations in long‐term follow‐up than patients with restrictive/pseudonormal MIP.

Conclusions

Acute HFpEF diagnosis could be assessed (based on the 2016 ESC criteria) in only a quarter of patients and confirmed in half of these. When assessed, only one in three patients had restrictive/pseudonormal MIP suggestive of elevated LA pressure. Patients with MIP other than restrictive/pseudonormal (suggestive of normal LA pressure) could have been misdiagnosed with acute HFpEF or had echocardiography performed after normalization of LA pressure. They were more often hospitalized for non‐HF reasons during follow‐up. Symptoms suggestive of acute HFpEF may in some patients represent non‐HF comorbidities.

Analysis Of Re-Hospitalizations For Patients With Heart Failure Caused By Coronary Heart Disease: Data Of First Event And Recurrent Event

BACKGROUND

The re-hospitalization rate of patients with heart failure remains at a high level, and studies of the subject have focused mainly on event-time outcomes. In addition to using re-hospitalization data with the outcomes of the event-time-count, this study introduces the conditional frailty model, which could help obtain more reasonable results.

MATERIALS AND METHODS

This prospective observational cohort study enrolled 1484 patients with heart failure caused by coronary heart disease. The outcomes of heart failure readmissions and the case report form data were collected. Based on the traditional Cox model with event-time outcomes, the mixed effects of a conditional frailty model were added to analyze the event-time-count longitudinal data.

RESULTS

The Cox regression model showed that non-manual work, diastolic dysfunction, and better medical compensation increased the risk of heart failure readmission, whereas treatment with beta-blockers decreased the risk. The conditional frailty model further revealed that age, female sex, non-manual work, better medical compensation, longer QRS duration, and treatment with percutaneous coronary intervention increased the risk of heart failure readmission.

CONCLUSION

This study obtained more reliable, reasonable results based on longitudinal data and a mixed model. The results could provide more clinical epidemiological evidence for the management of heart failure.