Readmission In Heart Failure Patients Research Articles

Prognostic Value of Inflammatory Cytokines in Predicting Hospital Readmissions in Heart Failure with Preserved Ejection Fraction.

The aim of this study was to explore the relationship between inflammatory cytokines and the risk of heart failure (HF) readmission in patients with heart failure with preserved ejection fraction (HFpEF). We enrolled 429 patients with HFpEF admitted to the cardiology department in our hospital from January 2020 to July 2022. The patients were divided into the readmission or non-readmission groups according to whether they were readmitted for heart failure within 1 year of discharge. The clinical features and laboratory date of the subjects were collected and analyzed. Multivariate cox regression analysis was used to identify predictors of HF readmission. In addition, receiver operating characteristic (ROC) curves were used to determine the prognostic value of each factor. The levels of IL-1β, IL-6, IL-10, IL-17, TNF-α, NT-proBNP, heart rate, total cholesterol and NYHA class were significantly higher in the readmission group than in the non-readmission group (p < 0.05). IL-1β, IL-6, IL-17, TNF-α, NT-proBNP, heart rate and NYHA class were identified as independent predictors of HF readmission. Inflammatory markers, including IL-1β, IL-6, IL-17 and TNF-α were related to the HF readmission in patients with HFpEF.

Comparative effectiveness of ehealth self-management interventions for patients with heart failure: A Bayesian network meta-analysis

ObjectiveThis study evaluated the effectiveness of electronic self-management support interventions in reducing all-cause mortality, cardiovascular mortality, readmission rates, and HF-related readmission in heart failure patients. MethodsFollowing the PRISMA-P guidelines and PRISMS taxonomy, we searched Pubmed, Cochrane Library, and Embase for RCTs and trials of electronic health technologies for heart failure interventions. Develop support programs in advance for education, monitoring, reminders, or a combination of these to screen and categorize studies. The Cochrane ROB2 tool was used to assess the risk of bias. ResultsThe monitoring interventions may improve all-cause mortality (OR 0.77, 95% CI 0.63 to 0.93) and cardiovascular mortality (OR 0.75, 95% CI 0.61 to 0.93) compared to usual care. Reminder interventions were associated with significantly reducing readmission rates (OR 0.07, 95% CI 0.00 to 0.94). Mixed interventions were most effective in reducing HF-related readmission rates (OR 0.75, 95% CI 0.56 to 0.99). ConclusionElectronic self-management interventions, particularly monitoring and reminders, can potentially improve outcomes of heart failure patients, including reducing all-cause mortality, cardiovascular mortality, and readmission rates. Practice ImplicationsThe eHealth model and the combination of self-management are significant for long-term intervention in patients with HF to improve their quality of life and prognosis.

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.

A machine learning model to predict heart failure readmission: toward optimal feature set.

Hospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance. This study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model. Heart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC. A total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters. The proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.

β-blockers may be detrimental in frail patients with heart failure with preserved ejection fraction.

The effectiveness of β-blocker in patients with heart failure with preserved ejection fraction (HFpEF) remains to be determined. We aimed to clarify the association between the use of β-blocker and prognosis according to the status of frailty. We compared prognosis between HFpEF patients with and without β-blockers stratified with the Clinical Frailty Scale (CFS), using data from the PURSUIT-HFpEF registry (UMIN000021831). Among 1159 patients enrolled in the analysis (median age, 81.4years; male, 44.7%), 580 patients were CFS ≤ 3, while 579 were CFS ≥ 4. Use of β-blockers was associated with a worse composite endpoint of all-cause death and heart failure readmission in patients with CFS ≥ 4 (adjusted hazard ratio (HR) 1.43, 95% CI 1.10-1.85, p = 0.007), but was not significantly associated with this endpoint in those with CFS ≤ 3 (adjusted HR 0.95, 95% CI 0.71-1.26, p = 0.719) in multivariable Cox proportional hazard models. These results were confirmed in a propensity-matched analysis (HR in those with CFS ≥ 4: 1.42, 95% CI 1.05-1.90, p = 0.020; that in those with CFS ≤ 3: 0.83, 95% CI 0.60-1.14, p = 0.249), and in an analysis in which patients were divided into CFS ≤ 4 and CFS ≥ 5. Use of β-blockers was significantly associated with worse prognosis specifically in patients with HFpEF and high CFS, but not in those with low CFS. Use of β-blockers in HFpEF patients with frailty may need careful attention.

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.

Preventing Heart Failure Readmission in Patients with Low Socioeconomic Position.

This review aims to summarize the current burden of heart failure (HF) in the United States, specifically in patients with low socioeconomic position (SEP), and synthesize recommendations to prevent HF-related hospital readmissions in this vulnerable population. As treatments have improved, HF-related mortality has declined over time, resulting in more patients living with HF. This has led to an increase in hospitalizations, however, putting excess strain on our healthcare system. HF patients with low SEP are a particularly vulnerable group, as they experience higher rates of hospitalization and readmission compared to their high SEP counterparts. The Hospital Readmission Reduction Program (HRRP) was created to motivate interventions that reduce hospital readmissions across diseases, with HF being a primary target. Numerous readmission prevention efforts have been suggested to target the pre-hospitalization, hospitalization, and post-hospitalization phases, including addressing social determinants of health (SDoH), improving coordination of care, optimizing discharge plans, and improving adherence to follow-up care and medication regimens. Many of these proposed interventions show promise in reducing HF-related readmissions and issues surrounding adequate caregiver support may be particularly important to reduce readmissions among persons in low SEP. Reducing HF-related hospital readmissions is possible, even in vulnerable populations like those with low SEP, but this will require coordinated efforts across the healthcare system and throughout the life course of these patients. Caregiver support is a necessary part of optimized care for low SEP HF patients and future efforts should consider interventions that support these caregivers.

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda.

High rates of hospital readmission and the cost of treating heart failure (HF) are significant public health issues globally and in Rwanda. Using machine learning (ML) to predict which patients are at high risk for HF hospital readmission 20 days after their discharge has the potential to improve HF management by enabling early interventions and individualized treatment approaches. In this paper, we compared six different ML models for this task, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), logistic regression (LR), decision trees (DT), random forests (RF), and support vector machines (SVM) with both linear and radial basis kernels. The outputs of the classifiers are compared using performance metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We found that RF outperforms all the remaining models with an AUC of 94% while SVM, MLP, and KNN all yield 88% AUC. In contrast, DT performs poorly, with an AUC value of 57%. Hence, hospitals in Rwanda can benefit from using the RF classifier to determine which HF patients are at high risk of hospital readmission.

Predicting readmission for heart failure patients by echocardiographic assessment of elevated left atrial pressure

BackgroundPathophysiologically, an elevated left ventricular (LV) filling pressure is the major reason for heart failure (HF) readmission. The 2016 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines provide a simplified algorithm for the echocardiographic assessment of LV filling pressure; however, this algorithm is yet to be sufficiently validated. Materials and MethodsWe retrospectively studied 139 consecutive patients with acute decompensated HF. High estimated left atrial pressure (eLAP) was defined according to the 2016 ASE/EACVI guidelines. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors for HF readmission within one year of discharge. ResultsAcross the study cohort, 68 patients (49%) did not have a high eLAP, 32 (23%) had an indeterminate eLAP, and 39 (28%) had a high eLAP. The number of HF readmission events within one year in the without high eLAP, indeterminate, and high eLAP groups were 4 (7.5%), 5 (18.5%), and 10 (33.3%), respectively. The HF readmission rate was significantly higher in patients with high eLAP than in those without high eLAP. Multivariate analysis revealed high eLAP (odds ratio, 5.924; 95% confidence interval, 1.664–21.087; P = 0.006) as a significant risk factor for HF readmission within one year. Furthermore, the exploratory analysis of the two-year outcomes revealed a similar finding: patients with high eLAP had a significantly higher rate of readmission for HF. ConclusionsThe present study demonstrated that echocardiographic assessment of elevated LAP based on the 2016 ASE/EACVI guidelines is clinically valid for predicting readmission in patients with HF.

Penggunaan Telemonitoring untuk Mengurangi Readmisi pada Pasien Gagal Jantung

This study aims to analyze the effectiveness of telemonitoring on the incidence of readmission in patients with heart failure. The method used in this study is a literature review focusing on articles published in the last five years and English, with the target population being patients aged over 18 years. The article search strategy uses online databases, namely "ScienceDirect," "ProQuest," "Sage," and "Scopus," using keywords and Boolean Operators. The results showed that using a telemonitoring system in most articles proved effective in reducing readmissions in heart failure patients. This difference occurs due to differences in the number of samples and the length of time of the study. In conclusion, telemonitoring is effective in reducing readmission in heart failure patients compared to those who receive regular care.&#x0D; &#x0D; Keywords: Heart failure, Rehospitalization, Telemonitoring

Prognostic Impact of Multiple Drug Use at Discharge in Patients Hospitalized with Heart Failure.

The association between polypharmacy/multiple drug use (MDU) and prognosis in patients hospitalized with heart failure (HF) is unclear. It is also unknown whether the prognostic values of MDU vary depending on the presence/absence of a previous history of HF and preserved/reduced left ventricular ejection fraction (LVEF). We analyzed consecutive 1,034 patients hospitalized with HF (age, 74.9 ± 11.5 years; 58.7% male). MDU was defined as ≥5 drugs at discharge. The primary endpoint was a composite of all-cause death and HF readmission. MDU was observed in 695 patients (67.2%). Patients with MDU use had higher prevalences of a previous history of HF, reduced LVEF, and comorbidities than those without MDU. Cox proportional hazard analysis showed that MDU was significantly associated with the primary endpoint after adjustment for possible confounders (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.03-1.79; P = 0.030). There was significant interaction between the presence/absence of a history of HF and the prognostic impact of MDU (HF history [-]: HR, 0.86; 95% CI, 0.54-1.40; P = 0.553; HF history [+]: HR, 1.72; 95% CI, 1.16-2.55; P = 0.007; P for interaction = 0.005). However, there was no significant interaction between preserved/reduced LVEF and the prognostic impact of MDU (P for interaction = 0.274). In conclusion, MDU at discharge is an independent risk factor for the composite of death or HF readmission in patients hospitalized with HF. We observed a significant interaction between the presence of de novo versus recurrent HF and the prognostic value of MDU.

Associations between source-apportioned PM2.5 and 30-day readmissions in heart failure patients

BackgroundAir pollution exposure is a significant risk factor for morbidity and mortality, especially for those with pre-existing chronic disease. Previous studies highlighted the risks that long-term particulate matter exposure has for readmissions. However, few studies have evaluated source and component specific associations particularly among vulnerable patient populations. ObjectivesUse electronic health records from 5556 heart failure (HF) patients diagnosed between July 5, 2004 and December 31, 2010 that were part of the EPA CARES resource in conjunction with modeled source-specific fine particulate matter (PM2.5) to estimate the association between exposure to source and component apportioned PM2.5 at the time of HF diagnosis and 30-day readmissions. MethodsWe used zero-inflated mixed effects Poisson models with a random intercept for zip code to model associations while adjusting for age at diagnosis, year of diagnosis, race, sex, smoking status, and neighborhood socioeconomic status. We undertook several sensitivity analyses to explore the impact of geocoding precision and other factors on associations and expressed associations per interquartile range increase in exposures. ResultsWe observed associations between 30-day readmissions and an interquartile range increase in gasoline- (16.9% increase; 95% confidence interval = 4.8%, 30.4%) and diesel-derived PM2.5 (9.9% increase; 95% confidence interval = 1.7%, 18.7%), and the secondary organic carbon component of PM2.5 (SOC; 20.4% increase; 95% confidence interval = 8.3%, 33.9%). Associations were stable in sensitivity analyses, and most consistently observed among Black study participants, those in lower income areas, and those diagnosed with HF at an earlier age. Concentration-response curves indicated a linear association for diesel and SOC. While there was some non-linearity in the gasoline concentration-response curve, only the linear component was associated with 30-day readmissions. DiscussionThere appear to be source specific associations between PM2.5 and 30-day readmissions particularly for traffic-related sources, potentially indicating unique toxicity of some sources for readmission risks that should be further explored.

Diastolic Dysfunction Criteria and Heart failure Readmission in Patients with Heart Failure and Reduced Ejection Fraction

Background: Advanced diastolic dysfunction (DDys) correlates with elevated LV filling pressures and predicts heart failure readmission (HF-R). As grade 2-3 DDys has predictive value for HF-R, and requires 2 of 3 criteria (left atrial volume index &gt;34 ml/m2, E/e’&gt;14, or tricuspid regurgitation velocity &gt;2.8 m/s), we hypothesized that all 3 criteria would predict greater HF risk than any 2 criteria. Methods: In this single-center retrospective study that included 380 patients in sinus rhythm with HF and reduced ejection, we recorded patient characteristics, Doppler-echo, and HF-R with follow-up to 2167 days (median=1423 days; interquartile range=992-1821 days). Results: For grade 1 DDys (223 patients), any single criteria resulted in greater HF-R as compared to 0 criteria (HR=2.52, (1.56-3.88) p&lt;0.0001) with an AUC (area under curve)=0.637, p&lt;0.001. For grade 2 DDys (94 patients), there was greater HF-R for all 3 (vs. 0 criteria: HR=4.0 (2.90-8.36), p&lt;0.0001). There was greater HF-R for 3 vs any 2 criteria (HR=1.81, (1.10-3.39), p=0.0222). For all 3 criteria, there was moderate predictability for HF-R (AUC=0.706, p&lt;0.0001) which was more predictive than any 2 criteria (AUC difference 0.057, (0.011-0.10), p=0.009). For grade 3 DDys (63 patients), E/A&gt;2+2-3 criteria identified a subgroup with the greatest risk of HF-R (HR=5.03 (4.62-22.72), p&lt;0.0001) compared with 0 DDys criteria with moderate predictability for 2-3 criteria (AUC=0.726, p&lt;0.0001) exceeding E/A&gt;2+0-1 criteria (AUC difference=0.120, (0.061-0.182), p&lt;0.001). Conclusion: Increasing the number of abnormal criteria increased the risk and predictive value of HF-R for grade 1-3 DDys in patients with HF with reduced ejection fraction.

Diastolic Dysfunction Criteria and Heart failure Readmission in Patients with Heart Failure and Reduced Ejection Fraction

Background: Advanced diastolic dysfunction (DDys) correlates with elevated LV filling pressures and predicts heart failure readmission (HF-R). As grade 2-3 DDys has predictive value for HF-R, and requires 2 of 3 criteria (left atrial volume index &gt;34 ml/m2, E/e’&gt;14, or tricuspid regurgitation velocity &gt;2.8 m/s), we hypothesized that all 3 criteria would predict greater HF risk than any 2 criteria. Methods: In this single-center retrospective study that included 380 patients in sinus rhythm with HF and reduced ejection, we recorded patient characteristics, Doppler-echo, and HF-R with follow-up to 2167 days (median=1423 days; interquartile range=992-1821 days). Results: For grade 1 DDys (223 patients), any single criteria resulted in greater HF-R as compared to 0 criteria (HR=2.52, (1.56-3.88) p&lt;0.0001) with an AUC (area under curve)=0.637, p&lt;0.001. For grade 2 DDys (94 patients), there was greater HF-R for all 3 (vs. 0 criteria: HR=4.0 (2.90-8.36), p&lt;0.0001). There was greater HF-R for 3 vs any 2 criteria (HR=1.81, (1.10-3.39), p=0.0222). For all 3 criteria, there was moderate predictability for HF-R (AUC=0.706, p&lt;0.0001) which was more predictive than any 2 criteria (AUC difference 0.057, (0.011-0.10), p=0.009). For grade 3 DDys (63 patients), E/A&gt;2+2-3 criteria identified a subgroup with the greatest risk of HF-R (HR=5.03 (4.62-22.72), p&lt;0.0001) compared with 0 DDys criteria with moderate predictability for 2-3 criteria (AUC=0.726, p&lt;0.0001) exceeding E/A&gt;2+0-1 criteria (AUC difference=0.120, (0.061-0.182), p&lt;0.001). Conclusion: Increasing the number of abnormal criteria increased the risk and predictive value of HF-R for grade 1-3 DDys in patients with HF with reduced ejection fraction.

Serum uric acid: A risk factor for right ventricular dysfunction and prognosis in heart failure with preserved ejection fraction.

Hyperuricemia and right ventricular dysfunction (RVD) are both widespread in heart failure with preserved ejection fraction (HFpEF) patients. RVD is associated with a poor prognosis in HFpEF. The correlation between serum uric acid (UA) levels and right ventricular function is unclear. The prognostic performance of UA in patients with HFpEF needs further validation. A total of 210 patients with HFpEF were included in the study and divided into two groups according to UA level: the normal UA group (≤7 mg/dl) and the high UA group (>7 mg/dl). The variables examined included clinical characteristics, echocardiography, and serum biochemical parameters. Right ventricular function was assessed by tricuspid annular plane systolic excursion (TAPSE) and tricuspid annular peak systolic velocity (TAPSV). Baseline characteristics were compared between the two groups, and the correlation between baseline UA and RVD was assessed using multifactorial binary logistic regression. Kaplan-Meier curves were used to describe all-cause mortality and heart failure readmission. Results showed that right ventricular function parameters were worse in the high UA group. After adjusting for UA, left ventricular posterior wall thickness (LVPWT), N-terminal B-type natriuretic peptide (NT-proBNP), atrial fibrillation (AF), and low-density lipoprotein cholesterol (LDL-C), UA (odds ratio = 2.028; p < 0.001) was independently associated with RVD, and UA >7 mg/dl (HR = 2.98; p < 0.001) was associated with heart failure readmission in patients with HFpEF. Elevated serum UA is closely associated with RVD and significantly associated with the heart failure readmission rate in patients with HFpEF.

ATA2023Annual Conference &amp; ExpoMarch 4–6, 2023San Antonio, Texas

ATA2023Annual Conference &amp; ExpoMarch 4–6, 2023San Antonio, Texas

Pulmonary Congestion on Lung Ultrasound Predicts Increased Risk of 30-Day Readmission in Heart Failure Patients.

Heart failure exacerbations are a common cause of hospitalizations with a high readmission rate. There are few validated predictors of readmission after treatment for acute decompensated heart failure (ADHF). Lung ultrasound (LUS) is sensitive and specific in the assessment of pulmonary congestion; however, it is not frequently utilized to assess for congestion before discharge. This study assessed the association between number of B-lines, on LUS, at patient discharge and risk of 30-day readmission in patients hospitalized for acute decompensated heart failure (ADHF). This was a single-center prospective study of adults admitted to a quaternary care center with a diagnosis of ADHF. At the time of discharge, the patient received an 8-zone LUS exam to evaluate for the presence of B-lines. A zone was considered positive if ≥3 B-lines was present. We assessed the risk of 30-day readmission associated with the number of lung zones positive for B-lines using a log-binomial regression model. Based on data from 200 patients, the risk of 30-day readmission in patients with 2-3 positive lung zones was 1.25 times higher (95% CI: 1.08-1.45), and in patients with 4-8 positive lung zones was 1.50 times higher (95% CI: 1.23-1.82, compared with patients with 0-1 positive zones, after adjusting for discharge blood urea nitrogen, creatinine, and hemoglobin. Among patients admitted with ADHF, the presence of B-lines at discharge was associated with a significantly increased risk of 30-day readmission, with greater number of lung zones positive for B-lines corresponding to higher risk.

Effect of estimated plasma volume status and left atrial diameter on prognosis of patients with acute heart failure.

Acute heart failure (AHF) is a frequent cardiovascular emergency presenting with high mortality as well as readmission rates. The aim was to investigate the predictive value of estimated plasma volume status (ePVs) and left atrial diameter (LAD) for the prognosis of patients with AHF. Clinical profiles were collected from 259 cases of AHF patients at the Affiliated Hospital of Putian University between September 2019 and October 2021. Six patients lost follow-up, resulting in 253 patients enrolled. Cardiogenic death and heart failure readmission during follow-up were defined as major cardiovascular events (MACE) group, other patients were defined as Non-MACE group. Apart from age, no significant differences were found between the two groups in demographics and comorbidities. The comparison between the two groups was statistically significant in terms of ePVs, LAD, and N-terminal-pro B-type natriuretic peptide (Nt-pro-BNP). On binary logistic regression analysis, ePVs (OR = 2.061, 95% CI 1.322∼3.214, P = 0.001), LAD (OR = 1.054, 95% CI 1.012∼1.098, P = 0.011), and Nt-pro-bnp (OR = 1.006, 95% CI 1.003∼1.010, P = 0.036) as predicting factors for MACE. Kaplan-Meier analysis indicated that the risk for cardiogenic death increasing with ePVs (p < 0.05). Estimated plasma volume status and LADs have some predictive value in assessing cardiogenic death and heart failure readmission in patients with AHF.

Integration of patient experience factors improves readmission prediction.

Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.

The Effect of Home Care Support Clinics on Hospital Readmission in Heart Failure Patients in Japan.

Heart failure is common and is associated with high rates of hospitalization. Home care support clinics/hospitals (HCSCs) and enhanced HCSCs were introduced in Japan in 2006 and 2012, respectively. This study aimed to examine the effect of post-discharge care by conventional or enhanced HCSCs on readmission, compared with general clinics. Retrospective cohort study using the Japanese nationwide health insurance claims database. Participants were ≥65 years of age, admitted for heart failure and discharged between July 2014 and August 2015 and received a home visit within a month following the discharge (n=12,393). The exposure was the type of medical facility that provides post-discharge home healthcare: general clinics, conventional HCSCs, and enhanced HCSCs. The primary outcome was all-cause readmission for 6 months after the first visit; the incidence of emergency house calls was a secondary outcome. We used a competing risk regression using the Fine and Gray method, in which death was regarded as a competing event. At 6 months, readmissions were lower in conventional (38%) or enhanced HCSCs (38%) than general clinics (43%). The adjusted subdistribution hazard ratio (sHR) of readmission was 0.87 (95% CI: 0.78-0.96) for conventional and 0.86 (0.78-0.96) for enhanced HCSCs. Emergency house calls increased with conventional (sHR: 1.77, 95% CI:1.57-2.00) and enhanced HCSCs (sHR: 1.93, 95% CI: 1.71-2.17). Older Japanese patients with heart failure receiving post-discharge home healthcare by conventional or enhanced HCSCs had lower readmission rates, possibly due to compensation with more emergency house calls. Conventional and enhanced HCSCs may be effective in reducing the risk of rehospitalization. Further studies are necessary to confirm the medical functions performed by HCSCs.