Predictors Of Hospital Readmission Research Articles

Readmission and mortality during the first year after an acute stroke: A prospective cohort study from Cameroon.

Hospital readmission after discharge for stroke is associated with high morbidity and mortality. There is a paucity of data on the burden of stroke readmission in most sub-Saharan African countries. We aimed to determine the rate, reasons and predictors of hospital readmission and the mortality rate within 12 months of discharge among stroke survivors in Cameroon. This prospective cohort included patients who survived hospitalization for an acute stroke and who were discharged from two referral hospitals in the capital city of Cameroon between January 2013 and December 2013.We performed logistic regression analysis to identify demographic and clinical factors associated with readmission within 1 year of discharge and causes of readmission. Of the 254 consecutive patients admitted for acute stroke, 198 were discharged alive. There were 107 (54%) males, and their mean age (SD) was 61.7 (13.9) years (range: 10 to 95 years). A total of 30 (15.2%) patients were readmitted following discharge from the index admission over a mean follow-up time of 286.9 (127.6) days. Of these, 14 (46.7%) were discharged and 16 (53.3%) died after readmission. Thirty percent (30%) of readmissions occurred within the first month. The most frequent causes of readmissions were infections (30%) and recurrent stroke (26.7%). Factors associated with readmission in bivariate analysis were initial admission temperature > 37.5°C (risk ratio [RR]: 1.3, p = 0.021) and initial admission Glasgow Coma Score < 14 (RR: p = 1.23, p = 0.019). After stratified adjustment for age and sex, temperature > 37.5°C (adjusted RR: 1.3, 95% CI: 1-1.7, p = 0.036) and GCS <14 (adjusted RR: 1.23, 95% CI: 1-1.6, p = 0.041) were associated with readmission. Readmission after discharge for stroke was common with nearly one third occurring within the first month and more than half dying following readmission. The most common causes of readmission were infections and recurrent stroke.

Predicting hospital readmission in multimorbid patients with the use of AI: A systematic review

Abstract Background Multimorbid patients are at higher risk of hospital readmission due to the complex nature of their conditions. Identifying those who may be at particularly high risk would allow us to intervene early and potentially delay or prevent such readmissions occurring, thus reducing healthcare costs. We conducted a systematic review investigating the use of machine learning models in predicting 30-days unplanned hospital readmission of multimorbid patients. Methods This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines, and was registered with PROSPERO (CRD42022373937). We searched MEDLINE, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Included studies developed an AI model for prediction of 30-days hospital readmission for adult patients with two or more health conditions. The CHARM and PROBAST checklists for data extraction and bias assessment were used. The quality of included studies was assessed with the CASP checklist. Results Eighteen papers were eligible for inclusion. A total of 669 predictors were reported with an average of 37 used per model. Predictors were classified as modifiable and non-modifiable with the most common modifiable predictors being hospital length of stay, hypertension, anaemia and obesity. Average sensitivity and specificity of the models was reported to be 72% in 13 studies and 79.2 in 11 studies, respectively. Area under the curve (AUC) was reported in 13 studies, five of which were considered to have good discrimination power (AUC&amp;gt;0.8). Conclusions Machine learning models are capable of accurately predicting 30-days hospital readmission of multimorbid patients. Identifying modifiable predictors with highest weight allows for better planning and resource allocation to potentially reduce the risk of 30-days readmissions. An important area for future work would be the implementation of these high performance models in practice. Key messages • Hospital readmissions are preventable and identifying those at higher risk allows us to intervene early. • Machine learning models are capable of predicting 30-days readmission of multimorbid patients.

Predicting hospital readmissions in diabetes patients: A comparative study of machine learning models

Predicting hospital readmissions in diabetes patients: A comparative study of machine learning models

Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning.

Early hospital readmission refers to unplanned emergency admission of patients within 30 days of discharge. Predicting early readmission risk before discharge can help to reduce the cost of readmissions for hospitals and decrease the death rate for Intensive Care Unit patients. In this paper, we propose a novel approach for prediction of unplanned hospital readmissions using discharge notes from the MIMIC-III database. This approach is based on first extracting relevant information from clinical reports using a pretrained Named Entity Recognition model called BioMedical-NER, which is built on Bidirectional Encoder Representations from Transformers architecture, with the extracted features then used to train machine learning models to predict unplanned readmissions. Our proposed approach achieves better results on clinical reports compared to the state-of-the-art methods, with an average precision of 88.4% achieved by the Gradient Boosting algorithm. In addition, explainable Artificial Intelligence techniques are applied to provide deeper comprehension of the predictive results.

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission. We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing. In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day. Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain. Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings.

NLP Based Prediction of Hospital Readmission using ClinicalBERT and Clinician Notes

NLP Based Prediction of Hospital Readmission using ClinicalBERT and Clinician Notes

Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis.

Numerous applications and strategies have been utilized to help assess the trends and patterns of readmissions after orthopaedic surgery in an attempt to extrapolate possible risk factors and causative agents. The aim of this work is to systematically summarize the available literature on the extent to which natural language processing, machine learning, and artificial intelligence (AI) can help improve the predictability of hospital readmissions after orthopaedic and spine surgeries. This is a systematic review and meta-analysis. PubMed, Embase and Google Scholar were searched, up until August 30, 2023, for studies that explore the use of AI, natural language processing, and machine learning tools for the prediction of readmission rates after orthopedic procedures. Data regarding surgery type, patient population, readmission outcomes, advanced models utilized, comparison methods, predictor sets, the inclusion of perioperative predictors, validation method, size of training and testing sample, accuracy, and receiver operating characteristics (C-statistic), among other factors, were extracted and assessed. A total of 26 studies were included in our final dataset. The overall summary C-statistic showed a mean of 0.71 across all models, indicating a reasonable level of predictiveness. A total of 15 articles (57%) were attributed to the spine, making it the most commonly explored orthopaedic field in our study. When comparing accuracy of prediction models between different fields, models predicting readmissions after hip/knee arthroplasty procedures had a higher prediction accuracy (mean C-statistic = 0.79) than spine (mean C-statistic = 0.7) and shoulder (mean C-statistic = 0.67). In addition, models that used single institution data, and those that included intraoperative and/or postoperative outcomes, had a higher mean C-statistic than those utilizing other data sources, and that include only preoperative predictors. According to the Prediction model Risk of Bias Assessment Tool, the majority of the articles in our study had a high risk of bias. AI tools perform reasonably well in predicting readmissions after orthopaedic procedures. Future work should focus on standardizing study methodologies and designs, and improving the data analysis process, in an attempt to produce more reliable and tangible results. Level III. See Instructions for Authors for a complete description of levels of evidence.

Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.

In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Machine Learning Algorithms for Predicting Hospital Readmission and Mortality Rates in Patients with Heart Failure

Purpose: The potential of predictive analytics in enhancing resource allocation and patient care for Heart failure (HF) outcomes is significant. This review aims to highlight this potential by analyzing existing studies and identifying the main barriers and challenges to applicability in all settings. Design/ Methodology/ Approach: A comprehensive search of related articles was meticulously conducted across electronic databases, including Google Scholar, Web of Science, and PubMed. Using precise search phrases and keywords, 1,835 scholarly articles published between 1 January 2017 and 14 May 2024 were retrieved. Only 23 articles that met the strict inclusion criteria were considered, ensuring the validity of the findings. A quantitative meta-analysis approach was utilised. Research Limitation/Implication: This research offers insights into enhancing healthcare outcomes as we analyse the challenges and feasibility of applying ML algorithms to predict heart failure outcomes in low-income settings. Findings: The challenges include scalability, ethical and legal issues, the choice of appropriate ML model, interpretability, data availability, and healthcare professional mistrust of these ML algorithms. Practical implications: This study offers practical strategies to bridge the gap between clinical practice and predictive analytics in these regions. These strategies should inspire and motivate healthcare professionals, researchers, and policymakers to consider and implement them. Social implication: This study provides insights that may improve HF outcomes and healthcare delivery. Originality/Value: The review identifies current gaps in the research, such as the need for more robust validation studies, the challenge of model interpretability, and the necessity for models that can be easily integrated into clinical workflows.

Lung Ultrasound Score at Hospital Discharge as a Predictor of Emergency Department Visit and Hospital Readmission in Patients With Acute Heart Failure.

Purpose The number of B-lines on lung ultrasound at hospital discharge in patients admitted with acute heart failure (AHF) is associated with poor outcomes. Assessing B-lines can be challenging to execute and replicate, depending on the clinical context. This study aims to determine whether the lung ultrasound score (LUS) at discharge predicts hospital readmission or emergency department (ED) visits in the 30 days after an AHF hospital admission. Methods We conducted an observational study at the medical ward of the emergency unit of the Clinics Hospital of the Ribeirao Preto Medical School, University of Sao Paulo, a tertiary university hospital in Ribeirao Preto, Sao Paulo, Brazil, where consecutive adults admitted with AHF were included. On the day of hospital discharge, we measured the LUS and tracked these patients for up to 30 days to monitor emergency department visits, hospital readmission, and the number of days free from hospital stay. Results A total of 46 patients were included in the study. A composite outcome of ED visits or hospital readmission in the 30 days after hospital discharge was achieved for 22 (47.8%) patients. The LUS at hospital discharge had a receiver operating characteristic (ROC) area of 0.93 (95% CI, 0.82-0.99) to predict the composite outcome, against 0.67 (95% CI, 0.52-0.81) for the clinical congestion score (CCS). A LUS ≥ 7 at discharge had a sensitivity of 95.5% and a specificity of 87.5% to predict the composite outcome. The average exam duration was 176±65 (sd) seconds. Conclusions The LUS at hospital discharge following admission for AHF proves to be an accurate tool for predicting the likelihood of return to the ED and/or hospital readmission within 30 days post discharge.

Predictors of Hospital Readmission, Institutionalization, and Mortality in Geriatric Rehabilitation Following Hospitalization According to Admission Reason.

Older adults following an inpatient geriatric rehabilitation (GR) program commonly experience adverse health outcomes such as hospital readmission, institutionalization, and mortality. Although several studies have explored factors related to these outcomes, the influence of admission reason on the predictive factors of adverse health outcomes in the rehabilitation process remains unclear. Therefore, this study aimed to identify predictive factors for adverse health outcomes in inpatients attending GR according to their admission reason. This retrospective study included patients with orthogeriatric (OG) conditions and patients with hospital-associated deconditioning (HAD) admitted to GR after an acute hospitalization between 2016 and 2020. Patients were evaluated by a comprehensive geriatric assessment at admission, including sociodemographic data, social resources, clinical data, cognitive, functional and nutritional status, and physical performance measurements. Adverse health outcomes were collected (hospital readmission, institutionalization, and mortality). Univariate analyses and multivariate backward binary logistic regressions were used to determine predictive factors. In this study, 290 patients were admitted for OG conditions, and 122 patients were admitted due to HAD. In patients with OG conditions, lower Mini-Mental State Examination (MMSE) predicted institutionalization and mortality. Lower Mini Nutritional Assessment-Short Form predicted institutionalization, whereas lower Barthel Index and lower Tinetti-Performance-Oriented Mobility Assessment scores were associated with higher mortality. In patients with HAD, higher age-adjusted comorbidity index predicted hospital readmission and mortality, and lower Short Physical Performance Battery scores predicted institutionalization and mortality. Finally, lower MMSE scores, worse values in Older Americans Resources and Services Scale and male gender were associated with a higher risk of institutionalization. Predictive factors for hospital readmission, institutionalization, and mortality in patients with OG conditions and HAD during GR were different. Some of those predictors, such as nutritional status and physical performance, are modifiable. Understanding predictive factors for adverse outcomes, and how these factors differ by admission diagnosis, improves our ability to identify patients most at risk. Early identification of these patients could assist with prevention efforts and lead to a reduction of negative outcomes.

WED-098-YI Low-density lipoprotein can predict hospital readmissions and outcomes following acute decompensation of cirrhosis

WED-098-YI Low-density lipoprotein can predict hospital readmissions and outcomes following acute decompensation of cirrhosis

A Preoperative Risk Assessment Tool for Predicting Adverse Outcomes among Total Shoulder Arthroplasty Patients

With the increased utilization of Total Shoulder Arthroplasty (TSA) in the outpatient setting, understanding the risk factors associated with complications and hospital readmissions becomes a more significant consideration. Prior developed assessment metrics in the literature either consisted of hard-to-implement tools or relied on postoperative data to guide decision-making. This study aimed to develop a preoperative risk assessment tool to help predict the risk of hospital readmission and other postoperative adverse outcomes. We retrospectively evaluated the 2019-2022(Q2) Medicare fee-for-service inpatient and outpatient claims data to identify primary anatomic or reserve TSAs and to predict postoperative adverse outcomes within 90days postdischarge, including all-cause hospital readmissions, postoperative complications, emergency room visits, and mortality. We screened 108 candidate predictors, including demographics, social determinants of health, TSA indications, prior 12-month hospital, and skilled nursing home admissions, comorbidities measured by hierarchical conditional categories, and prior orthopedic device-related complications. We used two approaches to reduce the number of predictors based on 80% of the data: 1) the Least Absolute Shrinkage and Selection Operator logistic regression and 2) the machine-learning-based cross-validation approach, with the resulting predictor sets being assessed in the remaining 20% of the data. A scoring system was created based on the final regression models' coefficients, and score cutoff points were determined for low, medium, and high-risk patients. A total of 208,634 TSA cases were included. There was a 6.8% hospital readmission rate with 11.2% of cases having at least one postoperative adverse outcome. Fifteen covariates were identified for predicting hospital readmission with the area under the curve of 0.70, and 16 were selected to predict any adverse postoperative outcome (area under the curve =0.75). The Least Absolute Shrinkage and Selection Operator and machine learning approaches had similar performance. Advanced age and a history of fracture due to orthopedic devices are among the top predictors of hospital readmissions and other adverse outcomes. The score range for hospital readmission and an adverse postoperative outcome was 0 to 48 and 0 to 79, respectively. The cutoff points for the low, medium, and high-risk categories are 0-9, 10-14, ≥15 for hospital readmissions, and 0-11, 12-16, ≥17 for the composite outcome. Based on Medicare fee-for-service claims data, this study presents a preoperative risk stratification tool to assess hospital readmission or adverse surgical outcomes following TSA. Further investigation is warranted to validate these tools in a variety of diverse demographic settings and improve their predictive performance.

Hospital readmission prediction with hybrid‐sampling and self‐paced balance learning

SummaryHospital readmission prediction is defined as an evaluation task to model the historical medical data to predict whether patients will be readmitted after discharge. In the past few years, many feasible and effective prediction methods have been proposed, however, most of them neglect the imbalanced distribution of medical data, which causes great difficulties in modeling. Thus, we proposed a new hospital readmission prediction method, which utilizes hybrid‐sampling and self‐paced balance learning strategies to solve the class‐imbalance problem. To be specifically, we first employ an interference negative sample deletion strategy to reduce the probability of important majority class samples being deleted. Then, we design a hard positive sample generation strategy to generate more positive samples. Meanwhile, we also introduce a self‐paced balance factor during the oversampling process to improve the similarity between newly generated minority class samples and hard positive samples. Finally, we perform the experiments on six real‐world readmission datasets to indicate the superiority of the proposed method.

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death

We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009–2011 inpatient episodes (approximately 1.2 million), and then tested the model on 2012 episodes (approximately 400 thousand). The model scored an out-of-sample AUROC of approximately 0.75 on predicting all-cause readmissions—defined using official Centers for Medicare and Medicaid Services (CMS) methodology—or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, it provides what blackboxes cannot—its exact gold-standard global interpretation, explicitly defining how the model performs its internal “reasoning” for mapping the input data features to predictions. In doing so, we identify relative risk factors and quantify the effect of discharge placement. We also show that the posthoc explainer SHAP provides explanations that are inconsistent with the ground truth model reasoning that our model readily admits.

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.

Factors Predicting Readmission and Mortality in Patients Admitted for Malignant Bowel Obstruction.

Malignant bowel obstruction (MBO) is a common complication of patients with advanced malignancies and has poor prognosis. Currently, there are limited guidelines for MBO management or predicting outcomes for these patients. To identify patient factors associated with readmission and mortality after hospital admission for MBO. A 5-year retrospective review was performed from 2017 to 2022 at a single tertiary institution to evaluate patients admitted for MBO. All patients had advanced cancer of gastrointestinal or gynecologic primary. Patient demographics, socioeconomic factors, tumor characteristics, and inpatient outcomes were collected. Multivariable analyses were performed to determine variables predicting hospital readmission for recurrent MBO and 90-day mortality. 210 patients were included. Mean age was 61 years, 28% were male, and 19% did not primarily speak English. 35% of patients lived over 50 miles from the hospital. On multivariable analysis, non-English speaking patients exhibited increased risk of readmission for MBO (OR = 2.82, P = .039). Older age was associated with decreased risk for MBO readmission (OR = .96, P = .007). Ascites was associated with increased mortality (OR = 2.17, P = .043). Earlier palliative care (PC) consultation predicted decreased readmission (OR = .24, P < .001) yet increased mortality at 90 days (OR = 3.20, P = .003). Patient age, primary language, and PC consult were predictors for MBO readmission, which may impact 90-day mortality. Given the palliative nature of MBO, modifiable factors such as PC consultation and multidisciplinary goals of care discussions should be prioritized in order to reduce readmissions and focus on quality of life (QOL) for this patient population.

Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.

Predicting Hospital Readmission in Medicaid Patients With COPD Using Administrative and Claims Data.

The goals of this study were to develop a model that predicts the risk of 30-d all-cause readmission in hospitalized Medicaid patients diagnosed with COPD and to create a predictive model in a retrospective study of a population cohort. We analyzed 2016-2019 Medicaid claims data from 7 United States states. A COPD admission was one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an International Classification of Diseases, Tenth Revision code for COPD. A readmission was an admission for any condition (not necessarily COPD) that occurred within 30 d of a COPD discharge. We estimated a mixed-effects logistic model to predict 30-d readmission from patient demographic data, comorbidities, past health care utilization, and features of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic (ROC) curve. Among 12,283 COPD hospitalizations contributed by 9,437 subjects, 2,534 (20.6%) were 30-d readmissions. The final model included demographics, comorbidities, claims history, admission and discharge variables, length of stay, and seasons of admission and discharge. The observed versus predicted plot showed reasonable fit, and the estimated area under the ROC curve of 0.702 was robust in sensitivity analyses. Our model identified with acceptable accuracy hospitalized Medicaid patients with a diagnosis of COPD who are at high risk of readmission. One can use the model to develop post-discharge management interventions for reducing readmissions, for adjusting comparisons of readmission rates between sites/providers or over time, and to guide a patient-centered approach to patient care.

Abstract P272: Association of Depression and Handgrip Strength With Morbidity and Mortality in Patients With Heart Failure: A Secondary Analysis From a Randomized Controlled Trial

Introduction: Depression is associated with increased morbidity and mortality in patients with heart failure (HF). Handgrip strength (HGS) is a measure of sarcopenia and lower HGS has been associated with adverse HF outcomes. We hypothesized that relationships between HGS and morbidity and mortality would be moderated by depression in HF patients. Methods: We conducted a secondary analysis of patients hospitalized with HF categorized by depression status. We calculated HGS adjusted for body weight and categorized the values into tertiles, from lowest to highest. We fitted Cox proportional hazards models to estimate hazard ratios (HRs) comparing HGS tertiles in relation to 5-year mortality and 1-year readmissions among depressed and non-depressed and tested whether relationships differ by depression status. Models included HGS tertile, depression status, tertile-by-depression interaction, age, sex, race, medical comorbidities, educational attainment, employment, and HF severity. Results: A total of 494 patients were included in the analysis (mean age 63.4 ± 13.4 years, 44% female, 26% non-white race). A total of 83% of patients had depression and the mean LVEF (SD) was 27.8% (9.2). At 12-months following the index hospitalization the readmission rate was 56% (275 of 494), and the 5-year mortality rate was 41% (204 of 494). Individuals with higher HGS (tertile 2) were less likely to be readmitted compared to tertile 1 (46% vs. 63%; P=0.013). Depression status significantly moderated the relationship between HGS and readmission such that strong handgrip strength predicted readmission among depressed (adjusted HR of 0.35 (95% CI; 0.16, 0.81) comparing tertile 2 to tertile 1) but not among non-depressed patients (0.77 (0.55, 1.06)). However, the HGS-mortality relationship was not moderated by depression. Conclusion: HGS is a predictor of hospital readmission among HFrEF patients, and depression is a moderator of the HGS-readmission relationship. We recommend routinely assessing hospitalized HFrEF patients’ HGS and mood symptoms prior to discharge home, and further research is needed to assess the effects of depression on sarcopenia in its relation to clinical outcomes in HF patients.