Get With The Guidelines-Heart Failure Research Articles

Sequential organ failure assessment score on admission predicts long-term mortality in acute heart failure patients.

AimsThe sequential organ failure assessment (SOFA) score has been a widely used predictor of outcomes in the intensive care unit, whereas short‐term and long‐term survivals of heart failure (HF) patients are predicted by the American Heart Association Get With the Guidelines–Heart Failure (GWTG‐HF) risk score. The purpose of present study was to examine whether the SOFA score on admission is more useful for predicting long‐term mortality in acute HF patients than the GWTG‐HF risk score.Methods and resultsA total of 269 patients (mean age, 78.5 ± 10.9 years; all‐cause mortality, 53.9%) seen in a single facility from January 2007 to December 2016 were enrolled retrospectively. They were followed up for a mean of 32.1 ± 22.3 months. All‐cause death was associated with higher SOFA and GWTG‐HF risk scores. However, no significant difference was observed in the area under the curve value between the scores. Kaplan–Meier survival analysis indicated that higher SOFA scores (P < 0.001) and GWTG‐HF risk scores (P < 0.001) were related to increased probabilities of all‐cause death. On multivariate Cox proportional hazard model analysis, the SOFA score (P < 0.001) and GWTG‐HF (P < 0.001) score were independent predictors of all‐cause death. Incorporating the SOFA score into the GWTG‐HF risk score yielded a significant net reclassification improvement and integrated discrimination improvement. On decision curve analysis, the net benefit of the SOFA score model when compared with the reference model was greater across the range of threshold probabilities.ConclusionsIn acute HF patients, long‐term all‐cause mortality can be predicted by the SOFA score. Discriminative performance metrics, such as net reclassification improvement, integrated discrimination improvement, and decision curve analysis, for predicting mortality were improved when the SOFA score was incorporated.

Rhythm Control Versus Rate Control in Patients With Atrial Fibrillation and Heart Failure With Preserved Ejection Fraction: Insights From Get With The Guidelines-Heart Failure.

BackgroundLimited data exist to guide treatment for patients with heart failure with preserved ejection fraction and atrial fibrillation, including the important decision regarding rate versus rhythm control.Methods and ResultsWe analyzed the Get With The Guidelines—Heart Failure (GWTG‐HF) registry linked to Medicare claims data from 2008 to 2014 to describe current treatments for rate versus rhythm control and subsequent outcomes in patients with heart failure with preserved ejection fraction and atrial fibrillation using inverse probability weighted analysis. Rhythm control was defined as use of an antiarrhythmic medication, cardioversion, or AF ablation or surgery. Rate control was defined as use of any combination of β‐blocker, calcium channel blocker, and digoxin without evidence of rhythm control. Among 15 682 fee‐for‐service Medicare patients, at the time of discharge, 1857 were treated with rhythm control and 13 825 with rate control, with minimal differences in baseline characteristics between groups. There was higher all‐cause death at 1 year in the rate control compared with the rhythm control group (37.5% and 30.8%, respectively, P<0.01). The lower 1‐year all‐cause death in the rhythm control group remained after risk adjustment (adjusted hazard ratio, 0.86; 95% CI, 0.75–0.98; P=0.02).ConclusionsRhythm control in patients aged 65 and older with heart failure with preserved ejection fraction and AF was associated with a lower risk of 1 year all‐cause mortality. Future prospective randomized studies are needed to explore this potential benefit.

Value of Neutrophil to Lymphocyte Ratio and Its Trajectory in Patients Hospitalized With Acute Heart Failure and Preserved Ejection Fraction

The neutrophil to lymphocyte ratio (NLR) has been proposed as a simple and routinely obtained marker of inflammation. This study sought to determine whether the NLR on admission as well as NLR trajectory would be complementary to the Get with the Guidelines Heart Failure (GWTG-HF) risk score in patients hospitalized with acute heart failure with preserved ejection fraction (HFpEF).Using the Stanford Translational Research Database, we identified 443 patients between January 2002 and December 2013 hospitalized with acute HFpEF and with complete data of NLR both on admission and at discharge. The primary endpoint was all-cause mortality. Mean age was 77 ± 16 years, 58% were female, with a high prevalence of diabetes mellitus (35.4%), coronary artery disease (58.2%), systemic hypertension (96.6%) and history of atrial fibrillation (57.5%). Over a median follow-up of 2.2 years, 121 (27.3%) patients died. The median NLR on admission was 6.5 (IQR 3.6 - 11.1); a majority of patients decreased their NLR during the course of hospitalization. On multivariable Cox modeling, both NLR on admission (HR 1.18 95% CI (1.00 - .38), p = 0.04) and absolute NLR trajectory (HR 1.26 95% CI (1.10 - 1.45), p = 0.001) were shown to be incremental to GWTG-HF risk score (p < 0.05) for outcome prediction. Adding the NLR or absolute NLR trajectory to the GWTG-HF risk score significantly improved the area under the operator-receiver curve and the reclassification up to 3 years after admission.This simple, readily available marker of inflammation may be useful when stratifying the risk of patients hospitalized with HFpEF.

P6339Sequential organ failure assessment score on admission predicts long-time mortality of the patients with acute heart failure

Abstract Background Despite the remarkable advances in the treatment options of acute heart failure (HF), prognosis assessment remains an ongoing challenge. Previous studies revealed only a moderate accuracy of models predicting mortality. Sequential Organ Failure Assessment (SOFA) Score are widely used in the intensive care unit (ICU) to predict outcome and predicted higher long-time mortality in unselected patients in cardiac ICU. In addition, the American Heart Association Get With the Guidelines–Heart Failure (GWTG-HF) risk score allows for risk stratification of 30-day outcome for patients hospitalized with HF. The purpose of this study was to evaluate whether SOFA score on admission is useful for long-time mortality prediction in acute HF patients and also to assess the discriminative performance as compared with GWTG-HF risk score. Methods This was a single-centre, retrospective cohort study. Between January 2007 and December 2016, we screened eligible 661 consecutive patients with acute HF administered at our hospital. SOFA score on admission of 294 patients was able to calculate retrospectively. We enrolled 269 patients who could complete follow up evaluation for more than 1 year. Endpoint was all-cause mortality after admission. Additive information of SOFA score was evaluated by area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA). Results The 269 patients were included in this study (78.5±10.9 years; 136 men; left ventricular ejection fraction [EF], 49.8±16.6%) during a mean follow-up of 32.1±22.3 months. Patients with all-cause death had higher SOFA score (4.2±2.3 versus 2.8±1.8, p&lt;0.001; AUC, 0.689) and GWTG-HF risk score (44.0±7.6 versus 38.1±7.9, P&lt;0.001, AUC, 0.692). Kaplan-Meier survival analysis demonstrated higher SOFA scores (P&lt;0.001) and GWTG-HF risk scores (P&lt;0.001) appears to be related to increase probabilities of all cause death. A multivariate Cox proportional hazard model were made with adjustment for SOFA score, GWTG-HF risk score, age, gender and ejection fraction. As a result, SOFA score (hazard ratio [HR] 1.227; 95% confidence interval [CI], 1.130 to 1.326; P&lt;0.001), GWTG-HF (HR, 1.054; 95% CI, 1.029 to 1.078; P&lt;0.001) and age (HR, 1.069; 95% CI 1.048 to 1.092; P&lt;0.001) were independent predictors of all cause death and HR of SOFA score was the highest in these parameters. Incorporating SOFA score into GWTG-HF score yielded a significant NRI (0.528 (95% CI 0.291 to 0.765) and IDI (0.046 (95% CI 0.020 to 0.072). In DCA, compared with the reference model, the net benefit for SOFA score model was greater across the range of threshold probabilities. Conclusions The SOFA score, simple and validated mortality risk score can predict long-term all-cause mortality in patients with acute HF. Discriminative performance metrics such as NRI, IDI and DCA were improved on incorporation of the SOFA score for prediction of mortality.

P762Usefulness of 2-year iodine-123 metaiodobenzylguanidine-based risk model for the post-discharge risk stratification in patients with acute decompensated heart failure

Abstract Background A four-parameter risk model including cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging and readily available clinical parameters has been recently developed for the prediction of 2-year cardiac mortality risk in patients with chronic heart failure (CHF) using a Japanese CHF database consisting of 1322 patients. On the other hand, the Acute Decompensated Heart Failure National Registry (ADHERE) and Get With The Guidelines-Heart Failure (GWTG-HF) risk scores, simple tools to predict risk of in-hospital mortality, have been reported to be predictive of post-discharge outcome in patients with acute decompensated heart failure (ADHF). However, there is no information available on the usefulness of 2-year MIBG-based cardiac mortality risk score for the prediction of post-discharge prognosis in ADHF patients and its comparison with the ADHERE and GWTG-HF risk scores. Purpose We sought to validate the predictability of the 2-year MIBG-based cardiac mortality risk score for post-discharge clinical outcome in ADHF patients, and to compare its prognostic value with those of ADHERE and GWTG-HF risk scores. Methods We studied 297 consecutive patients who were admitted for ADHF, survived to discharge, and had definitive 2-year outcomes. Venous blood sampling was performed on admission, and echocardiography and cardiac MIBG imaging were performed just before discharge. In cardiac MIBG imaging, the cardiac MIBG heart-to-mediastinum ratio (HMR) was measured from the chest anterior view images obtained at 20 and 200 min after isotope injection. The 2-year cardiac mortality risk score was calculated using four parameters, including age, left ventricular ejection fraction, NYHA functional class, and HMR on delayed image. The patients were stratified into three groups based on the 2-year cardiac mortality risk score: low- (&lt;4%), intermediate- (4–12%), and high-risk (&gt;12%) groups. The ADHERE and GWTG-HF risk scores were also calculated from admission data as previously reported. The predictive ability of the scores was compared using receiver operating characteristic curve analysis. The endpoint was a composite of all-cause mortality and unplanned hospitalization for worsening heart failure. Results During a follow-up period, 110 patients reached the primary endpoint. There was significant difference in the rate of primary endpoint among the three groups stratified by 2-year cardiac mortality risk score (low-risk group: 18%, intermediate-risk group: 36%, high-risk group: 64%, Figure 1A). The 2-year cardiac mortality risk score demonstrated a greater area under the curve for the primary endpoint compared to the ADHERE and the GWTG-HF risk scores (Figure 1B). Figure 1 Conclusions The 2-year MIBG-based cardiac mortality risk score is also useful for the prediction of post-discharge clinical outcome in ADHF patients, and its prognostic value is superior to those of the ADHERE and the GWTG-HF risk scores.

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure.

AimsThis study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).Methods and results12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines–Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876–0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720–0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001).ConclusionDAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.

Improving risk stratification in heart failure with preserved ejection fraction by combining two validated risk scores

IntroductionThe Intermountain Risk Score (IMRS) was developed and validated to predict short-term and long-term mortality in hospitalised patients using demographics and commonly available laboratory data. In this study, we sought...

Abstract 240: A Heart Failure Collaborative to Accelerate Improved Patient Outcomes in Three Metro Markets

Background &amp; Objectives: Nearly 6.5 million Americans are living with heart failure (HF), the leading cause of hospitalization among adults aged 65 and older. Despite advancements in medical therapy, HF patient outcomes remain of concern. HF readmission rates remain high and only half of patients diagnosed with heart failure are expected to live beyond 5 years of diagnosis. Increasing prevalence and poor outcomes provide significant opportunities for improvement. Hospital participation in a quality initiative to improve care by promoting adherence to the scientific guidelines, measured using Get With the Guidelines®-Heart Failure (GWTG-HF), assists hospitals in effectively delivering high quality care to improve patient outcomes. The objective of this initiative is to form multidisciplinary collaboratives that will rapidly implement evidence-based guidelines, develop and share best practices to improve heart failure patient outcomes. Methods: The AHA will lead a quality improvement initiative for transforming heart failure care in three major metropolitan cities including Chicago, Milwaukee and St. Louis. A retrospective review was conducted using GWTG-HF on measures with low adherence from participating hospitals in the metro markets. Baseline data of specific HF measures from Quarter 1, 2018 were analyzed to determine areas of improvement needed. Using regional and 1:1 hospital meetings, AHA will lead a quality improvement initiative to transform HF patient care. The HF project will focus on professional education, enhancing systems of care and facilitating the sharing of best practices. Measures: The patient population will include patients from GWTG-HF with a primary diagnosis of heart failure and focus on improving 7 HF measures. Results: The project goal within the 2-year timeframe of this initiative is to achieve 20% improvement from baseline data OR achieve AHA’s 85% adherence threshold in each measure. AHA Quality staff will observe and monitor market-level data within GWTG-HF to uncover and recommend improvements, provide consultation to clinicians, deliver targeted training and resources such as webinars, conferences, toolkits, care pathways, educational materials and to foster best-practice sharing to address common barriers. Limitations: Participating hospitals vary in size, discharge volume, GWTG participation tenure, FTE support and resources. There is incomplete baseline date for the three thirty day follow up measures. Initiative implementation may vary slightly in 3 metro markets Conclusions: The two-year multi-city quality initiative will bring together multiple hospitals to share best practices, develop resources and analyze Get With The Guidelines data for performance improvement to accelerate heart failure patient outcomes. Further investigation is merited evaluating the effectiveness of quality collaboratives to enhance care.

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during initial treatment or screening. This study aimed to derive and validate an echocardiography-based mortality prediction model for HD using deep learning (DL). In this multicenter retrospective cohort study, the subjects were admitted adult (age≥18years) HD patients who underwent echocardiography. The outcome was in-hospital mortality. We extracted predictor variables from echocardiography reports using text mining. We developed deep learning-based prediction model using derivation data of a hospital A. And we conducted external validation using echocardiography report of hospital B. We conducted subgroup analysis of coronary heart disease (CHD) and heart failure (HF) patients of hospital B and compared DL with the currently used predictive models (eg, Global Registry of Acute Coronary Events (GRACE) score, Thrombolysis in Myocardial Infarction score (TIMI), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and Get With The Guidelines-Heart Failure (GWTG-HF) score). The study subjects comprised 25776 patients with 1026 mortalities. The areas under the receiver operating characteristic curve (AUROC) of the DL model were 0.912, 0.898, 0.958, and 0.913 for internal validation, external validation, CHD, and HF, respectively, and these results significantly outperformed other comparison models. This echocardiography-based deep learning model predicted in-hospital mortality among HD patients more accurately than existing prediction models and other machine learning models.

Abstract 16745: Incremental Prognostic Value of Get With the Guidelines Heart Failure Risk Score and Comorbidity in Patients Admitted for Acute Decompensated Heart Failure: Insights From Osaka Prefectural Acute Heart Failure Registry (OPAR)

Background: The Get With the Guidelines-Heart Failure (GWTG-HF) risk score was recently developed to predict in-hospital mortality in patients (pts) admitted with acute decompensated heart failure (ADHF). On the other hand, comorbidity is also associated with poor clinical outcome in HF patients. However, there is no information available on the prognostic value of combining GWTG-HF risk score and comorbidity in patients admitted for ADHF. Methods: We prospectively studied 396 ADHF patients with survival discharge. GWTG risk score consists of 7 admission variables (blood urea nitrogen, systolic blood pressure, age, sodium, heart rate, race, and lung disease). Comorbidity was measured with the Age-adjusted Charlson comorbidity index (ACCI) which is commonly used for the evaluation of the comorbid condition which is weighted and scored, with additional points added for age. The endpoint was all-cause death (ACD). Results: During a follow-up period of 2.2±1.4 years, 114 patients had ACD. At multivariate Cox analysis, GWTG-HF risk score (p&lt;0.0001) and ACCI (p=0.0077) were significantly and independently associated with ACD, after the adjustment with sex, NYHA class, history of worsening heart failure hospitalization, LVEF, eGFR and plasma BNP level at the discharge. Patients with both higher GWTG-HF risk score (&gt;39 by ROC analysis, AUC 0.714 [95%CI 0.667-0.758]) and higher CCI (&gt;5 by ROC analysis, AUC 0.653 [95%CI 0.604-0.700]) had a significantly increased risk of ACD than either or none of them (46% vs 28% vs 7%, p&lt;0.0001). Adjusted hazard ratio in patients with both higher GWTG-HF risk score and ACCI was 9.4 (95%CI 4.3-20.6), which was two-fold of that in patients with either of them (4.5[95%CI 2.0-10.1]). Conclusions: GWTG-HF risk score and comorbidity had an prognostic incremental value in patients admitted for ADHF.

Clinical Significance of Get With the Guidelines-Heart Failure Risk Score in Patients With Chronic Heart Failure After Hospitalization.

BackgroundThe Get With the Guidelines–Heart Failure (GWTG‐HF) risk score was developed using American Heart Association GWTG‐HF program data and predicts in‐hospital mortality in patients with acute heart failure (HF). We aimed to clarify the prognostic impacts of the GWTG‐HF risk score in patients with HF after discharge.Methods and ResultsWe examined the GWTG‐HF score in 1452 patients with HF, who were admitted to our hospital and discharged after treatment, by calculating 7 predetermined variables. We divided all subjects into 3 groups according to the GWTG‐HF risk score (low, moderate, and high score groups). The plasma B‐type natriuretic peptide level significantly increased with increasing GWTG‐HF risk score severity (median values of B‐type natriuretic peptide: 167.0 in low, 260.7 in moderate, and 418.2 pg/mL in high score groups). We followed up all subjects after discharge, and there were 347 (23.9%) all‐cause deaths and 407 (28.0%) cardiac events in follow‐up periods. A Kaplan‐Meier survival curve demonstrated that event rates of all‐cause death and cardiovascular events, including worsening HF and cardiac death, significantly increased with increasing GWTG‐HF risk score severity in all subjects, and also in 749 patients with HF with preserved ejection fraction (ejection fraction ≥50%) and 703 patients with HF with reduced ejection fraction (ejection fraction <50%) patients. The multivariable Cox proportional hazard regression analysis demonstrated that the GWTG‐HF risk score was one of the significant predictors of all‐cause mortality and cardiac events (all‐cause mortality: hazard ratio, 1.537, 95% confidence interval, 1.172–2.023; cardiac events: hazard ratio, 1.584, 95% confidence interval, 1.344–1.860, per 10‐point increase of GWTG‐HF score).ConclusionsThe GWTG‐HF risk score is a useful multivariable score model for several years after hospitalization in patients with HF in a Japanese population.

Vaccination Trends in Patients With Heart Failure: Insights From Get With The Guidelines–Heart Failure

ObjectivesThis study sought to evaluate and contribute to the limited data on U.S. hospital practice patterns with respect to respiratory vaccination in patients hospitalized with heart failure (HF). BackgroundRespiratory infection is a major driver of morbidity in patients with HF, and many influenza and pneumococcal infections may be prevented by vaccination. MethodsThis study evaluated patients hospitalized at centers participating in the Get With The Guidelines–HF (GWTG-HF) registry from October 2012 to March 2017. The proportion of patients receiving vaccination was described for influenza and pneumococcal vaccination, respectively. The association of hospital-level vaccination rates with individual GWTG-HF performance measures and defect-free care was evaluated using multivariable modeling. ResultsThis study evaluated 313,761 patients discharged from 392 hospitals during the study period. The proportion of patients receiving influenza vaccination was 68% overall and declined from 70% in 2012 to 2013 to 66% in 2016 to 2017 (p < 0.001), although this was not statistically significant after adjustment (odds ratio: 1.05 per flu season; 95% confidence interval [CI]: 0.94 to 1.18). The proportion of patients receiving pneumococcal vaccination was 66% overall and decreased over the study period from 71% in 2013 to 60% in 2016 (p < 0.001), remaining significant after adjustment (odds ratio: 0.75 per calendar year; 95% CI: 0.67 to 0.84). Hospitals with higher vaccination rates were more likely to discharge patients with higher performance on defect-free care and individual GWTG-HF performance measures (p < 0.001). In a subset of patients with linked Medicare claims, vaccinated patients had similar rates of 1-year all-cause mortality (adjusted hazard ratio: 0.96 [95% CI: 0.89 to 1.03] for influenza vaccination; adjusted hazard ratio: 0.95 [95% CI: 0.89 to 1.01] for pneumococcal vaccination) compared with those not vaccinated. ConclusionsNearly 1 in 3 patients hospitalized with HF at participating hospitals were not vaccinated for influenza or pneumococcal pneumonia, and vaccination rates did not improve from 2012 to 2017. Hospitals that exhibited higher vaccination rates performed well with respect to other HF quality of care measures. Vaccination status was not associated with differences in clinical outcomes. Further randomized controlled data are needed to assess the relationship between vaccination and outcomes.

Home-Time After Discharge Among Patients Hospitalized With Heart Failure

BackgroundSurveys of patients with cardiovascular disease have suggested that "home-time"—being alive and out of any health care institution—is a prioritized outcome. This novel measure has not been studied among patients with heart failure (HF). ObjectivesThis study sought to characterize home-time following hospitalization for HF and assess its relationship with patient characteristics and traditionally reported clinical outcomes. MethodsUsing GWTG-HF (Get With The Guidelines-Heart Failure) registry data, patients discharged alive from an HF hospitalization between 2011 and 2014 and ≥65 years of age were identified. Using Medicare claims, post-discharge home-time over 30-day and 1-year follow-up was calculated for each patient as the number of days alive and spent outside of a hospital, skilled nursing facility (SNF), or rehabilitation facility. ResultsAmong 59,736 patients, 57,992 (97.1%) and 42,153 (70.6%) had complete follow-up for home-time calculation through 30 days and 1 year, respectively. The mean home-time was 21.6 ± 11.7 days at 30 days and 243.9 ± 137.6 days at 1 year. Contributions to reduced home-time varied by follow-up period, with days spent in SNF being the largest contributor though 30 days and death being the largest contributor through 1 year. Over 1 year, 2,044 (4.8%) patients had no home-time following index hospitalization discharge, whereas 8,194 (19.4%) had 365 days of home-time. In regression models, several conditions were associated with substantially reduced home-time, including chronic obstructive pulmonary disease, renal insufficiency, and dementia. Through 1 year, home-time was highly correlated with time-to-event endpoints of death (tau = 0.72) and the composite of death or HF readmission (tau = 0.59). ConclusionsHome-time, which can be readily calculated from administrative claims data, is substantially reduced for many patients following hospitalization for HF and is highly correlated with traditional time-to-event mortality and hospitalization outcomes. Home-time represents a novel, easily measured, patient-centered endpoint that may reflect effectiveness of interventions in future HF studies.

Abstract 278: Trends in Prescribing Patterns for Patients Hospitalized With Heart Failure With Preserved Ejection Fraction Before and After Clinical Trial Presentation

Background: There are limited treatment options for patients with HFpEF, with guidelines focusing on diuresis and management of associated cardiovascular conditions. In the TOPCAT trial, treatment with spironolactone in patients with HFpEF compared to placebo did not significantly decrease the incidence of the composite primary outcome, but the incidence of heart failure (HF) hospitalization was significantly lower. The impact of the TOPCAT results on mineralocorticoid antagonists (MRA) prescribing rates is unknown. We analyzed discharge prescribing rates of MRAs in the Get With The Guidelines-Heart Failure (GWTG-HF) registry to assess for practice changes following dissemination of TOPCAT results in 2013. Methods: All patients recorded in GWTG-HF with LVEF ≥ 50% and discharged between January 2009 and December 2016 were included in the study. Data were summarized by calendar year quarters (Q). The TOPCAT trial results were first presented in November 2013, so patients were dichotomized into those discharged before TOPCAT (2009Q1 through 2013Q3) and those after TOPCAT (2013Q4 through 2016Q4). Discharge prescribing rates for MRAs, beta blockers, ACE inhibitors, and ARBs were assessed for overall trends and change in prescribing rates after 2013Q3 by segmented regression. Results: Of 142,201 HFpEF patients included in the study, 18,581 (13.1%) were prescribed a MRA at discharge. Throughout the study period, MRA prescribing steadily increased (2009Q1 = 11.3%; 2016Q4 = 14.8%), without significant change in overall trend following announcement of TOPCAT results (p=0.17) (Figure 1). Discharge prescribing rates were largely stable for beta blockers but with a statistically significant change in trend after TOPCAT (2009Q1 = 82.8%; 2016Q4 = 80.5%; p &lt; 0.0001), while prescribing rates for ACE/ARB decreased significantly throughout the study period without change in trend following TOPCAT (2009Q1 = 74.7%; 2016Q4 = 53.7%; p = 0.58). Conclusions: The prescribing rates for MRAs in HFpEF at hospital discharge modestly increased over time, with little discernible influence of TOPCAT. These findings suggest there has been gradual, modest MRA adoption in the absence of other therapeutic options and highlight the need for further trials on the efficacy and safety of MRAs in HFpEF.

GET WITH THE GUIDELINES-HEART FAILURE RISK SCORE IS USEFUL FOR THE PREDICTION OF MODE OF DEATH IN PATIENTS ADMITTED FOR ACUTE DECOMPENSATED HEART FAILURE

The Get With the Guidelines-Heart Failure (GWTG-HF) risk score was recently developed to predict in-hospital mortality in patients (pts) admitted with acute decompensated heart failure (ADHF). However, little is known about the ability of the risk score to predict mode of death in ADHF pts. We

THE COMPLEMENTARY VALUE OF THE GET WITH THE GUIDELINES: HEART FAILURE RISK SCORE AND LABORATORY MARKERS AT DISCHARGE IN PREDICTING MORTALITY IN PATIENTS HOSPITALIZED WITH HEART FAILURE WITH PRESERVED EJECTION FRACTION

The aim of this study is to assess the complementary value of the Get with The Guidelines - Heart Failure (GWTG-HF) risk score and laboratory markers collected at discharge in predicting outcome in patients hospitalized with acute Heart Failure with preserved Ejection Fraction (HFpEF). Using

Performance of existing risk scores around heart transplantation: validation study in a 4-year cohort.

Several risk scores exist to help identify best candidate recipients for heart transplantation (HTx). This study describes the performance of five heart failure risk scores and two post-HTx mortality risk scores in a French single-centre cohort. All patients listed for HTx through a 4-year period were included. Waiting-list risk scores [Heart Failure Survival Score (HFSS), Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC), Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF) and Get With The Guidelines-Heart Failure (GWTG-HF)] and post-HTx scores Index for Mortality Prediction After Cardiac Transplantation (IMPACT and CARRS) were computed. Main outcomes were 1-year mortality on waiting list and after HTx. Performance was assessed using receiver operator characteristic (ROC), calibration and goodness-of-fit analyses. The cohort included 414 patients. Waiting-list mortality was 14.0%, and post-HTx mortality was 16.3% at 1-year follow-up. Heart failure risk scores had adequate discrimination regarding waiting-list mortality (ROC AUC for HFSS=0.68, SHFM=0.74, OPTIMIZE-HF=0.72, MAGGIC= 0.70 and GWTG=0.77; all P-values <0.05). On the contrary, post-HTx risk scores did not discriminate post-HTx mortality (AUC for IMPACT=0.58, and CARRS=0.48, both P-values >0.50). Subgroup analysis on patients undergoing HTx after ventricular assistance device (VAD) implantation (i.e. bridge-to-transplantation) (n=36) showed an IMPACT AUC=0.72 (P<0.001). In this single-centre cohort, existing heart failure risk scores were adequate to predict waiting-list mortality. Post-HTx mortality risk scores were not, except in the VAD subgroup.

Gender Differences in Presentation, Treatment, and In-Hospital Outcome of Patients Admitted With Heart Failure Complicated by Atrial Fibrillation (from the Get With the Guidelines—Heart Failure [GWTG-HF] Registry)

Almost 25% of patients with heart failure (HF) have coexisting atrial fibrillation (AF), the latter of which may increase morbidity and mortality. Despite the high prevalence of HF with concomitant AF, this subgroup of patients remains understudied. This study examines gender differences in presentation, treatment and in-hospital outcome of patients with HF and AF. The Get With the Guidelines-Heart Failure (GWTG-HF) database enrolled 6,496 patients with HF who presented to Cooper University Hospital from 2005 to 2012. Twenty-four percent (1,561 patients) had concomitant AF. Pearson chi-square tests and the Student T-tests were used to compare patient characteristics by gender. Multivariate logistic regression was used to predict in-hospital mortality. Six hundred sixty-nine (42.8%) patients with HF and AF were women. Women were older (p <0.001), had a higher ejection fraction (p <0.001), had systolic hypertension (p <0.001), and were more likely to have health insurance (p <0.001). Despite a higher CHADS2 score in women (p = 0.007), there was no gender difference in percent of anticoagulation medications prescribed before admission. Women were less likely to present with dizziness, lightheadedness, or syncope, and were more likely to be compliant with medications and diet recommendations before admission. Despite differences in presentation, co-morbidities, and therapy, in-hospital mortality was similar between men and women. Decreased appetite or early satiety predicted in-hospital mortality in women, whereas age, chest pain on admission, and decreased appetite or early satiety predicted in-hospital mortality in men. In conclusion, women presenting with HF complicated by AF clinically differ from men, but despite these differences, both groups shared similar symptom presentation and in-hospital mortality rates.

Novel risk stratification with time course assessment of in-hospital mortality in patients with acute heart failure

BackgroundPatients with acute heart failure (AHF) show various clinical courses during hospitalization. We aimed to identify time course predictors of in-hospital mortality and to establish a sequentially assessable risk model.Methods and resultsWe enrolled 1,035 consecutive AHF patients into derivation (n = 597) and validation (n = 438) cohorts. For risk assessments at admission, we utilized Get With the Guidelines-Heart Failure (GWTG-HF) risk scores. We examined significant predictors of in-hospital mortality from 11 variables obtained during hospitalization and developed a risk stratification model using multiple logistic regression analysis. Across both cohorts, 86 patients (8.3%) died during hospitalization. Using backward stepwise selection, we identified five time-course predictors: catecholamine administration, minimum platelet concentration, maximum blood urea nitrogen, total bilirubin, and C-reactive protein levels; and established a time course risk score that could sequentially assess a patient's risk status. The addition of a time course risk score improved the discriminative ability of the GWTG-HF risk score (c-statistics in derivation and validation cohorts: 0.776 to 0.888 [p = 0.002] and 0.806 to 0.902 [p<0.001], respectively). A calibration plot revealed a good relationship between observed and predicted in-hospital mortalities in both cohorts (Hosmer-Lemeshow chi-square statistics: 6.049 [p = 0.642] and 5.993 [p = 0.648], respectively). In each group of initial low-intermediate risk (GWTG-HF risk score <47) and initial high risk (GWTG-HF risk score ≥47), in-hospital mortality was about 6- to 9-fold higher in the high time course risk score group than in the low-intermediate time course risk score group (initial low-intermediate risk group: 20.3% versus 2.2% [p<0.001], initial high risk group: 57.6% versus 8.5% [p<0.001]).ConclusionsA time course assessment related to in-hospital mortality during the hospitalization of AHF patients can clearly categorize a patient's on-going status, and may assist patients and clinicians in deciding treatment options.

Prediction model of in-hospital mortality in elderly patients with acute heart failure based on retrospective study.

ObjectivesThe aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hospitalized with acute heart failure (AHF).Methods2486 patients who were 60 years and older from intensive care units of Cardiology Department in the hospital were analyzed. Independent risk factors for in-hospital mortality were obtained by binary logistic regression and then used to establish the risk prediction score system (RPSS). The area under the curve (AUC) of receiver operator characteristic and C-statistic test were adopted to assess the performance of RPSS and to compare with previous get with the guidelines–heart failure (GWTG-HF).ResultsBy binary logistic regression analysis, heart rate (OR: 1.043, 95% CI: 1.030–1.057, P < 0.001), left ventricular ejection fraction (OR: 0.918, 95% CI: 0.833–0.966, P < 0.001), pH value (OR: 0.001, 95% CI: 0.000–0.002, P < 0.001), renal dysfunction (OR: 0.120, 95% CI: 0.066–0.220, P < 0.001) and NT-pro BNP (OR: 3.463, 95% CI: 1.870–6.413, P < 0.001) were independent risk factors of in-hospital mortality for elderly AHF patients. Additionally, RPSS, which was composed of all the above-mentioned parameters, provided a better risk prediction than GWTG-THF (AUC: 0.873 vs. 0.818, P = 0.016).ConclusionsOur risk prediction model, RPSS, provided a good prediction for in-hospital mortality in elderly patients with AHF.