The PROTECT in-hospital risk model: 7-day outcome in patients hospitalized with acute heart failure and renal dysfunction

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients

AIMS

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF).

METHODS AND RESULTS

Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n = 4040) was split 4:1 into training (n = 3232) and test datasets (n = 808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9 ± 12.0. The in-hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in-hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815-0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680-0.685) and the RF model (0.755, 95% CI: 0.753-0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765-0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686-0.689) and the RF model (0.713, 95% CI: 0.711-0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in-hospital mortality and WHF were similar among each cluster in both the training and test datasets.

CONCLUSIONS

The XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in-hospital mortality and WHF for patients with AHF.

Titration of Medications After Acute Heart Failure Is Safe, Tolerated, and Effective Regardless of Risk

BACKGROUND

Guideline-directed medical therapy (GDMT) decisions may be less affected by single patient variables such as blood pressure or kidney function and more by overall risk profile. In STRONG-HF (Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure), high-intensity care (HIC) in the form of rapid uptitration of heart failure (HF) GDMT was effective overall, but the safety, tolerability and efficacy of HIC across the spectrum of HF severity is unknown. Evaluating this with a simple risk-based framework offers an alternative and more clinically translatable approach than traditional subgroup analyses.

OBJECTIVES

The authors sought to assess safety, tolerability, and efficacy of HIC according to the simple, powerful, and clinically translatable MAGGIC (Meta-Analysis Global Group in Chronic) HF risk score.

METHODS

In STRONG-HF, 1,078 patients with acute HF were randomized to HIC (uptitration of treatments to 100% of recommended doses within 2 weeks of discharge and 4 scheduled outpatient visits over the 2 months after discharge) vs usual care (UC). The primary endpoint was the composite of all-cause death or first HF rehospitalization at day 180. Baseline HF risk profile was determined by the previously validated MAGGIC risk score. Treatment effect was stratified according to MAGGIC risk score both as a categorical and continuous variable.

RESULTS

Among 1,062 patients (98.5%) with complete data for whom a MAGGIC score could be calculated at baseline, GDMT use at baseline was similar across MAGGIC tertiles. Overall GDMT prescriptions achieved for individual medication classes were higher in the HIC vs UC group and did not differ by MAGGIC risk score tertiles (interaction nonsignificant). The incidence of all-cause death or HF readmission at day 180 was, respectively, 16.3%, 18.9%, and 23.2% for MAGGIC risk score tertiles 1, 2, and 3. The HIC arm was at lower risk of all-cause death or HF readmission at day 180 (HR: 0.66; 95% CI: 0.50-0.86) and this finding was robust across MAGGIC risk score modeled as a categorical (HR: 0.51; 95% CI: 0.62-0.68 in tertiles 1, 2, and 3; interaction nonsignificant) for all comparisons and continuous (interaction nonsignificant) variable. The rate of adverse events was higher in the HIC group, but this observation did not differ based on MAGGIC risk score tertile (interaction nonsignificant).

CONCLUSIONS

HIC led to better use of GDMT and lower HF-related morbidity and mortality compared with UC, regardless of the underlying HF risk profile. (Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP testinG, of Heart Failure Therapies [STRONG-HF]; NCT03412201).

Worsening heart failure: a concept in evolution

PURPOSE OF REVIEW

Worsening heart failure (WHF) has developed as a unique definition within heart failure (HF) in recent years. It captures the disease as a dynamic process. This review describes what is currently known about WHF, why it should be considered a discrete scientific endpoint, and future directions for research.

RECENT FINDINGS

There is no single agreed upon definition for WHF. It can be identified as being due to treatment side-effects, related to concomitant comorbidity, or true disease progression. Risk scores based on criteria like those already developed for HF can be created to stratify risk for WHF.

CONCLUSIONS

WHF is an emerging entity within HF that defines itself as a unique point of interest. Understanding it as a clinical measure of where a patient's HF is evolving allows for identifying patients that require a refreshed approach to their care. Keeping this in mind will help redefine more patient-centric outcome measures in research to come.

Heart failure management at home: a non-randomised prospective case-controlled trial (HeMan at Home)

BACKGROUND/OBJECTIVES

Heart failure (HF) is a growing clinical and economic burden for patients and health systems. The COVID-19 pandemic has led to avoidance and delay in care, resulting in increased morbidity and mortality among many patients with HF. The increasing burden of HF during the COVID-19 pandemic led us to evaluate the quality and safety of the Hospital at Home (HAH) for patients presenting to their community providers or emergency department (ED) with symptoms of acute on chronic HF (CHF) requiring admission.

DESIGN/OUTCOMES

A non-randomised prospective case-controlled of patients enrolled in the HAH versus admission to the hospital (usual care, UC). Primary outcomes included length of stay (LOS), adverse events, discharge disposition and patient satisfaction. Secondary outcomes included 30-day readmission rates, 30-day ED usage and ED dwell time.

RESULTS

Sixty patients met inclusion/exclusion criteria and were included in the study. Of the 60 patients, 40 were in the HAH and 20 were in the UC group. Primary outcomes demonstrated that HAH patients had slightly longer LOS (6.3 days vs 4.7 days); however, fewer adverse events (12.5% vs 35%) compared with the UC group. Those enrolled in the HAH programme were less likely to be discharged with postacute services (skilled nursing facility or home health). HAH was associated with increased patient satisfaction compared with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) score in North Carolina. Secondary outcomes of 30-day readmission and ED usage were similar between HAH and UC.

CONCLUSIONS

The HAH pilot programme was shown to be a safe and effective alternative to hospitalisation for the appropriately selected patient presenting with acute on CHF.

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction

Background There is a need to develop electronic health record-based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Results We studied adults with heart failure (HF) from 2011 to 2019 within an integrated health care delivery system. WHF encounters were ascertained using natural language processing and structured data. We conducted boosted decision tree ensemble models to predict 1-year hospitalizations, emergency department visits/observation stays, and outpatient encounters for WHF and all-cause death within each LVEF category: HF with reduced ejection fraction (EF) (LVEF <40%), HF with mildly reduced EF (LVEF 40%-49%), and HF with preserved EF (LVEF ≥50%). Model discrimination was evaluated using area under the curve and calibration using mean squared error. We identified 338 426 adults with HF: 61 045 (18.0%) had HF with reduced EF, 49 618 (14.7%) had HF with mildly reduced EF, and 227 763 (67.3%) had HF with preserved EF. The 1-year risks of any WHF event and death were, respectively, 22.3% and 13.0% for HF with reduced EF, 17.0% and 10.1% for HF with mildly reduced EF, and 16.3% and 10.3% for HF with preserved EF. The WHF model displayed an area under the curve of 0.76 and mean squared error of 0.13, whereas the model for death displayed an area under the curve of 0.83 and mean squared error of 0.076. Performance and predictors were similar across WHF encounter types and LVEF categories. Conclusions We developed risk prediction models for 1-year WHF events and death across the LVEF spectrum using structured and unstructured electronic health record data and observed no substantial differences in model performance or predictors except for death, despite differences in underlying HF cause.

Impact of Naples Prognostic Score on midterm all-cause mortality in patients with decompensated heart failure

Aim: This work was designed to investigate the relationship between cardiac outcomes and Naples Prognostic Score (NPS) among heart failure (HF) patients. Materials & methods: This retrospective observational study enrolled 298 consecutive individuals hospitalized for New York Heart Association class 3-4 HF. The primary outcome was all-cause mortality. Secondary outcomes were rehospitalization and in-hospital death. Results: The high NPS group had a statistically greater rate of all-cause mortality (p < 0.001). In Cox regression analysis, integrating NPS considerably improved the performance of the full model over the baseline model (adjusted hazard ratio = 2.28; p = 0.004). Based on time-dependent receiver operating characteristic curve analysis, the NPS model outperformed the baseline and CONUT score models in discriminatory power in predicting the probability of survival. Conclusion: NPS was associated with short- and midterm mortality as well as rehospitalization.

Optimal Heart Rate Control Improves Long-Term Prognosis of Decompensated Heart Failure with Reduced Ejection Fraction

Background and Objectives: An elevated heart rate is an independent risk factor for cardiovascular disease; however, the relationship between heart rate control and the long-term outcomes of patients with heart failure with reduced ejection fraction (HFrEF) remains unclear. This study explored the long-term prognostic importance of heart rate control in patients hospitalized with HFrEF. Materials and Methods: We retrieved the records of patients admitted for decompensated heart failure with a left ventricular ejection fraction (LVEF) of ≤40%, from 1 January 2005 to 31 December 2019. The primary outcome was a composite of cardiovascular death or hospitalization for heart failure (HHF) during follow-up. We analyzed the outcomes using Cox proportional hazard ratios calculated using the patients' heart rates, as measured at baseline and approximately 3 months later. The mean follow-up duration was 49.0 ± 38.1 months. Results: We identified 5236 eligible patients, and divided them into five groups on the basis of changes in their heart rates. The mean LVEFs of the groups ranged from 29.1% to 30.6%. After adjustment for all covariates, the results demonstrated that lesser heart rate reductions at the 3-month screening period were associated with long-term cardiovascular death, HHF, and all-cause mortality (p for linear trend = 0.033, 0.042, and 0.003, respectively). The restricted cubic spline model revealed a linear relationship between reduction in heart rate and risk of outcomes (p for nonlinearity > 0.2). Conclusions: Greater reductions in heart rate were associated with a lower risk of long-term cardiovascular death, HHF, and all-cause mortality among patients discharged after hospitalization for decompensated HFrEF.

Effects of exertional dyspnea on early mobilization of patients with acute decompensated heart failure

[Purpose] In this study, we investigated the association between exertional dyspnea and length of the mobilization program in patients with acute decompensated heart failure. [Participants and Methods] We recruited all consecutive patients with heart failure who were hemodynamically stabilized after administration of intravenous medication and were able to walk >10 m before admission. Exertional dyspnea was evaluated using the visual analog scale in all patients after the 10-m walk during each session of the mobilization program. Multiple regression analysis was used to determine the factors associated with length of the mobilization program. [Results] Our study included 52 patients. Multiple regression analysis showed that the length of the mobilization program was significantly associated with the visual analog scale on day 3 and the length before the start of the mobilization program; however, the length of the mobilization program showed no significant association with age and blood urea nitrogen levels. The standardized coefficients for the visual analog scale scores on day 3 and the length before the start of the mobilization program were 0.49 and 0.33, respectively. [Conclusion] Exertional dyspnea is a good predictor of the length of the mobilization program. Our findings highlight the importance of evaluation of exertional dyspnea.

Different Renal Function Patterns in Patients With Acute Heart Failure: Relationship With Outcome and Congestion

Background

The role of worsening renal function during acute heart failure (AHF) hospitalization is still debated. Very few studies have extensively evaluated the renal function (RF) trend during hospitalization by repetitive measurements.

Objectives

To investigate the prognostic relevance of different RF trajectories together with the congestion status in hospitalized patients.

Methods

This is a post hoc analysis of a multi-center study including 467 patients admitted with AHF who were screened for the Diur-AHF Trial. We recognized five main RF trajectories based on serum creatinine and estimated glomerular filtration rate (eGFR) behavior. According to the RF trajectories our sample was divided into 1-stable (S), 2-transient improvement (TI), 3-permanent improvement (PI), 4-transient worsening (TW), and 5-persistent worsening (PW). The primary outcome was the combined endpoint of 180 days including all causes of mortality and re-hospitalization.

Results

We recruited 467 subjects with a mean congestion score of 3.5±1.08 and a median creatinine value of 1.28 (1.00-1.70) mg/dl, eGFR 50 (37-65) ml/min/m2 and NTpro B-type natriuretic peptide (BNP) 7,000 (4,200-11,700) pg/ml. A univariate analysis of the RF pattern demonstrated that TI and PW patterns were significantly related to poor prognosis [HR: 2.71 (1.81-4.05); p < 0.001; HR: 1.68 (1.15-2.45); p = 0.007, respectively]. Conversely, the TW pattern showed a significantly protective effect on outcome [HR:0.34 (0.19-0.60); p < 0.001]. Persistence of congestion and BNP reduction ≥ 30% were significantly related to clinical outcome at univariate analysis [HR: 2.41 (1.81-3.21); p < 0.001 and HR:0.47 (0.35-0.67); p < 0.001]. A multivariable analysis confirmed the independently prognostic role of TI, PW patterns, persistence of congestion, and reduced BNP decrease at discharge.

Conclusions

Various RF patterns during AHF hospitalization are associated with different risk(s). PW and TI appear to be the two trajectories related to worse outcome. Current findings confirm the importance of RF evaluation during and after hospitalization.

Risk prediction model of in-hospital mortality in heart failure with preserved ejection fraction and mid-range ejection fraction: a retrospective cohort study

Aim: To develop and validate internally a multivariate risk model for predicting the in-hospital mortality of patients with heart failure with preserved ejection fraction (HFpEF) and heart failure with mid-range ejection fraction (HFmrEF). Methods & results: The clinical data of 8172 inpatients with HFpEF and HFmrEF was used to establish a retrospective database. These patients, among whom 307 in-hospital deaths (3.8%) occurred, were randomly assigned to derivation and verification cohort. Among the extracted data from the derivation cohort were nine variables significantly related to in-hospital mortality, which were scored 0-4, for a total score of 24, which allowed formation of a risk predictive model. The verification cohort was then used to validate the discrimination and calibration capacities of this predictive model: the area under curve equaled 0.8575 (0.8285, 0.8865) for the derivation cohort, and 0.8323 (0.7999, 0.8646) for the verification cohort. According to this risk score, we divided patients into four risk classes (low-, medium-, high- and extremely high-risk) and revealed that the risk of in-hospital mortality increased with increasing risk class with an obvious linear relationship between actual and predicted mortality (r = 0.998, p < 0.001). Conclusion: The model based on nine common clinical variables should provide an accurate prediction of in-hospital mortality and appears to be a reliable risk classification system for patients with HFpEF and HFmrEF.

Prognostic Implications of Reductions in Heart Rates in Patients With Acute Heart Failure and Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a common arrhythmia in patients with acute heart failure (AHF). Heart rate (HR) also changes significantly over time. However, the association between changes in HR in AF patients and prognosis is uncertain.Methods and Results:We investigated the association between HR reduction in AF achieved within 48 h of admission and 60-day mortality in patients with AHF from the REALITY-AHF study. The percentage HR (%HR) reduction was calculated as (baseline HR-HR at 48 h) / baseline HR × 100. The primary endpoint was 60-day all-cause mortality. In 468 patients with confirmed AF at both admission and 48 h after admission, the median HR at these time points was 105±31 and 84±18 beats/min, respectively. The median %HR reduction was 15.4% (interquartile range 2.2-31.4%). During the 60 days of admission, 39 deaths (8.3%) were recorded, and the %HR reduction within 48 h was significantly associated with 60-day mortality in the unadjusted model (hazard ratio [HR] 0.85; 95% confidence interval [CI] 0.77-0.95; P=0.005) and after adjusting for other covariates (HR 0.81; 95% CI 0.68-0.96; P=0.016).Furthermore, the %HR reduction was associated with a significant reduction in 60-day mortality in patients with higher baseline HR.

CONCLUSIONS

%HR reduction is associated with a better short-term prognosis in patients with AHF presenting with AF, particularly in those with a rapid ventricular response.

Derivation and validation of a mortality risk prediction model using global longitudinal strain in patients with acute heart failure

AIMS

To develop a mortality risk prediction model in patients with acute heart failure (AHF), using left ventricular (LV) function parameters with clinical factors.

METHODS AND RESULTS

In total, 4312 patients admitted for AHF were retrospectively identified from three tertiary centres, and echocardiographic parameters including LV ejection fraction (LV-EF) and LV global longitudinal strain (LV-GLS) were measured in a core laboratory. The full set of risk factors was available in 3248 patients. Using Cox proportional hazards model, we developed a mortality risk prediction model in 1859 patients from two centres (derivation cohort) and validated the model in 1389 patients from one centre (validation cohort). During 32 (interquartile range 13-54) months of follow-up, 1285 patients (39.6%) died. Significant predictors for mortality were age, diabetes, diastolic blood pressure, body mass index, natriuretic peptide, glomerular filtration rate, failure to prescribe beta-blockers, failure to prescribe renin-angiotensin system blockers, and LV-GLS; however, LV-EF was not a significant predictor. Final model including these predictors to estimate individual probabilities of mortality had C-statistics of 0.75 [95% confidence interval (CI) 0.73-0.78; P < 0.001] in the derivation cohort and 0.78 (95% CI 0.75-0.80; P < 0.001) in the validation cohort. The prediction model had good performance in both heart failure (HF) with reduced EF, HF with mid-range EF, and HF with preserved EF.

CONCLUSION

We developed a mortality risk prediction model for patients with AHF incorporating LV-GLS as the LV function parameter, and other clinical factors. Our model provides an accurate prediction of mortality and may provide reliable risk stratification in AHF patients.

Clinical Outcome Predictions for the VerICiguaT Global Study in Subjects With Heart Failure With Reduced Ejection Fraction (VICTORIA) Trial

BACKGROUND

The prediction of outcomes in patients with heart failure (HF) may inform prognosis, clinical decisions regarding treatment selection, and new trial planning. The VerICiguaT Global Study in Subjects With Heart Failure With Reduced Ejection Fraction included high-risk patients with HF with reduced ejection fraction and a recent worsening HF event. The study participants had a high event rate despite the use of contemporary guideline-based therapies. To provide generalizable predictive data for a broad population with a recent worsening HF event, we focused on risk prognostication in the placebo group.

METHODS AND RESULTS

Data from 2524 participants randomized to placebo with chronic HF (New York Heart Association functional class II-IV) and an ejection fraction of less than 45% were studied and backward variable selection was used to create Cox proportional hazards models for clinical end points, selecting from 66 candidate predictors. Final model results were produced, accounting for missing data, and nonlinearities. Optimism-corrected c-indices were calculated using 200 bootstrap samples. Over a median follow-up of 10.4 months, the primary outcome of HF hospitalization or cardiovascular death occurred in 972 patients (38.5%). Independent predictors of increased risk for the primary end point included HF characteristics (longer HF duration and worse New York Heart Association functional class), medical history (prior myocardial infarction), and laboratory values (higher N-terminal pro-hormone B-type natriuretic peptide, bilirubin, urate; lower chloride and albumin). Optimism-corrected c-indices were 0.68 for the HF hospitalization/cardiovascular death model, 0.68 for HF hospitalization/all-cause death, 0.72 for cardiovascular death, and 0.73 for all-cause death.

CONCLUSIONS

Predictive models developed in a large diverse clinical trial with comprehensive clinical and laboratory baseline data-including novel measures-performed well in high-risk patients with HF who were receiving excellent guideline-based clinical care.

CLINICAL TRIAL REGISTRATION

Clinicaltrials.gov identifier, NCT02861534.Lay Summary: Patients with heart failure may benefit from tools that help clinicians to better understand a patient's risk for future events like hospitalization. Relatively few risk models have been created after the worsening of heart failure in a contemporary cohort. We provide insights on the risk factors for clinical events from a recent, large, global trial of patients with worsening heart failure to help clinicians better understand and communicate prognosis and select treatment options.

Pathophysiology and Therapeutic Approaches to Acute Decompensated Heart Failure

Acute decompensated heart failure (ADHF) is one of the leading admission diagnoses worldwide, yet it is an entity with incompletely understood pathophysiology and limited therapeutic options. Patients admitted for ADHF have high in-hospital morbidity and mortality, as well as frequent rehospitalizations and subsequent cardiovascular death. This devastating clinical course is partly due to suboptimal medical management of ADHF with persistent congestion upon hospital discharge and inadequate predischarge initiation of life-saving guideline-directed therapies. While new drugs for the treatment of chronic HF continue to be approved, there has been no new therapy approved for ADHF in decades. This review will focus on the current limited understanding of ADHF pathophysiology, possible therapeutic targets, and current limitations in expanding available therapies in light of the unmet need among these high-risk patients.

Prognostic role of albumin level in heart failure: A systematic review and meta-analysis

BACKGROUND

Hypoalbuminemia (HA) is common in HF, however, its pathophysiology and clinical implications are poorly understood. While multiple studies have been published in the past decade investigating the role of serum albumin in HF, there is still no consensus on the prognostic value of this widely available measure. The objective of this study is to assess the prognostic role of albumin in heart failure (HF) patient.

METHODS

Unrestricted searches of MEDLINE, EMBASE, Cochrane databases were performed. The results were screened for relevance and eligibility criteria. Relevant data were extracted and analyzed using Comprehensive Meta-Analysis software. The Begg and Mazumdar rank correlation test was utilized to evaluate for publication bias.

RESULTS

A total of 48 studies examining 44,048 patients with HF were analyzed. HA was found in 32% (95% confidence interval [CI] 28.4%-37.4%) HF patients with marked heterogeneity (I2 = 98%). In 10 studies evaluating acute HF, in-hospital mortality was almost 4 times more likely in HA with an odds ratios (OR) of 3.77 (95% CI 1.96-7.23). HA was also associated with a significant increase in long-term mortality (OR: 1.5; 95% CI: 1.36-1.64) especially at 1-year post-discharge (OR: 2.44; 95% CI: 2.05-2.91; I2 = 11%). Pooled area under the curve (AUC 0.73; 95% CI 0.67-0.78) was comparable to serum brain natriuretic peptide (BNP) in predicting mortality in HF patients.

CONCLUSION

Our results suggest that HA is associated with significantly higher in-hospital mortality as well as long-term mortality with a predictive accuracy comparable to that reported for serum BNP. These findings suggest that serum albumin may be useful in determining high-risk patients.

Designing an Elderly Hospital Admission Risk Prediction Model in Iran's Hospitals

Background:

The identification of elderly at risk of new functional disabilities in activities of daily living at admission to the hospital may facilitate referral for purposive interventions to prevent decline and institutionalization. This study was aimed at designing a risk prediction model for identifying the elderly at risk of admission in Iran's hospitals.

Materials and Methods:

This is a cross-sectional descriptive study conducted in 2017. In order to formulate and validate a prediction model, the study was done in two development and validation cohort study. Functional decline was defined as a decline of at least one point on the Katz ADL index at follow-up compared with preadmission status.

Results:

In development cohort, the mean age was 71 years including 54% of men and 46% women, 22% of men and 17% of women experienced functional decline after 3 months. In the validation cohort, the mean age was 70 years, including 49% of men and 51% women, 19% of men and 15% of women, functional decline after 3 months was observed.

Conclusion:

On the basis of the findings, aging at risk of hospital admission can be identified by easy designed model with four questions: (1) Is the patient's age more than 85 years? (2) Does the patient's mini mental status <22? (3) Does the patient need help for using general transporting? (4) Has the patient lost weight <5% over the past 6 months and body mass index <18.5? And also geriatrics experts can use the designed model as a predictive tool in order to improve the quality level of healthcare services to elderly as a vulnerable and high risk group. The important point of model is easy to use even for nonspecialists.

Acute Hemodynamic Index Predicts In-Hospital Mortality in Acute Decompensated Heart Failure

Fundamento

O exame físico permite a avaliação prognóstica de pacientes com insuficiência cardíaca (IC) descompensada, porém não é suficientemente confiável e depende da experiência clínica do profissional. Considerando as respostas hemodinâmicas a situações do tipo “luta ou fuga” tais como a admissão no serviço de emergência, foi proposto o índice hemodinâmico agudo (IHA), calculado a partir da frequência cardíaca e pressão de pulso.

Objetivo

avaliar a capacidade prognóstica intra-hospitalar do IHA na IC descompensada.

Métodos

estudo prospectivo, multicêntrico e observacional baseado no registro BREATHE, incluindo dados de hospitais públicos e privados no Brasil. Foram utilizadas análises ROC ( Receiver Operating Characteristic ), de estatística c e de regressão multivariada, assim como o critério de informação de Akaike, para testar a capacidade prognóstica do IHA. O valor-p < 0,05 foi considerado estatisticamente significativo.

Resultados

Foram analisados dados de 463 pacientes com IC com fração de ejeção reduzida a partir do registro BREATHE. A mortalidade intra-hospitalar foi de 9%. A mediana do IHA foi considerada o valor de corte (4 mmHg⋅bpm). Um baixo IHA (≤ 4 mmHg⋅bpm) foi encontrado em 80% dos pacientes falecidos. O risco de mortalidade intra-hospitalar em pacientes com baixo IHA foi 2,5 vezes maior que aquele para pacientes com IHA > 4 mmHg⋅bpm. O IHA foi capaz de predizer independentemente a mortalidade intra-hospitalar na IC aguda descompensada [sensibilidade: 0,786; especificidade: 0,429; AUC (área sob a curva): 0,607 (0,540-0,674), p = 0,010] mesmo depois dos ajustes para comorbidades e uso de medicamentos [razão de chances (RC): 0,061 (0,007-0,114), p = 0,025].

Conclusões

O IHA é capaz de predizer independentemente a mortalidade intra-hospitalar na IC aguda descompensada. Esse índice simples e realizado à beira do leito pode se mostrar útil em serviços de emergência. (Arq Bras Cardiol. 2021; 116(1):77-86)

Heart rate at discharge in patients with acute decompensated heart failure is a predictor of mortality

AIMS

Heart failure is a syndrome with increasing prevalence in concordance with the aging population and better survival rates from myocardial infarction. Morbidity and mortality are high in chronic heart failure patients, particularly in those with hospital admission for acute decompensation. Several risk stratification tools and score systems have been established to predict mortality in chronic heart failure patients. However, identification of patients at risk with easy obtainable clinical factors that can predict mortality in acute decompensated heart failure (ADHF) are needed to optimize the care-path.

METHODS AND RESULTS

We retrospectively analyzed electronic medical records of 78 patients with HFrEF and HFmrEF who were hospitalized with ADHF in the Heart Center of the University Hospital Cologne in the year 2011 and discharged from the ward after successful treatment. 37.6 ± 16.4 months after index hospitalization 30 (38.5%) patients had died. This mortality rate correlated well with the calculated predicted survival with the Seattle Heart Failure Model (SHFM) for each individual patient. In our cohort, we identified elevated heart rate at discharge as an independent predictor for mortality (p = 0.016). The mean heart rate at discharge was lower in survived patients compared to patients who died (72.5 ± 11.9 vs. 79.1 ± 11.2 bpm. Heart rate of 77 bpm or higher was associated with an almost doubled mortality risk (p = 0.015). Heart rate elevation of 5 bpm was associated with an increase of mortality of 25% (p = 0.022).

CONCLUSIONS

Patients hospitalized for ADHF seem to have a better prognosis, when heart rate at discharge is < 77 bpm. Heart rate at discharge is an easily obtainable biomarker for risk prediction of mortality in HFrEF and HFmrEF patients treated for acute cardiac decompensation. Taking into account this parameter could be useful for guiding treatment strategies in these high-risk patients. Prospective data for validation of this biomarker and specific intervention are needed.

Association of Heart Rate Trajectory Patterns with the Risk of Adverse Outcomes for Acute Heart Failure in a Heart Failure Cohort in Taiwan

BACKGROUND

Heart rate trajectory with multiple heart rate measurements is considered to be a more sensitive predictor of outcomes than single heart rate measurements. The association of heart rate trajectory patterns with acute heart failure outcomes has not been well studied. We examined the association of heart rate trajectory patterns with post-discharge outcomes.

METHODS

This prospective cohort study was based on an acute heart failure registry in Taiwan. A total of 1509 patients were enrolled in the Taiwan Society of Cardiology - Heart Failure with Reduced Ejection Fraction Registry from May 2013 to October 2015. The outcomes were post-discharge all-cause mortality and heart failure re-admission.

RESULTS

Two heart trajectory patterns were identified in group-based trajectory analysis. One started with a higher heart rate and had an increasing trend over 6 months then a subsequent decline (high-increasing-decreasing group; n = 352; 23.9%). The other started with a lower heart rate and had a relatively stable pattern (low-stable group; n = 1121; 76.1%). Compared with those in the low-stable group, patients in the high-increasing-decreasing group had a higher risk of events (all-cause mortality: hazard ratio 3.10 and 95% confidence interval 1.24-7.77; heart failure re-admission: hazard ratio 1.13 and 95% confidence interval 0.55-2.32).

CONCLUSIONS

Patients with a high-increasing-decreasing heart rate trajectory pattern had a higher risk of all-cause mortality than those with a low-stable pattern.

Heart rate as an independent predictor of long term mortality of acute heart failure patients in sinus rhythm according to their ejection fraction: data from the AHEAD registry

BACKGROUND

Heart rate (HR) at admission in patients with acute heart failure (AHF) has been shown to be an important risk marker of in-hospital mortality. However, its relation with mid and long-term prognosis as well as the impact of Ejection Fraction (EF) is unknown. Our objective was to study the relationship between long-term survival and HR at admission depending on EF in a cohort of patients hospitalized for AHF.

METHODS

We analyzed the data of 2335 patients in sinus rhythm hospitalized for AHF from AHEAD registry. Patients with cardiogenic shock and AHF from surgical or non-cardiac etiology were excluded.

RESULTS

Survival rates at 6 and 12 months were 84.8% and 78% respectively. Increased age, decreased diastolic BP, lack of PCI during hospitalization, increased creatinine level and increased HR (with different cut-offs according to EF categories) were found as predictors whatever the EF at 6 and 12 months. Optimal prognostic cut-offs of heart rate were identified for Heart Failure with reduced EF at 100 bpm, for Heart Failure with mid-range EF at 90 bpm and for Heart Failure with preserved EF at 80 bpm for both 6 and 12 months.

CONCLUSION

Our study suggests that HR at admission appears to be an independent prognostic parameter in AHF patients in sinus rhythm irrespective of EF and can be used to classify patients according to the severity of the disease.

One-Year Mortality Risk Stratification in Patients Hospitalized for Acute Decompensated Heart Failure: Construction of TSOC-HFrEF Risk Scoring Model

Background

Most previous risk prediction models in patients hospitalized for heart failure (HF) are derived from populations in Western countries, and it is unclear whether these models are applicable to Asian populations. This study aimed to construct a risk score system for predicting one-year mortality risk in Asian patients and to compare the applicability of this risk score system with the 3C-HF score system.

Methods

We used the population in the Taiwan Society of Cardiology-Heart Failure with Reduced Ejection Fraction (TSOC-HFrEF) registry, which is a prospective cohort of patients admitted for acute decompensated heart failure (ADHF) in Taiwan. The risk score system was constructed using multivariate Cox-model derived coefficients. A bootstrapping procedure was also used for bias-corrected evaluations. Comparisons between this constructed model and the 3C-HF score prediction model were evaluated using calibration plots and area under curve (AUC)/receiver operating characteristic (ROC) curve.

Results

Patients with complete data (n = 1127) in the TSOC-HFrEF registry were analyzed. During one year of follow-up, 14.5% (n = 163) of the patients died. A risk score system was constructed with the following predictors: body mass index, diastolic blood pressure, dyslipidemia, diabetes, aortic regurgitation, QRS duration, hemoglobin concentration, and digoxin usage. Compared to the 3C-HF score system, this risk score system had a similar discriminatory ability (AUC/ROC values of 0.675 and 0.636, p = 0.127) and both were well-calibrated in the Taiwan population.

Conclusions

The proposed risk score system for predicting one-year all-cause mortality in Taiwanese patients with ADHF may facilitate risk stratification in Asian patients with HF.

Risk assessment of post-discharge mortality among recently hospitalized Medicare heart failure patients with reduced or preserved ejection fraction

Objective: Targeted care management for hospitalized patients with acute decompensated heart failure (ADHF) with reduced or preserved ejection fraction (HFrEF/HFpEF) who are at higher risk for post-discharge mortality may mitigate this outcome. However, identification of the most appropriate population for intervention has been challenging. This study developed predictive models to assess risk of 30 day and 1 year post-discharge all-cause mortality among Medicare patients with HFrEF or HFpEF recently hospitalized with ADHF.Methods: A retrospective study was conducted using the 100% Centers for Medicare Services fee-for-service sample with complementary Part D files. Eligible patients had an ADHF-related hospitalization and ICD-9-CM diagnosis code for systolic or diastolic heart failure between 1 January 2010 and 31 December 2014. Data partitioned into training (60%), validation (20%) and test sets (20%) were used to evaluate the three model approaches: classification and regression tree, full logistic regression, and stepwise logistic regression. Performance across models was assessed by comparing the receiver operating characteristic (ROC), cumulative lift, misclassification rate, the number of input variables and the order of selection/variable importance.Results: In the HFrEF (N = 83,000) and HFpEF (N = 123,644) cohorts, 30 day all-cause mortality rates were 6.6% and 5.5%, respectively, and 1 year all-cause mortality rates were 33.6% and 29.5%. The stepwise logistic regression models performed best across both cohorts, having good discrimination (test set ROC of 0.75 for both 30 day mortality models and 0.74 for both 1 year mortality models) and the lowest number of input variables (18-34 variables).Conclusions: Post-discharge mortality risk models for recently hospitalized Medicare patients with HFrEF or HFpEF were developed and found to have good predictive ability with ROCs of greater than or equal to 0.74 and a reasonable number of input variables. Applying this risk model may help providers and health systems identify hospitalized Medicare patients with HFrEF or HFpEF who may benefit from more targeted care management.

Prognostic value of discharge heart rate in acute heart failure patients: More relevant in atrial fibrillation?

AIMS

The prognostic impact of heart rate (HR) in acute heart failure (AHF) patients is not well known especially in atrial fibrillation (AF) patients. The aim of the study was to evaluate the impact of admission HR, discharge HR, HR difference (admission-discharge) in AHF patients with sinus rhythm (SR) or AF on long- term outcomes.

METHODS

We included 1398 patients consecutively admitted with AHF between October 2013 and December 2014 from a national multicentre, prospective registry. Logistic regression models were used to estimate the association between admission HR, discharge HR and HR difference and one- year all-cause mortality and HF readmission.

RESULTS

The mean age of the study population was 72 ± 12 years. Of these, 594 (42.4%) were female, 655 (77.8%) were hypertensive and 655 (46.8%) had diabetes. Among all included patients, 745 (53.2%) had sinus rhythm and 653 (46.7%) had atrial fibrillation. Only discharge HR was associated with one year all-cause mortality (Relative risk (RR) = 1.182, confidence interval (CI) 95% 1.024-1.366, p = 0.022) in SR. In AF patients discharge HR was associated with one year all cause mortality (RR = 1.276, CI 95% 1.115-1.459, p ≤ 0.001). We did not observe a prognostic effect of admission HR or HRD on long-term outcomes in both groups. This relationship is not dependent on left ventricular ejection fraction.

CONCLUSIONS

In AHF patients lower discharge HR, neither the admission nor the difference, is associated with better long-term outcomes especially in AF patients.

Brachial pulse pressure in acute heart failure. Results of the Heart Failure Registry

Aims

To investigate the still uncertain independent prognostic impact of pulse pressure (PP) in acute heart failure (HF), in particular across the left ventricular ejection fraction (EF) phenotypes, and the potential contribution of PP in outlining the individual phenotypes.

Methods and results

We prospectively evaluated 1‐year death and rehospitalization in 4314 patients admitted for acute HF grouped by EF and stratified by their PP level on admission. In HF with reduced (< 40%) EF (HFrEF), the highest quartiles of PP had the lowest unadjusted [hazard ratio (HR) 0.77, 95% confidence interval (CI) 0.61–0.98] and adjusted (HR 0.64 0.50–0.82) risk of 1 year all cause death compared to the lowest quartile. Its prognostic impact was partially mediated by systolic blood pressure (SBP). In HF with preserved (≥ 50%) EF (HFpEF), the intermediate quartile of PP showed the lowest 1 year all cause mortality in unadjusted (HR 0.598, CI 0.416–0.858) and adjusted (HR 0.55, 95% CI 0.388‐0.801) models with no relationship with SBP. In a receiver operating characteristic analysis, a combination of PP > 60 mmHg and SBP > 140 mmHg was associated to a preserved EF with a high performance value. No prognostic significance of PP was found in the HF with mid‐range EF subgroup.

Conclusions

In acute HFrEF, there is an almost linear inverse relation between mortality and PP, partly mediated by SBP. In HFpEF, a J‐shaped relationship between mortality and PP was present with a better prognosis at the nadir. A combination of PP > 60 mmHg with SBP > 140 mmHg may be clinically helpful as marker of a preserved left ventricular EF.

Effect of Once-Weekly Exenatide on Clinical Outcomes According to Baseline Risk in Patients With Type 2 Diabetes Mellitus: Insights From the EXSCEL Trial

Background

In the EXSCEL (Exenatide Study of Cardiovascular Event Lowering), exenatide once‐weekly resulted in a nonsignificant reduction in major adverse cardiovascular events (MACEs) and a nominal 14% reduction in all‐cause mortality in 14 752 patients with type 2 diabetes mellitus (T2DM) with and without cardiovascular disease. Whether patients at increased risk for events experienced a comparatively greater treatment benefit with exenatide is unknown.

Methods and Results

In the EXSCEL population, we created risk scores for MACEs and all‐cause mortality using step‐wise selection of baseline characteristics. A risk score was calculated for each patient, and a time‐to‐event model for each end point was developed including the risk score, treatment assignment, and risk‐treatment interaction. Interaction P values evaluating for a differential treatment effect by baseline risk were reported. Over a median follow‐up of 3.2 years (interquartile range, 2.2, 4.4), 1091 (7.4%) patients died and 1744 (11.8%) experienced a MACE. Independent predictors of MACEs and all‐cause mortality included age, sex, comorbidities (eg, previous cardiovascular event), body mass index, blood pressure, hemoglobin A1c, and estimated glomerular filtration rate. The all‐cause mortality and MACE risk models had modest discrimination with optimism‐corrected c‐indices of 0.73 and 0.71, respectively. No interaction was observed between treatment effect and risk profile for either end point (both interactions, P>0.1).

Conclusions

Baseline characteristics (eg, age, previous cardiovascular events) and routine laboratory values (eg, hemoglobin A1c, estimated glomerular filtration rate) provided modest prognostic value for mortality and MACEs in a broad population of patients with type 2 diabetes mellitus. Exenatide's effects on mortality and MACEs were consistent across the spectrum of baseline risk.

Clinical Trial Registration

URL: https://www.clinicaltrials.gov. Unique identifier: NCT01144338.

Therapeutic Advances in the Management of Acute Decompensated Heart Failure

BACKGROUND

Acute decompensated heart failure (ADHF) is the most common presenting phenotype of acute heart failure (AHF). The main goal of this article was to review the contemporary management strategies in these patients and to describe how future clinical trials may address unmet clinical needs.

AREAS OF UNCERTAINTY

The current pathophysiologic understanding of AHF is incomplete. The guideline recommendations for the management of ADHF are based only on algorithms provided by expert consensus guided by blood pressure and/or clinical signs of congestion or hypoperfusion. The lack of adequately conducted trials to address the unmet need for evidence therapy in AHF has not yet been surpassed, and at this time, there is no evidence-based strategy for targeted decongestive therapy to improve outcomes. The precise time point for initiation of guideline-directed medical therapies (GDMTs), as respect to moment of decompensation, is also unknown.

DATA SOURCES

The available data informing current management of patients with ADHF are based on randomized controlled trials, observational studies, and administrative databases.

THERAPEUTIC ADVANCES

A major step-forward in the management of ADHF patients is recognizing congestion, either clinical or hemodynamic, as a major trigger for heart failure (HF) hospitalization and most important target for therapy. However, a strategy based exclusively on congestion is not sufficient, and at present, comprehensive assessment during hospitalization of cardiac and noncardiovascular substrate with identification of potential therapeutic targets represents "the corner-stone" of ADHF management. In the last years, substantial data have emerged to support the continuation of GDMTs during hospitalization for HF decompensation. Recently, several clinical trials raised hypothesis of "moving to the left" concept that argues for very early implementation of GDMTs as potential strategy to improve outcomes.

CONCLUSIONS

The management of ADHF is still based on expert consensus documents. Further research is required to identify novel therapeutic targets, to establish the precise time point to initiate GDMTs, and to identify patients at risk of recurrent hospitalization.

Can We Do More With Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions

Hospital readmission due to heart failure is a topic of concern for patients and hospitals alike: it is both the most frequent and expensive diagnosis for hospitalization. Therefore, accurate prediction of readmission risk while patients are still in the hospital helps to guide appropriate postdischarge interventions. As our understanding of the disease and the volume of electronic health record data both increase, the number of predictors and model-building time for predicting risk grow rapidly. This suggests a need to use methods for reducing the number of predictors without losing predictive performance. We explored and described three such methods and demonstrated their use by applying them to a real-world dataset consisting of 57 variables from health data of 1210 patients from one hospital system. We compared all models generated from predictor reduction methods against the full, 57-predictor model for predicting risk of 30-day readmissions for patients with heart failure. Our predictive performance, measured by the C-statistic, ranged from 0.630 to 0.840, while model-building time ranged from 10 minutes to 10 hours. Our final model achieved a C-statistic (0.832) comparable to the full model (0.840) in the validation cohort while using only 16 predictors and providing a 66-fold improvement in model-building time.

Clinical factors related to morbidity and mortality in high-risk heart failure patients: the GUIDE-IT predictive model and risk score

BACKGROUND

Most heart failure (HF) risk scores have been derived from cohorts of stable HF patients and may not incorporate up to date treatment regimens or deep phenotype characterization that change baseline risk over the short- and long-term follow-up period. We undertook the current analysis of participants in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment) trial to address these limitations.

METHODS AND RESULTS

The GUIDE-IT study randomized 894 high-risk patients with HF and reduced ejection fraction (≤ 40%) to biomarker-guided treatment strategy vs. usual care. We performed risk modelling using Cox proportional hazards models and analysed the relationship between 35 baseline clinical factors and the primary composite endpoint of cardiovascular (CV) death or HF hospitalization, the secondary endpoint of all-cause mortality, and the exploratory endpoint of 90-day HF hospitalization or death. Prognostic relationships for continuous variables were examined and key predictors were identified using a backward variable selection process. Predictive models and risk scores were developed. Over a median follow-up of 15 months, the cumulative number of HF hospitalizations and CV deaths was 328 out of 894 patients (Kaplan-Meier event rate 34.5% at 12 months). Frequency of all-cause deaths was 143 out of 894 patients (Kaplan-Meier event rate 12.2% at 12 months). Outcomes for the primary and secondary endpoints between strategy arms of the study were similar. The most important predictor that was present in all three models was the baseline natriuretic peptide level. Hispanic ethnicity, low sodium and high heart rate were present in two of the three models. Other important predictors included the presence or absence of a device, New York Heart Association class, HF duration, black race, co-morbidities (sleep apnoea, elevated creatinine, ischaemic heart disease), low blood pressure, and a high congestion score.

CONCLUSION

Risk models using readily available clinical information are able to accurately predict short- and long-term CV events and may be useful in optimizing care and enriching patients for clinical trials.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov ID number NCT01685840.

Prediction of Survival in Asian Patients Hospitalized With Heart Failure: Validation of the OPTIMIZE-HF Risk Score

BACKGROUND

Risk scores predicting in-patient mortality in heart failure patients have not been designed specifically for Asian patients. We aimed to validate and recalibrate the OPTIMIZE-HF risk model for in-hospital mortality in a multiethnic Asian population hospitalized for heart failure.

METHODS AND RESULTS

Data from the Singapore Cardiac Databank Heart Failure on patients admitted for heart failure from January 1, 2008, to December 31, 2013, were included. The primary outcome studied was in-hospital mortality. Two models were compared: the original OPTIMIZE-HF risk model and a modified OPTIMIZE-HF risk model (similar variables but with coefficients derived from our cohort). A total of 15,219 patients were included. The overall in-hospital mortality was 1.88% (n = 286). The original model had a C-statistic of 0.739 (95% CI 0.708-0.770) with a good match between predicted and observed mortality rates (Hosmer-Lemeshow statistic 13.8; P = .086). The modified model had a C-statistic of 0.741 (95% CI 0.709-0.773) but a significant difference between predicted and observed mortality rates (Hosmer-Lemeshow statistic 17.2; P = .029). The modified model tended to underestimate risk at the extremes (lowest and highest ends) of risk.

CONCLUSIONS

We provide the first independent validation of the OPTIMIZE-HF risk score in an Asian population. This risk model has been shown to perform reliably in our Asian cohort and will potentially provide clinicians with a useful tool to identify high-risk heart failure patients for more intensive management.

Economic Impact of Chronic Heart Failure Management in Today's Cost-Conscious Environment

"Heart failure is a global pandemic that is becoming an increasingly common diagnosis due to aging of the population and increased longevity. Understanding the scope and costs of current heart failure management will lead to improved health economic decision making. Interventions to reduce spending in heart failure care have been centered on reduction of readmissions, improvement in transitions of care, and innovations in technology that have further improved quality of life. Technological advancements in outpatient monitoring offers the hope of further reducing morbidity, mortality, and cost in heart failure."

Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review

Aims:

Readmission rates for patients with heart failure have consistently remained high over the past two decades. As more electronic data, computing power, and newer statistical techniques become available, data-driven care could be achieved by creating predictive models for adverse outcomes such as readmissions. We therefore aimed to review models for predicting risk of readmission for patients admitted for heart failure. We also aimed to analyze and possibly group the predictors used across the models.

Methods:

Major electronic databases were searched to identify studies that examined correlation between readmission for heart failure and risk factors using multivariate models. We rigorously followed the review process using PRISMA methodology and other established criteria for quality assessment of the studies.

Results:

We did a detailed review of 334 papers and found 25 multivariate predictive models built using data from either health system or trials. A majority of models was built using multiple logistic regression followed by Cox proportional hazards regression. Some newer studies ventured into non-parametric and machine learning methods. Overall predictive accuracy with C-statistics ranged from 0.59 to 0.84. We examined significant predictors across the studies using clinical, administrative, and psychosocial groups.

Conclusions:

Complex disease management and correspondingly increasing costs for heart failure are driving innovations in building risk prediction models for readmission. Large volumes of diverse electronic data and new statistical methods have improved the predictive power of the models over the past two decades. More work is needed for calibration, external validation, and deployment of such models for clinical use.

An integrative review of the literature on in-hospital worsening heart failure

A subset of patients hospitalized for acute exacerbation of chronic heart failure develop in-hospital worsening heart failure. The objective of this paper is to present an integrative review of in-hospital worsening heart failure, including definitions, incidence, prevalence, mechanisms, treatments, outcomes, and early identification by providers. A search of electronic databases was conducted from January 2000-August 2017 using multiple search terms. Papers were reviewed for relevance; retained papers were abstracted and data were reported in a narrative synthesis. Twenty papers were selected. Many papers were observational data from in-hospital events that occurred during research trials. There was great variability in in-hospital worsening heart failure definition, incidence, prevalence, and treatments offered. Despite rescue therapies, in-hospital worsening heart failure was associated with increased risk for longer hospital stays, higher readmission rates, and death. To date, there are no therapies that target the underlying mechanisms or minimize its occurrence.

Validation of U.S. mortality prediction models for hospitalized heart failure in the United Kingdom and Japan

AIMS

Prognostic models for hospitalized heart failure (HHF) were developed predominantly for patients of European origin in the United States of America; it is unclear whether they perform similarly in other health care systems or for different ethnicities. We sought to validate published prediction models for HHF in the United Kingdom (UK) and Japan.

METHODS AND RESULTS

Patients in the UK (n =894) and Japan (n =3158) were prospectively enrolled and were similar in terms of sex (∼60% men) and median age (∼77 years). Models predicted that British patients would have a higher mortality than Japanese, which was indeed true both for in-hospital (4.8% vs. 2.5%) and 180-day (20.7% vs. 9.5%) mortality. The model c-statistics for the published/derivation (range 0.70-0.76) and Japanese (range 0.75-0.77) cohorts were similar and higher than for the UK (0.62-0.75) but models consistently overestimated mortality in Japan. For in-hospital mortality, the OPTIMIZE-HF model performed best, providing similar discrimination in published/derivation, UK and Japanese cohorts [c-indices: 0.75 (0.74-0.77); 0.75 (0.68-0.81); and 0.77 (0.70-0.83), respectively], and least overestimated mortality in Japan. For 180-day mortality, the c-statistics for the ASCEND-HF model were similar in published/derivation (0.70) and UK [0.69 (0.64-0.74)] cohorts but higher in Japan [0.75 (0.71-0.79)]; calibration was good in the UK but again overestimated mortality in Japan.

CONCLUSION

Calibration of published prediction models appears moderately accurate and unbiased when applied to British patients but consistently overestimates mortality in Japan. Identifying the reason why patients in Japan have a better than predicted prognosis is of great interest.

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables

BACKGROUND

Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making.

METHODS

We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster analysis of 77 variables, including age, sex, HF etiology, comorbidities, physical findings, laboratory data, electrocardiogram, echocardiogram and treatment during hospitalization. Cox proportional hazards regression analysis was performed to estimate the association between the clusters and clinical outcomes.

RESULTS

Three clusters were identified. Cluster 1 (n=108) represented "vascular failure". This cluster had the highest average systolic blood pressure at admission and lung congestion with type 2 respiratory failure. Cluster 2 (n=89) represented "cardiac and renal failure". They had the lowest ejection fraction (EF) and worst renal function. Cluster 3 (n=148) comprised mostly older patients and had the highest prevalence of atrial fibrillation and preserved EF. Death or HF hospitalization within 12-month occurred in 23% of Cluster 1, 36% of Cluster 2 and 36% of Cluster 3 (p=0.034). Compared with Cluster 1, risk of death or HF hospitalization was 1.74 (95% CI, 1.03-2.95, p=0.037) for Cluster 2 and 1.82 (95% CI, 1.13-2.93, p=0.014) for Cluster 3.

CONCLUSIONS

Cluster analysis may be effective in producing clinically relevant categories of AHF, and may suggest underlying pathophysiology and potential utility in predicting clinical outcomes.

Prevalence of P wave dispersion and interatrial block in patients with systolic heart failure and their relationship with functional status, hospitalization and one year mortality

Background and objectives

P-wave dispersion (PWD) and Interatrial block (IAB) are common in heart failure (HF), and could be associated with adverse cardiac events. We aimed to assess the prevalence of PWD and IAB and their relationship with functional status, hospitalization and mortality rate in patients with systolic HF.

Methods

We enrolled 110 HF patients in sinus rhythm & LVEF <50%. Patients had undergone clinical evaluation, 6 min walking test (6MWT), 12-lead electrocardiography (ECG), 24-h Holter ECG & echocardiogram. Hospitalization and mortality rate were followed-up for one year. PWD was defined as the difference between maximum & minimum P-wave duration >40 ms. IAB was defined as maximum P duration >110 ms. Measurements were done by 2 blinded investigators using a caliber, a ruler and a magnifying lens.

Results

Mean age was 58.9 ± 9.7 years and 67.3% were males. Prevalence of PWD and IAB was 68.2% and 57.3%, respectively. Patients with PWD showed these features: 84% in NYHA class III or IV HF, 77.4% had LVEF <35%, 78.7% had paroxysmal Atrial Fibrillation (AF) and 89.4% couldn’t complete >200 m (m) in 6MWT. Patients with PWD had more hospitalizations (72% vs 28.6%, P value <0.02) and higher 1-year mortality rate (20% vs 8.6%, P value <0.04) than patients without PWD. Likewise, patients with IAB had nearly similar clinical features, hospitalization and mortality as patients with PWD.

Conclusion

PWD and IAB are prevalent in patients with systolic HF and they are significantly associated with low LVEF, paroxysmal AF, poor functional capacity, hospitalization and mortality rate.

Aetiology, timing and clinical predictors of early vs. late readmission following index hospitalization for acute heart failure: insights from ASCEND-HF

AIMS

Patients hospitalized for heart failure (HF) are at high risk for 30-day readmission. This study sought to examine the timings and causes of readmission within 30 days of an HF hospitalization.

METHODS AND RESULTS

Timing and cause of readmission in the ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide and Decompensated Heart Failure) trial were assessed. Early and late readmissions were defined as admissions occurring within 0-7 days and 8-30 days post-discharge, respectively. Patients who died in hospital or remained hospitalized at day 30 post-randomization were excluded. Patients were compared by timing and cause of readmission. Logistic and Cox proportional hazards regression analyses were used to identify independent risk factors for early vs. late readmission and associations with 180-day outcomes. Of the 6584 patients (92%) in the ASCEND-HF population included in this analysis, 751 patients (11%) were readmitted within 30 days for any cause. Overall, 54% of readmissions were for non-HF causes. The median time to rehospitalization was 11 days (interquartile range: 6-18 days) and 33% of rehospitalizations occurred by day 7. Rehospitalization within 30 days was independently associated with increased risk for 180-day all-cause death [hazard ratio (HR) 2.38, 95% confidence interval (CI) 1.93-2.94; P < 0.001]. Risk for 180-day all-cause death did not differ according to early vs. late readmission (HR 0.99, 95% CI 0.67-1.45; P = 0.94).

CONCLUSIONS

In this hospitalized HF trial population, a significant majority of 30-day readmissions were for non-HF causes and one-third of readmissions occurred in the first 7 days. Early and late readmissions within the 30-day timeframe were associated with similarly increased risk for death. Continued efforts to optimize multidisciplinary transitional care are warranted to improve rates of early readmission.

Negotiating renal dysfunction when treating patients with heart failure

INTRODUCTION

Chronic kidney disease (CKD) is one of the most prevalent comorbidities in HF, and no specific treatment is still available for the so-called cardiorenal syndrome. Areas covered: The aim of this review is to describe the interaction of heart and kidney function and the consequences of cardiorenal syndrome, focusing on the use of available therapeutics. Expert commentary: The presence of CKD has been associated with adverse outcomes in HF regardless of ejection fraction. On the other hand, cardiovascular events are the most common causes of morbidity and mortality among CKD patients, reflecting the close pathophysiological crosstalk between these organs. Multiple mechanisms are involved in the development of cardiorenal syndrome, including hemodynamic, neurohormonal and inflammatory mediators. The management of several HF drugs is a challenge in the presence of CKD mainly due to blunted diuretic response and increased risk of worsening of kidney function. Therefore, finding a balance between the optimization of cardiac and renal outcomes is a real negotiation in the everyday clinical practice.

Creation and validation of the acute heart failure risk score: AHFRS

Our aims were to create and validate a clinical decision rule to assess severity in acute heart failure. We conducted a prospective cohort study of patients with symptoms of acute heart failure who attended the emergency departments (EDs) of three hospitals between April 2011 and April 2013. The following data were collected on arrival to or during the stay in the ED: baseline severity of symptoms; presence of decompensated comorbidities; number of hospital admissions/visits to EDs for acute heart failure during the previous 24 months; triggers of the exacerbation; clinical signs and symptoms; results of ancillary tests requested in the ED; treatments prescribed; and response to the initial treatment in the ED. The main outcome was poor course during the acute phase, in-hospital for admitted patients and during the first week following the ED visit for discharged patients, this being a composite endpoint that included death, admission to an intensive care unit, need for invasive mechanical ventilation, cardiac arrest and use of non-invasive mechanical ventilation. Multivariate logistic regression models were developed. Predictors of poor course in acute heart failure were oedema on chest radiography, visits to the ED and/or admissions in the previous two years, and levels of glycemia and blood urea nitrogen (areas under the curve of 0.83 in the derivation sample, and 0.82 in the validation sample). Four clinical predictors available in the ED can be used to create a simple score to predict poor course in acute heart failure.Clinical Trials.gov ID: NCT02437058.

Implications of Using Different Definitions on Outcomes in Worsening Heart Failure

BACKGROUND

In-hospital worsening heart failure (WHF) is an important event that has inconsistent definitions used across trials. We used data from 2 acute heart failure (HF) trials from the National Institutes of Health HF Network, DOSE (Diuretic Optimization Strategies Evaluation) and ROSE (Renal Optimization Strategies), to understand event rates associated with different WHF definitions.

METHODS AND RESULTS

We pooled data from 668 patients in DOSE and ROSE and assessed the relationship between WHF and the composite end point of rehospitalization, emergency room visits for HF, and mortality through 60 days. We also assessed for a differential relationship between the timing of WHF development and outcomes. The overall incidence of WHF was 14.6% (24.1% in DOSE, 6.3% in ROSE, and 5.0% in DOSE using the ROSE definition). WHF was associated with an increase in the composite end point (hazard ratio [HR], 1.64; 95% confidence interval [CI], 1.11-2.42; P=0.01). However, the association between WHF and outcomes was significantly stronger in ROSE than in DOSE (HR, 2.67; 95% CI, 1.45-4.91; P<0.01 and HR, 1.28; 95% CI, 0.79-2.08; P=0.31, respectively). Development of WHF between baseline to 24 hours compared with 24 to 48 hours or 48 to 72 hours demonstrated a trend toward improved outcomes (HR, 0.49; 95% CI, 0.21-1.17; P=0.11 and HR, 0.45; 95% CI, 0.20-1.04; P=0.06, respectively).

CONCLUSIONS

A WHF definition that excluded the intensification of diuretics resulted in a lower event rate but a stronger association with outcomes. These data support the need for continued efforts to standardize WHF definitions in clinical trials.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00577135 (DOSE) and NCT01132846 (ROSE).

Clinical and laboratory characteristics of short-term mortality in Egyptian patients with acute heart failure

Objective

To identify the clinical and laboratory predictors of short-term mortality in patients with acute heart failure (AHF).

Subjects and methods

We conducted a prospective, single center study on 120 consecutive patients presented with acute heart failure to the emergency department. All patients had clinical, laboratory, electrocardiographic and echocardiographic evaluation. Short-term mortality was reported within 30 days of presentation.

Results

Mean age was 59.29 ± 10.1 years, 55.8% were males and 50.8% were smokers. The common AHF presentations were dyspnea (91.7%), chest tightness (62.5%) and lower limb edema (54.2%). Ischemic heart disease, diabetes and hypertension were present in 72.5%, 43.3% and 35% of patients, respectively.

Short-term mortality was reported in 29 patients (24.16%); most of them died in-hospital (19 patients, 65.52%). The following parameters were significantly associated with short-term mortality: hypoxia (P < 0.001), tachycardia (P < 0.01), raised jugular venous pressure (JVP) (P < 0.001), low systolic blood pressure (P < 0.01), prolonged PR interval (P < 0.007), atrial fibrillation (AF) (P < 0.038), left bundle branch block (LBBB) (P < 0.04), impaired kidney function (P < 0.007), anemia (P < 0.029), hyponatremia (P < 0.006), hypoalbuminemia (P < 0.005), dilated left ventricle (LV) (P < 0.001), low LV ejection fraction (LVEF) (P < 0.001), and dilated left atrium (LA) (P < 0.002).

ROC curve analysis showed that low LVEF (≤24%), dilated LV end diastolic diameter (LVESD) ≥ 66.5 mm, dilated LV end systolic diameter (LVESD) ≥ 53.5 mm, dilated LA diameter ≥ 48 mm, increased serum creatinine ≥ 1.6 mg/dl, and decreased serum albumin ≤ 3 g/dl can significantly predict short-term mortality in patients with acute heart failure.

Conclusion

Variable clinical, laboratory, electrocardiographic and echocardiographic parameters were associated with short-term mortality. Our study showed that low LVEF, dilated LV diameter, dilated LA diameter, impaired kidney function and low serum albumin can predict short-term mortality in patients with acute heart failure.