Risk Of All-cause Readmission Research Articles

Elevated levels of PDGF-BB and VEGF are associated with a decreased risk of readmission or death in children with severe malarial anemia.

Children with severe malarial anemia (SMA) typically have low in-hospital mortality but have a high risk of post-discharge readmission or death. We hypothesized that the dysregulation of hematopoiesis, vascular growth factors, and endothelial function that occurs in SMA might affect risk of readmission or death. Plasma was obtained from children 18 months to 12 years old with SMA (N=145) in Kampala, Uganda on admission, and outcomes were assessed over 12-month follow-up. Admission plasma levels of ten biomarkers of vascular growth, hematopoiesis, and endothelial function were compared to risk of readmission or death over 12-month follow-up. Over 12-month follow-up, 19 of 145 children with SMA were either readmitted or died: 15 children were readmitted (13 with malaria) and 4 children died. In multivariable analyses adjusted for age and sex, elevated plasma levels of platelet-derived growth factor-BB (PDGF-BB) and vascular endothelial growth factor (VEGF) on admission were independently associated with a decreased risk of all-cause readmission or death (adjusted hazard ratios [95% confidence intervals], 0.28 [0.16-0.51] and 0.19 [0.08-0.48], respectively) and a decreased risk of readmission due to severe malaria (0.27 [0.15, 0.51] and 0.16 [0.05, 0.47]) but not with risk of uncomplicated malaria (1.01 [0.53, 1.95] and 2.07 [0.93-4.64]). In children with severe malarial anemia, elevated plasma levels of PDGF-BB and VEGF, two factors that promote angiogenesis, are associated with a decreased risk of readmission or death in the year following admission, primarily driven by a decrease in the risk of recurrent severe malaria.

Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study.

Smart tracking technology (STT) that was applied for clinical use has the potential to reduce 30-day all-cause readmission risk through streamlining clinical workflows with improved accuracy, mobility, and efficiency. However, previously published literature has inadequately addressed the joint effects of STT for clinical use and its complementary health ITs (HITs) in this context. Furthermore, while previous studies have discussed the symbiotic and pooled complementarity effects among different HITs, there is a lack of evidence-based research specifically examining the complementarity effects between STT for clinical use and other relevant HITs. Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. This study uses a longitudinal in-patient dataset, including 879,122 in-patient hospital admissions for 347,949 patients in 61 hospitals located in Florida and New York in the United States, from 2014 to 2015. Logistic regression was applied to assess the effect of HITs on readmission risks. Time and hospital fixed effects were controlled in the regression model. Robust standard errors (SEs) were used to account for potential heteroskedasticity. These errors were further clustered at the patient level to consider possible correlations within the patient groups. The interaction between STT for clinical use and STT for supply chain management, mobile IT, and HIE was negatively associated with 30-day readmission risk, with coefficients of -0.0352 (P=.003), -0.0520 (P<.001), and -0.0216 (P=.04), respectively. These results indicate that the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Furthermore, the joint effects of these HITs varied depending on the hospital affiliation and patients' disease types. Our results reveal that while individual HIT implementations have varying impacts on 30-day readmission risk, their joint effects are often associated with a reduction in 30-day readmission risk. This study substantially contributes to HIT value literature by quantifying the complementarity effects among 4 different types of HITs: STT for clinical use, STT for supply chain management, mobile IT, and HIE. It further offers practical implications for hospitals to maximize the benefits of their complementary HITs in reducing the 30-day readmission risk in their respective care scenarios.

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Examining Heart Failure Outcomes Amid Housing Insecurity

How housing insecurity might affect patients with heart failure (HF) is not well characterized. Housing insecurity increases risks related to both communicable and noncommunicable diseases. For patients with HF, housing insecurity is likely to increase the risk for worse outcomes and rehospitalizations. We analyzed hospitalizations due to HF in the United States by using the 2020 National Inpatient Sample and Nationwide Readmissions Database to evaluate the impacts of housing insecurity on HF outcomes and hospital use. Individuals were identified as having housing insecurity by using diagnostic International Classification of Disease (ICD)-10 codes. Demographics and comorbidities were compared between patients with HF with and without housing insecurity. An adjusted logistic regression was performed to evaluate the relationships between housing insecurity and socioeconomic status on in-hospital mortality. Using a Cox proportional hazards model, patients with HF and without housing insecurity were evaluated for the risk of all-cause and HF-specific readmissions over time. Of the 1,003,270 hospitalizations for HF in the U.S. in 2020, 16,150 were identified as having housing insecurity (1.6%), and 987,120 were identified as having no housing insecurity (98.4%). The median age of patients with housing insecurity who were hospitalized for HF was 57, as compared to 73 in the population with no housing insecurity. A higher proportion of patients in the housing-insecurity group were Black (35% vs 20.1%) or Hispanic (11.1% vs 7.3%). Patients with housing insecurity were more likely to carry a diagnosis of alcohol-use disorder (15.2% vs 3.3%) or substance-use disorder (70.2% vs 17.8%) but were less likely to use tobacco (18.3% vs 28.7%). Patients with housing insecurity were over 4.5 times more likely to have Medicaid (52.4% vs 11.3%). Median length of stay did not differ between patients with housing insecurity vs those without it. Patients with housing insecurity were more likely to discharge against medical advice (11.4% vs 2.03%). After adjusting for patients' characteristics, housing insecurity was associated with lower in-hospital mortality rates (OR 0.60, 95% CI 0.39-0.92). Housing insecurity was associated with a higher risk of all-cause readmissions at 180 days (HR 1.13, 95% CI 1.12-1.14). However, there was no significant difference in the risk of HF-specific readmissions at 180 days (HR 1.07, 95% CI 0.998-1.14) CONCLUSIONS: Patients with HF and housing insecurity have distinct demographic characteristics. They are also more likely to be readmitted after their initial hospitalization when compared to those without housing insecurity. Identifying and addressing specific comorbid conditions for patients with housing insecurity who are hospitalized for HF may allow clinicians to provide more focused care, with the goal of preventing morbidity, mortality and unnecessary readmissions.

Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis.

To investigate the roles, challenges, and implications of using patient-reported outcome measures (PROMs) in predicting the risk of hospital readmissions. We systematically searched four bibliometric databases for peer-reviewed studies published in English between 1 January 2000 and 15 June 2023 and used validated PROMs to predict readmission risks for adult populations. Reported studies were analysed and narratively synthesised in accordance with the CHARMS and PRISMA guidelines. Of the 2858 abstracts reviewed, 23 studies met predefined eligibility criteria, representing diverse geographic regions and medical specialties. Among those, 19 identified the positive contributions of PROMs in predicting readmission risks. Seven studies utilised generic PROMs exclusively, eleven used generic and condition-specific PROMs, while 5 focussed solely on condition-specific PROMs. Logistic regression was the most used modelling approach, with 13 studies aiming at predicting 30-day all-cause readmission risks. The c-statistic, ranging from 0.54 to 0.84, was reported in 22/23 studies as a measure of model discrimination. Nine studies reported model calibration in addition to c-statistic. Thirteen studies detailed their approaches to dealing with missing data. Our study highlights the potential of PROMs to enhance predictive accuracy in readmission models, while acknowledging the diversity in data collection methods, readmission definitions, and model evaluation approaches. Recognizing that PROMs serve various purposes beyond readmission reduction, our study supports routine data collection and strategic integration of PROMs in healthcare practices to improve patient outcomes. To facilitate comparative analysis and broaden the use of PROMs in the prediction framework, it is imperative to consider the methodological aspects involved.

A Lower Remote Dielectric Sensing Value Was Associated with Hypovolemia and Worse Clinical Outcomes.

Background: Remote dielectric sensing (ReDS) systems can estimate the amount of lung fluid non-invasively and easily without expert techniques. The correlation between the elevated ReDS value and other modalities that estimate pulmonary congestion has been validated. The clinical implications of lower ReDS values, which may indicate hypovolemia, remain unknown. Methods: A total of 138 patients who were hospitalized for various cardiovascular-related problems and underwent ReDS value measurements at the index discharge in a blinded manner to the attending clinicians were eligible for inclusion. Patients with ReDS values > 30%, indicating the presence of pulmonary congestion, were excluded. The prognostic impact of lower ReDS values on all-cause readmission after index discharge was evaluated. Results: A total of 97 patients were included. The median age was 78 years, and 48 were men. The median ReDS value at index discharge was 26% (23%, 27%). A lower ReDS value correlated with smaller inferior vena cava maximum diameters (r = 0.46, p < 0.001) and higher blood urea nitrogen/creatinine ratios (r = -0.35, p < 0.001). A lower ReDS value (≤25%) was associated with a risk of all-cause readmissions with an unadjusted hazard ratio of 2.68 (95% confidence interval 1.09-6.59, p = 0.031) and an adjusted hazard ratio of 2.30 (95% confidence interval 0.92-5.78, p = 0.076). Its calculated cutoff of 25% significantly stratified the cumulative incidence of the primary outcome (36% versus 17%, p = 0.038). Conclusions: A lower ReDS value may indicate hypovolemia and be associated with the risk of all-cause readmission in patients hospitalized for cardiovascular diseases.

Association of marital/partner status with hospital readmission among young adults with acute myocardial infarction.

Despite evidence supporting the benefits of marriage on cardiovascular health, the impact of marital/partner status on the long-term readmission of young acute myocardial infarction (AMI) survivors is less clear. We examined the association between marital/partner status and 1-year all-cause readmission and explored sex differences among young AMI survivors. Data were from the VIRGO study (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients), which enrolled young adults aged 18-55 years with AMI (2008-2012). The primary end point was all-cause readmission within 1 year of hospital discharge, obtained from medical records and patient interviews and adjudicated by a physician panel. We performed Cox proportional hazards models with sequential adjustment for demographic, socioeconomic, clinical, and psychosocial factors. Sex-marital/partner status interaction was also tested. Of the 2,979 adults with AMI (2002 women [67.2%]; mean age 48 [interquartile range, 44-52] years), unpartnered individuals were more likely to experience all-cause readmissions compared with married/partnered individuals within the first year after hospital discharge (34.6% versus 27.2%, hazard ratio [HR] = 1.31; 95% confidence interval [CI], 1.15-1.49). The association attenuated but remained significant after adjustment for demographic and socioeconomic factors (adjusted HR, 1.16; 95% CI, 1.01-1.34), and it was not significant after further adjusting for clinical factors and psychosocial factors (adjusted HR, 1.10; 95%CI, 0.94-1.28). A sex-marital/partner status interaction was not significant (p = 0.69). Sensitivity analysis using data with multiple imputation and restricting outcomes to cardiac readmission yielded comparable results. In a cohort of young adults aged 18-55 years, unpartnered status was associated with 1.3-fold increased risk of all-cause readmission within 1 year of AMI discharge. Further adjustment for demographic, socioeconomic, clinical, and psychosocial factors attenuated the association, suggesting that these factors may explain disparities in readmission between married/partnered versus unpartnered young adults. Whereas young women experienced more readmission compared to similar-aged men, the association between marital/partner status and 1-year readmission did not vary by sex.

Inpatient Outcomes of Cirrhosis-related Cachexia in the United States

Background and AimsCachexia is a metabolic syndrome defined by a loss of more than 5% of body weight in patients with chronic diseases. The goal of this study was to investigate the link between cirrhotic cachexia and hospital mortality and the 30-day risk of all-cause readmission. MethodsThe study utilized Nationwide Readmission Database for the years 2016-2019 in which all patients older than 18 years old with a primary diagnosis of cirrhosis were included. We excluded patients with a concurrent diagnosis of Human Immunodeficiency Virus (HIV), chronic lung disease, end-stage renal disease, malignancy, heart failure, and certain neurological diseases. We compared baseline characteristics and outcomes between those who were cachectic and those who were not. Survey multivariate logistic regression was used to analyze the independent impact of cachexia on categorical outcomes. ResultsThe study cohort was 342,030 cases. Cachexia was identified in approximately 17% of the study population (58,509 discharges). The mean age was 56 years. Slightly more female patients noted in cachexia group (41% vs. 38%). Inpatient mortality during index hospitalization were higher in patients with cirrhotic cachexia (6.7% versus 3%, p<0.01). Inpatient mortality during first all-cause readmission within 30 days of index discharge was also higher in cachexia group (8.6% versus 6.5%, p<0.01). ConclusionsCachexia is an adverse prognosticator for inpatient outcomes in patients with cirrhosis. It is associated with greater readmission rates, inpatient mortality, and prolonged hospital admissions.

Associations Between Postdischarge Care and Cognitive Impairment-Related Hospital Readmissions for Ketoacidosis and Severe Hypoglycemia in Adults With Diabetes.

Patients with severe hypoglycemia (SH) or diabetic ketoacidosis (DKA) experience high hospital readmission after being discharged. Cognitive impairment (CI) may further increase the risk, especially in those experiencing an interruption of medical care after discharge. This study examined the effect modification role of postdischarge care (PDC) on CI-associated readmission risk among U.S. adults with diabetes initially admitted for DKA or SH. We used the Nationwide Readmissions Database (NRD) (2016-2018) to identify individuals hospitalized with a diagnosis of DKA or SH. Multivariate Cox regression was used to compare the all-cause readmission risk at 30 days between those with and without CI identified during the initial hospitalization. We assessed the CI-associated readmission risk in the patients with and without PDC, an effect modifier with the CI status. We identified 23,775 SH patients (53.3% women, mean age 65.9 ± 15.3 years) and 140,490 DKA patients (45.8% women, mean age 40.3 ± 15.4 years), and 2,675 (11.2%) and 1,261 (0.9%), respectively, had a CI diagnosis during their index hospitalization. For SH and DKA patients discharged without PDC, CI was associated with a higher readmission risk of 23% (adjusted hazard ratio [aHR] 1.23, 95% confidence interval 1.08-1.40) and 35% (aHR 1.35, 95% confidence interval 1.08-1.70), respectively. However, when patients were discharged with PDC, we found PDC was an effect modifier to mitigate CI-associated readmission risk for both SH and DKA patients (P < 0.05 for all). Our results suggest that PDC can potentially mitigate the excessive readmission risk associated with CI, emphasizing the importance of postdischarge continuity of care for medically complex patients with comorbid diabetes and CI.

Outcomes after ischemic stroke for dual-eligible Medicare-Medicaid beneficiaries in the United States.

Medicaid serves as a safety net for low-income US Medicare beneficiaries with limited assets. Approximately 7.7 million Americans aged ≥65 years rely on a combination of Medicare and Medicaid to obtain critical medical services, yet little is known about whether these patients have worse outcomes after stroke than patients with Medicare alone. We compared geographic patterns in dual Medicare-Medicaid eligibility and ischemic stroke hospitalizations and examined whether these dual-eligible beneficiaries had worse post-stroke outcomes than those with Medicare alone. We identified fee-for-service Medicare beneficiaries aged ≥65 years who were discharged from US acute-care hospitals with a principal diagnosis of ischemic stroke in 2014. Medicare beneficiaries with ≥1 month of Medicaid coverage were considered dual eligible. We mapped risk-standardized stroke hospitalization rates and percentages of beneficiaries with dual eligibility. Mixed models and Cox regression were used to evaluate relationships between dual-eligible status and outcomes up to 1 year after stroke, adjusting for demographic and clinical factors. At the national level, 12.5% of beneficiaries were dual eligible. Dual-eligible rates were highest in Maine, Alaska, and the southern half of the United States, whereas stroke hospitalization rates were highest in the South and parts of the Midwest (Pearson's r = 0.469, p<0.001). Among 254,902 patients hospitalized for stroke, 17.4% were dual eligible. In adjusted analyses, dual-eligible patients had greater risk of all-cause readmission within 30 days (hazard ratio 1.06, 95% confidence interval [CI] 1.03-1.09) and 1 year (hazard ratio 1.03, 95% CI 1.02-1.05) and had greater odds of death within 1 year (odds ratio 1.20, 95% CI 1.17-1.23) when compared with Medicare-only patients; there was no difference in in-hospital or 30-day mortality. Dual-eligible stroke patients had higher readmissions and long-term mortality than other patients, even after comorbidity adjustment. A better understanding of the factors contributing to these poorer outcomes is needed.

Early hospital readmission following stroke: the Florida Stroke Registry

BackgroundHospital readmission is an important indicator of poor transition of care post-stroke. Data on characteristics of patients at highest risk for readmission is limited and necessary to inform effective interventions. The goal is to identify risk factors at hospital discharge that predict 30-day readmission in the Florida Stroke Registry (FSR).MethodsThe study population included 45,877 patients discharged home or to rehabilitation with an ischemic stroke or intracerebral hemorrhage in the FSR between 2017 and 2019. The FSR is a voluntary statewide registry of stroke patients from 167 hospitals using data from Get With the Guideline-Stroke. Readmissions were ascertained by propensity matching FSR with the Florida Agency for Healthcare Administration dataset, which includes all hospital admissions in Florida. The primary outcome was 30-day hospital readmission for any cause, and secondary outcomes were vascular-related and stroke readmissions specifically. Multivariable logistic regression models identified patient characteristics that independently predicted 30-day readmissions, including sociodemographics, stroke clinical characteristics, in-hospital treatment, medical history, discharge status, and hospital characteristics.ResultsA hospital readmission within 30 days was experienced in 12% of cases; 6% had a vascular-related readmission, and 3% a recurrent stroke. The following characteristics were independently associated with an increased risk of all-cause readmission: Medicare or Medicaid insurance, large artery atherosclerosis as the stroke mechanism, increased stroke severity, diabetes, atrial fibrillation, peripheral vascular disease, coronary artery disease, prior stroke, chronic renal insufficiency, and depression. The following characteristics were independently associated with a decreased risk of all-cause readmission: ambulation, treated dyslipidemia, tPA treatment, discharge mRS 0–2, and treatment at a comprehensive stroke center.ConclusionsThe risk of 30-day hospital readmission was substantial, modifiable, and impacted by insurance status, medical history, stroke etiology and severity, stroke care, and functional status at discharge. These findings can inform strategies to target high-risk patients who can benefit from interventions to improve transitions of care post-stroke.

Hospital Frailty Risk Score (HFRS) Predicts Adverse Outcomes Among Hospitalized Patients with Chronic Pancreatitis.

The prevalence of frailty among patients with chronic pancreatitis (CP) and its impact on clinical outcomes is unclear. We report the impact of frailty on mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the United States. We extracted data on patients hospitalized with a primary or secondary diagnosis of CP from the Nationwide Readmissions Database 2019. We applied a previously validated hospital frailty risk scoring system to classify CP patients into frail and non-frail on index hospitalization and compared the characteristics of frail and non-frail patients. We studied the impact of frailty on mortality, readmission, and healthcare utilization. Of 56,072 patients with CP, 40.78% of patients were classified as frail. Frail patients experienced a higher rate of unplanned and preventable hospitalizations. Almost two-thirds of frail patients were younger than 65, and one-third had no or only single comorbidity. On multivariate analysis, frailty was independently associated with two times higher mortality risk (adjusted hazard ratio [aHR], 2.05; 95% CI 1.7-2.5). Frailty was also associated with a higher risk of all-cause readmission with an aHR of 1.07; (95% CI 1.03-1.1). Frail patients experienced a longer length of stay, higher hospitalization costs, and hospitalization charges. Infectious causes were the most common cause of readmission among frail patients compared to acute pancreatitis among non-frail patients. Frailty is independently associated with higher mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the US.

Predicting Hospital Readmissions in Patients Receiving Novel-Dose Sacubitril/Valsartan Therapy: A Competing-Risk, Causal Mediation Analysis.

Backgrounds: Our study aimed to identify and predict patients with heart failure (HF) taking novel-dose Sacubitril/Valsartan (S/V) at risk for all-cause readmission, as well as investigate the possible role of left ventricular reverse remodeling (LVRR). Methods and results: There were 464 patients recruited from December 2017 to September 2021 in our hospital with a median follow-up of 660 days (range, 17-1494). Competing risk analysis with Gray's Test showed statistically significant differences in all-cause readmission (p-value< .001) across the three different dose groups. Models 1 and 2 were developed based on the results of univariable competing risk analysis, least absolute shrinkage and selection operator approach, backward stepwise regression, and multivariable competing risk analysis. The internal verification (data-splitting method) indicated that Model 1 had better discrimination, calibration, and clinical utility. The corresponding nomogram showed that patients aged 75 years and above, or taking the lowest-dose S/V (≤50 mg twice a day), or diagnosed with ventricular tachycardia, or valvular heart disease, or chronic obstructive pulmonary disease, or diabetes mellitus were at the highest risk of all-cause readmission. In the causal mediation analysis, LVRR was considered as a critical mediator that negatively affected the difference of novel-dose S/V in readmission. Conclusions: A significant association was detected between novel-dose S/V and all-cause readmission in HF patients, in part negatively mediated by LVRR. The web-based nomogram could provide individual prediction of all-cause readmission in HF patients receiving novel-dose S/V. The effects of different novel-dose S/V are still needed to be explored further in the future.

1053. Comorbidities Associated with All-Cause Readmission to the Same Hospital after Index COVID-19 Hospitalization: A Retrospective Cohort Study of 232155 Patients in the United States

Abstract Background Understanding comorbidities that drive all-cause readmission in patients hospitalized with Coronavirus disease 2019 (COVID-19) can inform healthcare system capacity planning and improve post-discharge care. Methods This was a retrospective cohort study of patients hospitalized for COVID-19 between April 2020-December 2020 (index cohort) across 760 hospitals in the Premier Healthcare Database. Patients who died or left against medical advice were excluded from the index cohort. Surviving patients in the index cohort were followed until May 2021. First readmission to the same hospital as the COVID-19 index admission was considered all-cause readmission. The all-cause 14-month risk (95% confidence interval) of readmission was calculated using the Kaplan-Meier approach. A multivariable Cox proportional hazards model adjusted for demographic variables, hospital characteristics, co-existing comorbidities, and COVID-19 severity was built to study the association between Elixhauser comorbidities and readmission. Results Among 232155 unique patients in the index cohort, 36680 were readmitted to the same hospital at least once, followed through May 2021. The 14-month risk of readmission was 16.2% (95% CI:16.1% - 16.4%). The most frequent primary diagnosis on readmission was infectious disease (14240, 38.8%), of which 8754 (24%) were for COVID-19. With each additional comorbidity, the readmission hazard increased by 19% (HR, 1.19; 95% CI:1.18 - 1.19). In the multivariable Cox proportional hazards model, many comorbidity categories were associated with an increased risk of readmission. Metastatic cancer (HR, 1.74; 95% CI:1.60 –1.89), lymphoma (HR, 1.61; 95% CI:1.47 – 1.77), drug abuse (HR, 1.51; 95% CI:1.41 – 1.62), congestive heart failure (HR, 1.47; 95% CI:1.44– 1.51), and alcohol abuse (HR, 1.46; 95% CI:1.36– 1.56) were associated with the highest hazard for readmission. Conclusion COVID-19 patients have a high risk of all-cause readmission and are frequently readmitted for COVID-19. With the continued emergence of COVID-19 variants, this study provides valuable insights into developing more informed discharge plans and improving post-discharge care for COVID-19 patients with existing comorbidities to prevent readmission. Disclosures Lyndsay M. O'Hara, PhD, MPH, PDI: Grant/Research Support.

Abstract 14135: Neighborhood Socioeconomic Status and Heart Failure or All-Cause Readmission Within a Safety-Net System

Introduction: Neighborhood socioeconomic status (NSES) independently predicts higher rates of heart failure (HF) readmission, but whether this is true in a safety-net system is uncertain. Methods: We conducted a retrospective EHR-based cohort study of adults with HF between 2001-2019 at a municipal safety-net hospital in SF using Fine-Gray survival models. Most recent residential addresses were geocoded. NSES index is a composite measure including income, education, employment, and housing scaled to the SF Bay Area as quintiles (1=lowest, 5=highest). Results: Among 2,507 individuals with index HF hospitalization, 179 (7.1%) were readmitted within 30 days for HF and 374 (14.9%) for any cause. Within 1 year, 639 (25.5%) were readmitted for HF and 1,185 (47.3%) for any cause. In unadjusted analyses, NSES is associated with HF readmission at 30 days (p=0.013) or 1 year (p=0.021) and all-cause readmission at 30 days (p=0.0015) or 1 year (p=0.0049). Adjusted for demographics, substance use, and comorbidities, Quintile 1 has 58% higher risk of HF readmission (HR=1.58, 95% CI 1.07-2.33, p=0.021) and 56% higher risk of all-cause readmission (HR=1.56, 95% CI 1.19-2.05, p=0.001) over 1 year than Quintile 5. Similarly, compared to Quintile 5, all quintiles have higher risk of all-cause readmission over 1 year from Quintile 2 (HR=1.53, 95% CI 1.16-2.03, p=0.003) to Quintile 4 (HR=1.50, 95% CI 1.10-2.03, p=0.010). Including GDMT prescription does not change these results. Conclusion: NSES is associated with a higher risk of HF and all-cause readmissions over 1 year in a safety-net hospital. Further studies should investigate NSES domains to identify interventions and address disparities.

Outcomes of Coronary Artery Bypass Grafting in Patients With Previous Mediastinal Radiation

Studies have shown that patients with radiation therapy-associated coronary artery disease tend to have worse outcomes with percutaneous revascularization. Previous irradiation has been linked with future internal mammary artery graft disease. Studies investigating the outcomes of coronary artery bypass surgery (CABG) among patients with previous radiation are limited. The Nationwide Readmission Database for the years 2016 to 2019 was queried for hospitalizations with CABG and history of mediastinal radiation. Complex samples multivariable logistic and linear regression models were used to determine the association between the history of mediastinal radiation and in-hospital mortality, 90 days all-cause unplanned readmission rates, and acute coronary syndrome readmission rates. A total of 533,702 hospitalizations (2,070 in the irradiation history group and 531,632 in the control group) were included in this analysis. Patients with radiation therapy history were less likely to have traditional coronary artery disease risk factors and more likely to have associated valvular disease. Patients with a history of irradiation had similar in-hospital mortality and 90-day readmission risk at the expense of higher hospitalizations costs (β coefficient: $2,764; p=0.005). They had a higher likelihood of readmission with acute coronary syndrome within 90 days (adjusted odds ratio 1.67, p=0.02). In a conclusion, a history of mediastinal irradiation is not associated with increased rates of short-term mortality or increased all-cause readmission risk after CABG. However, it may be associated with increased acute coronary syndrome readmission rates.

Inequities in Mental Health Care Quality and Clinical Outcomes Among Inpatients with Depression Within a Tax-Financed Universal Health Care System.

PurposeThe objective was to examine potential socioeconomic inequities in guideline recommended quality of care as well as several clinical outcomes among first-time inpatients with major depressive disorder (MDD) in a tax-financed universal health care system.Patients and MethodsA nationwide cohort study was performed based on individual-level record linkage of public registers in Denmark. The study included all adult incident inpatients with MDD at Danish psychiatric hospitals in the period 2011–2017 (n=10,949). Socioeconomic position was assessed according to the level of education and income. Outcomes included quality of depression care for inpatients as reflected by the fulfillment of guideline recommended quality of care measures as well as clinical outcomes in terms of all-cause mortality, suicidal behavior, readmission for depression and all-cause readmission.ResultsPatients with low-level education or low-level income were statistically significantly less likely to receive high quality of in-hospital care, defined as fulfillment of at least 70% of relevant performance measures (adjusted relative risk (RR) 0.92 and 0.87, respectively). In addition, the same patients had a higher all-cause mortality (adjusted RR 1.22 and 1.41, respectively). Patients with low education or middle income were associated with a higher risk of suicidal behavior (adjusted RR 1.28 and 1.19, respectively). While no differences were found in the risk of all-cause readmission, low-level education and income was associated with a lower risk of readmission due to MDD (adjusted RR 0.91 and 0.87, respectively).ConclusionInequities in quality of care and clinical outcomes were observed among MDD inpatients in a tax-financed universal health care system, indicating that lack of access to care and insurance is not the only explanation for inequity in health.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

One-year readmission and mortality following ischaemic stroke by diabetes status, sex, and socioeconomic disadvantage: An analysis of 27,802 strokes from 2012 to 2017

ObjectiveWe estimated the 1-year incidence of readmission to hospital and 1-year all-cause mortality following ischaemic stroke (IS), by diabetes status, sex, and socioeconomic disadvantage. MethodsThis study included all individuals aged ≥30 years who were discharged from hospital following an IS between July 2012 and June 2017 in Victoria, Australia (n = 25,421). Individuals were followed from discharge until readmission (for all-causes, cardiovascular readmission, and readmission for IS) or death, censoring at 1-year of follow-up. ResultsThe 1-year cumulative incidence of all-cause readmission following an IS was 56.1% (95% CI: 55.5–56.7); 24% of first all-cause admission were attributed to cardiovascular disease. People with diabetes were at an excess risk of all-cause, cardiovascular, and IS readmission following an IS (adjusted sub-HRs: 1.13 [95% CI: 1.09–1.17], 1.14 [1.07–1.21], and 1.17 [1.06–1.29], for people with vs. without diabetes, respectively) and 1-year mortality (adjusted HR: 1.11 [1.03–1.19]). There was no significant difference between men and women in all-cause and cardiovascular readmission risk, while women were at higher risk of IS readmission (sub-HR: 1.10 [1.01–1.21] for women vs. men) and mortality (HR: 1.12 [1.05–1.20]). There was no relationship between socioeconomic disadvantage and risk of cardiovascular or IS readmission, while 1-year mortality risk did increase with increasing socioeconomic disadvantage (HR for most vs. least disadvantaged quintile: 1.15 [1.03–1.27]; ptrend = 0.006), and all-cause readmission risk decreased (sub-HR: 0.94 [0.90–0.99]; ptrend = 0.001). ConclusionsThere is a high risk of readmission following IS. Decreasing the readmission rate will require more complex interventions than solely improving post-discharge cardiovascular management.

The Moderating Effect of Caregiver Engagement in Transitional Care Intervention Outcomes: A Meta-Analysis

As chronically ill adults age, increased fluctuations in health status result in frequent care transitions. Caregiver engagement is often a core component of evidence-based transitional care interventions, yet little is known about the relative contribution of this element to observed outcomes. This meta-analysis aimed to synthesize evidence of caregiver engagement in randomized control trials (RCT’s) of transitional care interventions, estimate the overall intervention effects on all-cause hospital readmissions, and test caregiver engagement as a moderator of interventions’ effects. Relative risk was the effect size, and the overall effect was estimated using inverse variance weighting. Fifty-four studies met criteria, representing 31,399 participants and 65 effect sizes. The weighted sample mean age was 64 years. The majority (64%) of interventions targeted participants with specific diagnoses, such as heart disease, but more than half (54%) lacked caregiver engagement components. Among all reviewed studies of transitional care interventions, the overall effect on all-cause readmissions at 1 month was non-significant (p=.123, k=28). However, intervention effects at 2 or more months were significant (RR=0.89, 95% CI: 0.82, 0.97, p=.007, k=26), indicating a 12% reduction in the relative risk of all-cause readmissions among intervention participants compared to controls. Caregiver engagement was found to moderate intervention effects (p=.05). Specifically, interventions that included caregiver engagement produced more robust effects (RR=0.83, 95% CI: 0.75, 0.92, p=.001), than those without such engagement (RR=0.97, 95% CI: 0.87, 1.08, p=.550). Findings suggest that transitional care interventions need to more explicitly engage caregivers as active partners in order to optimize patient outcomes.