Predictors Of 30-day Readmission Research Articles

Predictive Factors for 30-Day Readmission and Increased Healthcare Utilization in Sickle Cell Disease Patients: A Single-Center Comparative Retrospective Study.

SCD poses a significant healthcare burden. Understanding the factors contributing to high healthcare utilization and readmissions is crucial for improving the quality of care provided. This retrospective comparative observational study was conducted at King Saud University Medical City and included 160 SCD patients. A comparison was made between patients with no readmission and patients with at least one 30-day readmission. Another comparison was done between high healthcare-utilizing patients and low healthcare-utilizing patients. A regression model for 30-day readmission prediction was created. Readmission was significantly higher in patients using opioids, following up with pain clinics, and having a history of AVN (p= 0.002, p=0.028 and p=0.025 respectively). Higher healthcare utilization was associated with older age, smoking, use of opioids and GABA analogs, and psychiatric illnesses, including depression, substance use disorder, and anxiety. Predictors of 30-day readmission were hydroxyurea use (odds ratio, 2.819 [95% CI,1.082 to 7.34], p = 0.034), follow-up with pain management clinics (odds ratio, 2.248 [95% CI,1.547 to 3.266], p = 0.001), and SCD genotype (SS genotype) (odds ratio, 1.754 [95% CI,1.012 to 3.042], p = 0.045). Using the Paracetamol/Codeine combination significantly reduced the likelihood of readmission within 30 days of discharge. This study identified factors associated with 30-day readmission rate and high healthcare utilization among SCD patients. Strategies to reduce readmissions may include specialized SCD clinics, educational programs for patients, improved physician awareness of mental health screening, and further research on the impact of opioid use. Limitations include retrospective nature, single-center design, reliance on self-reported data, and exclusion of critically ill patients. However, despite the limitations, this study could lay a foundation for future projects aiming to optimize care and outcomes for patients living with SCD.

Integration of Chronological Age Does Not Improve the Performance of a Mixed-Effect Model Using Comorbidity Burden and Frailty to Predict 90-Day Readmission After Surgery for Degenerative Scoliosis

ObjectiveWe evaluated the contributions of chronological age, comorbidity burden, and/or frailty in predicting 90-day readmission in patients undergoing degenerative scoliosis surgery. MethodsPatients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, and/or ECI. Area under the curve (AUC) were compared using DeLong's test. Results8,104 patients were identified. Readmission rate was 9.8%, with infection representing the most common cause (3.5%). Our first model utilized chronological age, ECI, and/or frailty as primary predictors. The combination of ECI+frailty+age performed best, but the inclusion of chronological age did not significantly improve performance compared to ECI+frailty alone (AUC 0.603 vs. 0.599, p=0.290). A second model using only chronological age and frailty as primary predictors performed better, however the inclusion of chronological age worsened performance when compared to frailty alone (AUC 0.747 vs. 0.743, p=0.043). ConclusionsThese data support frailty as a predictor of 90-day readmission within a nationally representative sample. Frailty alone performed better than combinations of ECI and age. Interestingly, the integration of chronological age did not dramatically improve the model’s performance. Limitations include the use of a national registry and a single frailty index. This provides impetus to explore biological age, rather than chronological age, as a potential tool for surgical risk assessment.

Triglyceride Glucose Index as a Predictor of 30-Day Readmission and 6 Months Mortality After Hospitalization in Acute Decompensated Heart Failure

Triglyceride Glucose Index as a Predictor of 30-Day Readmission and 6 Months Mortality After Hospitalization in Acute Decompensated Heart Failure

Outcomes and Predictors of 30-Day Readmission in Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization between 2016 and 2018

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. The 5-year survival rate for liver cancer in the US has improved from 3% four decades ago to 20% now. Transarterial chemoembolization (TACE) is the treatment of choice for stage B/intermediate-stage HCC. Complications of TACE include hepatic encephalopathy, liver failure, post-embolization syndrome, duodenal ulcers, liver abscesses, acute cholecystitis, and injury to the biliary tract. This study evaluates the 30-day readmission rate and predictors of readmission among patients with HCC undergoing TACE. Methods: The 2016–2018 Healthcare Cost and Utilization Project (HCUP) database, which includes the National Readmission Database (NRD), was used. All adult patients with HCC who underwent TACE were identified using the International Classification of Diseases (ICD-10). The rate of 30-day readmissions after TACE and the associated diagnoses were identified. Logistic regression was used to obtain adjusted odds ratios for variables associated with 30-day readmission. Results: A total of 566 patients underwent TACE between 2016–2018. Sixty-five patients were excluded due to death and unavailability of 30-day readmission data. The procedure was performed in large (80.4%), metro-teaching hospitals (94.5%). Mean patient age was 65.1 ± 9.9 years, and 74% of patients were male. Among the 501 patients, 81 (16.2%) were readmitted within 30 days. The mean age for readmitted patients was 63.2 ± 11.0 and 69.1% were male. The mean length of stay at readmission was 5.5 ± 7.3 days. A total of 7.4% of patients had neurological disorders, 17.3% had weight loss, 30.9% had fluid and electrolyte imbalance, and 21.0% had hepatic encephalopathy. The most common primary diagnoses at 30-day readmission were liver cell carcinoma, sepsis, and liver failure. Univariate analysis for variables associated with 30-day readmission included hepatic encephalopathy (OR 3.45; 95% CI 1.8–6.62; p = 0.0002), underlying neurological disorders (OR 3.28; 95% CI 1.16–9.3; p = 0.03), weight loss (OR 2.82; 95% CI 1.42–5.61; p = 0.003), and Medicaid status (OR 1.74; 95% CI 1.05–2.88; p = 0.03). Multivariable analysis showed hepatic encephalopathy (OR 2.91; 95% CI 1.4, 6.04; p = 0.04) and weight loss (OR 2.37; 95% CI 1.13–4.96; p = 0.02) were associated with hospital readmission. Conclusions: Weight loss and hepatic encephalopathy were predictors for 30-day readmission after a TACE procedure for HCC.

Targeted use of postoperative discharge phone calls to reduce early readmission rates

ObjectivesPostoperative readmissions are common and costly. Office-initiated phone calls to patients shortly after discharge may identify concerns and allow for early intervention to prevent readmission. We sought to evaluate our 30-day readmission rate after the implementation of a standardized postoperative discharge phone call (PODPC) intervention, compared with a historical aggregated cohort. MethodsFrom July 2020 to 21, postoperative patients were prospectively identified at 48 hour after discharge. Medical assistants performed PODPCs, administering a survey designed to identify medical/surgical issues that could signify a complication and warrant escalation to a nurse practitioner (NP) for further management. Demographics, comorbidities, and procedure type were obtained retrospectively. Descriptive statistics were used to evaluate PODPC responses, frequency of escalation, readmission, and reasons. The electronic medical record identified a historical aggregated cohort (July 2018 to 2019) and the 30-day readmission rate. A χ2 analysis was used to compare readmission rates between the preintervention historical and PODPC intervention groups. Predictors of 30-day readmission were modeled with multivariable logistic regression. ResultsOf 411 PODPCs conducted, 106 patients (26%) reported not feeling well; having concerns. Eighty-four PODPCs (20%) triggered escalation to a NP; of these, 60 patients (71%) were counseled over the phone by an NP, 16 (19%) were brought into clinic, 6 (7%) were sent to the emergency department, and 2 (2%) did not answer the NP call. Of 411 patients, 17% (n = 68) were readmitted within 30 days. Comparatively, the historical aggregated cohort readmission rate was significantly higher at 28% (n = 346; P < .001). On multivariable analysis, chronic obstructive pulmonary disease (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.01-3.65; P = .046), and feeling run down; having difficulty with movement; needing assistance for most activities (OR, 3.94; 95% CI, 2.09-7.43; P < .0001) were predictive of 30-day readmission when controlling for procedure type. ConclusionsAlthough readmissions remained common (>15%), being in the intervention cohort was associated with a significantly lower readmission rate compared with the historical aggregated cohort. One-fifth of PODPCs identified a concern; however, >90% of these could be managed by an NP by phone or in clinic. This PODPC intervention holds promise as a viable mechanism for decreasing readmissions.

Abstract 18513: Causes and Predictors of 30-day Readmissions Following Covid-19 Hospitalizations and Co-Existing Acute Heart Failure: Insights From National Readmission Database 2020

Introduction: COVID-19 infection notably impacts the cardiovascular system, with evidence of higher in-hospital mortality in patients with acute heart failure (AHF) and COVID-19 infection. However, less is known about short-term outcomes following discharge. Therefore, we sought to analyze readmission data in COVID-19 hospitalizations with AHF from the National Readmission Database (NRD). Methods: NRD (2020) was queried to identify all COVID-19 hospitalizations and co-existing AHF using ICD-10 CM codes. The group was then stratified into heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). The outcomes were 30-day readmission rate, length of stay (LOS), total hospital charge (THC), and predictors of readmission. Multivariate/univariate logistic and linear regression analyses were used to analyze the outcomes and adjust for possible confounders. Results: A total of 53,337 COVID-19 admissions with co-existing AHF were identified, of which 38,401 were discharged alive. Within 30 days from discharge, 8,159 (21.26%) were readmitted. Upon stratification, 51% belonged to the HFrEF group and were found to have scarcely higher 30 days readmission rates than encounters with HFpEF (21.46% vs. 21.14%). Readmissions lead to an additional mean THC of $85,687 and a mean LOS of 7.8 days. The top five all-causes of readmissions were COVID-19 infection (24.8%), heart failure (16.2%), sepsis (15.4%), acute kidney injury (2.2%), and pneumonia (1.2%). Figure-1 shows a multivariate analysis of independent factors associated with readmissions. Conclusions: Our analysis showed a 21.26% 30-days readmission rate following acute heart failure during COVID-19 infection. Cirrhosis, anemia, diabetes mellitus, chronic lung diseases, and discharge against medical advice were significant readmission predictors. Optimizing these co-morbidities during index admission may reduce readmissions and overall financial burden.

Transthoracic echocardiography parameters as predictors of 30-day readmission after admission for new-onset heart failure

Abstract Funding Acknowledgements Type of funding sources: None. Background In an aging population. heart failure (HF) is one of the biggest challenges facing healthcare systems in the coming years. Hospital readmissions after discharge continue to be one of the main problems for both patients and health systems as they represent costs and work overload for staff. The aim of this study is to re-visit the prognostic value of transthoracic echo parameters (TTE) in readmissions after a new-onset HF episode. Methods Consecutive patients admitted with a first episode of HF were prospectively included into a registry. All patients underwent transthoracic echocardiography based on current guidelines for chamber quantification within 24hrs after admission. Conventional systolic and diastolic function parameters were obtained. In addition. longitudinal strain and ventricular-arterial coupling of both ventricles were evaluated. Results 218 consecutive patients were included. Clinical and imaging characteristics are described in Table 1. 32 patients (14.7%) had readmission within 30-days. Mean age of the cohort was 73.4±13.4 years; mean LV ejection fraction (LVEF) was 42.3±14.2% and mean LV global longitudinal strain (LV-GLS) was −9.2±3.4%. Patients with 30-day readmissions had lower LVEF (37.8±3.1 vs 42.7±1.5%. p = 0.255) and significantly lower LV-GLS (−7.4±2.4 vs −9.5±3.4%. p = 0.042). Regarding diastolic function, patients with 30-day readmissions had a slightly higher E/e’ ratio (15.1±10.3 vs 14.3±7.2, p = 0.747) and a significantly larger left atrium volume index (57.2ml/m2 vs 45.5ml/m2). Right ventricular-arterial coupling (RVAc) was significantly lower in patients with readmissions (0.43±0.11 vs 0.69±0.9, p = 0.031). As for clinicals variables; only obesity was significantly associated with 30-day readmissions (Table 1). A model to predict 30-days readmissions that included LVEF, LV-GLS, LAVI and RVAc had an area under the ROC curve of 0.831. CI (0.72–0.95). Figure 1. Conclusions Several clinical scores have been proposed. however. cardiac function parameters are barely taken into account in most scores. Simple and feasible TTE parameters can accurately predict readmissions after a first episode of heart failure and might be included in clinical scores for better risk stratification of patients with new-onset HF.

Predictors of 30-day readmission, mortality, and length of stay for hospitalized U.S. patients with Alzheimer’s and related dementias from 2010 to 2015

Objectives Examine predictors of clinical and resource utilization outcomes associated with Alzheimer’s disease and related dementias (ADRD), stratified by patient severity profiles. Methods Cross-sectional study of adults (30+ year old) with ADRD discharged from US hospitals to home health care (HHC) and identified from the 2010-2015 Nationwide Readmissions Database (NRD) using ICD 9th-10th codes. Outcomes of interest included 30-day hospital readmissions, in-hospital mortality, and hospital length of stay (LOS). Covariates consisted of sociodemographic and clinical variables. Multiple logistic regressions (for readmissions and mortality) and generalized linear regressions (for LOS) were used to examine associations between outcomes and study covariates, stratified by patient severity profiles. Results Of 164,598 ADRD patients, 3,848 were mild, 68,803 were moderate, 72,428 were severe, and 19,519 were extreme. The 30-day readmission rate was 3.2%, death rate was 14.5%, and LOS was 3.0 days, (95%, CI: 15.0, 17.0) to 5.0 days, (95%, CI: 18.0, 19.0), all with a p-value < 0.0001. Across outcomes and severity levels, the top predictors included number of diagnoses, gender, hospital bed size, primary and secondary diagnoses, and income size. Conclusions Severe and extreme stages of HHC discharge may lead to increased readmissions, death, LOS, and costs. Specialized care is needed to reduce these negative outcomes in the ADRD patient population.

Value of Ambulatory Modified Radical Mastectomy.

Modified radical mastectomy (MRM) still is largely performed in inpatient settings. This study sought to determine the value (expenditures and complications) of ambulatory MRM. Health Care Utilization Project (HCUP) state databases from 2016 were queried for patients who underwent MRM. The study examined rates of 30-day readmission for surgical-site infection (SSI) or hematoma, charges by index care setting, and predictors of 30-day readmission. Overall, 8090 patients underwent MRM: 5113 (63 %) inpatient and 2977 (37 %) ambulatory patients. Compared with the patients who underwent inpatient MRM, those who underwent ambulatory MRM were older (61 vs. 59 years), more often white (66 % vs. 57 %), in the lowest income quartile (28 % vs. 21 %), insured by Medicare (43 % vs. 33 %) and residents in a small metro area (6 % vs. 4 %) (all p < 0.01). Of the 5113 patients treated as inpatients, 126 (2.5 %) were readmitted, whereas 50 (1.7 %) of the ambulatory patients were readmitted (p = 0.02). The adjusted charge for inpatient MRM without readmission was $113,878 (range, $107,355-120,402) compared with $94,463 (range, $86,021-102,907) for ambulatory MRM, and the charge for inpatient MRM requiring readmission was $159,355 (range, $147,142-171,568) compared with $139,940 (range, $125,808-154,073) for ambulatory MRM (all p < 0.01). This difference remained significant after adjustment for hospital length of stay. Adjusted logistic regression showed that the ambulatory setting was protective for readmission (odds ratio, 0.49; 95 % confidence interval, 0.35-0.70; p < 0.01). The analyses suggest that ambulatory MRM is both safe and less expensive. The findings advocate that MRM, a last holdout of inpatient care within breast surgical oncology, can be transitioned to the ambulatory setting for appropriate patients.

Rates and predictors of 30-day hospital readmissions in adults for drug-induced acute pancreatitis: A retrospective study from the United States National Readmission Database.

Drug-induced acute pancreatitis (DIAP) linked to several medications is a diagnosis of exclusion and is associated with significant morbidity and mortality, contributing to the US healthcare cost burden. Existing studies on DIAP focus on the drug classes that can cause acute pancreatitis. Hence, our retrospective study aims to determine the rates and predictors for 30-day readmissions (30-DR) in patients with index hospitalization for DIAP. From the Nationwide Readmissions Database, we followed adults admitted for DIAP who were discharged alive for 30days. During 30-DR, we evaluated the rates, predictors, and outcomes of DIAP. Of the 4457 DIAP patients surviving at discharge, 12.5% were readmitted at 30days. During readmissions, the predictors of 30-DR for DIAP were young age, the Charlson-Deyo Comorbidity Index of 2 and 3, protein-energy malnutrition, and dyslipidemia. During 30-DR, DIAP had a higher mortality rate (2.4% vs. 0.7%; P<0.020), extended hospital stays (5.6days vs. 4days, 0.000), and higher hospital charges ($12983.6 vs. $8255.6; P0.000). DIAP has high 30-DR rates and poorer outcomes.

Dementia and readmission risk in patients with heart failure participating in a transitional care program

BackgroundCognitive impairment is prevalent in patients hospitalized for heart failure (HF). We aimed to generate further evidence on the value of dementia screening in hospitalized HF patients by examining whether and when dementia would be an independent risk factor for 30-day readmission while modeling permutations of known risk factors such as patient demographics, disease burden, prior utilization, and index hospitalization characteristics. Methods and resultsA retrospective cohort study was employed, consisting of 26,128 patients (2,075 or 7.9% with dementia) in a transitional care program post HF hospitalization. The overall 30-day all-cause readmission rate was 18.1%. Patients with dementia had higher unadjusted rates of readmission (22.0 vs 17.8%) and death (4.5 vs. 2.2%) within 30 days post hospitalization, compared to those without dementia. Hierarchical multivariable proportional hazards regression results showed that dementia independently predicted readmission when both patient demographics and disease burden variables were controlled for (HR=1.15, p=0.02). However, the association between dementia and readmission was attenuated in the full model when prior utilization and index hospitalization characteristics were added (HR=1.04, p=0.55). For dementia patients, Charlson comorbidity index, prior ED visits, and length of stay were significant risk factors of readmission. ConclusionsThe presence of dementia and the predictors of 30-day readmission in those with dementia may help identify this subset of high-risk HF patients for potential efforts to improve their prognosis.

Etiologies and Predictors of 30-Day Readmission in Heart Failure: An Updated Analysis.

Readmissions in heart failure (HF), historically reported as 20%, contribute to significant patient morbidity and high financial cost to the healthcare system. The changing population landscape and risk factor dynamics mandate periodic epidemiologic reassessment of HF readmissions. National Readmission Database (NRD, 2019) was used to identify HF-related hospitalizations and evaluated for demographic, admission characteristics, and comorbidity differences between patients readmitted vs. those not readmitted at 30-days. Causes of readmission and predictors of all-cause, HF-specific, and non-HF-related readmissions were analyzed. Of 48,971 HF patients, the readmitted cohort was younger (mean 67.4 vs. 68.9 years, p≤0.001), had higher proportion of males (56.3% vs. 53.7%), lowest income quartiles (33.3% vs. 28.9%), Charlson comorbidity index (CCI) ≥3 (61.7% vs. 52.8%), resource utilization including large bed-size hospitalizations, Medicaid enrollees, mean length of stay (6.2 vs. 5.4 days), and disposition to other facilities (23.9% vs. 20%) than non-readmitted. Readmission (30-day) rate was 21.2% (10,370) with cardiovascular causes in 50.3% (HF being the most common: 39%), and non-cardiac in 49.7%. Independent predictors for readmission were male sex, lower socioeconomic status, nonelective admissions, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, anemia, and CCI ≥3. HF-specific readmissions were significantly associated with prior coronary artery disease and Medicaid enrollment. Our analysis revealed cardiac and noncardiac causes of readmission were equally common for 30-day readmissions in HF patients with HF itself being the most common etiology highlighting the importance of addressing the comorbidities, both cardiac and non-cardiac, to mitigate the risk of readmission.

Predictors of 30-Day Re-admission in Cardiac Patients at Heart Hospital, Qatar.

Cardiovascular disease patients are more likely to be readmitted within 30 days of being discharged alive. This causes an enormous burden on health-care systems in terms of poor care of patients and misutilization of resources. This study aims to find out the risk factors associated with 30-day readmission in cardiac patients at Heart Hospital, Qatar. A total of 10,550 cardiac patients who were discharged alive within 30 days at the heart hospital in Doha, Qatar, from January 2015 and December 2019 were analyzed. The bootstrap method, an internal validation statistical technique, was applied to present representative estimates for the population. Out of the 10,550 cardiac patients, there were 8418 (79.8%) index admissions and 2132 (20.2%) re-admitted at least once within 30 days after the index admission. The re-admissions group was older than the index admission group (65.6 ± 13.2 vs. 56.0 ± 13.5, P = 0.001). Multinomial regression analysis showed that females were 30% more likely to be re-admitted than males (adjusted odds ratio [aOR] 1.30, 95% confidence interval [CI]: 1.11-1.50, P = 0.001). Diabetes (aOR 1.36, 95% CI: 1.20-1.53, P = 0.001), chronic renal failure (aOR 1.93, 95% CI: 1.66-2.24, P = 0.001), previous MI (aOR 3.22, 95% CI: 2.85-3.64, P = 0.001), atrial fibrillation (aOR 2.17, 95% C.I. : 1.10-2.67, P = 0.01), cardiomyopathy (aOR 1.72, 95% CI 1.47-2.02, P = 0.001), and chronic heart failure (aOR 1.56, 95% C.I.: 1.33-1.82, P = 0.001) were also independent predictors for re-admission in the regression model. C-statistics showed these variables could predict 82% accurately hospital readmissions within 30 days after being discharged alive. The model was more than 80% accurate in predicting 30-day readmission after being discharged alive. The presence of five or more risk factors was found to be crucial for readmissions within 30 days. The study may help design interventions that may result in better outcomes with fewer resources in the population.

PREDICTIVE MODEL OF 30-DAY HOSPITAL READMISSION FOR PATIENTS WITH ALZHEIMER’S DISEASE

Abstract Hospitals are insufficiently equipped for patients with Alzheimer’s disease and related dementia (ADRD). Thus, 30-day hospital readmission is higher and costlier among ADRD patients than the general population of older adults. Our objective was to develop a risk-assessment tool for hospitalized patients with ADRD. We used 2016-2019 electronic medical record (EMR) data from the University of Michigan health system and applied machine learning techniques to develop a readmission risk-assessment tool. We identified 2,899 individuals with ADRD who had at least one index hospital admission. All data features available in EMR – demographics, lab results, prior counts of healthcare use, and characteristics of index hospitalization – were included in our predictive models. Additionally, we geocoded the street address of patients using the National Neighborhood Data Archive using the U.S. Census tract-level information to include two composite measures of socioeconomic status: disadvantage and affluence. The readmission rate for ADRD patients was 22% versus 17% for the general population. The best predictive model was the Random Forest (area under the receiver operating characteristic curve=0.66; sensitivity=0.64; specificity=0.61). The accuracy of our model (0.61) was 42% higher than the LACE score (0.43), which is currently used by the hospital for all patients. The top 5 predictors of 30-day readmission among people with ADRD included length of hospital stay, frailty index, living in a disadvantaged neighborhood, and total prior-year healthcare charges. Our risk-assessment tool identifies ADRD patients at high risk of readmission and why they are at higher risk. The tool enables better decision-making before discharge.

Predictors of 30-Day Readmission Following Cervical Laminoplasty in 3,085 Patients: An American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) Database Study.

Background Cervical laminoplasty is a surgical alternative to laminectomy and fusion for treating multi-level cervical spondylotic myelopathy. There is limited evidence evaluating readmission incidence and risk factors following cervical laminoplasty. Here, we provide a retrospective review evaluating preoperative risk for 30-day readmission following cervical laminoplasty. Methodology The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was used to identify patients undergoing laminoplasty as defined by Current Procedural Terminology codes 63050 and 63051. Patients were then categorized based on whether 30-day readmission occurred, and preoperative risk factors were examined. Continuous and categorical variables were analyzed using Student's t-test or Fisher's exact test. Multivariate regression analysis was performed for each variable, with p-values of <0.05 considered significant. Results In total, 3,085 patients were identified as undergoing posterior cervical laminoplasty. Of these, 2,938 patients did not require readmission, and 147 patients were readmitted, representing a 4.77% 30-day readmission rate. For all patients, sepsis (odds ratio (OR) = 5.58, p = 0.03), dialysis (OR = 3.46, p = 0.01), American Society of Anesthesiologists class >2 (OR = 1.69, p = 0.011), and hypertension (OR = 1.51, p = 0.04) were predictive of readmission. A subgroup analysis was performed for all geriatric patients (aged >65). In total, 1,353 patients were identified, of whom 76 were readmitted, demonstrating a readmission rate of 5.62%. For the elderly patients, hypertension (OR = 1.98, confidence interval (CI) = 1.04-3.75, p = 0.04) and independent status (OR = 0.39, CI = 0.21-0.74, p = 0.004) were predictive of readmission. Conclusions Assessment of predictors for readmission is important for patient education and setting appropriate clinical expectations for surgeons and providers. Preoperative hypertension, dialysis, and sepsis were risk factors for 30-day readmission following cervical laminoplasty, with functional status being a unique risk factor for elderly patients.

Epidemiology and Predictors of 30-Day Readmission in CAR-T Cell Therapy Recipients

The novel cellular immunotherapy using chimeric antigen receptor (CAR) T cells has transformed the management of several previously incurable hematologic malignancies. Since the first CAR-T cell product was approved by the Food and Drug Administration in 2017, five additional products have been approved for various hematologic malignancies. Although there is now more experience with outpatient administration, CAR-T therapy was initially delivered in an inpatient setting. The unique complications of cytokine release syndrome (CRS) and neurologic side effects (commonly known as immune effector cell-associated neurotoxicity syndrome [ICANS]), along with a higher risk for infection, increase the risk for hospital readmission. Given the recent approval of CAR-T therapy, large-scale epidemiologic data are lacking. The present study aimed to characterize the epidemiology of hospitalizations, readmissions, and factors associated with all-cause 30-day readmission post CAR-T therapy. This retrospective cohort study used the Nationwide Readmissions Database from 2017 to 2019 to identify hospitalizations for CAR-T therapy administration. A descriptive analysis was performed after categorizing these hospitalizations as non-Hodgkin lymphoma, multiple myeloma, or leukemia. The readmission rate was calculated, and etiologies of readmission were identified. A Cox proportional hazards model was used to elucidate factors associated with 30-day readmission. We also estimated the healthcare utilization related to readmissions, including total hospital charges and length of stay. The 2,964 CAR-T therapy-related admissions included 2,176 with a diagnosis of non-Hodgkin lymphoma, 344 with multiple myeloma, and 445 with leukemia. The median length of stay was 15 days. Most CAR-T therapy recipients were male (63.4%), admitted to a teaching hospital (99.2%); 49.3% had private insurance, and 33.2% belonged to the highest-income communities. CAR-T therapy was administered mostly in privately owned (89.5%) large-sized hospitals (74.4%) in large metropolitan regions (91.4%). Median total hospital charges were highest for non-Hodgkin lymphoma, followed by leukemia and multiple myeloma ($945,645 versus $265,034 versus $184,194; P < .001). All-cause mortality during index hospitalization was highest for leukemia at 8.6%, followed by 3.6% for non-Hodgkin lymphoma and 1.4% for multiple myeloma (P < .001). The 30-day all-cause readmission rate was 23.6%, and the median time to readmission was 7 days. The readmission rate was highest for leukemia, followed by non-Hodgkin lymphoma and multiple myeloma (34.2% versus 22.8% versus 15.7%; P < .001). Readmission incurred an additional median total hospital charge of $64,561. During readmission, median length of stay was 5 days, and in-hospital mortality was 4.9%. Top etiologies for readmission were cancer or treatment-related (22%), sepsis or infection (18%), neurologic events (15%), neutropenia or pancytopenia (11%), and fever, hypotension, or hypoxia (8%). On multivariable analysis, non-Hodgkin lymphoma and leukemia (compared with multiple myeloma), transfer to a facility at discharge, chronic renal disease, cerebrovascular disease, and noninvasive ventilation were associated with higher odds of readmission. In contrast, admission to a teaching hospital predicted lower odds of readmission. Almost a quarter of CAR-T therapy recipients are readmitted within the first 30 days resulting in additional economic burden and substantial healthcare utilization.

Abstract 15115: Causes and Predictors of 90-Day Readmission Among Patients Admitted With Acute Myocarditis

Introduction: Myocarditis contributes to the global burden of cardiovascular disease primarily through sudden death and dilated cardiomyopathy. We aimed to study patient characteristics, national readmission rates and predictors for patients admitted for acute myocarditis utilizing a nationally representative cohort of patients from the National Readmission Database. Methods: We utilized the National Readmission Database 2018 to identify the hospitalized adults with a primary diagnosis of acute myocarditis. We included subjects who were readmitted within 90 days after index admission. We excluded subjects with elective and traumatic admissions. We utilized a multivariate cox regression model to identify independent predictors of readmission. Results: During the study period, 3,524 hospitalized patients who had a primary diagnosis of acute myocarditis were discharged alive. Within 90 days of discharge, 356 (10.1%) subjects were readmitted. The top three causes of readmission were heart failure (37.1%), sepsis (16.1%) and ventricular tachycardia (10.6%). The in-hospital mortality rate (5.5 vs. 2.6%, p&lt;0.001) was significantly higher during readmission. There was a trend in higher length of stay in readmission (5.6 vs. 4.3 days, p=0.055), however this difference was not statistically significant. Figure 1 shows the Forest plot of multivariate analysis of independent factors associated with readmissions. Conclusion: Our study identified significant factors such as heart failure, atrial fibrillation, hypertension related to higher readmission odds for myocarditis patients. This data may contribute to the healthcare system to help modify strategies to minimize readmission.

Abstract 15895: Causes and Predictors of 30-Day Readmission After Implantable Cardioverter/Defibrillator Insertion in Heart Failure Patients: Insight From the National Readmissions Database

Introduction: Implantable Cardioverter/Defibrillator (ICD) insertion in heart failure (HF) patients with ejection fraction below 30% and at least 40 days post-infarction is rapidly becoming commonplace. Given the high prevalence of ICD insertions in HF patients with reduced ejection fraction (HFrEF) we aimed to analyze the rates and causes of readmissions in this specific population. Methods: We utilized the National Readmission Database from 2018 to identify hospitalized adults with HF who underwent ICD insertion. We included those who were readmitted within 30 days after the index admission. We excluded elective and traumatic admissions. A multivariate cox regression model was used to identify independent predictors of readmission. Results: During the study period, 283,539 hospitalized patients with HF who underwent ICD insertion were discharged alive. Within 30-days from discharge, 46,537 (16.4%) were readmitted. The top three causes of readmission were heart failure (35.2%), sepsis (5.26%), and ventricular tachycardia (4%). Readmitted patients were more likely to be discharged to nursing facility (45% vs 38%; p&lt;0.001), had higher rates of Medicare/Medicaid (89.2% vs 78.7%; p&lt;0.001) and annual income &lt; $44,000 (35.7% vs 33%; p&lt;0.001). In-hospital mortality (5.6% vs 3.4%; p&lt;0.001) was higher during readmission. Figure 1 shows the Forrest plot of multivariate analysis of independent factors associated with readmissions. Conclusions: We identified social and clinical predictors of readmission after ICD insertion in those with HF such as household income, discharge to a nursing home, renal/lung disease, and intubation during their index admission. Healthcare systems should use this data to target modifiable factors to prevent readmissions as this can help ease the burden placed on the resources of the American healthcare system and improve clinical outcomes for patients.

S125 Predictors and Causes of 30-Day Readmissions in Primary Biliary Cholangitis: Analysis of the Nationwide Readmission Database

Introduction: Primary biliary cholangitis (PBC) is an autoimmune disorder that most commonly affects middle-aged women. Cirrhosis, hepatocellular carcinoma, metabolic bone disease, and malabsorption can complicate PBC. In this study we aimed to describe the causes and predictors of 30-day readmissions in patients with PBC. Methods: This was an observational retrospective study involving adult patients hospitalized with PBC in the US. We utilized the Nationwide Readmission Databases 2016 to 2019. The first hospitalization for PBC within the year was marked as the index admission. We identified one subsequent hospitalization within 30 days, this was marked as a readmission. We analyzed the rates, causes, and outcomes of the readmitted cohort. We used multivariable cox regression analysis to identify independent predictors of readmissions. Results: We included a total of 3954 hospitalizations with PBC as the primary diagnosis for admission. 30.5% of the discharged cohort were readmitted within 30 days. Excluding PBC, the most common reasons for readmission were hepatic failure (9.6%) and sepsis (6.7%). Fluid and electrolyte disorders were the most common comorbidities in both the index hospitalization and readmission cohorts. Mortality was higher in the readmitted patients compared to index hospitalizations (10.1% and 4.3%, respectively, p < 0.001). Predictors of 30-day readmissions were peptic ulcer disease (aHR 1.64, p = 0.040), renal failure (aHR 1.29, p = 0.038), weight loss (aHR 1.28, p = 0.024), fluid and electrolyte disorders (aHR 1.28, p = 0.011), and coagulopathy (aHR 1.22, p = 0.029). Conclusion: Patients with PBC have high readmission rates, these readmissions are associated with high inpatient mortality. PBC patients are at risk of liver cirrhosis and malabsorption which explains the high rates of sepsis and electrolyte abnormalities observed in our study. Coagulopathy and weight loss might be reflective of the severity of liver dysfunction and the malabsorptive state, hence, there were associated higher readmission risk. Excellent short and long-term survival have been described following liver transplantation for PBC patients. However, recent data demonstrated that they have higher wait-list mortality among patients listed for liver transplantation.

In-hospital mortality rate and predictors of 30-day readmission in patients with iron deficiency anemia and diastolic heart failure: A cross-sectional study

Introduction: There is currently strong evidence of the adverse effects of anemia on the prognosis of heart failure with reduced ejection fraction. Unfortunately, the data on the effects of anemia on a large sample of patients with diastolic heart failure (DHF) is lacking. In this study, we aimed to evaluate the effect of iron deficiency anemia on DHF readmission rates and its corresponding causes and burdens on the healthcare system. Methods: We utilized 2018 Nationwide readmission data and included patients aged ≥18 years with ICD-10 CM code indicating acute or acute chronic diastolic heart failure and iron deficiency anemia was included in the study. The primary outcome is 30-day readmission rates. Secondary outcomes were mortality rates, common causes of readmission, and healthcare utilization. Independent predictors for readmission were identified using cox regression analysis. Results: The total number of admissions in our study was 795,777. The mean age was 74.4 years (SD=13.7), and 63.54% were females. The 30-day readmission rate in patients with diastolic heart failure and iron deficiency anemia was 18.32 % vs. 16.01% in patients without anemia. The mortality rate at index admission and readmission was 3.62 % (2601) and 5.82 % (737), respectively. The most common cause of readmission was hypertensive heart and kidney disease with heart failure (17.74%). The independent predictors of readmission were age&lt;85 years, household income &lt;59000$/per year, Medicare and Medicaid insurance, higher Elixhauser comorbidities score, longer Length of stay during the index admission, discharge to a nursing home, hospital located in a large metropolitan area. The financial burden on healthcare for all the readmission was $837 million for 2018. Conclusion: The 30-day readmission rate in patients with diastolic heart failure and iron deficiency anemia is 18.32% in the year 2018. The mortality rate increased from 3.62 % to 5.82 % with readmission. The financial burden for readmission during that year was $837 million. Future studies are warranted to treat iron deficiency anemia to prevent readmissions in diastolic heart failure.