Predicted Heart Failure Readmission Research Articles

HR-BGCN [formula omitted] Predicting readmission for heart failure from electronic health records

Heart failure has become a huge public health problem, and failure to accurately predict readmission will further lead to the disease’s high cost and high mortality. The construction of readmission prediction model can assist doctors in making decisions to prevent patients from deteriorating and reduce the cost burden. This paper extracts the patient discharge records from the MIMIC-III database. It divides the patients into three research categories: no readmission, readmission within 30 days, and readmission after 30 days, to predict the readmission of patients. We propose the HR-BGCN model to predict the readmission of patients. First, we use the Adaptive-TMix to improve the prediction indicators of a few categories and reduce the impact of unbalanced categories. Then, the knowledge-informed graph attention mechanism is proposed. By introducing a document-level explicit diagram structure, the coding ability of graph node features is significantly improved. The paragraph-level representation obtained through graph learning is combined with the context token-level representation of BERT, and finally, the multi-classification task is carried out. We also compare several typical graph learning classification models to verify the model’s effectiveness, such as the IA-GCN model, GAT model, etc. The results show that the average F1 score of the HR-BGCN model proposed in this paper for 30-day readmission of heart failure patients is 88.26%, and the average accuracy is 90.47%. The HR-BGCN model is significantly better than the graph learning classification model for predicting heart failure readmission. It can help doctors predict the 30-day readmission of patients, then reduce the readmission rate of patients.

Predicting heart failure readmission and all-cause mortality in patients with acute heart failure: a simple predischarge risk score

Abstract Introduction Predischarge risk stratification of patients hospitalized for 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. Purpose We sought to develop a congestion-focused risk score to predict HF readmission and all-cause mortality after discharge in patients hospitalized for AHF. Methods We used data from a prospective, two-center observational study in adults hospitalized for AHF. Laboratory data were collected on admission. Patients underwent a physical examination, 4-zone, and in a subset 8-zone, lung ultrasound (LUS), and conventional echocardiography at baseline (within a median of 1 day after admission). A second LUS was performed before discharge in a subset of patients. The primary endpoint was the composite of HF rehospitalization or all-cause death. HF hospitalizations were adjudicated blinded to LUS findings. Results Among 297 patients with complete data (median age 74 years, 43% women, mean left ventricular ejection fraction 39%), 76 patients (26%) were hospitalized for HF 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). The weighted 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; Figure 1). In a subset of patients with 8-zone LUS data (n=176), results were similar (C-statistic: 0.72). Conclusion A four-variable, weighted risk score integrating clinical, laboratory and ultrasound imaging data provided adequate risk discrimination for 90-day adverse outcomes in patients hospitalized for AHF. If validated in a larger dataset, this score could be used to identify patients needing the closest monitoring, more intensive follow-up and early treatment adjustment.Figure 1.

Diagnostic and prognostic value of an ejection fraction corrected for myocardial remodelling

Abstract Introduction Despite clinical guideline recommendations to use left ventricular ejection fraction (EF) as a marker of left ventricular function in heart failure (HF), it is prone to measurement error, is load dependent and is skewed during geometric cardiac remodelling. EF also cannot distinguish between patients with HF with preserved EF (HFpEF) and those with a normal heart. By evaluating EF at the ventricular mid-wall layer (the corrected EF (EFc)) instead of the endocardial surface, the geometric dependency shortcomings of EF can be overcome. Purpose We evaluated whether EFc can provide additional diagnostic and prognostic information compared with traditional EF. Methods We conducted a retrospective cohort study using a dataset of all consecutive patients who were admitted to hospital following the request of a troponin across three centres between 2010 and 2017. Patients who underwent an echocardiogram either within 3 months before or 5 months after their hospital admission were included. Patients were classified according to whether their index hospital admission was due to HF, and further classified according to EF ((i) EF<40, HF with reduced EF (HFrEF); (ii) 40≤EF<50, HF with mid-range EF (HFmrEF); (iii) EF≥50, HFpEF). All-cause mortality was assessed as a competing risk to HF admission. Cox-proportional regression models were constructed to compare the EFc to EF in evaluating risk of HF admission. Results The study population consisted of 2752 individuals (56.5% male, age 69.3±16.4 years). Comparing patients with HFpEF to those with an index admission without HF, there was no difference in EF between the groups (62.3% versus 64.2%, p = 0.79), however a significant difference was observed when comparing EFc (56.6% versus 61.8%, p = 0.0007) (Figure 1). Both low EF (<50%) and low EFc (<50%) were associated with a high HF readmission risk, which were comparable. However, in the cohort with a normal EF (EF≥50%), predictive models using EFc were significantly more accurate in predicting HF readmissions within 3 years, where the leave one out cross validation ROC analysis showed a 18.6% reduction in errors when EFc replaces EF in the risk prediction models. Table 1 shows the specific IDI and NRI scores to quantify the improvements to predictions when EF or EFc are added to the baseline model (age, gender and creatinine), for assessing readmissions within 3 years in the EF≥50% cohort. The higher IDI and NRI scores for EFc compared to EF shows that adding EFc to "baseline" improves predictions more than adding EF to "baseline". The results also show that using EFc instead of EF will lead to 12.2% more patients in the event group (HF readmission) being correctly identified as higher risk, and 16.6% more patients in the non-event group (no HF readmission) being correctly identified as lower risk. Conclusions In comparison to EF, the EFc improves the prediction of hospital admissions due to HF for patients where the EF≥50%.Figure 1Table 1

Hyperuricemia is associated with heart failure readmission in patients with heart failure and preserved ejection fraction-an observational study in Chinese

Background and aimsThis study aimed to explore the association between hyperuricemia and heart failure (HF) readmission in HF patients with preserved ejection fraction (HFpEF) because the impact of hyperuricemia on the prognosis of these patients has not been fully understood. Methods and resultsThis retrospective observational study included 538 hospitalized patients diagnosed with HFpEF. A total of 57.6 % of patients with HFpEF suffered from hyperuricemia (serum uric acid (SUA) was >7 mg/dL in men and >6 mg/dL in women). Compared to those without hyperuricemia, patients with hyperuricemia were more likely to be female (62.6 % vs. 53.9 %, p = 0.044) and older (78.0 ± 8.4 vs. 75.9 ± 9.0 years, p = 0.008). Our Cox analysis revealed that SUA level (hazard ratio (HR) = 1.158, 95 % confidence interval (CI): 1.087–1.234, p<0.001) and hyperuricemia (HR = 1.846, 95 % CI: 1.308–2.606, p<0.001) were associated with HF readmission in patients with HFpEF, respectively. Kaplan-Meier analysis showed that patients with hyperuricemia had a significantly worse prognosis (p<0.001). The receiver operating characteristic analysis revealed that the area under the ROC curve of SUA for predicting HF readmission was 0.6276 (95 % CI: 0.5763–0.6790) and a designated cut-off value of 7.53 mg/dL. ConclusionsHyperuricemia is a common comorbidity among patients with HFpEF. Moreover, SUA level and hyperuricemia have been shown to be associated with HF readmission. Therefore, it is meaningful to monitor SUA levels in patients with HFpEF during the whole treatment period of HF. Whereas, whether intervention of hyperuricemia could benefit patients with HFpEF needs further studies.

Role of the right ventricular contractile reserve during low-load exercise in predicting heart failure readmission

BackgroundExercise intolerance in patients with heart failure (HF) increases HF-associated readmission, and right ventricular (RV) contractile reserve assessed by low-load exercise stress echocardiography (ESE) is associated with exercise intolerance. This study investigated the impact of RV contractile reserve evaluated by low-load ESE on HF readmission. MethodsWe prospectively examined 81 consecutive patients hospitalized for HF who underwent low-load ESE under a stabilized HF condition between May 2018 and September 2020. We performed a 25-W low-load ESE and defined RV contractile reserve as the increment in RV systolic velocity (RV s′). The primary outcome was hospital readmission. Incremental values of the change in RV s′ over a readmission risk (RR) score were analyzed using the receiver operating characteristic (ROC) area under the curve; internal validation using bootstrapping was performed. The association between RV contractile reserve and HF readmission was illustrated with the Kaplan-Meier curve. ResultsEighteen (22 %) patients were readmitted due to worsening HF during the observation period (median 15.6 months). The cut-off value of 0.68 cm/s for the change in RV s′ to predict HF readmission with the ROC curve analysis indicated good sensitivity (100 %) and specificity (76.2 %). The discriminatory ability for HF readmission was significantly improved by adding the change in RV s′ to the RR score (p = 0.006), and the c-statistic using the bootstrap method was 0.92. The cumulative survival rate free of HF readmission was significantly lower in patients with reduced-RV contractile reserve (log-rank test, p < 0.001). ConclusionsThe change in RV s′ during low-load exercise had an incremental prognostic value for predicting HF readmission. The results demonstrated the loss of RV contractile reserve assessed by low-load ESE was associated with HF readmission.

The Charlson comorbidity index and short-term readmission in patients with heart failure: A retrospective cohort study.

The relationship between the Charlson comorbidity index (CCI) and short-term readmission is as yet unknown. Therefore, we aimed to investigate whether the CCI was independently related to short-term readmission in patients with heart failure (HF) after adjusting for other covariates. From December 2016 to June 2019, 2008 patients who underwent HF were enrolled in the study to determine the relationship between CCI and short-term readmission. Patients with HF were divided into 2 categories based on the predefined CCI (low < 3 and high > =3). The relationships between CCI and short-term readmission were analyzed in multivariable logistic regression models and a 2-piece linear regression model. In the high CCI group, the risk of short-term readmission was higher than that in the low CCI group. A curvilinear association was found between CCI and short-term readmission, with a saturation effect predicted at 2.97. In patients with HF who had CCI scores above 2.97, the risk of short-term readmission increased significantly (OR, 2.66; 95% confidence interval, 1.566-4.537). A high CCI was associated with increased short-term readmission in patients with HF, indicating that the CCI could be useful in estimating the readmission rate and has significant predictive value for clinical outcomes in patients with HF.

Prognostic significance of diastolic blood pressure in patients with heart failure with preserved ejection fraction

Although systolic blood pressure (SBP) is routinely considered when treating acute heart failure (HF), diastolic blood pressure (DBP) is hardly been assessed in the situation. There are no previous studies regarding the predictive value of DBP in elderly patients with HF with preserved ejection fraction (HFpEF) in Japan. This study aimed to investigate the prognostic significance of DBP in patients with acute decompensated HFpEF. We analyzed data of all HFpEF patients admitted to Shinonoi General Hospital for HF treatment between July 2016 and December 2018. We excluded patients with acute coronary syndrome and severe valvular disease. Patients were divided into two groups according to their median DBP; the low DBP group (DBP ≤ 77 mmHg, n = 106) and the high DBP group (DBP > 77 mmHg, n = 100). The primary outcome was HF readmission. In 206 enrolled patients (median 86 years), during a median follow-up of 302 days, the primary outcome occurred in 48 patients. The incidence of HF readmission was significantly higher in the low DBP group (33.0% vs 18.5%, p = 0.024). In Kaplan–Meier analysis, low DBP predicted HF readmission (Log-rank test, p = 0.013). In Cox proportional hazard analysis, low DBP was an independent predictor of HF readmission after adjustment for age, sex, SBP, hemoglobin, serum albumin, serum creatinine, B-type natriuretic peptide, renin-angiotensin system inhibitors, calcium channel blockers, left ventricular ejection fraction, coronary artery disease, and whether they live alone (hazard ratio, 2.229; 95% confidence interval, 1.021–4.867; p = 0.044). Low DBP predicted HF readmission in patients with HFpEF.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

AimsThis study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.Methods and resultsFollowing Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835).ConclusionsML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867

A Wearable Device for Predicting Heart Failure Readmissions

Predicting readmissions for heart failure (HF) is notoriously difficult. Statistical models employing traditional clinical variables discriminate

Combinations of cardiac and non-cardiac predictors for prognoses in patients with acute heart failure

In contemporary heart failure (HF) practice, prognostic value for combinations of cardiac and non-cardiac predictors remains poorly understood. We analysed the combinatorial predictors of outcomes in acute HF patients. This longitudinal cohort study included consecutive patients admitted for acute decompensated HF between April 2015 and March 2018 in an urban hospital. The main outcomes are HF readmission within 6 months after discharge or all-cause death. A total of 451 patients with 662 admissions were enrolled and the data including frailty and echocardiographic parameters were analysed by multivariate and matched cohort analyses. The mean age of the patients was 76.8 years. We constructed a multi-frailty index (MFI) ranging from 0 to 3 points as a composite of non-cardiac comorbidities and biopsychosocial frailty. In matched cohort of patients with ejection fraction ≧50% (HFpEF), MFI ≧1, pulmonary hypertension (PH; peak flow velocity of tricuspid regurgitation ≧2.9 m/s by echocardiography), and pancytopenia at discharge were strong predictors of HF readmission [odds ratios (ORs), 4.33, 2.5, and 2.86; P = 0.02, 0.05, and 0.02, respectively], and MFI ≧2 was the only predictor for all-cause death. For ejection fraction <40%, age, BNP ≧800 pg/mL, increase in estimated glomerular filtration rate during hospitalization, and lymphocytopenia plus anaemia predicted HF readmission (ORs, 1.77, 2.72, 0.73, and 2.89; P = 0.001, 0.05, 0.04, and 0.03, respectively). In contrast, diabetes mellitus was the only specific predictor found in patients over 80 years old. These data identified multi-frailty and PH or mild pancytopenia as synergistic predictors of HF readmission in HFpEF patients.

P757Methamphetamine-associated cardiomyopathy: a case-controlled study of clinical characteristics, management and outcomes

Abstract Background Methamphetamine-associated cardiomyopathy (MAC) is an increasingly diagnosed condition with poor prognosis, and there remains paucity of literature including how MAC differs from other cardiomyopathies. We compared the characteristics and outcomes MAC patients with non-ischaemic cardiomyopathy controls at our centre. Methods Clinical profile, management and outcomes were prospectively assessed in consecutive patients with MAC at our hospital from 2006–2018. They were compared with randomly chosen controls with non-ischaemic dilated cardiomyopathy of similar age-group (20–65 year). Results Both groups had 62 patients followed for 3.0±2.9 years. MAC patients were younger, with higher proportion of Maori ethnicity, unemployment and cardiogenic shock during index admission and lower proportion of Pacific ethnicity, cardiovascular risk factors and atrial fibrillation. MAC patients also had higher peak NT-proBNP, lower ejection fraction, and lower attendance rate to outpatient appointments. There was no index admission mortality in both groups. MAC had higher mortality and trend to higher heart failure re-admissions rates during follow-up (Figure 1). Amongst MAC patients, baseline left ventricular end diastolic diameter and failure of improvement in right ventricular systolic function by one category during follow-up were independent predictors of mortality, while failure of improvement of left ventricular ejection fraction by one category predicted heart failure readmission. Figure 1 Conclusions MAC patients were younger but sicker on presentation, with higher mortality and trend towards higher heart failure readmission rates during medium-term follow-up than controls. Adherence to therapy and attendance to appointments may improve cardiac systolic function over time to reduce adverse clinical endpoints.

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.

Methamphetamine-Associated Cardiomyopathy Versus Other Non-Ischaemic Cardiomyopathies: A Case-Controlled Study

Background: Methamphetamine-associated cardiomyopathy (MAC) is an increasingly diagnosed condition with poor prognosis, and there is a paucity of literature, including how MAC differs from other cardiomyopathies. This study compared the characteristics and outcomes of MAC patients with non-ischaemic cardiomyopathy controls at the current centre. Methods: Clinical profile, management, and outcomes were prospectively assessed in consecutive patients with MAC at Middlemore Hospital from 2006–2018. They were compared with randomly chosen controls with non-ischaemic dilated cardiomyopathy of a similar age group (20–65 years). Results: Both groups had 62 patients who were followed for 3.0 ± 2.9 years. The MAC patients were younger, with a higher proportion of Maori ethnicity, unemployment and cardiogenic shock during index admission, and a lower proportion of Pacific ethnicity, cardiovascular risk factors, and atrial fibrillation. The MAC patients also had higher peak N-terminal pro-B-type natriuretic peptide (NT-proBNP), lower ejection fraction, and lower attendance rate to outpatient appointments. There was no index admission mortality in either group. The MAC patients had higher mortality and a trend to higher heart failure re-admissions rates during follow-up. Amongst MAC patients, baseline left ventricular end diastolic diameter and failure of improvement in right ventricular systolic function by one category during follow-up were independent predictors of mortality, while failure of improvement of left ventricular ejection fraction by one category predicted heart failure readmission. Conclusions: The MAC patients were younger but sicker on presentation, with higher mortality and trend towards higher heart failure readmission rates during medium-term follow-up than controls. Adherence to therapy and attendance to appointments may improve cardiac systolic function over time, and reduce adverse clinical endpoints.

The clinical use of red cell distribution width in advanced heart failure following cardiac resynchronization therapy

Introduction High Red Cell Distribution Width (RDW) is linked to poor outcomes in advanced Heart Failure (HF). Whether or not RDW levels predict response to Cardiac Resynchronization Therapy (CRT) remains unclear. We examined the role of RDW in predicting clinical outcomes of HF following CRT insertion. Further, we examined if follow-up RDW levels correlated with response and mortality. Methodology A retrospective chart review was carried out for all patients undergoing CRT insertion from April 2009 to April 2015. RDW was measured at baseline and three different time points after CRT insertion (3 months, 6 months, and 1 year). Comparisons were done according to the median RDW value (RDW >14.1 vs. RDW ⩽14.1). Our primary endpoints were significant reduction (⩾15%) in Left Ventricular End Systolic Diameter (LVESD), HF readmissions, and all-cause mortality. The outcomes were examined over a median follow-up duration of 36 months. Results One-hundred and eighty-nine patients were eligible for the present analysis. Of which, the majority were males (72.5%) with a mean age of 59.5. Elevated baseline RDW was significantly associated with HF readmissions (29.3% vs. 9.5%; P = 0.001) but not with significant reduction in LVESD (30.4% vs. 46.9%; P = 0.064) or all-cause mortality (11.1% vs. 10.0%; P = 0.808). Multivariate analysis showed no independent role of RDW in predicting HF readmissions (OR = 2.86, 95% CI 0.79–10.29, P = 0.108). Follow-up RDW levels remained persistently elevated in patients with poor reverse ventricular remodeling, HF readmissions, and all-cause mortality (Figure). Conclusion Baseline RDW had no independent role in predicting outcomes of advanced HF after CRT insertion. During follow-up, persistently elevated RDW correlated with HF morbidity and mortality.

Developing and Testing a New Machine-Learning Method to Identify Patients with Heart Failure Who Are at Risk of 30-Day Readmission or Mortality

Background: Predicting readmissions or mortality following hospital discharge in patients with heart failure (HF) is a major challenge. Commonly used machine learning (ML) algorithms have reported suboptimal performances in predicting HF readmissions or mortality due to class imbalance in the outcome levels (e.g. low number of readmissions vs no readmission). Moreover, the area under the receiver operating characteristic curve (AUC), a commonly used indicator of model performance, is a crude indicator for data with class imbalances. Our main aim was to develop ML models to predict 30-day HF readmission or mortality and compare various measures of model performance. Methods: We identified all Western Australian patients 65 years or older admitted for HF in 2003–08 in the linked Hospital Morbidity Data Collection. In addition to common ML models to predict 30-day HF readmissions or mortality, we also developed multi-layer perceptron (MLP) with a synthetic minority oversampling technique, considered superior in data with class imbalances. We calculated the AUC, sensitivity, and specificity of the models. Results: Of the 10,735 patients with HF, 23.6% were readmitted or died within 30 days. We observed AUCs of 0.51, 0.50, and 0.51 for linear regression, least absolute shrinkage and selection operator, and random forest models, respectively. The MLP model had a higher AUC (0.58), with a sensitivity of 57% and a specificity of 56%. Conclusion: The MLP method may be a better ML technique for data with outcome class imbalances. In addition to AUC, other metrics such as sensitivity and specificity should also be considered for such data.

122 - Prediction of Heart Failure Readmission Using Echocardiographic Variables in Patients with Heart Failure with Preserved Ejection Fraction in an Urban Inner City Teaching Hospital

Objective: The purpose of this study was to determine the relationship between echocardiographic variables and hospital readmission rates in patients with Heart Failure with preserved EF (HFpEF) admitted for acute decompensated heart failure (HF). Background: Acute HF exacerbation in patients with HFpEF is caused by congestion which may be not clinically evident. Congestion is due to elevated LV filling pressure which is often reflected by high levels of natriuretic peptides. Using the echocardiogram to determine specific parameters such as LVEDP, E/e' ratio, left atrial volume index can help to identify patients with elevated filling pressures that are congested but may not be reflected clinically and may be increased risk for HF readmission. Methods: One hundred sixteen patients with HFpEF who were admitted for acute HF exacerbation with an echocardiogram done during initial presentation were included. Patients were divided based on readmission in 60 days. Continuous variables were analyzed using Wilcoxon rank sum test and categorical variables(atrial fibrillation, coronary artery disease, diabetes mellitus, hypertension, hyperlipidemia, gender, race) were analyzed using Fisher's exact test. The best subset selection method was performed to assess the best predictive model of readmission. Results: There were no significant relationship between readmission and any of the categorical variables. A significant relationship was noted between readmission and both NT pro-BNP and left atrial (LA) Volume index (ml/m2). The best fit model predicting readmission was applied to E/e' ratio and LA volume index (ml/m2). For every unit increase in E/e' ratio, the odds of readmission decreased. For every unit increase in LA Volume index, the odds of readmission increased. Conclusion: Our results reveal that LA volume index predicted HF readmission in HFpEF patients, while E/e' ratio did not. This could be due to the dynamic nature of E/e' being volume related and unclear timing of the echocardiogram in relation to diuresis. Increased LA volume index correlating with readmission appropriately reflects the pathophysiology of HFpEF and LA remodeling in these patients. The analysis showed significant correlation with HF readmission rates in patients with higher NT proBNP levels. LA volume index can be considered as an additional prognostic marker for HF readmission. Further studies are warranted to evaluate echocardiographic variables at the time of admission for acute decompensated HF in HFpEF patients.

Predicting Heart Failure Readmissions.

In this study, the effects of sociodemographic and clinical factors on heart failure (HF) readmission risk were examined. Hospitals now incur financial penalties for excessive HF readmission rates; therefore, identifying factors associated with risk is essential for designing risk-reduction strategies. A retrospective cohort study using chart reviews compared HF inpatients (N = 245) who were readmitted with those who were not readmitted. The sample included mostly white (64%) elderly (mean [SD] age, 69.8 [15.1] years) men (49%) and women (51%). Using Cox regression, the number of comorbidities (3-4 or 5-8) and type of comorbidities, specifically renal insufficiency (readmission ratio [RR], 1.7; P = .003), atrial fibrillation (RR, 1.7; P = .005), cardiomyopathy (RR, 1.5; P = .020), followed by a history of myocardial infarction/coronary artery disease (RR, 1.4; P = .055), were the predictors of HF readmission. Targeting those with high-risk comorbidities is important in designing measures to prevent or delay readmission of HF patients.

The Intermountain Risk Score (including the red cell distribution width) predicts heart failure and other morbidity endpoints

The complete blood count (CBC) and basic metabolic profile are common, low-cost blood tests, which have previously been used to create and validate the Intermountain Risk Score (IMRS) for mortality prediction. Mortality is the most definitive clinical endpoint, but medical care is more easily applied to modify morbidity and thereby prevent death. This study tested whether IMRS is associated with clinical morbidity endpoints. Patients seen for coronary angiography (n = 3927) were evaluated using a design similar to a genome-wide association study. The Bonferroni correction for 102 tests required a P-value of ≤ 4.9 × 10⁻⁴ for significance. A second set of angiography patients (n = 10 413) was used to validate significant findings from the first patient sample. In the first patient sample, IMRS predicted heart failure (HF) (P(trend) = 1.6 × 10(-26)), coronary disease (P(trend) = 2.6 × 10(-11)), myocardial infarction (MI) (P(trend) = 3.1 × 10(-25)), atrial fibrillation (P(trend) = 2.5 × 10(-20)), and chronic obstructive pulmonary disease (P(trend) = 4.7 × 10⁻⁴). Even more, IMRS predicted HF readmission [hazard ratio (HR) = 2.29/category, P(trend) = 1.2 × 10⁻⁶), incident HF (HR = 1.88/category, P(trend) = 0.02), and incident MI (HR = 1.56/category, P(trend) = 4.7 × 10⁻⁴). These findings were verified in the second patient sample. Intermountain Risk Score, a predictor of mortality, was associated with morbidity endpoints that often lead to mortality. Further research is required to fully characterize its clinical utility, but its low-cost CBC and basic metabolic profile composition may make it ideal for initial risk estimation and prevention of morbidity and mortality. An IMRS web calculator is freely available at http://intermountainhealthcare.org/IMRS.