Risk Of Readmission In Patients Research Articles

Frequency of Readmission Among American Society of Anesthesia Classification after Total Hip and Total Knee Arthroplasty

Background: With the aging population and the increasing prevalence of degenerative joint diseases, the need for total joint arthroplasty (TJA) has surged. The American Society of Anesthesiologists (ASA) Physical Status Classification System plays a pivotal role in preoperative evaluation, predicting the risk of postoperative complications and mortality. Understanding the impact of ASA classification on surgical outcomes can guide improvements in patient care and surgical planning. Objective: This study aimed to investigate the relationship between ASA classification and the risk of postoperative readmission in patients undergoing total joint arthroplasty. Methods: Conducted at a single urban medical center, this descriptive, non-randomized study involved 352 patients who underwent primary TJA due to osteoarthritis, rheumatoid arthritis, or avascular necrosis. Participants were assigned an ASA classification preoperatively. The primary outcome measured was readmission within three months post-surgery, with secondary outcomes including the incidence of complications such as deep vein thrombosis (DVT), pulmonary embolism, and surgical site infections. Data were analyzed using SPSS version 21, with significance set at p<0.05. Results: Of the 352 participants, 165 (46.9%) were from rural areas, and 187 (53.1%) were from urban locations. The readmission rate was 6.8%, with ASA III and IV classifications showing significantly higher readmission rates (9.7% and 28.6%, respectively) compared to ASA I and II (1.8% and 4.4%). The most common reasons for readmission were chest infections (37.5%), followed by cardiac complications, pulmonary embolism, and neurological complications (20.8% each). The study found a significant correlation between higher ASA classification and increased readmission risk (p=0.021). Conclusion: Higher ASA classifications are associated with a greater risk of postoperative readmission in patients undergoing total joint arthroplasty. These findings underscore the importance of comprehensive preoperative assessments and tailored postoperative care strategies to mitigate the risk of readmission, particularly in patients with higher ASA scores.

Plasma SMOC2 Predicts Prognosis in Patients with Heart Failure: A Prospective Cohort.

Heart failure (HF) is a chronic disease with a poor prognosis, making it extremely important to assess the prognosis of patients with HF for accurate treatment. Secreted modular calcium-binding protein 2 (SMOC2) is a cysteine-rich acidic secreted protein that plays a pathophysiological role in many diseases, including regulation of vascular growth factor activity. It has previously been found that SMOC2 plays an essential role in cardiac fibrosis in our previous preclinical study, but whether it can be used as a clinical marker in heart failure patients remains unclear. The purpose of this research was to evaluate the correlation between plasma levels of SMOC2 and the prognosis for individuals with HF. HF patients diagnosed with ischemic cardiomyopathy were enrolled from January to December 2021. Baseline plasma levels of SMOC2 were measured after demographic and clinical features were collected. Linear and nonlinear multivariate Cox regression models were used to determine the association between plasma SMOC2 and patient outcomes during follow-up. All analysis was performed using SPSS, EmpowerStats, and R software. The study included 188 patients, and the average follow-up time was 489.5±88.3 days. The plasma SMOC2 concentrations were positively correlated with N-terminal pro-B-type Natriuretic Peptide (NT-proBNP), left ventricular end-diastolic diameter (LVEDd), and length of hospital stay and were negatively correlated with left ventricular ejection fraction (LVEF) at baseline. A total of 53 patients (28.2%) were rehospitalized due to cardiac deterioration, 14 (7.4%) died, and 37 (19.7%) developed malignant arrhythmias. A fully adjusted multivariate COX regression model showed that SMOC2 is associated with readmission (HR = 1.02, 95% CI:1.012-1.655). A significant increase in rehospitalization risk was observed in group Q2 (HR =1.064, 95% CI: 1.037, 3.662, p=0.005) and group Q3 (HR =1.085, 95% CI:1.086, 3.792, p=0.009) in comparison with group Q1. The p for trend also shows a linear correlation across the three models (P < 0.001). SMOC2 was associated with the severity of HF in patients, but not with all-cause deaths and arrhythmias during follow-up. Plasma SMOC2 is associated with the severity of HF and readmission rate, and is a good predictor of the risk of readmission in patients.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5days), body mass index (BMI) (> 33.21kg/m2), and operation time (> 93min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

COPD Assessment Test and risk of readmission in patients with bronchiectasis: a prospective cohort study.

Readmission following bronchiectasis exacerbation is a common and challenging clinical problem and few simple predictive tools exist. The COPD Assessment Test (CAT) is an easy-to-use questionnaire. This study aims to evaluate the predictive value of CAT scores in determining the risk of readmission in patients with bronchiectasis exacerbation. We conducted a prospective cohort study in 106 bronchiectasis patients admitted with exacerbation. All patients completed the CAT at admission and at discharge. Patients were followed-up for 12 months to collect data on readmission. The area under the curve was used to measure the predictive value of CAT at admission, CAT at discharge and change in CAT for readmission due to bronchiectasis exacerbation. 46 patients were readmitted for bronchiectasis exacerbation within 12 months. High CAT at admission was an independent risk factor for readmission within 12 months in patients with acute exacerbation of bronchiectasis (hazard ratio 3.201, 95% CI 1.065-9.624; p<0.038) after adjustment for confounding variables. The cut-off value of CAT at admission and CAT at discharge to predict 12-month readmission in patients with acute exacerbation of bronchiectasis was 23.5 (sensitivity 62.2%, specificity 83.6%) and 15.5 (sensitivity 52.2%, specificity 87.0%). CAT at admission is a strong predictor of readmission in patients with bronchiectasis exacerbation.

The effect of neuroendocrine abnormalities on the risk of psychiatric readmission after hospitalization for bipolar disorder: A retrospective study

BackgroundThe correlation between the endocrine system and bipolar disorder(BD) has been well recognized, yet the influence of neuroendocrine hormones on readmission risk post-hospitalization for BD remains largely unexplored. This retrospective cohort study was to scrutinize the impact of neuroendocrine functionality on the readmission of patients with BD post-hospitalization for mental disorders. MethodsThe dataset was derived from the electronic medical records of the First Affiliated Hospital of Jinan University in Guangzhou, China. Both univariate and multivariate logistic regression analysis were conducted on all patients hospitalized for BD, and from 1 January 2017 to October 2022. ResultsOf the 1110 eligible patients, 83 and 141 patients experienced psychiatric readmissions within 90 and 180 days post-discharge, respectively. Multivariate analysis revealed that high serum TSH levels (aOR = 1.079; 95%CI = 1.003–1.160) and thyroid disease comorbidities (aOR = 2.899; 95%CI = 1.303–6.452) were independently correlated with the risk of 90-day readmission; while increased serum TSH levels (aOR = 1.179; 95%CI = 1.081–1.287) represented a risk factor for 180-day readmission. These results indicate that high serum TSH levels and thyroid disease comorbidities may contribute to an elevated readmission risk in patients with BD following hospitalization. ConclusionRoutinely evaluating and intervening in thyroid function is crucial in the treatment of BD, as it may aid in preventing re-hospitalization.

The impact of metabolic overweight/obesity phenotypes on unplanned readmission risk in patients with COPD: a retrospective cohort study

Background: There is an inconsistent association between overweight/obesity and chronic obstructive pulmonary disease (COPD). Considering that different metabolic characteristics exist among individuals in the same body mass index (BMI) category, the classification of overweight/obesity based on metabolic status may facilitate the risk assessment of COPD. Our study aimed to explore the relationship between metabolic overweight/obesity phenotypes and unplanned readmission in patients with COPD.Methods: We conducted a retrospective cohort study using the Nationwide Readmissions Database (NRD). According to metabolic overweight/obesity phenotypes, patients were classified into four groups: metabolically healthy non-overweight/obesity (MHNO), metabolically unhealthy non-overweight/obesity (MUNO), metabolically healthy with overweight/obesity (MHO), and metabolically unhealthy with overweight/obesity (MUO). The primary outcome was unplanned readmission to hospital within 30 days of discharge from index hospitalization. Secondary outcomes included in-hospital mortality, length of stay (LOS) and total charges of readmission within 30 days.Results: Among 1,445,890 patients admitted with COPD, 167,156 individuals were unplanned readmitted within 30 days. Patients with the phenotype MUNO [hazard ratio (HR), 1.049; 95%CI, 1.038–1.061; p &amp;lt; 0.001] and MUO (HR, 1.061; 95%CI, 1.045–1.077; p &amp;lt; 0.001) had a higher readmission risk compared with patients with MHNO. But in elders (≥65yr), MHO also had a higher readmission risk (HR, 1.032; 95%CI, 1.002–1.063; p = 0.039). Besides, the readmission risk of COPD patients with hyperglycemia or hypertension regardless of overweight/obesity increased (p &amp;lt; 0.001).Conclusion: In patients with COPD, overweight/obesity alone had little effect on unplanned readmission, whereas metabolic abnormalities regardless of overweight/obesity were associated with an increased risk of unplanned readmission. Among the metabolic abnormalities, particular attention should be paid to hyperglycemia and hypertension. But in elders (≥65yr) overweight/obesity and metabolic abnormalities independently exacerbated the adverse outcomes.

The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study.

Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. The primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF. Additionally, the study aims to provide explanations for the model predictions by highlighting important features both on a global scale across the patient cohort and on a local level for individual patients. The retrospective data for this study were obtained from the Regional Health Care Information Platform in Region Halland, Sweden. The study cohort consisted of patients diagnosed with HF who were over 40 years old and had been hospitalized at least once between 2017 and 2019. Data analysis encompassed the period from January 1, 2017, to December 31, 2019. Two ML models were developed and validated to predict 100-day readmissions, with a focus on the explainability of the model's decisions. These models were built based on decision trees and recurrent neural architecture. Model explainability was obtained using an ML explainer. The predictive performance of these models was compared against 2 risk assessment tools using multiple performance metrics. The retrospective data set included a total of 15,612 admissions, and within these admissions, readmission occurred in 5597 cases, representing a readmission rate of 35.85%. It is noteworthy that a traditional and explainable model, informed by clinical knowledge, exhibited performance comparable to the DL model and surpassed conventional scoring methods in predicting readmission among patients with HF. The evaluation of predictive model performance was based on commonly used metrics, with an area under the precision-recall curve of 66% for the deep model and 68% for the traditional model on the holdout data set. Importantly, the explanations provided by the traditional model offer actionable insights that have the potential to enhance care planning. This study found that a widely used deep prediction model did not outperform an explainable ML model when predicting readmissions among patients with HF. The results suggest that model transparency does not necessarily compromise performance, which could facilitate the clinical adoption of such models.

Evaluation of the Retrospective LACE Index in Predicting the Risk of Readmission in Patients with Hereditary Angioedema in an Emergency Department.

This study aimed to calculate the LACE index in patients who admitted to the emergency department (ED) with hereditary angioedema (HA) diagnosed and to predict recurrent admissions of patients. In this single-center study, patients aged 18 or higher who were admitted to the ED diagnosed with HA were included over a 12-year period. 35 patients diagnosed with code E88.0 were evaluated according to electronic file records. The number of admissions to the ED in the last 6 months was 2. The LACE index was 4, and risk was 71.4%. The patients admitted to the hospital in the last 30 days had a higher rate of admission to the hospital in the last 6 months (p < 0.001). The LACE index at admission predicted 30 days admission with (AUC = 0.75, 95% CI (0.56-0.91)) acceptable discrimination. The LACE index and the number of admissions in the last 6 months included in the evaluation can be considered predictive in recurrent ED admissions of HA patients. However, the distribution of LACE-risk groups is no priority. Therefore, the low-, medium-, or high-risk level of LACE index values should be not taken into consideration in readmission of such patients.

Considering Mobility Status and Home Environment in Readmission Risk After Total Knee Arthroplasty.

Discharge disposition following total knee arthroplasty (TKA) offers varying levels of post-acute care monitoring depending on the medical status of the patient and his or her ability to function independently. Discharge disposition following TKA is associated with 30-day and 90-day hospital readmission, but prior studies have not consistently considered confounding due to mobility status after TKA, available caregiver support, and measures of home area deprivation. The purpose of this study was to examine 30-day and 90-day readmission risk for patients discharged to a skilled nursing facility (SNF) following TKA after controlling specifically for these factors, among other covariates. This was a retrospective cohort study of patients undergoing TKA at any of 11 hospitals in a single, large, academic health-care system between January 2, 2017, and August 31, 2022, who were discharged to an SNF or home health care (HHC). The adjusted relative risk of readmission within 30 and 90 days of discharge to an SNF compared with HHC was estimated using modified Poisson regression models. There were 15,212 patients discharged to HHC and 1,721 patients discharged to SNFs. Readmission within 30 days was 7.1% among patients discharged to SNFs and 2.4% among patients discharged to HHC; readmission within 90 days was 12.1% for the SNF group and 4.8% for the HHC group. The adjusted relative risk after discharge to an SNF was 1.07 (95% confidence interval [CI], 0.79 to 1.46; p = 0.65) for 30-day readmission and 1.45 (95% CI, 1.16 to 1.82; p < 0.01) for 90-day readmission. Discharge to an SNF compared with HHC was independently associated with 90-day readmission, but not with 30-day readmission, after controlling for mobility status after TKA, available caregiver support, and home Area Deprivation Index, among other covariates. Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

The Effect of ASA Score on Total Ankle Arthroplasty

Category: Ankle; Other Introduction/Purpose: The American Society of Anesthesiologist (ASA) physical status classification system serves as a useful measure of patient comorbidities. Multiple studies have found an increased risk of readmission in patients undergoing total knee or hip arthroplasty with elevated ASA scores. There is a paucity of literature investigating the association of ASA score and postoperative outcomes following total ankle arthroplasty (TAA). Thus, this study seeks to evaluate the relationship between ASA score and postoperative outcomes following TAA. Methods: The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database was queried from 2007-2020 to identify 2210 TAA patients. Patients were stratified based on preoperative ASA score, 1-normal healthy patient (n=101), 2-mild systemic disease (n=1228), 3-severe systemic disease (n=855), 4-severe, life-threatening systemic disease (n=26). Demographics, medical comorbidities, concomitant procedures, hospital length of stay (LOS), and 30-day complication, readmission, and reoperation rates were compared between groups. The cohort was predominantly male (54.0%) and mean patient age was 63.99 (range 19-87) years with a mean BMI of 31.01 (range 17.14-57.78) m/kg2. Results: Patients with an ASA score ≥3 were significantly older (≤2 =62.78, ≥3 =65.81%; p&lt;.001), with higher BMI (≤2 =29.72, ≥3=32.97; p&lt;.001). 2210 patients were discharged following TAA, with 37 readmitted within 30 days following surgery. There was no statistically significant difference in readmission or reoperation rate based on ASA score. (Table 1) The average LOS following TAA was 1.81 (range 0-28) days, and increased ASA score was statistically significantly associated with longer length of stay (≤2=1.69 days, ≥3 =1.98 days; p&lt;.001). Patients with ASA score ≥3 were discharged home at a significantly lower rate than those with lower ASA scores (≤2 =95.3%, ≥3 =87.4%; p&lt;.001). There was no statistically significant difference in postoperative complication rates within 30 days of surgery. Conclusion: Higher ASA score was statically significantly associated with increased length of stay and non-home discharge disposition. These findings are valuable for physicians and patients to consider prior to TAA given the increased utilization of resources and cost associated with higher ASA scores.

Huangqin Qingre Chubi Capsule is Associated with Reduced Risk of Readmission in Patients with Rheumatoid Arthritis: A Real-World Retrospective Cohort Study.

The therapeutic effects of Huangqin Qingre Chubi (HQC) in rheumatoid arthritis (RA) have been documented. However, there is a lack of real-world clinical evidence supporting its efficacy. Patients diagnosed with RA were recruited from the First Affiliated Hospital of the Anhui University of Chinese Medicine. Patient information was obtained from the hospital's database. Propensity score matching (PSM), Kaplan-Meier curve, and Cox proportional hazards model were used to control confounding factors and analyze the factors influencing readmission. Association rule analysis and random walk evaluation models were used to evaluate the correlations among HQC treatment, inflammation indicators, and self-perception of patients (SPP) scale. After PSM, 3423 patients were enrolled, with 1142 in the HQC group and 2281 in the non-HQC group. The readmission risk of the HQC group was significantly lower than that of the non-HQC group. Combined univariate and multivariate analysis results revealed that risk factors for readmission were age >60 years, female sex, hypertension, chronic gastritis, and elevated levels of laboratory indices, including anticyclic citrullinated peptide and complement component 3 (C3) and C4. HQC, disease-modifying antirheumatic drugs, nonsteroidal anti-inflammatory drugs, and glucocorticoid therapy were protective factors for readmission. HQC treatment was closely associated with improvements in many factors, including erythrocyte sedimentation rate, C-reactive protein, C3, rheumatoid factor levels, visual analog scale, depression self-assessment scale, and patient-reported activity index scores with RA. HQC treatment can reduce the risk of readmission and significantly improve immune inflammatory indicators and SPP in patients with RA, with no risk of hepatorenal toxicity.

Impact of Social Support on Cardiovascular Risk Prediction Models: A Systematic Review.

Cardiovascular diseases (CVD) stand as the primary causes of both mortality and morbidity on a global scale.Social factors such as low social support can increase the risk of developing heart diseases and have shown poor prognosis in cardiac patients. Resources such as PubMed and Google Scholar were searched using a boolean algorithm for articles published between 2003 and 2023. Eligible articles showed an association between social support and cardiovascular risks. A systematic review was conducted using the guidance published in the Cochrane Prognosis Method Group and the PRISMA checklist, for reviews of selected articles. A total of five studies were included in our final analysis. Overall, we found that participants with low social support developed cardiovascular events, and providing a good support system can decrease the risk of readmission in patients with a history of CVD. We also found that integrating social determinants in the cardiovascular risk prediction model showed improvement in accessing the risk. Population with good social support showed low mortality and decreased rate of readmission. There are various prediction models, but the social determinants are not primarily included while calculating the algorithms. Although it has been proven in multiple studies that including the social determinants of health (SDOH) improves the accuracy of cardiovascular risk prediction models. Hence, the inclusion of SDOH should be highly encouraged.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study.

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

Data-Driven Hospital Admission Control: A Learning Approach

A Data-Driven Approach to Improve Care Unit Placements in Hospitals The choice of care unit upon hospital admission is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. These decisions require carefully weighing the benefits of improved health outcomes against the opportunity cost of reserving higher level care beds for potentially more complex patients arriving in the future. In “Data-Driven Hospital Admission Control: A Learning Approach,” Zhalechian, Keyvanshokooh, Shi, and Van Oyen introduce a data-driven algorithm to address this challenging task. By focusing on reducing the readmission risk of patients, the algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) determine the best care unit placement for a patient based on the observed information and occupancy levels to minimize total readmission risk. The algorithm is supported by a performance guarantee, and its effectiveness is showcased using real-world hospital system data.

The Prognostic Value of Blood Eosinophil Level in AECOPD is Influenced by Corticosteroid Treatment During Hospitalization.

Blood eosinophil is a promising biomarker for phenotyping patients with acute exacerbation of COPD (AECOPD). We aimed to evaluate the prognostic value of eosinophil on short- and long-term outcomes stratified by corticosteroid treatment among AECOPD inpatients. In this retrospective cohort study, we included patients hospitalized for AECOPD from July 2013 to June 2021 in Beijing, China. Clinical data were collected from electronic medical records. The blood eosinophil count was measured within 24h after admission. Eosinophilic AECOPD was defined as having an eosinophil percentage ≥ 2%. The study outcomes were length of stay (LOS), treatment failure, and AECOPD readmission risk within 3 years of discharge. Multivariable models were used to analyze the associations between blood eosinophil count and outcomes stratified by corticosteroid treatment during hospitalization. A total of 2406 AECOPD patients were included. The median LOS of AECOPD patients was 10 (interquartile range: 8-14) days. The eosinophil percentage was negatively associated with LOS (P-trend=0.014). Compared with the non-eosinophilic AECOPD group, the eosinophilic group had a 58% lower risk of treatment failure (OR=0.42, 95% CI: 0.20-0.89) in patients treated with systemic corticosteroids, but no association was observed in those treated with inhaled corticosteroids (ICS) only (OR=0.95, 95% CI: 0.60-1.52). The eosinophilic group had an increased risk of 90-day re-admission in patients treated with ICS only (HR=1.51, 95% CI: 1.00-2.29), but not in patients treated with systemic corticosteroids during hospitalization (HR=0.67, 95% CI: 0.39-1.15). No statistically significant results were found for 180-day, 1-year, or 3-year readmission risk. Elevated blood eosinophils in AECOPD were associated with shorter length of stay and improved response to treatment with systemic corticosteroids, but not inhaled corticosteroids. Our study suggested that a therapeutic approach of using systemic corticosteroid may benefit patients present with eosinophilic AECOPD.

A nationwide study of temporal trends of cause-specific hospital readmissions in patients with heart failure.

The impact of hospital readmissions on the outcomes of heart failure (HF) patients is well known. However, data on temporal trends of cause-specific hospital readmissions in these patients are limited. From 1987 to 2014, we identified and followed up for 1year 608135 patients ≥18years hospitalized with HF according to the International Classification of Diseases (ICD) 9 and 10 from the National Inpatient Register. Readmissions for cardiovascular (CVD) and non-CVD causes and co-morbidities were defined according to ICD-9 and ICD-10 codes. We analysed trends in the incidence rate of readmissions, the median time to the first rehospitalization, and the time to readmission, stratified by sex, age groups and cause of rehospitalization using linear regression. During our study, 1year all-cause mortality decreased (β=-4.93, P<0.0001), but the incidence rate of readmissions per 1000 person-years remained unchanged. The readmission rate for CVD causes decreased; in contrast, the readmission rate increased across all age and sex groups for non-CVD causes. Analysing the patients by study periods (1987-1997, 1998-2007 and 2008-2014), CVD and non-CVD co-morbidities had a statistically significant increasing trend (P<0.001). The median time in hospital decreased and the median time to the first readmission were almost unchanged. Contrary to a declining mortality rate, the incidence rate of readmissions saw no change, possibly because of divergent trends in cause-specific readmissions. An increasing rate of readmissions for non-CVD causes underscores the importance of optimising multimorbidity management to reduce the risk of readmissions in patients with HF.

WED-486 - Changes in frailty as a predictor of readmission risk in patients with liver cirrhosis. Preliminary results

WED-486 - Changes in frailty as a predictor of readmission risk in patients with liver cirrhosis. Preliminary results

Outcomes of patients with a myeloproliferative neoplasm and atrial fibrillation.

7070 Background: Patients with myeloproliferative neoplasms (MPN), including essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (PMF), are associated with increased risk of cardiovascular (CV) disease including arterial thrombosis (AT). Atrial fibrillation (AF) is also associated with increased risk of AT and often co-exists with MPN. However, thrombotic and bleeding outcomes in patients with MPN compared with those without have not been thoroughly investigated. Methods: Patients aged ≥ 18 years with a hospital admission for AF with and without a history of MPN in 2017 and 2018 were identified using the National Readmission Database. Primary outcomes were 30-day (D) and 90-D any-cause, AT-related (including stroke, myocardial infarction, arterial thromboembolism), and bleeding-related readmissions. Propensity score weighting (PSW) was performed in order to adjust for imbalances between MPN and non-MPN patients. PSW Cox proportional hazards was performed to estimate risk of 90-D readmission in patients with MPN compared with patients without. Results: A total of 468,094 patients with AF were identified with 1,617 patients having a history of MPN. CHA2DS2-VASC (mean 3.8 vs 3.7) and HAS-BLED scores (mean 2.4 vs 2.3), risk scores for AT and bleeding in AF, were similar between MPN and non-MPN patients. After PSW Cox proportional hazards, patients with MPN had an increased risk of 30-D all-cause (HR 1.72, 95% CI 1.70 – 1.74), AT (HR 1.45, 95% CI 1.38 – 1.53), and bleeding (HR 2.21, 95% CI 2.01 – 2.44) re-admissions. MPN patients had an increased risk of 90-day any cause (HR 1.38, 95% CI 1.37 – 1.40) and bleeding (HR 1.76, 95% CI 1.65 – 1.88) but not AT (HR 1.03, 95% CI 0.99 – 1.08) readmissions. Conclusions: Among patients admitted for AF, MPN is associated with increased risk of 30-D and 90-D any-cause and bleeding readmissions and 30-D AT readmissions despite similar CHA2DS2-VASC and HAS-BLED risk scores. Further studies are needed to identify risk factors for bleeding and AT in patients with MPN and AF and improve on current risk scores, which do not include MPN status. [Table: see text]

Thirty-Day readmissions among COVID-19 patients hospitalized during the early pandemic in the United States: Insights from the Nationwide Readmissions Database

BackgroundHospital readmissions are core indicators of the quality of health care provision. ObjectiveTo understand factors associated with 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database. MethodsThis retrospective study characterized the 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database. ResultsThe 30-day, all-cause hospital readmission rate in this population was 3.2%. We found the most common diagnoses at readmission to be sepsis, acute kidney injury, and pneumonia. Chronic alcoholic liver cirrhosis and congestive heart failure were prominent predictors of readmission among patients with COVID-19. Moreover, we found that younger patients and patients from economically disadvantaged backgrounds were at higher risk of 30-day readmission. Acute complications during index hospitalization, including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy, also increased the risk of 30-day readmission for patients with COVID-19. ConclusionBased on the results of our study, we advise clinicians to promptly recognize patients with COVID-19 who are at high risk of readmission, and to subsequently manage their underlying comorbidities, to institute timely discharge planning, and to allocate resources to underprivileged patients in order to decrease the risk of 30-day hospital readmissions.

A-BBL: A Risk Prediction Model for Patient Readmission based on Electronic Medical Records

With the spread of medical digitization, electronic health record data has been accumulated in large quantities, laying the foundation for intelligent medical changes. ICU data is mined and analyzed to identify the risk of patient readmission in a timely manner, prevent and control the deterioration of patients' conditions, and reduce the burden of patient costs. However, due to the poor quality of medical data, potential information cannot be effectively mined. In view of the above problems, a patient readmission risk prediction model A-BBL is proposed. By extracting and analyzing the patient 's discharge summary information, the readmission risk of discharged patients within 30 days is predicted. The A-BBL model consists of three parts: firstly, BioBert is used to pre-train the medical text data, extract the semantic information of the medical text, and then generate the corresponding word vector. Then, the sequence model BiLSTM is used to capture the context information and model the input sequence. Finally, the self-attention mechanism is used to extract the key information in the input sequence, enhance the vector representation ability of the sequence, thereby improving the performance and accuracy of the model, so as to predict the readmission rate of patients. Based on the MIMIC-III real medical data set, the A-BBL model for patient readmission prediction proposed in this paper is verified. Compared with the baseline model, the accuracy is improved by 7.2 %. This study can help medical staff better understand and pay attention to the progression of critically ill patients, im-prove the survival rate of patients, and reduce the readmission rate of patients.