Predictors of clinical outcomes in acute decompensated heart failure: Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure outcome models

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).

Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data

BACKGROUND

The risk assessment for survival in heart failure (HF) remains one of the key focuses of research. This study aims to develop a simple and feasible nomogram model for survival in HF based on the Heart Failure-A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) to support clinical decision-making.

METHODS

The HF patients were extracted from the HF-ACTION database and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate Cox regression was used to identify and integrate significant prognostic factors to form a nomogram, which was displayed in the form of a static nomogram. Bootstrap resampling (resampling = 1000) and cross-validation was used to internally validate the model. The prognostic performance of the model was measured by the concordance index (C-index), calibration curve, and the decision curve analysis.

RESULTS

There were 1394 patients with HF in the overall analysis. Seven prognostic factors, which included age, body mass index (BMI), sex, diastolic blood pressure (DBP), exercise duration, peak exercise oxygen consumption (peak VO2), and loop diuretic, were identified and applied to the nomogram construction based on the training cohort. The C-index of this model in the training cohort was 0.715 (95% confidence interval (CI): 0.700, 0.766) and 0.662 (95% CI: 0.646, 0.752) in the validation cohort. The area under the ROC curve (AUC) value of 365- and 730-day survival is (0.731, 0.734) and (0.640, 0.693) respectively in the training cohort and validation cohort. The calibration curve showed good consistency between nomogram-predicted survival and actual observed survival. The decision curve analysis (DCA) revealed net benefit is higher than the reference line in a narrow range of cutoff probabilities and the result of cross-validation indicates that the model performance is relatively robust.

CONCLUSIONS

This study created a nomogram prognostic model for survival in HF based on a large American population, which can provide additional decision information for the risk prediction of HF.

Predictors of heart failure readmission and all-cause mortality in patients with acute heart failure

BACKGROUND

Predischarge risk stratification of patients with acute heart failure (AHF) could facilitate tailored treatment and follow-up, however, simple scores to predict short-term risk for HF readmission or death are lacking.

METHODS

We sought to develop a congestion-focused risk score using data from a prospective, two-center observational study in adults hospitalized for AHF. Laboratory data were collected on admission. Patients underwent physical examination, 4-zone, and in a subset 8-zone, lung ultrasound (LUS), and echocardiography at baseline. A second LUS was performed before discharge in a subset of patients. The primary endpoint was the composite of HF hospitalization or all-cause death.

RESULTS

Among 350 patients (median age 75 years, 43% women), 88 participants (25%) were hospitalized or died within 90 days after discharge. A stepwise Cox regression model selected four significant independent predictors of the composite outcome, and each was assigned points proportional to its regression coefficient: NT-proBNP ≥2000 pg/mL (admission) (3 points), systolic blood pressure < 120 mmHg (baseline) (2 points), left atrial volume index ≥60 mL/m2 (baseline) (1 point) and ≥ 9 B-lines on predischarge 4-zone LUS (3 points). This risk score provided adequate risk discrimination for the composite outcome (HR 1.48 per 1 point increase, 95% confidence interval: 1.32-1.67, p < 0.001, C-statistic: 0.70). In a subset of patients with 8-zone LUS data (n = 176), results were similar (C-statistic: 0.72).

CONCLUSIONS

A four-variable risk score integrating clinical, laboratory and ultrasound data may provide a simple approach for risk discrimination for 90-day adverse outcomes in patients with AHF if validated in future investigations.

Predicting the risk of 1-year mortality among patients hospitalized for acute heart failure in China

BACKGROUND

We aimed to develop and validate a model to predict 1-year mortality risk among patients hospitalized for acute heart failure (AHF), build a risk score and interpret its application in clinical decision making.

METHODS

By using data from China Patient-Centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, which prospectively enrolled patients hospitalized for AHF in 52 hospitals across 20 provinces, we used multivariate Cox proportional hazard model to develop and validate a model to predict 1-year mortality.

RESULTS

There were 4,875 patients included in the study, 857 (17.58%) of them died within 1-year following discharge of index hospitalization. A total of 13 predictors were selected to establish the prediction model, including age, medical history of chronic obstructive pulmonary disease and hypertension, systolic blood pressure, Kansas City Cardiomyopathy Questionnaire-12 score, angiotensin converting enzyme inhibitor or angiotensin receptor blocker at discharge, discharge symptom, N-terminal pro-brain natriuretic peptide, high-sensitivity troponin T, serum creatine, albumin, blood urea nitrogen, and highly sensitive C-reactive protein. The model showed a high performance on discrimination (C-index was 0.759 [95% confidence interval: 0.739, 0.778] in development cohort and 0.761 [95% confidence interval: 0.731, 0.791] in validation cohort), accuracy, calibration, and outperformed than several existed risk scores. A point-based risk score was built to stratify low- (0-12), intermediate- (13-16), and high-risk group (≥17) among patients.

CONCLUSIONS

A prediction model using readily available predictors was developed and internal validated to predict 1-year mortality risk among patients hospitalized for AHF. It may serve as a useful tool for individual risk stratification and informing decision making to improve clinical care.

Prognostic Value of a Multi-mRNA Signature for 1-Year All-Cause Death in Hospitalized Patients With Heart Failure With a Preserved Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction is a major global public health problem, while effective risk stratification tools are still lacking. We sought to construct a multi-mRNA signature to predict 1-year all-cause death.

METHODS

We selected 30 patients with heart failure with preserved ejection fraction who died during 1-year follow-up and 30 who survived in the discovery set. One hundred seventy-one and 120 patients with heart failure with preserved ejection fraction were randomly selected as a test set and a validation set, respectively. We performed mRNA microarrays in all patients.

RESULTS

We constructed a 5-mRNA signature for predicting 1-year all-cause death. The scores of the 5-mRNA signature were significantly associated with the 1-year risk of all-cause death in both the test set (hazard ratio, 2.72 [95% CI, 1.98-3.74]; P<0.001) and the validation set (hazard ratio, 3.95 [95% CI, 2.40-6.48]; P<0.001). Compared with a reference model, which included sex, ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) score, history of HF and NT-proBNP (N-terminal pro-B-type natriuretic peptide), the 5-mRNA signature had a better discrimination capability, with an increased area under the curve from 0.696 to 0.813 in the test set and from 0.712 to 0.848 in the validation set. A composite model integrating the 5-mRNA risk score and variables in the reference model demonstrated an excellent discrimination capability, with an area under the curve of 0.861 (95% CI, 0.784-0.939) in the test set and an area under the curve of 0.859 (95% CI, 0.755-0.963) in the validation set. The net reclassification improvement and integrated discrimination improvement indicated that the composite model significantly improved patient classification compared with the reference model in both sets (P<0.001).

CONCLUSIONS

The 5-mRNA signature is a promising predictive tool for 1-year all-cause death and shows improved prognostic power over the established risk scores and NT-proBNP in patients with heart failure with preserved ejection fraction.

Examining predictors for 6-month mortality and healthcare utilization for patients admitted for heart failure in the acute care setting

BACKGROUND

Acute heart failure (AHF) is a leading cause of mortality and hospitalization. Past studies reported increased healthcare spending in the last year of life in high-income countries, and this has been characterized as inappropriate healthcare resource utilization. The study aimed to examine potentially (in)appropriate healthcare utilization by comparing healthcare utilization patterns across predicted and observed 6-month mortality among patients admitted for HF.

METHODS

We conducted a retrospective cohort study among patients presenting at the emergency department (ED) of a tertiary hospital with HF as primary diagnosis and admitted after their ED discharge. We used LASSO Cox proportional hazards models to predict 6-month mortality, and estimated healthcare utilization patterns of predicted and observed mortality across inpatient healthcare services.

RESULTS

3946 patients were admitted into the emergency department with a primary diagnosis of HF. From 57 candidate variables, 17 were retained in the final 6- month mortality model (C-statistic 0.66). Patients who died within 6-months of ED admission had longer length of stay (LOS) and less inpatient surgeries than those who survived. Patients with a greater predicted mortality risk were admitted to the ICU more often and had a longer LOS than those with a lower predicted mortality risk.

CONCLUSIONS

There were significant differences in healthcare resource utilization in patients admitted for AHF across predicted versus actual mortality. Lack of information on patients' preferences prevents the estimation of (in)appropriateness. Future studies should account for these considerations to estimate inappropriate healthcare utilization among these patients.

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.

Hospitalization for acute heart failure during non-working hours impacts on long-term mortality: the REPORT-HF registry

AIMS

Hospital admission during nighttime and off hours may affect the outcome of patients with various cardiovascular conditions due to suboptimal resources and personnel availability, but data for acute heart failure remain controversial. Therefore, we studied outcomes of acute heart failure patients according to their time of admission from the global International Registry to assess medical practice with lOngitudinal obseRvation for Treatment of Heart Failure.

METHODS AND RESULTS

Overall, 18 553 acute heart failure patients were divided according to time of admission into 'morning' (7:00-14:59), 'evening' (15:00-22:59), and 'night' (23:00-06:59) shift groups. Patients were also dichotomized to admission during 'working hours' (9:00-16:59 during standard working days) and 'non-working hours' (any other time). Clinical characteristics, treatments, and outcomes were compared across groups. The hospital length of stay was longer for morning (odds ratio: 1.08; 95% confidence interval: 1.06-1.10, P < 0.001) and evening shift (odds ratio: 1.10; 95% confidence interval: 1.07-1.12, P < 0.001) as compared with night shift. The length of stay was also longer for working vs. non-working hours (odds ratio: 1.03; 95% confidence interval: 1.02-1.05, P < 0.001). There were no significant differences in in-hospital mortality among the groups. Admission during working hours, compared with non-working hours, was associated with significantly lower mortality at 1 year (hazard ratio: 0.88; 95% confidence interval: 0.80-0.96, P = 0.003).

CONCLUSIONS

Acute heart failure patients admitted during the night shift and non-working hours had shorter length of stay but similar in-hospital mortality. However, patients admitted during non-working hours were at a higher risk for 1 year mortality. These findings may have implications for the health policies and heart failure trials.

A novel polygenic risk score improves prognostic prediction of heart failure with preserved ejection fraction in the Chinese Han population

AIMS

Mortality risk assessment in patients with heart failure (HF) with preserved ejection fraction (HFpEF) presents a major challenge. We sought to construct a polygenic risk score (PRS) to accurately predict the mortality risk of HFpEF.

METHODS AND RESULTS

We first carried out a microarray analysis of 50 HFpEF patients who died and 50 matched controls who survived during 1-year follow-up for candidate gene selection. The HF-PRS was developed using the independent common (MAF > 0.05) genetic variants that showed significant associations with 1-year all-cause death (P < 0.05) in 1442 HFpEF patients. Internal cross-validation and subgroup analyses were performed to evaluate the discrimination ability of the HF-PRS. In 209 genes identified by microarray analysis, 69 independent variants (r < 0.1) were selected to develop the HF-PRS model. This model yielded the best discrimination capability for 1-year all-cause mortality with an area under the curve (AUC) of 0.852 (95% CI 0.827-0.877), which outperformed the clinical risk score consisting of 10 significant traditional risk factors for 1-year all-cause mortality (AUC 0.696, 95% CI 0.658-0.734, P = 4 × 10-11), with net reclassification improvement (NRI) of 0.741 (95% CI 0.605-0.877; P < 0.001) and integrated discrimination improvement (IDI) of 0.181 (95% CI 0.145-0.218; P < 0.001). Individuals in the medium and the highest tertile of the HF-PRS had nearly a five-fold (HR = 5.3, 95% CI 2.4-11.9; P = 5.6 × 10-5) and 30-fold (HR = 29.8, 95% CI 14.0-63.5; P = 1.4 × 10-18) increased risk of mortality compared to those in the lowest tertile, respectively. The discrimination ability of the HF-PRS was excellent in cross validation and throughout the subgroups regardless of comorbidities, gender, and patients with or without a history of heart failure.

CONCLUSION

The HF-PRS comprising 69 genetic variants provided an improvement of prognostic power over the contemporary risk scores and NT-proBNP in HFpEF patients.

Common Variants on FGD5 Increase Hazard of Mortality or Rehospitalization in Patients With Heart Failure From the ASCEND-HF Trial

BACKGROUND

Heart failure remains a global health burden, and patients hospitalized are particularly at risk, but genetic associates for subsequent death or rehospitalization are still lacking.

METHODS

The genetic substudy of the ASCEND-HF trial (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) was used to perform genome-wide association study and transethnic meta-analysis. The overall trial included the patients of self-reported European ancestry (n=2173) and African ancestry (n=507). The end point was death or heart failure rehospitalization within 180 days. Cox models adjusted for 11 a priori predictors of rehospitalization and 5 genetic principal components were used to test the association between single-nucleotide polymorphisms and outcome. Summary statistics from the 2 populations were combined via meta-analysis with the significance threshold considered P<5×10-8.

RESULTS

Common variants (rs2342882 and rs35850039 in complete linkage disequilibrium) located in FGD5 were significantly associated with the primary outcome in both ancestry groups (European Americans: hazard ratio [HR], 1.38; P=2.42×10-6; African ancestry: HR, 1.51; P=4.43×10-3; HR in meta-analysis, 1.41; P=4.25×10-8). FGD5 encodes a regulator of VEGF (vascular endothelial growth factor)-mediated angiogenesis, and in silico investigation revealed several previous genome-wide association study hits in this gene, among which rs748431 was associated with our outcome (HR, 1.20; meta P<0.01). Sensitivity analysis proved FGD5 common variants survival association did not appear to operate via coronary artery disease or nesiritide treatment (P>0.05); and the signal was still significant when changing the censoring time from 180 to 30 days (HR, 1.39; P=1.59×10-5).

CONCLUSIONS

In this multiethnic genome-wide association study of ASCEND-HF, single-nucleotide polymorphisms in FGD5 were associated with increased risk of death or rehospitalization. Additional investigation is required to examine biological mechanisms and whether FGD5 could be a therapeutic target.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT00475852.

Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure

Background: Among the clinical predictors of a heart failure (HF) prognosis, different personal factors have been established in previous research, mainly age, gender, anemia, renal insufficiency and diabetes, as well as mediators (pulmonary embolism, hypertension, chronic obstructive pulmonary disease (COPD), arrhythmias and dyslipidemia). We do not know the role played by contextual and individual factors in the prediction of in-hospital mortality. Methods: The present study has added hospital and management factors (year, type of hospital, length of stay, number of diagnoses and procedures, and readmissions) in predicting exitus to establish a structural predictive model. The project was approved by the Ethics Committee of the province of Almeria. Results: A total of 529,606 subjects participated, through databases of the Spanish National Health System. A predictive model was constructed using correlation analysis (SPSS 24.0) and structural equation models (SEM) analysis (AMOS 20.0) that met the appropriate statistical values (chi-square, usually fit indices and the root-mean-square error approximation) which met the criteria of statistical significance. Individual factors, such as age, gender and chronic obstructive pulmonary disease, were found to positively predict mortality risk. Isolated contextual factors (hospitals with a greater number of beds, especially, and also the number of procedures performed, which negatively predicted the risk of death. Conclusions: It was, therefore, possible to introduce contextual variables to explain the behavior of mortality in patients with HF. The size or level of large hospital complexes, as well as procedural effort, are key contextual variables in estimating the risk of mortality in HF.

Prognostic value of Growth differentiation factors 15 in Acute heart failure patients with preserved ejection fraction

AIMS

There is an increasing proportion of hospitalized heart failure (HF) patients classified as HF with preserved ejection fraction (HFpEF) around the world. Growth differentiation factor 15 (GDF-15) is a promising biomarker in HFpEF prognostication; however, the majority of the existing data has been derived from the research on undifferentiated HF, whereas the studies focusing on HFpEF are still limited. This study aimed to determine the prognostic power of GDF-15 in the hospitalized patients with HFpEF in a Chinese cohort.

METHODS AND RESULTS

We analysed the levels of serum GDF-15 in 380 patients hospitalized for acute onset of HFpEF measured by heart ultrasound at admission in a prospective cohort. The associations of GDF-15 with 1 year risk of all-cause death and 1 year HF readmission were assessed by the Cox proportional hazards model. Area under the receiver operating characteristic curves was used to compare predictive accuracy. GDF-15 was strongly correlated with 1 year HF readmission and 1 year all-cause death, with event rates of 24.8%, 40.0%, and 50.0% for 1 year HF readmission (P < 0.001), respectively, and with 11.2%, 13.6%, and 24.6% for 1 year all-cause death (P = 0.004) in the corresponding tertile, respectively. In the multivariate linear regression model, GDF-15 had a significantly negative correlation with haemoglobin (P = 0.01) and a positive correlation with creatinine (P = 0.01), alanine transaminase (P = 0.001), and therapy of aldosterone antagonist (P = 0.018). The univariate Cox regression model of GDF-15 showed that c-statistic was 0.632 for 1 year HF readmission and 0.644 for 1 year all-cause death, which were superior to the N-terminal pro-brain natriuretic peptide (NT-proBNP) model with c-statistics of 0.595 and 0.610, respectively. In the multivariable Cox regression model, GDF-15 tertiles independently predicted 1 year HF readmission (hazard ratio 2.25, 95% confidence interval: 1.43-3.54, P < 0.001) after adjusting for baseline Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND-HF) risk score, history of HF, NT-proBNP, and high-sensitivity cardiac troponin T. Compared with the model including all the adjusted variables, the model with the addition of GDF-15 improved predictive power, with c-statistic increasing from 0.643 to 0.657 for 1 year HF readmission and from 0.638 to 0.660 for 1 year all-cause death.

CONCLUSIONS

In hospitalized patients with HFpEF, GDF-15 measured within 48 h of admission is a strong independent biomarker for 1 year HF readmission and even better than NT-proBNP. GDF-15 combined with the traditional indicators provided incremental prognostic value in this population.

Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF

BACKGROUND

Heart Failure with Preserved Ejection Fraction (HFpEF) is a heterogenous disease with few therapies proven to provide clinical benefit. Machine learning can characterize distinct phenotypes and compare outcomes among patients with HFpEF who are hospitalized for acute HF.

METHODS

We applied hierarchical clustering using demographics, comorbidities, and clinical data on admission to identify distinct clusters in hospitalized HFpEF (ejection fraction >40%) in the ASCEND-HF trial. We separately applied a previously developed latent class analysis (LCA) clustering method and compared in-hospital and long-term outcomes across cluster groups.

RESULTS

Of 7141 patients enrolled in the ASCEND-HF trial, 812 (11.4%) were hospitalized for HFpEF and met the criteria for complete case analysis. Hierarchical Cluster 1 included older women with atrial fibrillation (AF). Cluster 2 had elevated resting blood pressure. Cluster 3 had young men with obesity and diabetes. Cluster 4 had low resting blood pressure. Mortality at 180 days was lowest among Cluster 3 (KM event-rate 6.2 [95% CI: 3.5, 10.9]) and highest among Cluster 4 (18.8 [14.6, 24.0], P < .001). Twenty four-hour urine output was higher in Cluster 3 (2700 mL [1800, 3975]) than Cluster 4 (2100 mL [1400, 3055], P < .001). LCA also identified four clusters: A) older White or Asian women, B) younger men with few comorbidities, C) older individuals with AF and renal impairment, and D) patients with obesity and diabetes. Mortality at 180 days was lowest among LCA Cluster B (KM event-rate 5.5 [2.0, 10.3]) and highest among LCA Cluster C (26.3 [19.2, 35.4], P < .001).

CONCLUSIONS

In patients hospitalized for HFpEF, cluster analysis demonstrated distinct phenotypes with differing clinical profiles and outcomes.

Alerting Clinicians to 1-Year Mortality Risk in Patients Hospitalized With Heart Failure: The REVEAL-HF Randomized Clinical Trial

Importance

Heart failure is a major cause of morbidity and mortality worldwide. The use of risk scores has the potential to improve targeted use of interventions by clinicians that improve patient outcomes, but this hypothesis has not been tested in a randomized trial.

Objective

To evaluate whether prognostic information in heart failure translates into improved decisions about initiation and intensity of treatment, more appropriate end-of-life care, and a subsequent reduction in rates of hospitalization or death.

Design, Setting, and Participants

This was a pragmatic, multicenter, electronic health record-based, randomized clinical trial across the Yale New Haven Health System, comprising small community hospitals and large tertiary care centers. Patients hospitalized for heart failure who had N-terminal pro-brain natriuretic peptide (NT-proBNP) levels of greater than 500 pg/mL and received intravenous diuretics within 24 hours of admission were automatically randomly assigned to the alert (intervention) or usual-care groups.

Interventions

The alert group had their risk of 1-year mortality calculated using an algorithm that was derived and validated using similar historic patients in the electronic health record. This estimate, including a categorical risk assessment, was presented to clinicians while they were interacting with a patient's electronic health record.

Main Outcomes and Measures

The primary outcome was a composite of 30-day hospital readmissions and all-cause mortality at 1 year.

Results

Between November 27, 2019, through March 7, 2021, 3124 patients were randomly assigned to the alert (1590 [50.9%]) or usual-care (1534 [49.1%]) group. The alert group had a median (IQR) age of 76.5 (65-86) years, and 796 were female patients (50.1%). Patients from the following race and ethnicity groups were included: 13 Asian (0.8%), 324 Black (20.4%), 136 Hispanic (8.6%), 1448 non-Hispanic (91.1%), 1126 White (70.8%), 6 other ethnicity (0.4%), and 127 other race (8.0%). The usual-care group had a median (IQR) age of 77 (65-86) years, and 788 were female patients (51.4%). Patients from the following race and ethnicity groups were included: 11 Asian (1.4%), 298 Black (19.4%), 162 Hispanic (10.6%), 1359 non-Hispanic (88.6%), 1077 White (70.2%), 13 other ethnicity (0.9%), and 137 other race (8.9%). Median (IQR) NT-proBNP levels were 3826 (1692-8241) pg/mL in the alert group and 3867 (1663-8917) pg/mL in the usual-care group. A total of 284 patients (17.9%) and 270 patients (17.6%) were admitted to the intensive care unit in the alert and usual-care groups, respectively. A total of 367 patients (23.1%) and 359 patients (23.4%) had a left ventricular ejection fraction of 40% or less in the alert and usual-care groups, respectively. The model achieved an area under the curve of 0.74 in the trial population. The primary outcome occurred in 619 patients (38.9%) in the alert group and 603 patients (39.3%) in the usual-care group (P = .89). There were no significant differences between study groups in the prescription of heart failure medications at discharge, the placement of an implantable cardioverter-defibrillator, or referral to palliative care.

Conclusions and Relevance

Provision of 1-year mortality estimates during heart failure hospitalization did not affect hospitalization or mortality, nor did it affect clinical decision-making.

Trial Registration

ClinicalTrials.gov Identifier NCT03845660.

Predictive Model for Heart Failure Readmission Using Nationwide Readmissions Database

Objective

To generate a heart failure (HF) readmission prediction model using the Nationwide Readmissions Database to guide management and reduce HF readmissions.

Patients and Methods

A retrospective analysis was performed for patients listed for HF admissions in the Nationwide Readmissions Database from January 1, 2010, to December 31, 2014. A Cox proportional hazards model for sample survey data for the prediction of readmission for all patients with HF was implemented using a derivation cohort (2010-2012). We generated receiver operating characteristic (ROC) curves and estimated area under the ROC curve at each time point (30, 60, 90, and 180 days) to assess the accuracy of our predictive model using the derivation cohort (2010-2012) and compared it with the validation cohort (2013-2014). A risk score was computed for the validation cohort. On the basis of the total risk score, we calculated the probability of readmission at 30, 60, 90, and 180 days.

Results

Approximately 1,420,564 patients were admitted for HF, contributing to 1,817,735 total HF admissions. Of these, 665,867 patients had at least 1 readmission for HF. The 10 most common comorbidities for readmitted patients included hypertension, diabetes mellitus, renal failure, chronic pulmonary disease, deficiency anemia, fluid and electrolyte disorders, obesity, hypothyroidism, peripheral vascular disorders, and depression. The area under the ROC curve for the prediction model was 0.58 in the derivation cohort and 0.59 in the validation cohort.

Conclusion

The prediction model will find clinical utility at point of care in optimizing the management of patients with HF and reducing HF readmissions.

Emergency Department Visits Versus Hospital Readmissions Among Patients Hospitalized for Heart Failure

BACKGROUND

Worsening heart failure (HF) often requires hospitalization but in some cases may be managed in the outpatient or emergency department (ED) settings. The predictors and clinical significance of ED visits without admission vs hospitalization are unclear.

METHODS

The ASCEND-HF trial included 2661 US patients hospitalized for HF with reduced or preserved ejection fraction. Clinical characteristics were compared between patients with a subsequent all-cause ED visit (with ED discharge) within 30 days vs all-cause readmission within 30 days. Factors associated with each type of care were assessed in multivariable models. Multivariable models landmarked at 30 days evaluated associations between each type of care and subsequent 150-day mortality.

RESULTS

Through 30-day follow-up, 193 patients (7%) had ED discharge, 459 (17%) had readmission, and 2009 (76%) had neither urgent visit. Patients with ED discharge vs readmission were similar with respect to age, sex, systolic blood pressure, ejection fraction, and coronary artery disease, whereas ED discharge patients had a modestly lower creatinine (P < .01). Among patients with either event within 30 days, a higher creatinine and prior HF hospitalization were associated with a higher likelihood of readmission, as compared with ED discharge (P < .02). Landmarked at 30 days, rates of death during the subsequent 150 days were 21.0% for patients who were readmitted and 11.4% for patients discharged from the ED. Compared with patients who were readmitted, ED discharge was independently associated with lower 150-day mortality (adjusted hazard ratio 0.58, 95% confidence interval 0.36-0.92, P = .02).

CONCLUSIONS

In this cohort of US patients hospitalized for HF, worse renal function and prior HF hospitalization were associated with a higher likelihood of early postdischarge readmission, as compared with ED discharge. Although subsequent mortality was high after discharge from the ED, this risk of mortality was significantly lower than patients who were readmitted to the hospital.

Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure

BACKGROUND

This study aimed to develop a risk prediction model (AUS-HF model) for 30-day all-cause re-hospitalisation or death among patients admitted with acute heart failure (HF) to inform follow-up after hospitalisation. The model uses routinely collected measures at point of care.

METHODS

We analyzed pooled individual-level data from two cohort studies on acute HF patients followed for 30-days after discharge in 17 hospitals in Victoria, Australia (2014-2017). A set of 58 candidate predictors, commonly recorded in electronic medical records (EMR) including demographic, medical and social measures were considered. We used backward stepwise selection and LASSO for model development, bootstrap for internal validation, C-statistic for discrimination, and calibration slopes and plots for model calibration.

RESULTS

The analysis included 1380 patients, 42.1% female, median age 78.7 years (interquartile range = 16.2), 60.0% experienced previous hospitalisation for HF and 333 (24.1%) were re-hospitalised or died within 30 days post-discharge. The final risk model included 10 variables (admission: eGFR, and prescription of anticoagulants and thiazide diuretics; discharge: length of stay>3 days, systolic BP, heart rate, sodium level (<135 mmol/L), >10 prescribed medications, prescription of angiotensin converting enzyme inhibitors or angiotensin receptor blockers, and anticoagulants prescription. The discrimination of the model was moderate (C-statistic = 0.684, 95%CI 0.653, 0.716; optimism estimate = 0.062) with good calibration.

CONCLUSIONS

The AUS-HF model incorporating routinely collected point-of-care data from EMRs enables real-time risk estimation and can be easily implemented by clinicians. It can predict with moderate accuracy risk of 30-day hospitalisation or mortality and inform decisions around the intensity of follow-up after hospital discharge.

Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF.

Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771–0.895) and validation set (AUC 0.798, 95% CI: 0.719–0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05).

Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

Early diuretic strategies and the association with In-hospital and Post-discharge outcomes in acute heart failure

BACKGROUND

Decongestion is a primary goal during hospitalizations for decompensated heart failure (HF). However, data surrounding the preferred route and strategy of diuretic administration are limited with varying results in prior studies.

METHODS

This is a retrospective analysis using patients from ASCEND-HF with a stable diuretic strategy in the first 24 hours following randomization. Patients were divided into three groups: intravenous (IV) continuous, IV bolus and oral strategy. Baseline characteristics, in-hospital outcomes, 30-day composite cardiovascular mortality or HF rehospitalization and 180-day all-cause mortality were compared across groups. Inverse propensity weighted modeling was used for adjustment.

RESULTS

Among 5,738 patients with a stable diuretic regimen in the first 24 hours (80% of overall ASCEND trial), 3,944 (68.7%) patients received IV intermittent bolus administration of diuretics, 799 (13.9%) patients received IV continuous therapy and 995 (17.3%) patients with oral administration. Patients in the IV continuous group had a higher baseline creatinine (IV continuous 1.4 [1.1-1.7]; intermittent bolus 1.2 [1.0-1.6]; oral 1.2 [1.0-1.4] mg/dL; P <0.001) and high NTproBNP (IV continuous 5,216 [2,599-11,603]; intermittent bolus 4,944 [2,339-9,970]; oral 3,344 [1,570-7,077] pg/mL; P <0.001). There was no difference between IV continuous and intermittent bolus group in weight change, total urine output and change in renal function till 10 days/discharge (adjusted P >0.05 for all). There was no difference in 30 day mortality and HF readmission (adjusted OR 1.08 [95%CI: 0.74, 1.57]; P = 0.701) and 180 days mortality (adjusted OR 1.04 [95%CI: 0.75, 1.43]; P = 0.832).

CONCLUSION

In a large cohort of patients with decompensated HF, there were no significant differences in diuretic-related in-hospital, or post-discharge outcomes between IV continuous and intermittent bolus administration. Tailoring appropriate diuretic strategy to different states of acute HF and congestion phenotypes needs to be further investigated.

Health Status Predicts Short- and Long-Term Risk of Composite Clinical Outcomes in Acute Heart Failure

OBJECTIVES

This study aims to examine the association between the Kansas City Cardiomyopathy Questionnaire (KCCQ)-12 score and the 30-day and 1-year rates of composite events of cardiovascular death and heart failure (HF) rehospitalization in patients with acute HF.

BACKGROUND

Few studies reported the prognostic effects of KCCQ in acute HF.

METHODS

This study prospectively enrolled adult patients hospitalized for HF from 52 hospitals in China and collected the KCCQ-12 score within 48 hour of index admission. The study used multivariable Cox regression to examine the association between KCCQ-12 score and 30-day and 1-year composite events and was further stratified by new-onset HF and acutely decompensated chronic heart failure (ADCHF). Subgroup analyses were performed to explore the potential heterogeneity. The study evaluated the incremental prognostic value of KCCQ-12 score over N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels and established risk scores by C-statistics, net reclassification improvement, and integrated discrimination improvement.

RESULTS

Among 4,898 patients, 29.4% had new-onset HF. After adjustment, each 10-point decrease in the KCCQ-12 score was associated with a 13% increase in 30-day risk and a 7% increase in 1-year risk. The associations were consistent regardless of new-onset HF or ADCHF, age, sex, left ventricular ejection fraction, New York Heart Association functional class, NT-proBNP level, comorbidities, and renal function. Adding KCCQ-12 score to NT-proBNP and established risk scores significantly improved prognostic capabilities measured by C-statistics, net reclassification improvement, and integrated discrimination improvement.

CONCLUSIONS

In acute HF, a poor KCCQ-12 score predicted short- and long-term risks of cardiovascular death and HF rehospitalization. KCCQ-12 could serve as a convenient tool for rapid initial risk stratification and provide additional prognostic value over NT-proBNP and established risk scores.

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.

REVeAL-HF: Design and Rationale of a Pragmatic Randomized Controlled Trial Embedded Within Routine Clinical Practice

Heart failure (HF) is one of the most common causes of hospitalization in the United States and carries a significant risk of morbidity and mortality. Use of evidence-based interventions may improve outcomes, but their use is encumbered in part by limitations in accurate prognostication. The REVeAL-HF (Risk EValuation And its Impact on ClinicAL Decision Making and Outcomes in Heart Failure) trial is the first to definitively evaluate the impact of knowledge about prognosis on clinical decision making and patient outcomes. The REVeAL-HF trial is a pragmatic, completely electronic, randomized controlled trial that has completed enrollment of 3,124 adults hospitalized for HF, defined as having an N-terminal pro-B-type natriuretic peptide level of >500 pg/ml and receiving intravenous diuretic agents within 24 h of admission. Patients randomized to the intervention had their risk of 1-year mortality generated with information in the electronic health record and presented to their providers, who had the option to give feedback on their impression of this risk assessment. The authors are examining the impact of this information on clinical decision-making (use of HF pharmacotherapies, referral to electrophysiology, palliative care referral, and referral for advanced therapies like heart transplantation or mechanical circulatory support) and patient outcomes (length of stay, post-discharge 30-day rehospitalizations, and 1-year mortality). The REVeAL-HF trial will definitively examine whether knowledge about prognosis in HF has an impact on clinical decision making and patient outcomes. It will also examine the relationship between calculated, perceived, and real risk of mortality in this patient population. (Risk EValuation And Its Impact on ClinicAL Decision Making and Outcomes in Heart Failure [REVeAL-HF]; NCT03845660).

Assessment of acute heart failure prognosis: the promising role of prognostic models and biomarkers

Numerous models and biomarkers have been proposed to estimate prognosis and improve decision-making in patients with acute heart failure (AHF). The present literature review provides a critical appraisal of externally validated prognostic models in AHF, combining clinical data and biomarkers. We perform a literature review of clinical studies, using the following terms: "acute heart failure," "acute decompensated heart failure," "prognostic models," "risk scores," "mortality," "death," "hospitalization," "admission," and "biomarkers." We searched MEDLINE and EMBASE databases from 1990 to 2020 for studies documenting prognostic models in AHF. External validation of each prognostic model to another AHF cohort, containing at least one biomarker, was prerequisites for study selection. Among 358 initially screened studies, 9 of them fulfilled all searching criteria. The majority of prognostic models were simplified, including a narrow number of variables (up to 10), with good performance regarding calibration and discrimination (c-statistics > 0.65). Unfortunately, the derived and validated cohorts showed a wide variety in patients' characteristics (e.g., cause of AHF and therapy). Moreover, the prognostic models used various time-points and a plethora of combinations of variables determining different cut-off values. Although the application of valid prognostic models in AHF population is quite promising, a precise methodological approach should be set for the derivation and validation of prognostic models in AHF with unified characteristics to establish an effective performance in clinical practice.

Natriuretic Peptides as the Basis of Peptide Drug Discovery for Cardiovascular Diseases

Cardiovascular diseases (CVDs) are the leading global cause of death, accounting for more than 17.6 million deaths per year in 2016, a number that is expected to grow to more than 23.6 million by 2030. While many technologies are currently under investigation to improve the therapeutic outcome of CVD complications, only a few medications have been approved. Therefore, new approaches to treat CVD are urgently required. Peptides regulate numerous physiological processes, mainly by binding to specific receptors and inducing a series of signals, neurotransmissions or the release of growth factors. Importantly, peptides have also been shown to play an important role in the circulatory system both in physiological and pathological conditions. Peptides, such as angiotensin II, endothelin, urotensin-II, urocortins, adrenomedullin and natriuretic peptides have been implicated in the control of vascular tone and blood pressure as well as in CVDs such as congestive heart failure, atherosclerosis, coronary artery disease, and pulmonary and systemic hypertension. Hence it is not surprising that peptides are becoming important therapeutic leads in CVDs. This article will review the current knowledge on peptides and their role in the circulatory system, focusing on the physiological roles of natriuretic peptides in the cardiovascular system and their implications in CVDs.

Plasma Volume Status and Its Association With In-Hospital and Postdischarge Outcomes in Decompensated Heart Failure

BACKGROUND

Prior analyses suggest an association between formula-based plasma volume (PV) estimates and outcomes in heart failure (HF). We assessed the association between estimated PV status by the Duarte-ePV and Kaplan Hakim (KH-ePVS) formulas, and in-hospital and postdischarge clinical outcomes, in the ASCEND-HF trial.

METHODS AND RESULTS

The KH-ePVS and Duarte-ePV were calculated on admission. We assessed associations with in-hospital worsening HF, 30-day composite cardiovascular mortality or HF rehospitalization and 180-day all-cause mortality. There were 6373 (89.2%), and 6354 (89.0%) patients who had necessary characteristics to calculate KH-ePVS and Duarte-ePV, respectively. There was no association between PV by either formula with in-hospital worsening HF. KH-ePVS showed a weak correlation with N-terminal prohormone BNP, and with measures of decongestion such as body weight change and urine output (r < 0.3 for all). Duarte-ePV was trending toward an association with worse 30-day (adjusted odds ratio 1.07, 95% confidence interval [CI] 1.00-1.15, P = .058), but not 180-day outcomes (adjusted hazard ratio 1.03, 95% CI 0.97-1.09, P = .289). A continuous KH-ePVS of >0 (per 10-unit increase) was associated with improved 30-day outcomes (adjusted odds ratio 0.75, 95% CI 0.62-0.91, P = .004). The continuous KH-ePVS was not associated with 180-day outcomes (adjusted hazard ratio 1.05, 95% CI 0.98-1.12, P = .139).

CONCLUSIONS

Baseline PV estimates had a weak association with in-hospital measures of decongestion. The Duarte-ePV trended toward an association with early clinical outcomes in decompensated HF, and may improve risk stratification in HF.

The burden of non-cardiac comorbidities and association with clinical outcomes in an acute heart failure trial - insights from ASCEND-HF

AIMS

Non-cardiac comorbidities are highly prevalent in patients with heart failure (HF). Our objective was to define the association between non-cardiac comorbidity burden and clinical outcomes, costs of care, and length of stay within a large randomized trial of acute HF patients.

METHODS AND RESULTS

Patients with complete medical history for the following comorbidities were included: diabetes mellitus, chronic obstructive pulmonary disease, chronic liver disease, history of cancer within the last 5 years, chronic renal disease (baseline serum creatinine >3.0 mg/mL), current smoking, alcohol abuse, depression, anaemia, peripheral arterial disease, and cerebrovascular disease. Patients were classified by overall burden of non-cardiac comorbidities (0, 1, 2, 3, and 4+). Hierarchical generalized linear models were used to assess associations between comorbidity burden and 30-day all-cause death or HF hospitalization and 180-day all-cause death in addition to costs of care and length of stay. A total of 6945 patients were included in the final analysis. Mean comorbidity number was 2.2 (± 1.34). Patients with 4+ comorbidities had higher rates of 30-day all-cause death/HF hospitalization as compared with patients with no comorbidities [odds ratio (OR) 3.32, 95% confidence interval (CI) 1.61-6.84; P < 0.01]. Similar results were seen with respect to 180-day death (OR 2.13, 95% CI 1.33-3.43; P < 0.01). Higher comorbidity burden was associated with higher 180-day costs of care and length of stay.

CONCLUSIONS

Higher comorbidity burden is associated with poor clinical outcomes, higher costs of care, and extended length of stay. Further studies are needed to define the impact of comorbidity management programmes on outcomes for HF patients.

Risk scores did not reliably predict individual risk of mortality for patients with decompensated heart failure

OBJECTIVE

We investigated the performance of four prognostic tools in predicting 180-day mortality for patients admitted for acute decompensated heart failure (ADHF) by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) over a range of risk thresholds, in addition to discrimination and calibration.

STUDY DESIGN AND SETTING

We studied 1,458 patients. The risk assessment was performed using the Acute Decompensated Heart Failure National Registry (ADHERE) model and the Get With The Guidelines (GWTG), ADHF/NT-proBNP, and Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores.

RESULTS

C-statistics ranged from 0.727 for the ADHERE model to 0.767 for the ADHF/NT-proBNP score. The ADHF/NT-proBNP risk score, the ADHERE model, and the ASCEND risk score, but not the GWTG risk score, were also well calibrated. Sensitivity and PPV were modest at the >30% risk threshold and ranged from 55% for the ADHF/NT-proBNP risk score to 38.8% for the ADHERE model and from 46.7% for the ADHF/NT-proBNP risk score to 42.1% for the ASCEND risk score, respectively. There was a modest agreement between the risk scores in classifying the patients across risk strata or in classifying those who died as being at >30% risk of death.

CONCLUSION

Although risk assessment tools work well for stratifying patients, their use in estimating the risk of mortality for individuals has limited clinical utility.

Pharmacological interventions for heart failure in people with chronic kidney disease

BACKGROUND

Approximately half of people with heart failure have chronic kidney disease (CKD). Pharmacological interventions for heart failure in people with CKD have the potential to reduce death (any cause) or hospitalisations for decompensated heart failure. However, these interventions are of uncertain benefit and may increase the risk of harm, such as hypotension and electrolyte abnormalities, in those with CKD.

OBJECTIVES

This review aims to look at the benefits and harms of pharmacological interventions for HF (i.e., antihypertensive agents, inotropes, and agents that may improve the heart performance indirectly) in people with HF and CKD.

SEARCH METHODS

We searched the Cochrane Kidney and Transplant Register of Studies through 12 September 2019 in consultation with an Information Specialist and using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.

SELECTION CRITERIA

We included randomised controlled trials of any pharmacological intervention for acute or chronic heart failure, among people of any age with chronic kidney disease of at least three months duration.

DATA COLLECTION AND ANALYSIS

Two authors independently screened the records to identify eligible studies and extracted data on the following dichotomous outcomes: death, hospitalisations, worsening heart failure, worsening kidney function, hyperkalaemia, and hypotension. We used random effects meta-analysis to estimate treatment effects, which we expressed as a risk ratio (RR) with 95% confidence intervals (CI). We assessed the risk of bias using the Cochrane tool. We applied the GRADE methodology to rate the certainty of evidence.

MAIN RESULTS

One hundred and twelve studies met our selection criteria: 15 were studies of adults with CKD; 16 studies were conducted in the general population but provided subgroup data for people with CKD; and 81 studies included individuals with CKD, however, data for this subgroup were not provided. The risk of bias in all 112 studies was frequently high or unclear. Of the 31 studies (23,762 participants) with data on CKD patients, follow-up ranged from three months to five years, and study size ranged from 16 to 2916 participants. In total, 26 studies (19,612 participants) reported disaggregated and extractable data on at least one outcome of interest for our review and were included in our meta-analyses. In acute heart failure, the effects of adenosine A1-receptor antagonists, dopamine, nesiritide, or serelaxin on death, hospitalisations, worsening heart failure or kidney function, hyperkalaemia, hypotension or quality of life were uncertain due to sparse data or were not reported. In chronic heart failure, the effects of angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) (4 studies, 5003 participants: RR 0.85, 95% CI 0.70 to 1.02; I2 = 78%; low certainty evidence), aldosterone antagonists (2 studies, 34 participants: RR 0.61 95% CI 0.06 to 6.59; very low certainty evidence), and vasopressin receptor antagonists (RR 1.26, 95% CI 0.55 to 2.89; 2 studies, 1840 participants; low certainty evidence) on death (any cause) were uncertain. Treatment with beta-blockers may reduce the risk of death (any cause) (4 studies, 3136 participants: RR 0.69, 95% CI 0.60 to 0.79; I2 = 0%; moderate certainty evidence). Treatment with ACEi or ARB (2 studies, 1368 participants: RR 0.90, 95% CI 0.43 to 1.90; I2 = 97%; very low certainty evidence) had uncertain effects on hospitalisation for heart failure, as treatment estimates were consistent with either benefit or harm. Treatment with beta-blockers may decrease hospitalisation for heart failure (3 studies, 2287 participants: RR 0.67, 95% CI 0.43 to 1.05; I2 = 87%; low certainty evidence). Aldosterone antagonists may increase the risk of hyperkalaemia compared to placebo or no treatment (3 studies, 826 participants: RR 2.91, 95% CI 2.03 to 4.17; I2 = 0%; low certainty evidence). Renin inhibitors had uncertain risks of hyperkalaemia (2 studies, 142 participants: RR 0.86, 95% CI 0.49 to 1.49; I2 = 0%; very low certainty). We were unable to estimate whether treatment with sinus node inhibitors affects the risk of hyperkalaemia, as there were few studies and meta-analysis was not possible. Hyperkalaemia was not reported for the CKD subgroup in studies investigating other therapies. The effects of ACEi or ARB, or aldosterone antagonists on worsening heart failure or kidney function, hypotension, or quality of life were uncertain due to sparse data or were not reported. Effects of anti-arrhythmic agents, digoxin, phosphodiesterase inhibitors, renin inhibitors, sinus node inhibitors, vasodilators, and vasopressin receptor antagonists were very uncertain due to the paucity of studies.

AUTHORS' CONCLUSIONS

The effects of pharmacological interventions for heart failure in people with CKD are uncertain and there is insufficient evidence to inform clinical practice. Study data for treatment outcomes in patients with heart failure and CKD are sparse despite the potential impact of kidney impairment on the benefits and harms of treatment. Future research aimed at analysing existing data in general population HF studies to explore the effect in subgroups of patients with CKD, considering stage of disease, may yield valuable insights for the management of people with HF and CKD.

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.

Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure

BACKGROUND

Several heart failure (HF) risk models exist, however, most of them perform poorly when applied to real-world situations. This study aimed to develop a convenient and efficient risk model to identify patients with high readmission risk within 90 days of HF.

METHODS

A multivariate logistic regression model was used to predict the risk of 90-day readmission. Data were extracted from electronic medical records from January 1, 2017 to December 31, 2017 and follow-up records of patients with HF within 3 months after discharge. Model performance was evaluated using a receiver operating characteristic curve. All statistical analysis was done using R version 3.5.0.

RESULTS

A total of 350 patients met the inclusion criterion of being readmitted within in 90 days. All data sets were randomly divided into derivation and validation cohorts at a 7/3 ratio. The baseline data were fairly consistent among the derivation and validation cohorts. The variables most clearly related to readmission were logarithm of serum N-terminal pro b-type natriuretic peptide (NT-proBNP) level, red cell volume distribution width (RDW-CV), and Charlson comorbidity index (CCI). The model had good discriminatory ability (C-statistic = 0.73).

CONCLUSIONS

We developed and validated a multivariate logistic regression model to predict the 90-day readmission risk for Chinese patients with HF. The predictors included in the model are derived from electronic medical record (EMR) admission data, making it easier for physicians and pharmacists to identify high-risk patients and tailor more intensive precautionary strategies.

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.

Relationship between intrarenal renin-angiotensin activity and re-hospitalization in patients with heart failure with reduced ejection fraction

OBJECTIVE

Heart failure (HF) is a clinical syndrome resulting from structural or functional damages. Although clinical trials have shown that the plasma renin-angiotensin system (RAS) activation decreases HF functional status and increases hospitalization for HF patients, the effect of intrarenal RAS activity is still unknown. In this study, we investigated the relationship between the New York Heart Association (NYHA) class, duration, and number of hospitalizations in the previous year and urinary angiotensinogen (UAGT) in patients with HF with reduced ejection fraction (HFrEF).

METHODS

This study included 85 patients who had an ejection fraction of <40% and were receiving optimal medical treatment. Among these, 22 were excluded from the study for various reasons. Demographically and biochemically, the remaining 63 patients were compared according to the NYHA functional classes and re-hospitalization status.

RESULTS

When the groups were compared in terms of N-terminal pro-B-type natriuretic peptide (NT-proBNP), UAGT, and high-sensitivity C-reactive protein (Hs-CRP), it was found that these parameters were significantly higher in patients who were hospitalized more than two times in the previous year [p<0.001; p=0.007; p<0.001, respectively]. There was a significant correlation between number of hospitalizations and NT-proBNP (r=0.507, p<0.001), Hs-CRP (r=0.511, p<0.001), hemoglobin (r=-0.419, p=0.001), serum sodium (r=-0.26, p=0.04), and systolic blood pressure (r=-0.283, p=0.02). When the independence of multiple correlations was assessed using multiple linear regression analysis, NT-proBNP, Hs-CRP, and hemoglobin levels were independent predictors of re-hospitalization, but this was not the same for UAGT.

CONCLUSION

Although UAGT levels are high in patients with poor NYHA functional class and repeated hospitalizations, this marker is not valuable for predicting repeated hospitalization in patients with HFrEF.

Relationship Between Enrolling Country Income Level and Patient Profile, Protocol Completion, and Trial End Points

BACKGROUND

Globalization of clinical trials fosters inclusion of higher and lower income countries, but the influence of enrolling country income level on heart failure trial performance is unclear. This study sought to evaluate associations between enrolling country income level, acute heart failure patient profile, protocol completion, and trial end points.

METHODS AND RESULTS

The ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) trial included 7141 patients with acute heart failure from 30 countries. Country income data in gross national income per capita in current US dollars from the year 2007 (ie, the year trial enrollment began) were abstracted from the World Bank. Patients were grouped by enrolling country income level (ie, high [>$11 455], upper middle [$3706-$11 455], lower middle [$936-$3705], and low [<$936]). Income data were available for 29 (97%) countries (N=7064). There were 3996 (57%), 1518 (21%), and 1550 (22%) patients from high-income, upper-middle-income, and lower-middle-income countries, respectively. There were no patients from low-income countries. Patients from lower-middle-income countries tended to be younger with fewer comorbidities and lower utilization of guideline-directed therapies. Rates of adverse events (13.8%) and protocol noncompletion (4.9%) during 180-day follow-up were highest among high-income countries (all P <0.01). After adjustment for race, geographic region, and clinical characteristics, compared with lower-middle-income countries, enrollment from higher income countries was associated with increased 30-day mortality or rehospitalization (high income: odds ratio, 1.70; 95% CI, 1.02-2.85; upper-middle-income: odds ratio, 2.16; 95% CI, 1.23-3.81), driven by higher rates of rehospitalization. Mortality was similar at 30 and 180 days. The association between higher country income and the 30-day composite end point was similar across geographic regions, with exception of Latin America ( P for interaction, 0.03).

CONCLUSIONS

In this global acute heart failure trial, patients from higher income countries had lower rates of protocol completion, higher rates of adverse events, and similar mortality rates. After adjustment for race, geographic region, and clinical factors, enrollment from a higher income country was associated with worse clinical outcomes, driven by higher rates of rehospitalization. Variation in enrolling country income level may influence study end points and trial performance independent of geographic region.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov . Unique identifier: NCT00475852.

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.

Rate pressure product and the components of heart rate and systolic blood pressure in hospitalized heart failure patients with preserved ejection fraction: Insights from ASCEND-HF

BACKGROUND

Heart rate and systolic blood pressure (SBP) are prognostic markers in heart failure (HF) with reduced ejection fraction (HFrEF). Their combination in rate pressure product (RPP) as well as their role in heart failure with preserved ejection fraction (HFpEF) remains unclear.

HYPOTHESIS

RPP and its components are associated with HFpEF outcomes.

METHODS

We performed an analysis of Acute Study of Clinical Effectiveness of Nesiritide in Subjects With Decompensated Heart Failure (ASCEND-HF; http://www.clinicaltrials.gov NCT00475852), which studied 7141 patients with acute HF. HFpEF was defined as left ventricular ejection fraction ≥40%. Outcomes were assessed by baseline heart rate, SBP, and RPP, as well as the change of these variables using adjusted Cox models.

RESULTS

After multivariable adjustment, in-hospital change but not baseline heart rate, SBP, and RPP were associated with 30-day mortality/HF hospitalization (hazard ratio [HR]: 1.17 per 5-bpm heart rate, HR: 1.20 per 10-mm Hg SBP, and HR: 1.02 per 100 bpm × mm Hg RPP; all P < 0.05). Baseline SBP was associated with 180-day mortality (HR: 0.88 per 10-mm Hg, P = 0.028). Though change in RPP was associated with 30-day mortality/HF hospitalization, the RPP baseline variable did not provide additional associative information with regard to outcomes when compared with assessment of baseline heart rate and SBP variables alone.

CONCLUSIONS

An increase in heart rate and SBP from baseline to discharge was associated with increased 30-day mortality/HF hospitalization in HFpEF patients with acute exacerbation. These findings suggest value in monitoring the trend of vital signs during HFpEF hospitalization.

Mortality in Advanced Chronic Obstructive Pulmonary Disease and Heart Failure Following Cardiopulmonary Rehabilitation

Cardiopulmonary rehabilitation (CR) improves physical function and quality of life (QoL) in chronic obstructive pulmonary disease (COPD) and heart failure (HF), but it is unknown if CR improves outcomes in very severe disease. This study's purpose was to describe functional capacity (6-min walk distance [6MWD], steps/day), symptoms (dyspnea, depression), QoL (Short-Form Health Survey-Veterans [SF-36 V]) and cardiopulmonary function ( N-terminal pro-brain natriuretic peptide [NT-proBNP], forced expiratory volume in 1 s [FEV1]), and derive predictors of mortality among patients with severe COPD and HF who participated in CR.

METHODS AND RESULTS

In this secondary analysis of a randomized controlled trial comparing two CR methods in severe COPD and HF, 90 (COPD = 63, HF = 27) male veterans, mean age 66 ± 9.24 years, 79% Caucasian, and body mass index 31 kg/m2, were followed for 12 months after CR. The COPD group had greater functional decline than the HF group (6MWD, p = .006). Dyspnea was lower ( p = .001) and QoL higher ( p = .006) in the HF group. Mean NT-proBNP was higher in the HF group at all time points. FEV1 improved over 12 months in both groups ( p = .01). Mortality was 8.9%, 16.7%, and 37.8% at 12, 24, and 60 months, respectively. One-year predictors of mortality were baseline total steps (<3,000/day), 6MWD (<229 meters), and NT-proBNP level (>2,000 mg/pg).

CONCLUSIONS

In very severe COPD and HF, risks of mortality over 12 months can predict patients unlikely to benefit from CR and should be considered at initial referral.

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.

Is Time of the Essence? The Impact of Time of Hospital Presentation in Acute Heart Failure: Insights From ASCEND-HF Trial

OBJECTIVES

As the largest acute heart failure (AHF) trial conducted to date, the global ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) trial database presented an opportunity to systematically describe the relationship among time of hospital presentation, clinical profile, inpatient management, and outcomes among patients admitted with AHF.

BACKGROUND

Time of hospital presentation has been shown to impact outcomes among patients hospitalized with many conditions. However, the association among time of presentation and patient characteristics, management, and clinical outcomes among patients hospitalized with AHF has not been well characterized.

METHODS

A post hoc analysis of the ASCEND-HF trial was performed, which enrolled 7,141 patients hospitalized for AHF. Patients were divided based on when they presented to the hospital; regular hours were defined as 9 am to 5 pm, Monday through Friday, and off hours were defined as 5 pm to 9 am, Monday through Friday and weekends. Clinical characteristics and outcomes were compared by time of presentation.

RESULTS

Overall, 3,298 patients (46%) presented during off hours. Off-hour patients were more likely to have orthopnea (80% vs. 74%, respectively) and rales (56% vs. 49%, respectively) than regular-hour patients. Off-hour patients were more likely to receive intravenous (IV) nitroglycerin (18% vs. 11%, respectively) and IV loop diuretics (92% vs. 86%, respectively) as initial therapy and reported greater relief from dyspnea at 24 h (odds ratio [OR]: 1.14; 95% confidence interval [CI]: 1.04 to 1.24; p = 0.01) than regular-hour patients. After adjustment, off-hour presentation was associated with significantly lower 30-day mortality (OR: 0.74; 95% CI: 0.57 to 0.96; p = 0.03) and 180-day mortality (hazard ratio [HR]: 0.82; 95% CI: 0.72 to 0.94; p = 0.01) but similar 30-day rehospitalization rates (p = 0.40).

CONCLUSIONS

In this AHF trial, patients admitted during off hours exhibited a distinct clinical profile, experienced greater dyspnea relief, and had lower post-discharge mortality than regular-hour patients. These findings have implications for future AHF trials.

The '6W' multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions

OBJECTIVES

To synthesize concepts and approaches related to the analysis of patterns or processes of care and patient's outcomes into a comprehensive model of care trajectories, focusing on hospital readmissions for patients with chronic ambulatory care sensitive conditions (ACSCs).

STUDY DESIGN

Narrative literature review.

METHODS

Published studies between January 2000 and November 2017, using the concepts of 'continuity', 'pathway', 'episode', and 'trajectory', and focused on readmissions and chronic ACSCs, were collected in electronic databases. Qualitative content analysis was performed with emphasis on key constituents to build a comprehensive model.

RESULTS

Specific common constituents are shared by the concepts reviewed: they focus on the patient, aim to measure and improve outcomes, follow specific periods of time and consider other factors related to care providers, care units, care settings, and treatments. Using these common denominators, the comprehensive '6W' multidimensional model of care trajectories was created. Considering patients' attributes and their chronic ACSCs illness course ('who' and 'why' dimensions), this model reflects their patterns of health care use across care providers ('which'), care units ('where'), and treatments ('what'), at specific periods of time ('when').

CONCLUSIONS

The '6W' model of care trajectories could provide valuable information on 'missed opportunities' to reduce readmission rates and improve quality of both ambulatory and inpatient care.

Interaction of Body Mass Index on the Association Between N-Terminal-Pro-b-Type Natriuretic Peptide and Morbidity and Mortality in Patients With Acute Heart Failure: Findings From ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure)

BACKGROUND

Higher body mass index (BMI) is associated with lower circulating levels of N-terminal-pro-b-type natriuretic peptide (NT-proBNP). The Interaction between BMI and NT-proBNP with respect to clinical outcomes is not well characterized in patients with acute heart failure.

METHODS AND RESULTS

A total of 686 patients from the biomarker substudy of the ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated HF ) clinical trial with documented NT-proBNP levels at baseline were included in the present analysis. Patients were classified by the World Health Organization obesity classification (nonobese: BMI <30 kg/m², Class I obesity: BMI 30-34.9 kg/m², Class II obesity BMI 35-39.9 kg/m², and Class III obesity BMI ≥40 kg/m²). We assessed baseline characteristics and 30- and 180-day outcomes by BMI class and explored the interaction between BMI and NT-proBNP for these outcomes. Study participants had a median age of 67 years (55, 78) and 71% were female. NT-proBNP levels were inversely correlated with BMI (P<0.001). Higher NT-proBNP levels were associated with higher 180-day mortality (adjusted hazard ratio for each doubling of NT-proBNP, 1.40; 95% confidence interval, 1.16, 1.71; P<0.001), but not 30-day outcomes. The effect of NT-proBNP on 180-day death was not modified by BMI class (interaction P=0.24).

CONCLUSIONS

The prognostic value of NT-proBNP was not modified by BMI in this acute heart failure population. NT-proBNP remains a useful prognostic indicator of long-term mortality in acute heart failure even in the obese patient.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00475852.

Heart failure labelled patients with missing ejection fraction in primary care: prognosis and determinants

Background

It is common to find a high variability in the accuracy of heart failure (HF) diagnosis in electronic primary care medical records (EMR). Our aims were to ascertain (i) whether the prognosis of HF labelled patients whose ejection fraction (EF) was missing in their EMR differed from those that had it registered, and (ii) the causes contributing to the differences in the availability of EF in EMR.

Methods

Retrospective cohort analyses based on clinical records of HF and attended at 52 primary healthcare centres of Barcelona (Spain). Information of 8376 HF patients aged > 40 years followed during five years was analyzed.

Results

EF was available only in 8.5% of primary care medical records. Cumulate incidence for mortality and hospitalization from 1st January 2009 to 31th December 2012 was 37.6%. The highest rate was found in patients with missing EF (HR 1.84, 95% CI 1.68 -1.95) compared to those with preserved EF. Patients hospitalized the previous year and those requiring home healthcare (HR 1.81, 95% Confidence Interval 1.68-1.95 and HR 1.58, 95% CI 1.46-1.71, respectively) presented a higher risk of having an adverse outcome. Older patients, those more socio-economically disadvantaged, obese, requiring home healthcare, and taking loop diuretics were less likely to have an EF registered.

Conclusions

EF is poorly recorded in primary care. HF patients with EF missing at medical records had the worst prognosis. They tended to be older, socio-economically disadvantaged, and more fragile.