Decompensated Heart Failure National Registry Research Articles

Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21802 patients, of which 14539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF≤40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12910 (59.2%) had heart failure with preserved EF (EF≥50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P<0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in-hospital mortality among patients hospitalized with a diagnosis of heart failure.

Use of the ADHERE Risk Model as a Predictor of Risk of in-Hospital Worsening Heart Failure in a Cohort.

Patients admitted with acute decompensated heart failure (HF) are subject to developing worsening episodes that require more complex interventions. The Acute Decompensated Heart Failure National Registry (ADHERE) risk model was developed in the United States to predict the risk of in-hospital worsening HF. To use the ADHERE risk model in the assessment of risk of in-hospital worsening HF and to determine its sensitivity and specificity in hospitalized patients. This cohort study was conducted at a Brazilian public university hospital, and data from 2013 to 2020 were retrospectively collected. P values < 0.05 were considered statistically significant. A total of 890 patients with a mean age of 74 ± 8 years were included. The model showed that, in the group of 490 patients at risk, 254 (51.8%) developed in-hospital worsening HF. In the group of 400 patients not at risk, only 109 (27.2%) experienced worsening HF. The results demonstrated a statistically significant curve (area under the curve = 0.665; standard error = 0.018; P < 0.01; confidence interval = 0.609 to 0.701), indicating good accuracy. The model had a sensitivity of 69.9% and a specificity of 55.2%, with a positive predictive value of 52% and a negative predictive value of 72.7%. In this cohort, we showed that the ADHERE risk model was able to discriminate patients who in fact developed worsening HF during the admission period, from those who did not.

Abnormal plasma ceramides refine high-risk patients with worsening heart failure.

Worsening heart failure (WHF) is a heterogeneous clinical syndrome with poor prognosis. More effective risk stratification tools are required to identify high-risk patients. Evidence suggest that aberrant ceramide accumulation can be affected by heart failure risk factors and as a driver of tissue damage. We hypothesized that specific ceramide lengths and ratios serve as biomarkers for risk stratification in WHF patients by reflecting pathological changes of distinct organ dysfunctions. We measured seven plasma ceramides using liquid chromatography-mass spectrometry (LC-MS) in 1,558 patients, including 1,262 participants in retrospective discovery set and 296 WHF patients in prospective validation set in BIOMS-HF study (Registry Study of Biomarkers in Heart Failure). Univariable and multivariable logistic regression models were constructed to identify associations of ceramides with organ dysfunctions. We constructed three ceramide-based scores linked independently to heart, liver, and kidney dysfunction, with ceramides and ratios included in each score specifying systemic inflammation, chronic metabolic disorder, and water-sodium retention. The combined ceramide heart failure score (CHFS) was independently associated with adverse outcomes [Hazard Ratio, 2.80 (95% CI: 1.78-4.40; P < 0.001); 2.68 995% CI: 1.12-6.46; P = 0.028)] and improved the predictive value of Acute Decompensated Heart Failure National Registry score and BNP [net reclassification index, 0.34 (95% confidence interval, CI: 0.19-0.50); 0.42 (95% CI: 0.13-0.70)] in the discovery and validation set, respectively. Lower BNP levels, but higher CHFS had the highest hazard of future adverse events in WHF patients. Abnormal plasma ceramides, associated with heart and peripheral organ dysfunctions, provide incremental prognostic information over the ADHERE score and brain natriuretic peptide concentration for risk stratification in WHF patients. This may facilitate the reclassification of high-risk patients in need of aggressive therapeutic interventions.

Derivation and validation of a machine learning-based risk prediction model in patients with acute heart failure

BackgroundRisk stratification is important in patients with acute heart failure (AHF), and a simple risk score that accurately predicts mortality is needed. The aim of this study is to develop a user-friendly risk-prediction model using a machine-learning method. MethodsA machine-learning-based risk model using least absolute shrinkage and selection operator (LASSO) regression was developed by identifying predictors of in-hospital mortality in the derivation cohort (REALITY-AHF), and its performance was externally validated in the validation cohort (NARA-HF) and compared with two pre-existing risk models: the Get With The Guidelines risk score incorporating brain natriuretic peptide and hypochloremia (GWTG-BNP-Cl-RS) and the acute decompensated heart failure national registry risk (ADHERE). ResultsIn-hospital deaths in the derivation and validation cohorts were 76 (5.1 %) and 61 (4.9 %), respectively. The risk score comprised four variables (systolic blood pressure, blood urea nitrogen, serum chloride, and C-reactive protein) and was developed according to the results of the LASSO regression weighting the coefficient for selected variables using a logistic regression model (4 V-RS). Even though 4 V-RS comprised fewer variables, in the validation cohort, it showed a higher area under the receiver operating characteristic curve (AUC) than the ADHERE risk model (AUC, 0.783 vs. 0.740; p = 0.059) and a significant improvement in net reclassification (0.359; 95 % CI, 0.10–0.67; p = 0.006). 4 V-RS performed similarly to GWTG-BNP-Cl-RS in terms of discrimination (AUC, 0.783 vs. 0.759; p = 0.426) and net reclassification (0.176; 95 % CI, −0.08–0.43; p = 0.178). ConclusionsThe 4 V-RS model comprising only four readily available data points at the time of admission performed similarly to the more complex pre-existing risk model in patients with AHF.

COMPARISON OF THE PREDICTIVE PERFORMANCE OF CARDIOGENIC SHOCK SCORES IN A REAL-WORLD LATIN AMERICA COUNTRY.

Background : Mortality in cardiogenic shock (CS) is up to 40%, and although risk scores have been proposed to stratify and assess mortality in CS, they have been shown to have inconsistent performance. The purpose was to compare CS prognostic scores and describe their performance in a real-world Latin American country. Methods : We included 872 patients with CS. The Society for Cardiovascular Angiography and Interventions (SCAI), CARDSHOCK, IABP-Shock II, Cardiogenic Shock Score, age-lactate-creatinine score, Get-With-The-Guidelines Heart Failure score, and Acute Decompensated Heart Failure National Registry scores were calculated. Decision curve analyses were performed to evaluate the net benefit of the different scoring systems. Logistic and Cox regression analyses were applied to construct area under the curve (AUC) statistics, this last one against time using the Inverse Probability of Censoring Weighting method, for in-hospital mortality prediction. Results: When logistic regression was applied, the scores had a moderate-good performance in the overall cohort that was higher AUC in the CARDSHOCK ( c = 0.666). In acute myocardial infarction-related CS (AMI-CS), CARDSHOCK still is the highest AUC (0.68). In non-AMI-CS only SCAI (0.668), CARDSHOCK (0.533), and IABP-SHOCK II (0.636) had statistically significant values. When analyzed over time, significant differences arose in the AUC, suggesting that a time-sensitive component influenced the prediction of mortality. The highest AUC was for the CARDSHOCK score (0.658), followed by SCAI (0.622). In AMI-CS-related, the highest AUC was for the CARDSHOCK score (0.671). In non-AMI-CS, SCAI was the best (0.642). Conclusions : Clinical scores show a time-sensitive AUC, suggesting that performance could be influenced by time and the type of CS. Understanding the temporal influence on the scores could provide a better prediction and be a valuable tool in CS.

Performance of the heart failure risk scores in predicting 1year mortality and short-term readmission of patients.

The aim of this study was to assess the performance of these main scores in predicting prognosis in patients with heart failure (HF). A total of 2008 patients who were admitted to the Fourth People's Hospital of Zigong, Sichuan, from December 2016 to June 2019 and diagnosed with HF were included in the study. We compared the prognostic predictive performance of Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Get With the Guidelines-Heart Failure programme (GWTG-HF), Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores, the Acute Decompensated Heart Failure National Registry (ADHERE) model, Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, and Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure (GISSI-HF) for the endpoints. The primary endpoint was 1year all-cause mortality and the secondary endpoint was the incidence of 28day readmission post-discharge. At 1year follow-up, 44 (2.21%) patients with HF died. Discrimination analyses showed that all risk scores performed reasonably well in predicting 1year mortality, with areas under the receiver operating characteristic curve (AUCs) fluctuating between 0.757 and 0.822. GISSI-HF showed the best discrimination with the AUC of 0.822 (0.768-0.876), followed by MAGGIC-HF, BCN-Bio-HF, ASCEND, SHFM, GWTG-HF, and ADHERE with AUCs of 0.819 (0.756-0.883), 0.812 (0.758-0.865), 0.802 (0.742-0.862), 0.787 (0.725-0.849), 0.762 (0.684-0.840), and 0.757 (0.681-0.833), respectively. All risk scores were similarly predictive of 28day emergency readmissions, with AUCs fluctuating between 0.609 and 0.680. Overestimation of mortality occurred in all scores except the ASCEND. The risk scores remained with good prognostic discrimination in patients with biventricular HF and in the subgroup of patients taking angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker. Currently assessed risk scores have limited clinical utility, with fair accuracy and calibration in assessing patients' 1year risk of death and poor accuracy in assessing patients' risk of readmission. There is a need to incorporate more patient-level information, use more advanced technologies, and develop models for different subgroups of patients to achieve more practical, innovative, and accurate risk assessment tools.

Derivation and validation of a machine learning-based risk prediction model for in-hospital mortality in patients with acute heart failure

Abstract Background Although risk stratification is important in patients with acute heart failure (AHF) to predict patient prognosis, pre-existing risk models have not often been used due to its complexity. Recently, machine learning methods have been presented as an alternative approach to analyzing the predictive probability of large clinical datasets. Purpose The aim of this study is to develop a user-friendly risk score developed by one of machine learning methods and compare the performance of the new risk score to the existing conventional risk models. Methods A machine-learning-based risk model was developed using least absolute shrinkage and selection operator (LASSO) regression by identifying predictors of in-hospital mortality in the derivation cohort (REALITY-AHF) and externally validating and comparing its performance with two pre-existing risk models: the Get With The Guidelines risk score incorporating brain natriuretic peptide and hypochloremia (GWTG-BNP-Cl-RS) and the acute decompensated heart failure national registry (ADHERE) risk model. Results In-hospital deaths in the derivation and validation (NARA-HF) cohorts were 76 (5.1%) and 61 (4.9%), respectively. The risk score comprised four variables (systolic blood pressure, blood urea nitrogen, serum chloride, and C-reactive protein) and was developed according to the results of the LASSO regression weighting the coefficient for selected variables using a logistic regression model (4V-RS). Even though 4V-RS comprised fewer variables, In the validation cohort, it showed a higher area under the receiver operating characteristic curve (AUC) than the ADHERE risk model (AUC, 0.783 vs. 0.740; P=0.059) and a significant improvement in net reclassification (0.359; 95% CI, 0.10–0.67; p=0.006). 4V-RS performed similarly to GWTG-BNP-Cl-RS in terms of discrimination (AUC, 0.783 vs. 0.759; p=0.426) and net reclassification (0.176; 95% CI, −0.08–0.43; p=0.178). Conclusions The 4V-RS model comprising only four readily available data points at the time of admission performed similarly to the more complex pre-existing risk model in patients with AHF. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Cardiovascular Research Fund

Development and validation of gender specific risk models in prediction of mortality for hospitalized heart failure patients in a multiethnic asian population

Abstract Funding Acknowledgements Type of funding sources: None. Background/Introduction Heart failure carries substantial morbidity and mortality. Female has different characteristics compared to male which may affect prognosis and are not represented well in many trials. Multiple risk scores for in-hospital mortality have been created and validated such as ADHERE (Acute Decompensated Heart Failure National Registry) and GWTG (Get With The Guidelines). However, these risk scores were in Western population and not gender specific. Purpose To develop and validate a gender specific risk model in predicting mortality amongst Asian patients admitted for acute decompensated heart failure (ADHF). Methods We analyzed data from our national centre’s ADHF registry. Epidemiological, clinical, laboratory and imaging variables were ascertained. Univariate and multivariate analysis using forward stepwise logistic regression were performed to identify predictors for all-cause in hospital mortality with emphasis on disparities between male and female. The accuracy of risk score was assessed using the concordance statistics while calibration was done using the Hosmer-Lemeshow method. The validity of the risk score was determined using separate gender validation cohorts. Results A total of 10148 patients admitted for ADHF were analyzed. 8262 patients admitted for ADHF from January 2009 to December 2018 (6008 males and 2254 females) were in the derivation cohort where as 1886 patients (1304 males and 582 females) were selected randomly from January 2019 to July 2021 for our validation cohort. Female had better survival (p value =0.04). There were 8 predictive variables for female cohort and 10 predictive variables for male cohort. Similar variables were low systolic blood pressure, loop diuretic use, dialysis, mechanical ventilation and CPR (cardiopulmonary resuscitation). Interestingly, there were significant differences. For female, other variables that had impact on mortality were hypertension (AOR, 0.23; 95% CI,0.09–0.58), high urea (AOR, 3.32; 95% CI, 1.36–8.09) and hyponatremia (AOR, 2.87, 95% CI, 1.21–6.78). However for male, the variables were renal insufficiency (AOR, 1.749; 95% CI, 1.13–2.72), history of stroke (AOR, 2.386, 95% CI, 1.22–4.65), LVEF less than 40% (AOR, 2.81; 95% CI, 1.40–5.63), absence of ARB/ACEi (AOR, 2.31; 95% CI, 1.48–3.60) and absence of beta blockers (AOR, 2.65; 95% CI, 1.73–4.05). The AUC for the female derivation cohort was 0.97 (95% CI, 0.95–0.99) and 0.94 (95% CI, 0.92–0.96) for male derivation cohort. AUC in the female validation cohort was 0.91 (95% CI, 0.83-0.98) and male was 0.90 (95% CI, 0.84–0.97). The risk scores showed good calibration (female; x²=8.88, p = 0.18 and male; x²=13.11, p = 0.07). Conclusion A gender specific risk score for ADHF was developed and validated successfully in our Asian population. This will change our practice in providing valuable prognostic information in both female and male thereby guiding the need for more intensive treatment. Abstract Figure. ROC Curves (Female and Male) Abstract Figure. Key Variables (Female and Male)

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

A four-parameter risk model that included cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging and readily available clinical parameters was recently developed for prediction of 2-year cardiac mortality risk in patients with chronic heart failure. We sought to validate the ability of this risk model to predict post-discharge clinical outcomes in patients with acute decompensated heart failure (ADHF) and to compare its prognostic value with that of the Acute Decompensated Heart Failure National Registry (ADHERE) and Get With The Guidelines-Heart Failure (GWTG-HF) risk scores. We studied 407 consecutive patients who were admitted for ADHF and survived to discharge, with definitive 2-year outcomes (death or survival). Cardiac MIBG imaging was performed just before discharge. The 2-year cardiac mortality risk was calculated using four parameters, namely age, left ventricular ejection fraction, New York Heart Association functional class, and cardiac MIBG heart-to-mediastinum ratio on delayed images. Patients were stratified into three groups based on the 2-year cardiac mortality risk: low- (< 4%), intermediate- (4-12%), and high-risk (> 12%) groups. The ADHERE and GWTG-HF risk scores were also calculated. There was a significant difference in the incidence of cardiac death among the three groups stratified using the 2-year cardiac mortality risk model (p < 0.0001). The 2-year cardiac mortality risk model had a higher C-statistic (0.732) for the prediction of cardiac mortality than the ADHERE and GWTG-HF risk scores. The 2-year MIBG-based cardiac mortality risk model is useful for predicting post-discharge clinical outcomes in patients with ADHF. UMIN000015246, 25 September 2014.

Association of gut-related metabolites with outcome in acute heart failure

Trimethylamine N-oxide (TMAO), a gut-related metabolite, is associated with heart failure (HF) outcomes. However, TMAO is the final product of a complex metabolic pathway (ie, choline/carnitine) that has never been entirely investigated in HF. The present study investigates a panel of metabolites involved in the TMAO-choline/carnitine metabolic pathway for their associations with outcome in acute HF patients. In total, 806 plasma samples from acute HF patients were analyzed for TMAO, trimethyllysine, L-carnitine, acetyl-L-carnitine, γ-butyrobetaine, crotonobetaine, trimethylamine, betaine aldehyde, choline, and betaine using a developed liquid chromatography-tandem mass spectrometry method. Associations with outcome of all-cause mortality (death) and a composite of all-cause mortality and/or rehospitalization caused by HF (death/HF) at 30 days and 1 year were investigated. TMAO, trimethyllysine, L-carnitine, acetyl-L-carnitine, and γ-butyrobetaine were associated with death and death/HF at 30 days (short term; hazard ratio 1.30-1.49, P≤ .021) and at 1 year (long term; hazard ratio 1.15-1.25, P≤ .026) when adjusted for cardiac risk factors. L-carnitine and acetyl-L-carnitine were superior for short-term outcomes whereas TMAO was the superior metabolite for association with long-term outcomes. Furthermore, acetyl-L-carnitine and L-carnitine were superior for in-hospital mortality and improved risk stratification when combined with current clinical risk scores (ie, Acute Decompensated HEart Failure National REgistry, Organized Program To Initiate Lifesaving Treatment In Hospitalized Patients With Heart Failure, and Get With The Guidelines-Heart Failure; odds ratio (OR) ≥ 1.52, P≤ .020). Carnitine-related metabolites show associations with adverse outcomes in acute HF, in particular L-carnitine and acetyl-L-carnitine for short-term outcomes, and TMAO for long-term outcomes. Further studies are warranted to investigate the role and implications of carnitine metabolites including intervention in the pathogenesis of HF.

Abstract 12789: Clinical Factors Associated With Mortality Among Patients Hospitalized for Heart Failure in Egypt

Introduction: Heart failure (HF) is a growing public health burden in many low and middle-income countries (LMIC). However, most HF registries were conducted in high income countries, which often have different ethnic and cultural backgrounds from that of LMIC. Hypothesis: Independent clinical variables associated with mortality in patients hospitalized for HF in Egypt are different from those established in the United States (US). Methods: Between 2011 and 2014, 1,660 patients hospitalized for HF were enrolled from 20 centers across Egypt as part of the European Society of Cardiology HF long-term Registry. Deceased patients were compared to survivors, to identify demographic, clinical and biochemical variables associated with in-hospital and one-year mortality. Variables associated with mortality on univariate analysis, and independent variables identified in the Acute Decompensated Heart Failure National Registry (ADHERE) and in the Seattle Heart Failure Model, both based in the US, were entered into the multivariate logistic regression model. Results: In-hospital mortality was 5%. Only two independent clinical factors associated with in-hospital mortality were identified: elevated serum creatinine (sCr), OR=1.47 [95% CI: 1.23, 1.74] for every point increases above one mg/dl; and low admission systolic blood pressure (SBP), OR=1.54; [95% CI: 1.43, 1.65] for every 10 points decrease in SBP below 140 mmHg. At one-year follow up, mortality was 27%. Independent predictors of one-year mortality were: age, OR=1.47; [95% CI: 1.23,1.75] for every 10-year increase above 40; low discharge SBP, OR=1.30 [95% CI: 1.08, 1.52] for every 10 points decrease below 140 mmHg; low ejection fraction, OR=1.51 [95% CI: 0.59,0.73] for every 5 points decrease from 65%; chronic liver disease, OR=3.0 [95% CI: 1.51,5.88]; history of stroke, OR=3.2 [95% CI: 1.52,6.65]. These variables overlapped with those identified in US registries. Conclusions: Independent clinical variables associated with mortality after HF hospitalization in Egypt are similar to those reported in HF registries in the US.

Implementation of The Surviving Sepsis Campaign In Patients With Heart Failure And Sepsis. Does The Age Matter?

Objective The impact of the Survival Sepsis Campaign in patients with heart failure (HF) and sepsis is unknown. Furthermore, there is limited data on the impact of age on clinically relevant outcomes in this population. Methods This retrospective cohort study included 618 patients with HF that developed sepsis. Based on age, patients were stratified into the elderly (age ≥75 years), middle-aged (55-74 years), and young adults (18-54 years). Primary outcomes included all-cause in-hospital mortality. Secondary outcomes include the development of pulmonary edema requiring interventions (diuretics vs. mechanical ventilation), cardiogenic shock, post-admission acute kidney injury (AKI), hospital length of stay (LOS), and 30-day HF-related readmission rate. Results chronic comorbidities such as coronary artery disease, hypertension, atrial fibrillation, and HF with preserved ejection fraction were more common in the elderly, while end-stage renal disease and HF with reduced ejection fraction were most likely present in young adults (See table 1).Predictors of poor outcomes such as quick sequential organ failure assessment (qSOFA) (p=0.1752), systemic inflammatory response syndrome (SIRS) (p=0.1186), and acute decompensated heart failure national registry (ADHERE) (p=0.1465) were similar among the three age groups. However, younger aged patients were most likely to have elevated NT-pro BNP, lactic acid, total bilirubin, and low albumin. The Surviving Sepsis Campaign Bundle (i.e., broad-spectrum antimicrobials and intravenous crystalloids) was equally implemented in all three age groups (p=NS). Outcomes for the elderly, middle age, and young adult population, in-hospital mortality was 33% vs. 30% vs. 24%, p=0.3881, respectively. The elderly had the lowest incidence of cardiogenic shock (p Conclusion Elderly patients with HF that develop sepsis had proportionally higher mortality (33% vs. 30% vs. 24%) despite having fewer predictors of mortality, less in-hospital complications, and shorter LOS than other age groups. These findings suggest that the generalization of the surviving sepsis campaign in patients with HF and sepsis may be deleterious. It should be individualized based on the age group.

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

ObjectiveWe 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 SettingWe 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. ResultsC-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. ConclusionAlthough risk assessment tools work well for stratifying patients, their use in estimating the risk of mortality for individuals has limited clinical utility.

Predictive Value of the Get With The Guidelines Heart Failure Risk Score in Unselected Cardiac Intensive Care Unit Patients.

BackgroundThe cardiac intensive care unit (CICU) population is no longer composed of only patients with acute coronary syndromes, and includes those with acute heart failure and multiple comorbidities. We hypothesized that the GWTG‐HF (Get With The Guidelines–Heart Failure) risk score that predicts inpatient mortality in hospitalized patients with heart failure would predict mortality in CICU patients.Methods and ResultsWe retrospectively analyzed CICU patients at a tertiary care hospital from 2007 to 2015. The GWTG‐HF risk score was calculated at CICU admission. As a secondary analysis, the EFFECT (Enhanced Feedback for Effective Cardiac Treatment), OPTIMIZE‐HF (Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure), and ADHERE (Acute Decompensated Heart Failure National Registry) risk scores were calculated. Kaplan–Meier survival analysis and the area under the receiver operating characteristic curve value were determined for inpatient and 1‐year mortality. The GWTG‐HF risk score was calculated in 9532 (95%) patients, with a median value of 40 (interquartile range, 35–47). Inpatient mortality occurred in 824 (8.6%) patients, and 2075 (21.8%) patients died by 1 year. Patients who died in hospital had a significantly higher mean GWTG‐HF score (47.7 versus 40.2; P<0.001). Inpatient and 1‐year mortality increased in each GWTG‐HF risk score quartile (P<0.0001). Discrimination of the GWTG‐HF, EFFECT, OPTIMIZE‐HF, and ADHERE risk scores was assessed using area under the receiver operating characteristic curve values for hospital mortality, and were similar for all risk scores (0.72–0.74; P>0.05). The Hosmer–Lemeshow statistic suggested poor calibration for hospital mortality by the GWTG‐HF risk score (P<0.001).ConclusionsThe GWTG‐HF risk score and other heart failure prediction tools demonstrate good discrimination for inpatient and 1‐year mortality in a heterogeneous cohort of CICU patients. Our study emphasizes that prognostic variables overlap in cardiac patients, regardless of the admission diagnosis.

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.

Prediction of risk and diagnostic accuracy in patients hospitalized for decompensated heart failure: cohort study.

To analyze the diagnostic accuracy in nursing in patients with predicted risk of clinical worsening during hospitalization for acutely decompensated heart failure. Cohort study with data collection in medical records according to the Acute Decompensated Heart Failure National Registry risk model. After defining the patients at risk, the Nursing Diagnosis Accuracy Scale version 2 was applied. The scale classifies the accuracy as null, low, moderate or high. Of the 43 patients at risk of worsening, 22 (51%) did not worsen and 21 (49%) worsened; in both, the diagnostic accuracy was in the Moderate / High category in 22 (89%) and 16 (88%), respectively. Only Decreased cardiac output and Excessive fluid volume were scored with 100% in the High category. Patients with acute decompensated heart failure and risk of clinical worsening during hospitalization were identified with moderate or high diagnostic accuracy by nurses.

Permanent pacemaker use among patients with heart failure and preserved ejection fraction: Findings from the Acute Decompensated Heart Failure National Registry (ADHERE) National Registry

BackgroundHeart failure with preserved ejection fraction may be associated with chronotropic incompetence, but little is known about the incidence and prevalence of permanent pacemaker use in this population or factors associated with its use. MethodsWe analyzed patients with heart failure with preserved ejection fraction (ie, left ventricular ejection fraction greater than 40%) from the ADHERE registry (2001-2006) linked with Medicare claims. We described the use of both prevalent and incident permanent pacemakers in heart failure with preserved ejection fraction and determined factors associated with pacemaker use with logistic regression models. ResultsAmong 13,881 patients with heart failure with preserved ejection fraction, 3136 (22.6%) had a permanent pacemaker, and of these patients, 636 had a permanent pacemaker implanted during hospitalization. Permanent pacemaker use was more common among older patients (81 vs 79 years; P < .001), men (38% vs 34%; P < .001), patients with atrial fibrillation (58% vs 36%; P < .001), and patients with wider QRS duration (140 ms vs 94 ms; P < .001). Rates of digoxin, aldosterone antagonist, and loop diuretic use were slightly higher in patients with a permanent pacemaker compared with patients with no permanent pacemaker. Factors associated with both prevalent and incident pacemaker use included age, fast or slow heart rate, atrial fibrillation, and lower body mass index. ConclusionsUse of permanent pacemakers is relatively common among patients with heart failure with preserved ejection fraction.

Inpatient Mortality Risk Scores and Postdischarge Events in Hospitalized Heart Failure Patients: A Community-Based Study.

The Acute Decompensated Heart Failure National Registry (ADHERE) and Get With The Guidelines (GWTG) registries have developed simple heart failure (HF) in-hospital mortality risk scores. We hypothesized that HF scores predictive of in-hospital mortality would perform as well for early postdischarge mortality risk stratification. In this single-center, community-based, retrospective study of all consecutive primary HF hospitalizations (6203 hospitalizations in 3745 patients) from 2000 to 2013, the ADHERE and GWTG risk scores were calculated from admission data. There were 176 (3.0%) and 399 (6.7%), 869 (14.7%), and 1272 (21.5%) deaths in-hospital and at 30, 90, and 180 days postdischarge, respectively. The GWTG but not ADHERE risk score was well calibrated for in-hospital mortality. Both the ADHERE (C statistic 0.66 and 0.67, 0.64, and 0.64) and GWTG (C statistic 0.74 and 0.73, 0.71, and 0.70) HF risk scores were similarly predictive of in-hospital and 30-, 90-, and 180-day postdischarge mortality. The ADHERE risk score identified 10% and the GWTG risk score identified 20% of hospitalizations where 180-day postdischarge mortality was 50%, a prognostic bench mark for hospice referral. In contrast, hospitalizations characterized as lowest risk by the ADHERE (57% of hospitalizations; 180-day mortality 16.2%) or GWTG score (20% of hospitalizations; 180-day mortality 8.0%) had substantially lower mortality (odds ratios high versus low risk of 5-8 [ADHERE] and 11-18 [GWTG] across time points; P<0.0001 for all). The simple ADHERE and GWTG scores stratify hospitalized HF patients for both inpatient and early postdischarge mortality risk, allowing comprehensive risk assessment on admission.

Development and validation of a risk model for in-hospital worsening heart failure from the Acute Decompensated Heart Failure National Registry (ADHERE)

A subset of patients hospitalized with acute heart failure experiences in-hospital worsening heart failure, defined as persistent or worsening signs or symptoms requiring an escalation of therapy. We analyzed data from the Acute Decompensated Heart Failure National Registry (ADHERE) linked to Medicare claims to develop and validate a risk model for in-hospital worsening heart failure. Our definition of in-hospital worsening heart failure included events such as escalation of medical therapy (eg, inotropic medications) >12hours after admission. We considered candidate risk prediction variables routinely assessed at admission, including age, medical history, biomarkers, and renal function. We used logistic regression with robust standard errors to generate a risk model in a 66% random derivation sample; we validated the model in the remaining 34%. We evaluated the calibration and discrimination of the model in both samples. We evaluated 23,696 patients hospitalized with acute heart failure. Baseline characteristics were well matched in the derivation and validation samples, and the occurrence of in-hospital worsening heart failure was similar in both samples (15.4% and 15.6%, respectively). In the multivariable model, the strongest predictors of in-hospital worsening heart failure were increased troponin and creatinine. The model was well calibrated and had good discrimination in the derivation sample (c statistic, 0.74) and validation sample (c statistic, 0.72). The ADHERE worsening heart failure risk model is a clinical tool with good discrimination for use in patients hospitalized with acute heart failure to identify those at increased risk for in-hospital worsening heart failure. This tool may be useful to target treatment strategies for patients at high risk for in-hospital worsening heart failure.

MODEL OF END-STAGE LIVER DISEASE EXCLUDING INR (MELD-XI) SCORING SYSTEM PROVIDES THE ADDITIONAL LONG-TERM PROGNOSTIC INFORMATION TO ADHERE RISK LEVEL IN PATIENTS ADMITTED WITH ACUTE DECOMPENSATED HEART FAILURE

The Acute Decompensated Heart Failure National Registry (AHDERE) risk levels are a validated tool to assess the risk of in-hospital mortality in patients (pts) with acute decompensated heart failure (ADHF). Liver dysfunction has a prognostic impact on the outcome of pts with advanced heart failure.