Strongest Predictor Of Readmission Research Articles

Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model

BackgroundUnplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility. This study aims to compare the predictive accuracy of the SRC with a novel artificial neural network (ANN) algorithm for 30-day readmission after primary TKA, using the same set of clinical variables from a large national database.MethodsPatients undergoing primary TKA between 2013 and 2020 were identified from the ACS-NSQIP database and randomly stratified into training and validation cohorts. The ANN was developed using data from the training cohort with fivefold cross-validation performed five times. ANN and SRC performance were subsequently evaluated in the distinct validation cohort, and predictive performance was compared on the basis of discrimination, calibration, accuracy, and clinical utility.ResultsThe overall cohort consisted of 365,394 patients (trainingN = 362,559; validationN = 2835), with 11,392 (3.1%) readmitted within 30 days. While the ANN demonstrated good discrimination and calibration (area under the curve (AUC)ANN = 0.72, slope = 1.32, intercept = −0.09) in the validation cohort, the SRC demonstrated poor discrimination (AUCSRC = 0.55) and underestimated readmission risk (slope = −0.21, intercept = 0.04). Although both models possessed similar accuracy (Brier score: ANN = 0.03; SRC = 0.02), only the ANN demonstrated a higher net benefit than intervening in all or no patients on the decision curve analysis. The strongest predictors of readmission were body mass index (> 33.5 kg/m2), age (> 69 years), and male sex.ConclusionsThis study demonstrates the superior predictive ability and potential clinical utility of the ANN over the conventional SRC when constrained to the same variables. By identifying the most important predictors of readmission following TKA, our findings may assist in the development of novel clinical decision support tools, potentially improving preoperative counseling and postoperative monitoring practices in at-risk patients.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Does CT scanning after pancreatoduodenectomy reduce readmission rates: an analysis of 900 resections at a high-volume center.

Pancreatoduodenectomy (PD) remains associated with significant complication and readmission rates. Infection constitutes a significant proportion of morbidity. We aim to evaluate whether CT scans performed prior to discharge for suspected infection prevents readmission. A retrospective review of patients undergoing PD at a tertiary referral center from 2010 to 2018. A total of 982 patients underwent PD: 74% had no clinical infection at the index admission. Of the non-infected patients, 59% exhibited leukocytosis, 27% underwent a CT scan, and 33.6% were readmitted. Of the non-infected patients, 148 (20.3%) experienced major complications, and this was the strongest predictor of readmission (OR: 10.5, [95% CI: 6.5-17], p=0.0001). In the non-infected patients who had major complications, CT scanning was predictive of lower risk of readmission (OR: 0.38, [95% CI: 0.17-0.83], p=0.015). Leukocytosis was also found to be predictive of lower risk of readmission (OR: 0.42, [95% CI: 0.18-0.98], p=0.044). These findings did not hold true for those who had yet to experience major complications on their index admission. CT scanning without evidence of infection was associated with reduction of readmission in the cohort with major complications and showed a trend towards preventing readmission in the overall cohort. Development of clinical algorithms to maximize the utility of this test is warranted.

Abstract 13264: Gender-Based Risk Prediction for 1-year Readmission Among Younger Women Hospitalized for Acute Myocardial Infarction

Background: Younger women (≤55 years) are at higher risk for readmission within one-year of hospitalization following acute myocardial infarction (AMI) compared to similarly aged men, yet there are no gender-specific risk prediction models for this population. Prior research suggests that gender-related factors more comprehensively explain sex-based differences in readmission risk. The objective was to develop and validate a risk prediction model of 1-year post-AMI readmission in young women considering demographic, clinical, and gender-related factors. Methods: We used data from women enrolled in the VIRGO study (n =2,007), a prospective observational study of young patients aged ≤55 years hospitalized with AMI in the US. Data were obtained from patient interviews, medical record abstraction, and adjudicated hospitalization records. Bayesian Model Averaging was used for model selection in a derivation cohort of 1338 women and subsequently validated in the remaining 669 women. Results: Within 1-year post-AMI, 684 (34.1%) women were readmitted at least once with a majority of readmissions due to cardiac causes (57.5%). The final model contained 9 predictors: experiencing any in-hospital complications, physical health at baseline (SF-12), disease-specific quality of life (Seattle Angina Questionnaire), diabetes, history of congestive heart failure, low income (≤30,000 USD), depression, length of hospital stay, and employment status (Figure) . Of the 9 predictors, 5 were gender-related. The model was well calibrated (calibration plots) and exhibited modest discrimination (C statistic=0.66 in development and validation cohorts). Conclusions: Younger women with diabetes, depression, history of congestive heart failure, and longer hospital stays were more likely to be readmitted. While clinical factors were the strongest predictors of readmission within 1-year among younger women with AMI, gender-related variables were important complements.

Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

IntroductionThis study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions.MethodsPatients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree models were evaluated to generate a robust prediction model.ResultsWe identified 16,745 patients who underwent CAS, of whom 7.4% were readmitted within 30 days. Depression [p < 0.001, OR 1.461 (95% CI 1.231–1.735)], heart failure [p < 0.001, OR 1.619 (95% CI 1.363–1.922)], cancer [p < 0.001, OR 1.631 (95% CI 1.286–2.068)], in-hospital bleeding [p = 0.039, OR 1.641 (95% CI 1.026–2.626)], and coagulation disorders [p = 0.007, OR 1.412 (95% CI 1.100–1.813)] were the strongest predictors of readmission. The artificial intelligence machine learning DNN prediction model has a C-statistic value of 0.79 (validation 0.73) in predicting the patients who might have all-cause unplanned readmission within 30 days of the index CAS discharge.ConclusionsMachine learning derived models may effectively identify high-risk patients for intervention strategies that may reduce unplanned readmissions post carotid artery stenting.Central IllustrationFigure 2: ROC and AUPRC analysis of DNN prediction model with other classification models on 30-day readmission data for CAS subjectsSupplementary InformationThe online version contains supplementary material available at 10.1007/s12325-021-01709-7.

Predictors of 30-day readmission and resource utilization after thoracic endovascular aortic repair.

The introduction and expansion of thoracic endovascular aortic repair (TEVAR) have revolutionized the treatment of a variety of thoracic aortic diseases. We sought to evaluate the incidence, causes, predictors and costs associated with 30-day readmission after TEVAR in a nationally representative cohort. Adult patients undergoing isolated TEVAR were identified in the National Readmissions Database from 2010 to 2014. Hospital costs were estimated by converting individual hospital charge data adjusted to 2014 consumer price indices. Multivariable logistic regression was utilized to determine hospital- and patient-level factors associated with readmissions. A total of 24983 TEVARs were noted during the study period; the average age of the patients was 65 ± 16 years; 40% were women. The most common indication was an intact thoracic aneurysm (43.5%), followed by aortic dissection (30.5%). The average cost of the index admission was $63644 ± $52312; the average hospital stay was 11 ± 14 days; the index mortality rate was 6.7%. Readmissions within 30 days occurred in 17.4% of patients. Indications for readmission were varied; the most common aetiologies were cardiac (17.8%), infectious (16.0%) and pulmonary (12.1%). On multivariable analysis, the strongest predictor of readmission was the diagnosis, with a ruptured thoraco-abdominal aneurysm having the highest readmission burden (adjusted odds ratio 2.23, 1.17-4.24; P = 0.015). Notably, hospital volume did not predict index hospital length of stay, costs or 30-day readmissions (all P > 0.10). Annual TEVAR volume was not associated with any of the outcomes assessed. Rather, indication for TEVAR was the strongest predictor for many outcomes. As TEVAR becomes increasingly utilized, a focus on cardiac and vascular diseases may reduce readmissions and improve quality of care.

P2668Causes and predictors of 30 and 90 days readmission after cardiac arrest: insights from the nationwide readmission database

Abstract Background Cardiac arrest (CA) is a lethal condition with high public health burden and grave long-term implications. We aimed to evaluate the rate and predictors of readmission after CA Methods We used the Nationwide Readmission Database 2014 to determine patient with index admission CA by using relevant ICD-9 codes. We excluded patients &lt;18 years and admissions in October, November, and December to allow 90 days readmission follow up. We evaluated rates and causes of 30 and 90 days readmission. We investigated disposition options for CA survivors and rates of readmissions. Multivariable analysis was done for relevant variables to demonstrated predictors of readmission. Results A total cohort of 71,780 patients suffered CA and survived to hospital discharge. Mean age was 63.9±15 years and 61.3% were males. Mean length of stay was 14.2±18.3 days, primary payer was Medicare 58%, 42.2% were discharged home, 33% to skilled nursing/intermediate care facility, 18.5% to home with home health services, 5% to short term hospital, and 0.8% left against medical advice (AMA). All-cause 30 and 90 days readmission rates were 18% and 28.9%, respectively. According to each disposition group 30/90 days readmission rates were; home 13.6/23%, facility 22/35.7%, home health 21/31.9%, short term hospital 16.8/24.1%, and 33.7/42.4% left AMA. The most common causes of readmission at 30 and 90 days were congestive heart failure and infection (11% each). CA on readmission was 1.5% at 30 and 90 days. The most common predictors of readmission were CHF odds ratio (OR) 1.3, (95% CI: 1.3–1.4; P&lt;0.001), renal failure OR 1.7, (95% CI: 1.7–1.8; P&lt;0.001), chronic lung disease OR 1.3, (95% CI: 1.2–1.; P&lt;0.001), and depression OR 1.1 (95% CI: 1.1–1.2; P&lt;0.001). Conclusion Readmissions are common at 30 and 90 days in CA survivors and are associated with high cost. CHF and infection are the most common causes of 30 and 90-day readmission, while renal failure is the strongest predictor of readmission after adjusting for unbalanced covariates. Discharge to home was the most common disposition, and had the lowest rate for readmission. Leaving AMA had the highest rate of readmission.

Factors associated with 30-day readmission for patients hospitalized for seizures.

We sought to determine the cumulative incidence of readmissions after a seizure-related hospitalization and identify risk factors and readmission diagnoses. We performed a retrospective cohort study of adult patients hospitalized with a primary discharge diagnosis of seizure (International Classification of Diseases, Ninth Edition, Clinical Modification codes 345.xx and 780.3x) using the State Inpatient Databases across 11 states from 2009 to 2012. Hospital and community characteristics were obtained from the American Hospital Association and Robert Wood Johnson Foundation. We performed logistic regressions to explore effects of patient, hospital, and community factors on readmissions within 30 days of discharge. Of 98,712 patients, 13,929 (14%) were readmitted within 30 days. Reasons for readmission included epilepsy/convulsions (30% of readmitted patients), mood disorders (5%), schizophrenia (4%), and septicemia (4%). The strongest predictors of readmission were diagnoses of CNS tumor (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9-2.4) or psychosis (OR 1.8, 95% CI 1.7-1.8), urgent index admission (OR 2.0, 95% CI 1.8-2.2), transfer to nonacute facilities (OR 1.7, 95% CI 1.6-1.8), long length of stay (OR 1.7, 95% CI 1.6-1.8), and for-profit hospitals (OR 1.7, 95% CI 1.6-1.8). Our main model's c-statistic was 0.66. Predictors of readmission for status epilepticus included index admission for status epilepticus (OR 3.5, 95% CI 2.6-4.7), low hospital epilepsy volume (OR 0.4, 95% CI 0.3-0.7), and rural hospitals (OR 4.8, 95% CI 2.1-10.9). Readmission is common after hospitalization for seizures. Prevention strategies should focus on recurrent seizures, the most common readmission diagnosis. Many factors were associated with readmission, although readmissions remain challenging to predict.

Early Unplanned Readmissions After Admission to Hospital With Heart Failure

Hospital readmissions remain a continued challenge in the care of patients with heart failure (HF). This study aims to examine the rates, temporal trends, predictors and causes of 30-day unplanned readmissions after admission with HF. Patients hospitalized with a primary or secondary diagnosis of HF in the U.S. Nationwide Readmission Database were included. We examined the incidence, trends, predictors and causes of unplanned all-cause readmissions at 30-days. A total of 2,635,673 and 8,342,383 patients were included in the analyses for primary and secondary diagnoses of HF, respectively. The 30-day unplanned readmission rate was 15.1% for primary HF and 14.6% for secondary HF. Predictors of readmission in primary HF included renal failure (OR 1.27 (1.25 to 1.28)), cancer (OR 1.26 (1.22 to 1.29)), receipt of circulatory support (OR 2.81 (1.64 to 4.81)) and discharge against medical advice (OR 2.29 (2.20 to 2.39)). In secondary HF, the major predictors were receipt of circulatory support (OR 1.43 (1.12 to 1.84)) and discharge against medical advice (OR 2.01 95%CI (1.95 to 2.07)). In primary HF 52.4% of patients were readmitted for a noncardiac cause while for secondary HF 73.9% were readmitted for a noncardiac cause. For secondary HF, the strongest predictor of readmission was discharge against medical advice (OR 2.06 95%CI 2.01 to 2.12, p < 0.001). Early unplanned readmissions are common among patients hospitalized with HF, and a majority of readmissions are due to causes other than HF. Our results highlight the need to better manage comorbidities in patients with HF.

Nonspecific Chest Pain and 30-Day Unplanned Readmissions in the United States (From the Nationwide Readmission Database)

Chest pain is a common reason for admission to hospital and little is known regarding 30-day unplanned readmissions after an admission with a primary discharge diagnosis of nonspecific chest pain. We analyzed patients with a primary diagnosis of nonspecific chest pain in the Nationwide Readmission Database who were admitted in 2010 to 2014. Rates, causes, and predictors of 30-day unplanned readmissions were determined. A total of 1,842,270 patients had a diagnosis of nonspecific chest pain. The 30-day unplanned readmission rate was 8.6%. From 2010 to 2014, there was an increase in 30-day unplanned readmissions from 8.1% to 9.5%. The majority of 30-day unplanned readmissions were for noncardiac reasons (73.4%). The 3 most prevalent noncardiac causes for readmissions were neuropsychiatric (10.9%), gastrointestinal (10.5%), and infections (9.9%), while the 3 most prevalent cardiac causes were coronary artery disease including angina (8.4%), arrhythmias (6.6%), and heart failure 5.5%. The strongest predictors of readmission were alcohol misuse ([OR] odds ratio 1.74 95% [CI] confidence interval 1.66-1.81), renal failure (OR 1.82 95%CI 1.76-1.87), cancer (OR 2.40 95%CI 2.27-2.53), discharge to a nursing home (OR 2.26 95%CI 2.18-2.34), and discharge against medical advice (OR 1.94 95%CI 1.86-2.02). The rate of 30-day unplanned readmission was 6.1% among those who received any test compared to 9.3% in those who did not receive any test. Rates of early unplanned readmissions occur following 1 in 12 admissions for nonspecific chest pain with noncardiac causes being the most common reason. Patients who receive a cardiovascular investigation appear to have fewer unplanned readmissions.

Trends in Hospice Discharge and Relative Outcomes Among Medicare Patients in the Get With The Guidelines–Heart Failure Registry

While 1 in 10 older patients hospitalized with heart failure (HF) die within 30 days, end-of-life care for this population is not well described. To assess rates of discharge to hospice, readmission after hospice, and survival in hospice in patients following hospital discharge. In this observational cohort analysis of patients in the multicenter American Heart Association Get With The Guidelines (GWTG)-HF registry linked to Medicare fee-for-service claims data, we analyzed patients 65 years and older discharged alive from the hospital between 2005 and 2014. We compared 4588 patients discharged to hospice with 4357 patients with advanced HF (ejection fraction ≤25% and any of the following: inpatient inotrope use, serum sodium level ≤130 mEq/L, blood urea nitrogen level ≥45 mg/dL [to convert to micromoles per liter, multiply by 0.357], systolic blood pressure ≤90 mm Hg, or comfort measures during hospitalization) not discharged to hospice and with 113 045 other patients with HF in the GWTG-HF registry. Data were analyzed from October 2017 to June 2018. Discharge to hospice, rehospitalization, and mortality. Of the 4588 patients discharged to hospice, 2556 (55.7%) were female and 4047 (88.2%) were white, and they had a median (interquartile range) age of 86 (80-90) years. Hospice accounted for 4588 of 121 990 discharges (3.8%), of which 2424 (52.8%) were discharges to home hospice and 2164 (47.2%) were to a hospice facility. Hospice discharges increased from 2.0% (109 of 5528) in 2005 to 4.9% (968 of 19 590) in 2014. Patients discharged to hospice were older, white, and more symptomatic compared with patients with advanced HF (n = 4357) and other patients in the GWTG-HF registry (n = 113 045). The median (interquartile range) postdischarge survival time in patients discharged to hospice was 11 (3-63) days compared with 318 (78-1105) days in patients with advanced HF and 754 (221-1868) days in other patients in the GWTG-HF registry. A total of 739 patients (34.1%) discharged to hospice facilities died in less than 72 hours, while 295 (12.2%) discharged to home hospice died in less than 72 hours; 690 patients (15.0%) discharged from hospice lived for 6 months or more. Among hospitals with more than 25 hospice discharges, the median (interquartile range) hospice discharge rate was 3.5% (2.0%-5.7%). Readmission at 30 days was lower in patients discharged to hospice (189 [4.1%]) compared with patients with advanced HF (1185 [27.2%]) and others in the GWTG-HF registry (25 022 [22.2%]). Nonwhite race and younger age were the strongest predictors of readmission from hospice. Hospice use has grown to about 4.9% of Medicare HF hospital discharges, with significant hospital-level variation. Almost a quarter of patients discharged to hospice die within 3 days of discharge, and about 4.1% of patients are readmitted to the hospital within 30 days.

Emergency department management of acute exacerbations of chronic obstructive pulmonary disease: factors predicting readmission.

RationaleReadmissions are common following acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and are partially responsible for increased morbidity and mortality in COPD. Numerous factors have been shown to predict readmission of patients previously admitted to hospital for AECOPD; however, factors related to readmission in patients who are triaged in emergency departments (EDs) and sent directly home are poorly understood. We postulate that patients seen in the ED for AECOPD and directly sent home have a high readmission rate, and we suspect that inadequate management and follow-up contribute to this high readmission rate.MethodsWe conducted a 1-year retrospective study of all patients seen in the ED for AECOPD at an inner-city tertiary care hospital; 30- and 90-day readmission rates for COPD and all-cause admissions to the ED and hospital were determined. Patients discharged directly home from the ED were compared with those admitted to hospital for management. Patient, treatment, and system variables that could potentially impact readmission were documented. Multivariate Poisson regression models were used to determine which factors predicted readmissions.ResultsThe readmission rates in the ED group (n=240) were significantly higher than that in the hospitalized group (n=271): 1) the 90-day ED readmissions (1.29 vs 0.51, p<0.0001) and 30-day ED readmissions (0.54 vs 0.20, p<0.0001) (ED vs hospitalized groups) were significantly higher in the ED group; 2) the time to first readmission was significantly shorter in the ED group than in the hospitalized group (24.1±22 vs 31.8±27.8 days; p<0.05). Cardiovascular comorbidities (p<0.00001), substance abuse disorder (p<0.001), and mental illness (p<0.001) were the strongest predictors of readmission in the ED group. Age (p<0.01), forced expiratory volume in 1 second (p<0.001), and cardiovascular comorbidities (p<0.05) were the best predictors for both 30- and 90-day COPD readmission rates in the ED group. Only 50% of the ED group patients received bronchodilators, oral steroids, and antibiotics inclusively, and only 68% were referred for community follow-up. The need for oral steroids to treat AECOPD predicted future 90-day COPD readmissions in the ED group (p<0.003).ConclusionPatients discharged directly home from EDs have a significantly higher risk of readmission to EDs than those who are hospitalized. One possible reason for this is that COPD management is variable in EDs with <50% receiving appropriate therapy.

Abstract 150: Care for Heart Failure Patients at the End of Life: Hospital Readmission and Mortality Amongst Heart Failure Patients Discharged to Hospice

Background: While 1 in 10 patients hospitalized with heart failure (HF) die within 30 days, end-of-life care for this high-risk population is not well described. Methods: We analyzed patients discharged alive from the Get With The Guidelines-HF registry between 2005-2014, linked to Medicare claims. We compared patients discharged to hospice to non-hospice “advanced HF” patients (ejection fraction ≤25% and either on inotropes, sodium ≤130, blood urea nitrogen ≥45 mg/dL, systolic blood pressure ≤90 mmHg or comfort measures) and to other GWTG-HF patients. Results: Of 121,990 US patients, hospice patients (n=4588, 2164 facility-based, 2424 home hospice) compared with advanced HF (n=4357) and others (113,045) were older (median age 86 years vs 78 years vs 81 years), more likely white race (88% vs 80% vs 82%), have intravenous loop diuretics used (74% vs 57% vs 63%), have an advanced care plan/surrogate decision maker discussed or documented (76% vs 62% vs 66%), had more dyspnea at rest (55% vs 46% vs 48%) and worse/unchanged symptoms at discharge (35% vs 2% vs 1%) (all p&lt;0.01). Discharge to hospice increased from 2% (n=109) in 2005 to 5% (n=968) in 2014. Median survival in hospice was 11 days (25 th , 75 th percentile: 3, 65 days) compared with advanced HF (318 days) and others (754 days); 34% of patients discharged to a hospice facility and 12% to home hospice died in &lt;3 days. (Figure) Median survival in hospice did not change significantly from 2005 to 2014. Hospital readmission at 30 days was 4% among hospice, 27% for advanced HF, and 22% for others. Median per-hospital hospice discharge rate over the study perior was 3% (1%, 6%). Hospice discharges had lower adjusted hazards of all-cause readmission (hospice compared with others: advanced HF odd ratio (OR) 0.15 (95% confidence interval (CI) 0.13-0.18), others OR 0.15 (95% CI 0.13-0.18). Hospice patients also had lower 6-month and 1-year readmission rate. Non-white race (OR 1.59 [95% CI 1.18-2.17]) and younger age (OR per 5 years 1.18 [95% CI 1.10-1.27)] were the strongest predictors of readmission from hospice. Conclusion: Hospice use in patients hospitalized with HF is limited but increasing. Few hospice patients are rehospitalized and almost a quarter die within 3 days of discharge. These findings may inform interventions to improve hospice care for HF patients.

Abstract 149: Care for Heart Failure Patients at the End of Life: Hospital Readmission and Mortality Amongst Heart Failure Patients Discharged to Hospice

Background: While 1 in 10 patients hospitalized with heart failure (HF) die within 30 days, end-of-life care for this high-risk population is not well described. Methods: We analyzed patients discharged alive from the Get With The Guidelines-HF registry between 2005-2014, linked to Medicare claims. We compared patients discharged to hospice to non-hospice “advanced HF” patients (ejection fraction ≤25% and either on inotropes, sodium ≤130, blood urea nitrogen ≥45 mg/dL, systolic blood pressure ≤90 mmHg or comfort measures) and to other GWTG-HF patients. Results: Of 121,990 US patients, hospice patients (n=4588, 2164 facility-based, 2424 home hospice) compared with advanced HF (n=4357) and others (113,045) were older (median age 86 years vs 78 years vs 81 years), more likely white race (88% vs 80% vs 82%), have intravenous loop diuretics used (74% vs 57% vs 63%), have an advanced care plan/surrogate decision maker discussed or documented (76% vs 62% vs 66%), had more dyspnea at rest (55% vs 46% vs 48%) and worse/unchanged symptoms at discharge (35% vs 2% vs 1%) (all p&lt;0.01). Discharge to hospice increased from 2% (n=109) in 2005 to 5% (n=968) in 2014. Median survival in hospice was 11 days (25 th , 75 th percentile: 3, 65 days) compared with advanced HF (318 days) and others (754 days); 34% of patients discharged to a hospice facility and 12% to home hospice died in &lt;3 days. (Figure) Median survival in hospice did not change significantly from 2005 to 2014. Hospital readmission at 30 days was 4% among hospice, 27% for advanced HF, and 22% for others. Median hospice discharge rate was 3.0 (0.7, 5.5). Hospice discharges had lower adjusted hazards of all-cause readmission (hospice compared with others: advanced HF odd ratio (OR) 0.15 (95% confidence interval (CI) 0.13-0.18), others OR 0.15 (95% CI 0.13-0.18). Hospice patients also had lower 6-month and 1-year readmission rate. Non-white race (OR 1.59 [95% CI 1.18-2.17]) and younger age (OR per 5 years 1.18 [95% CI 1.10-1.27)] were the strongest predictors of readmission from hospice. Conclusion: Hospice use in patients hospitalized with HF is limited but increasing. Few hospice patients are rehospitalized and almost a quarter die within 3 days of discharge. These findings may inform interventions to improve hospice care for HF patients.

Development and Validation of a Predictive Model for Short- and Medium-Term Hospital Readmission Following Heart Valve Surgery.

BackgroundAlthough models exist for predicting hospital readmission after coronary artery bypass surgery, no such models exist for predicting readmission after heart valve surgery (HVS).Methods and ResultsUsing a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database, January 2007–June 2011), we examined patient, hospital, and clinical factors predictive of short‐ and medium‐term hospital readmission post‐HVS. We set aside 20% of hospitals for model validation. A generalized estimating equation model accounted for clustering within hospitals. At 219 hospitals, we identified 38 532 patients (67 years, 56% male, 62% aortic valve surgery) who underwent HVS. A total of 3125 (7.8%) and 4943 (12.8%) patients were readmitted to the index hospital within 1 and 3 months, respectively. Our 3‐month model predicted readmission rates between 3% and 61% with fair discrimination (C‐statistic, 0.67) and good calibration (predicted vs observed differences in validation cohort averaged 1.9% across all deciles of predicted readmission risk). Results were similar for our 1‐month model and our simplified 3‐month model (suitable for clinical use), which used the 5 strongest predictors of readmission: transfused units of packed Red blood cells, presence of End‐stage renal disease, type of Valve surgery, Emergency hospital admission, and hospital Length of stay (REVEaL).ConclusionsWe described and validated key factors that predict short‐ and medium‐term hospital readmission post‐HVS. These models should enable clinicians to identify individuals with HVS who are at increased risk for hospital readmission and are most likely to benefit from improved postdischarge care and follow‐up.

Readmissions in HIV-Infected Inpatients: A Large Cohort Analysis.

Hospital readmissions impose considerable physical and psychological hardships on patients and represent a high, but possibly preventable, cost for insurers and hospitals alike. The objective of this study was to identify patient characteristics associated with 30-day readmission among persons living with HIV/AIDS (PLWH) using a statewide administrative database and to characterize the movement of patients between facilities. Retrospective cohort analysis of HIV-infected individuals in New York State using a comprehensive, all-payer database. All hospitals in New York State. HIV-infected adults admitted to a medical service in 2012. PLWH identified using International Classification of Disease (ICD)-9 diagnosis codes 042 and V08. Of 23,544 index hospitalizations, 21.8% (5121) resulted in readmission. Multivariable predictors of readmission included insurance status, housing instability, psychoses, multiple comorbid chronic conditions, substance use, and past inpatient and emergency department visits. Over 30% of readmissions occurred at a different facility than that of the initial hospitalization. A number of patient characteristics were independently associated with hospital readmission within 30 days. Behavioral health disorders and comorbid conditions may be the strongest predictors of readmission in PLWH. Readmissions, especially those in urban areas, often result in fragmented care which may compromise the quality of care and result in harmful discontinuity of medical treatment.

Hospital Acquired Conditions Are the Strongest Predictor for Early Readmission: An Analysis of 26,710 Arthroplasties

Hospital readmission is a metric of hospital quality of care, yet little is known what factors predict hospital readmission following arthroplasty. Our aim was to identify variables associated with early readmission following knee and hip arthroplasty, with focus upon hospital acquired conditions (HACs). Retrospective cohort analysis using Surgical Care Improvement Project (SCIP) and Veteran’s Affairs Surgical Quality Improvement Program (VASQIP) data was performed over a five-year period. Following 26,710 total and partial primary arthroplasties (16,808 knees and 9902 hips), the overall 30-day readmission was 7.3% (1940) with readmission rates of 6.6% for knee arthroplasty and 8.4% for hip arthroplasty. HACs accounted for 42% of all complications and were the strongest predictor of readmission. Efforts to reduce these events may improve cost and safety of arthroplasty.

Emergency readmissions are substantially determined by acute illness severity and chronic debilitating illness: A single centre cohort study

BackgroundThe factors influencing hospital readmissions are debated. We assessed whether readmissions could be predicted using routinely collected hospital data. MethodsAll emergency admissions to a single institution over 12years (2002–2013) were included. The predictor variables, of acute illness severity, Manchester Triage Category, chronic disabling disease and Charlson co-morbidity scores, were studied univariably and entered into a multivariable logistic regression model to predict the bivariate of any readmission or none. A zero truncated Poisson regression model assessed the predictors against the readmission count and incidence rate ratios were calculated. Factors reflecting the clinical load on the emergency department were examined. Results66,933 admissions were recorded in 36,271 patients. The readmission rates at 1, 3, 6 and 9years were 29.5%, 38.9%, 42.9% and 44.1%. Early readmissions represented 14.1%. In the multivariable model, an admission in the previous 6months was the strongest predictor of readmission, OR of 5.02 (95% CI: 4.86, 5.18). Acute illness severity — OR of 2.68 (95% CI: 2.33, 3.09) for group VI vs group I, and chronic disabling score — OR of 2.08 (95% CI: 1.87, 2.32) for a score of 4+ vs 0 were significant predictors of readmission in the multivariable model. Both of these predictors demonstrated a linear relationship. Illness severity was the strongest predictor of an early readmission within 4weeks. ConclusionReadmissions increase as a function of time; illness severity, chronic disabling disease score and a recent admission are the strongest predictors of readmission.

Capsule comment on Harrison et al., Assessing the impact of nurse post-discharge telephone calls on 30-day hospital readmission rates.

The transition from an acute care hospital to a post-hospitalization setting presents numerous logistical challenges. Failures in any of the aspects of the transition are associated with short-term (30-day) re-hospitalizations, emergency department (ED) visits, and increased costs of care. Currently, 20 % of Medicare beneficiaries are readmitted within 30 days,1 and approximately 8 % of all adults are readmitted within 30 days of an index hospital admission.2 Electronic medical records and electronic based ancillary services hold the promise of a more efficient and safer process to reduce readmissions and increase patient satisfaction. Harrison and colleagues studied the use of a 72-hour telephone follow-up after acute care hospitalization to reduce hospital readmission rates.3 The study encountered several problems, including “no call attempted” to 25 % of patients and call completion to less than 50 % of discharged patients. The content of the post-discharge call is also not clear. In multivariable models, including a propensity score analysis, the authors identified a patient’s ability to answer a telephone call as the strongest predictor of readmission and concurrently, the “success” of the program. This ability to answer the telephone is reminiscent of the relationship between lower hospital readmission rates and more favorable 6-minute walk test results in heart failure patients4 and global assessments of functional status performed in rehabilitation facilities.5 The ability to answer the phone emerged as a functional status proxy predicting both hospital readmission and the “success” of the program. The phone call itself had no effect. Reducing avoidable readmissions is a laudable goal. Research suggests that avoidable readmissions can be reduced by improving discharge planning, care coordination, coaching, education and patient self-management. It is disappointing that the post-hospitalization telephone call had no effect in this study, as it theoretically could impact factors known to reduce readmissions. However, before abandoning post-discharge calls as ineffective, future research should focus on the goals these phone calls aim to achieve and the format and content of these calls. Further, the value and impact of the call intervention could be more rapidly and effectively evaluated by enriching the study population for patients with impaired functional status.

Abstract 231: Predicting Readmission Risk Following Coronary Revascularization at the Time of Admission

Objective: Reducing readmissions following hospitalization for coronary revascularization is a national priority. Identifying patients at high risk for readmission early in a hospitalization would enable hospitals to target these individuals for enhanced discharge planning. Traditional risk models identify patients based on characteristics that may not be available until after discharge. We sought to compare the model performance for models based on data that is available earlier in the hospitalization. Methods: We developed models to predict 30-day inpatient readmission to our instititution for a cohort of patients who were revascularized at our institution between January 2010 and December 2013. We developed separate models for patients with percutaneous coronary revascularization (PCI) and coronary artery bypass graft surgery (CABG). We developed three models using data available at three different time points in the hospitalization: 1) at admission, 2) early in the hospitalization and 3) at discharge. Candidate variables for the admission model included demographics, comorbidities, and previous utilization within our system. The second model added initial vital signs and laboratory values. The discharge model added discharge vital signs and laboratory values, discharge medications, new comorbidities, and length of stay. We assessed each model using the c-index. Results: Our cohort included 4,941 PCI patients and 1,633 CABG patients. The readmission rate was 8.4% in the PCI group and 14.2% in the CABG group. For both populations, the discriminative ability of the admission model was high (0.805 for PCI, 0.824 for CABG). The addition of laboratory values and vital signs was associated with slight improvement in discrimination (0.812 for PCI, 0.829 for CABG). The addition of data available at the time of discharge further increased model discrimination (0.833 for PCI, 0.848 for CABG). Except for one model, the strongest predictor of readmission was carrying a diagnosis of hypertension at baseline (OR &gt; 2 for all)). Previous hospitalization within six months was a strong predictor among PCI patients (OR 1.18, p&lt;0.001), and having a previous acute myocardial infarction was the strongest predictor among CABG patients (OR 2.46, p = 0.002). Discussion: Risk prediction models based on data available only at discharge minimally improved the performance of models based solely on demographic and utilization data available at the time of admission. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitlization. This would allow providers and health systems to target high-risk patients with enhanced discharge planning during the course of the hospitalization, and this may improve the ability to avoid readmissions.