Readmission In Adults Research Articles

SARC-Calf using calf circumference adjusted for BMI predicts six-months readmission and mortality in hospitalized patients: a secondary analysis of a cohort study

BackgroundSarcopenia is a prevalent condition associated with worse clinical outcomes in hospitalized patients. The SARC-Calf is an accurate instrument for its screening; however, it includes the calf circumference (CC) measure as a criterion, which is influenced by adiposity. An adjustment for CC based on body mass index (BMI) has been proposed, but the literature lacks studies evaluating the SARC-Calf using adjusted CC. ObjectiveThis study aimed to evaluate the prognostic value of the SARC-Calf with BMI-adjusted CC and compare it between adult and older hospitalized patients. MethodsThis is a secondary analysis of a cohort with prospective data collection, including individuals aged ≥18 years, lucid and able to communicate. SARC-Calf was applied using BMI-adjusted CC, obtained by subtracting 3, 7, and 12 cm from CC values when BMI was 25–29.99, 30–39.99, and ≥ 40kg/m2, respectively. Outcomes of interest included prolonged hospital stay, in-hospital death, hospital readmission, and mortality six months after discharge. Logistic and Cox regression analyses, adjusted for Charlson comorbidity index and sex, were performed. ResultsWe analyzed data from 554 patients (mean age 55.2±14.9 years, 52.9% males). Suggestive signs of sarcopenia by SARC-Calf with BMI-adjusted CC were identified in 40.4% of patients (38.6% in adults and 42.7% in older patients, p=0.380). Suggestive signs of sarcopenia were associated with hospital readmission in adults (OR=1.8; 95%CI 1.1, 2.9), and six-month death in both adult (OR= 4.0; 95% CI 1.3, 12.1) and older patients (OR=2.8; 95%CI 1.2, 6.6). It was not independently associated with in-hospital outcomes. ConclusionsSARC-Calf with BMI-adjusted CC identified a high frequency of patients with suggestive signs of sarcopenia, regardless of age, and it was independently associated with worse outcomes six months after discharge. Statement of SignificanceSuggestive signs of sarcopenia cannot be accurately identified by SARC-Calf in individuals with high BMI due to the influence of adiposity on calf circumference (CC) measurements. In this secondary analysis, we explored the prognostic value of SARC-Calf with BMI-adjusted CC, aiming to correct the influence of adiposity on the CC measurement. We confirmed that this approach identifies a high number of patients with suggestive signs of sarcopenia and showed that it was independently related to worse outcomes six months after hospital discharge in both adults and older patients.

Prevalence of malnutrition and impact on 30-day hospital readmission in adults receiving home care and ambulatory care: A descriptive cohort study.

Little is known about the prevalence of malnutrition among patients receiving home care (HC) and ambulatory care (AC) services. Further, the risk of hospital readmission in malnourished patients transitioning from hospital to HC or AC is also not well established. This study aims to address these two gaps. A descriptive cohort study of newly referred HC and AC patients between January and December 2019 was conducted. Nutrition status was assessed by clinicians using the Mini Nutritional Assessment-Short Form (MNA-SF). Prevalence of malnutrition and at risk of malnutrition (ARM) was calculated, and a log-binomial regression model was used to estimate the relative risk of hospital readmission within 30 days of discharge for those who were malnourished and referred from hospital. A total of 3704 MNA-SFs were returned, of which 2402 (65%) had complete data. The estimated prevalence of malnutrition and ARM among newly referred HC and AC patients was 21% (95% CI: 19%-22%) and 55% (95% CI: 53%-57%), respectively. The estimated risk of hospital readmission for malnourished patients was 2.7 times higher (95% CI: 1.9%-3.9%) and for ARM patients was 1.9 times higher (95% CI: 1.4%-2.8%) than that of patients with normal nutrition status. The prevalence of malnutrition and ARM among HC and AC patients is high. Malnutrition and ARM are correlated with an increased risk of hospital readmission 30 days posthospital discharge.

Clinical and health care utilization variables can predict 90-day hospital re-admission in adults with Crohn’s disease for point of care risk evaluation

BackgroundHospital re-admission for persons with Crohn’s disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk.MethodsWe performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden’s index.ResultsThere were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76–6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950–0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500–0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950–1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%.ConclusionsDemographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.

Health-Related Social Needs, Social Determinants, and Readmissions in Adults With Congenital Heart Disease.

Health-Related Social Needs, Social Determinants, and Readmissions in Adults With Congenital Heart Disease.

Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.

In this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults. We searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality. We screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance. This review has identified considerable concerns regarding methodological issues and reporting quality in studies investigating ML to predict hospitalizations. To ensure the acceptability of these models in clinical settings, it is crucial to improve the quality of future studies.

Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence.

Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. Patient admissions are preventable events with a considerable impact on global functioning and social adjustment. While machine learning (ML) approaches have proven prediction ability in other diseases, little is known about their utility to predict patient admissions in this pathology. To develop prediction models for hospital admission/readmission within 5 years of diagnosis in patients with BD using ML techniques. The study utilized data from patients diagnosed with BD in a major healthcare organization in Colombia. Candidate predictors were selected from Electronic Health Records (EHRs) and included sociodemographic and clinical variables. ML algorithms, including Decision Trees, Random Forests, Logistic Regressions, and Support Vector Machines, were used to predict patient admission or readmission. Survival models, including a penalized Cox Model and Random Survival Forest, were used to predict time to admission and first readmission. Model performance was evaluated using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC) and concordance index. The admission dataset included 2,726 BD patients, with 354 admissions, while the readmission dataset included 352 patients, with almost half being readmitted. The best-performing model for predicting admission was the Random Forest, with an accuracy score of 0.951 and an AUC of 0.98. The variables with the greatest predictive power in the Recursive Feature Elimination (RFE) importance analysis were the number of psychiatric emergency visits, the number of outpatient follow-up appointments and age. Survival models showed similar results, with the Random Survival Forest performing best, achieving an AUC of 0.95. However, the prediction models for patient readmission had poorer performance, with the Random Forest model being again the best performer but with an AUC below 0.70. ML models, particularly the Random Forest model, outperformed traditional statistical techniques for admission prediction. However, readmission prediction models had poorer performance. This study demonstrates the potential of ML techniques in improving prediction accuracy for BD patient admissions.

Risk factors for medication-related short-term readmissions in adults – a scoping review

BackgroundHospital readmissions due to medication-related problems occur frequently, burdening patients and caregivers emotionally and straining health care systems economically. In times of limited health care resources, interventions to mitigate the risk of medication-related readmissions should be prioritized to patients most likely to benefit. Focusing on general internal medicine patients, this scoping review aims to identify risk factors associated with drug-related 30-day hospital readmissions.MethodsWe began by searching the Medline, Embase, and CINAHL databases from their inception dates to May 17, 2022 for studies reporting risk factors for 30-day drug-related readmissions. We included all peer-reviewed studies, while excluding literature reviews, conference abstracts, proceeding papers, editorials, and expert opinions. We also conducted backward citation searches of the included articles. Within the final sample, we analyzed the types and frequencies of risk factors mentioned.ResultsAfter deduplication of the initial search results, 1159 titles and abstracts were screened for full-text adjudication. We read 101 full articles, of which we included 37. Thirteen more were collected via backward citation searches, resulting in a final sample of 50 articles. We identified five risk factor categories: (1) patient characteristics, (2) medication groups, (3) medication therapy problems, (4) adverse drug reactions, and (5) readmission diagnoses. The most commonly mentioned risk factors were polypharmacy, prescribing problems—especially underprescribing and suboptimal drug selection—and adherence issues. Medication groups associated with the highest risk of 30-day readmissions (mostly following adverse drug reactions) were antithrombotic agents, insulin, opioid analgesics, and diuretics. Preventable medication-related readmissions most often reflected prescribing problems and/or adherence issues.ConclusionsThis study’s findings will help care teams prioritize patients for interventions to reduce medication-related hospital readmissions, which should increase patient safety. Further research is needed to analyze surrogate social parameters for the most common drug-related factors and their predictive value regarding medication-related readmissions.

Age-related differences in risks and outcomes of 30-day readmission in adults with sickle cell disease.

Literature on 30-day readmission in adults with sickle cell disease (SCD) is limited. This study examined the overall and age-stratified rates, risk factors, and healthcare resource utilization associated with 30-day readmission in this population. Using the Nationwide Readmissions Database, a retrospective cohort study was conducted to identify adult patients (aged ≥ 18) with SCD in 2016. Patients were stratified by age and followed for 30days to assess readmission following an index discharge. The primary outcome was 30-day unplanned all-cause readmission. Secondary outcomes included index hospitalization costs and readmission outcomes (e.g., time to readmission, readmission costs, and readmission lengths of stay). Separate generalized linear mixed models estimated the adjusted odds ratios (aORs) for associations of readmission with patient and hospital characteristics, overall and by age. Of 15,167 adults with SCD, 2,863 (18.9%) experienced readmission. Both the rates and odds of readmission decreased with increasing age. The SCD complications vaso-occlusive crisis and end-stage renal disease (ESRD) were significantly associated with increased likelihood of readmission (p < 0.05). Age-stratified analyses demonstrated that diagnosis of depression significantly increased risk of readmission among patients aged 18-to-29years (aOR = 1.537, 95%CI: 1.215-1.945) but not among patients of other ages. All secondary outcomes significantly differed by age (p < 0.05). This study demonstrates that patients with SCD are at very high risk of 30-day readmission and that younger adults and those with vaso-occlusive crisis and ESRD are among those at highest risk. Multifaceted, age-specific interventions targeting individuals with SCD on disease management are needed to prevent readmissions.

Using machine learning methods to predict all-cause somatic hospital admissions and readmissions in adults: A systematic review

Using machine learning methods to predict all-cause somatic hospital admissions and readmissions in adults: A systematic review

Association of Surgical Complications With Resource Utilization in Adult Congenital Cardiac Surgery

BackgroundAs patients with congenital heart disease are increasingly surviving well into adulthood, the morbidity, mortality, and resource utilization of adult congenital cardiac operations are of increasing interest. Therefore, we evaluated factors associated with perioperative morbidity and outcomes in adults undergoing congenital operations. MethodsThe Nationwide Readmissions Database was tabulated for all adults (≥18 years old) with congenital heart disease between 2010 and 2017. Congenital operations were identified by previously published International Classification of Diseases, Ninth Revision and Tenth Revision codes. Complications were selected on the basis of The Society of Thoracic Surgeons short list of complications. Multivariable regression models were used to assess adjusted odds ratios (AORs) and β coefficients for select clinical outcomes. ResultsOf 52,360 adults identified who underwent congenital cardiac operations, 14,123 (27%) suffered a complication. The presence of a complication increased the odds of index death (AOR, 11.46; 95% CI, 8.58-15.31), nonhome discharge (AOR, 2.09; 95% CI, 1.91-2.29), 30-day readmission (AOR, 2.12; 95% CI, 1.88-2.39), 90-day readmission (AOR, 2.17; 95% CI, 1.94-2.43), costs (β, +$37,000; 95% CI, $34,000-$40,000), and hospital duration (β, +7.86 days; 95% CI, 7.3-8.4). ConclusionsPerioperative complications portend in-hospital death, resource use, and readmissions in adults undergoing congenital heart operations. As complications are present in 27% of this population, identification of risk stratification and complication-reducing strategies may improve patient morbidity, mortality, and resource utilization.

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.

A snapshot of patient experience of illness control after a hospital readmission in adults with chronic heart failure

BackgroundApproximately 6.5 million adults have chronic heart failure (HF), the number one cause of 30-day hospital readmission. Managing HF and its symptoms is critical for patients. Hospitalization may impact patients’ perceptions of illness control, which can affect illness management. However, how hospital readmissions are perceived as related to one’s ability to control their HF and its symptoms has not been examined.ObjectiveThe purpose was to explore the experiences of people with HF in managing their illness (i.e., illness control), understand their perceptions of illness control after recent hospital readmission, and clarify the concept of illness control in people with chronic HF.MethodsA qualitative approach, applied thematic analysis was employed. Purposive sampling was used to identify participants. Semi-structured interviews were conducted in 10 participants’ homes. Ongoing, concurrent, and comparative data analysis was used with ATLASti© data management software.ResultsTwo themes were identified, strategies to control HF and barriers to controlling HF. Strategies to control HF included four subthemes: managing dietary intake and medications; self- advocacy; monitoring symptoms; and support. Barriers to control also had four subthemes: healthcare systems issues; health care professional relationships and interactions; personal characteristics; and knowledge deficits.ConclusionPeople use many different strategies to control HF. Control comes from both within and outside of the individual. The desire to control HF and its symptoms was evident, but implementing strategies is challenging and takes time, experience, and trial and error. Individuals did not view readmission negatively but as necessary to help them control their symptoms.

ODP216 Interventional Study To Reduce The Readmission Rate In Type 1 Diabetics Hospitalized For Diabetic Ketoacidosis

Abstract Introduction Diabetic ketoacidosis (DKA) is an emergency associated with a high mortality rate. 25% of DKA readmissions in the U. S. A are preventable and they lead to an increase in the mortality rate and the length of the hospital stay with each new admission. In our study, we set out to decrease the DKA readmission rate at our hospital which is a community teaching hospital by assessing the predictors of readmission in adults with type 1 diabetics hospitalized for DKA. Methods The study was an interventional study conducted between February 2021 and May 2021. All the type 1 diabetics who were on insulin and had DKA admissions in the past three months were included in the study. After a literature review of the risk factors of DKA readmission, a questionnaire was designed to gather information from the patients. The ABCD (A: age; B: behavioral health; C: coverage; D: drug/alcohol abuse) score was used for DKA readmission risk assessment on admission. The insulin delivery technique of the patients was assessed and proper training on the technique was given before discharge. All the DKA admissions received mandatory endocrinology consult and the note template of the endocrinology consult note in the electronic medical record system was modified to let the primary team know about the discharge instructions, medications, and follow-up appointments in detail. Patients were followed up with a telehealth visit in a week and physical endocrinology clinic follow-up 3 weeks after discharge. Results Our study included 69 patients admitted with a diagnosis of DKA. The average HbA1C in the study was 11.8. Predictors of readmission were female sex (Hazard Ratio [HR]: 1.74, 95% CI: 1.64 - 1.80, p&amp;lt;0. 001), hypertension (HR: 1.98, 95% CI: 1.80 - 1.95, p&amp;lt;0. 001), discharge against medical advice (HR: 1.74, 95% CI: 1.53 - 1.96, p&amp;lt;0. 001). The readmission rate at the hospital was 20.8% initially and dropped to 13.7% after the interventions. 78% of the population were administering insulin the wrong way. The 'ABCD' score of the patients had a statistically significant correlation with the readmission rate. A difference in difference study model was used to assess the significance of the interventions on the readmission rate. Linear regression was run with significant R square, F- test values. Conclusions The study revealed that the major risk factors for readmission were lower socioeconomic status, co-morbidities, poor health literacy, and drug abuse. The study highlighted the importance of inpatient education, specialty care, better discharge instructions, coordination of care, and post-discharge support to reduce the DKA readmission rate. Presentation: No date and time listed

Factors associated with racial differences in all-cause 30-day readmission in adults with cardiovascular disease: an observational study of a large healthcare system

ObjectiveTo examine factors contributing to racial differences in 30-day readmission in patients with cardiovascular disease (CVD).DesignPatients were enrolled from 1 January 2015 to 31 August 2017 and data were collected...

Predicting and Validating 30-day Hospital Readmission in Adults With Diabetes Whose Index Admission Is Diabetes-related

ObjectiveThe primary objective is to develop a prediction model of 30-day hospital readmission among adults with diabetes mellitus (DM) whose index admission was DM-related. The secondary aims are to internally and externally validate the prediction model and compare its performance with 2 existing models. Research Design and SettingData of inpatients aged ≥ 18 years from 2008 to 2015 were extracted from the electronic medical record system of the National University Hospital, Singapore. Unplanned readmission within 30 days was calculated from the discharge date of the index hospitalization. Multivariable logistic regression and 10-fold cross-validation were performed. For external validation, simulations based on prevalence of 30-day readmission, and the regression coefficients provided by referenced papers were conducted.ResultsEleven percent of 2355 patients reported 30-day readmission. The prediction model included 4 predictors: length of stay, ischemic heart disease, peripheral vascular disease, and number of drugs. C-statistics for the prediction model and 10-fold cross-validation were 0.68 (95% CI 0.66, 0.70) and 0.67 (95% CI 0.63 to 0.70), respectively. Those for the 3 simulated external validation data sets ranged from 0.64 to 0.68.ConclusionThe prediction model performs well with good internal and external validity for identifying patients with DM at risk of unplanned 30-day readmission.

Vaso-Occlusive Events in Pediatric Sickle Cell Disease: Quantifying Social Disadvantage and Its Impact on Hospitalizations

Background: Sickle Cell Disease (SCD) has established socioeconomic disadvantage resulting in increased healthcare utilization. Among the pediatric population, the implementation of preventative care measures within the medical home model has improved outcomes and lifespan. Area deprivation index (ADI) is an established method for quantifying socioeconomic disadvantage and has been shown to be associated with increased hospital readmission in adults and pediatric patients with chronic disease (Singh Am J Public Health. 2003, Kind et al. Ann Intern Med. 2014). ADI has been applied to adults with SCD and vaso-occlusive events (VOE) but has not been investigated in pediatric patients. We explored the role of ADI in pediatric patients with SCD admitted with VOE in order to characterize disadvantage and the hospitalization characteristics that impact hospital readmission.Methods: This retrospective review included 675 consecutive emergency department and hospital admissions for VOE among 101 pediatric (≤ 21 years old) patients with SCD from 2016-2019 at a single urban, US-based medical center. One hospital admission for each patient was selected at random. Information extracted included demographics, SCD characteristics, admission complications, entry and discharge pain scores, length of stay, and management characteristics. Variables were compiled in a descriptive table and compared in logistic regression models against a primary outcome of 7-day readmission.The 2018 ADI dataset was used to assign an ADI value based on the census block corresponding to the patient-reported zip-code for the specific hospital admission (https://www.neighborhoodatlas.medicine.wisc.edu). ADI values were grouped by most and least deprived with higher scores (6-10) indicating more deprivation. Summary statistics described patient and disease characteristics in these groups.Results: Within our cohort, the state and national ADI was calculated for 101 patients with a median age of 14 years (50.4% female; 98.0% Black) (Table 1). Most patients were publicly insured (80.2%). The Hb SS genotype was the most common genotype, followed by Hb SC (22.7%). Median ADI rank was 9 with 11 patients (10.9%) classified as less disadvantaged (ADI 1-5) and 90 patients (89.1%) as more disadvantaged (ADI 6-10).We found the less disadvantaged group had even numbers of public and private insurance use while 83.3% of the patients in the more disadvantaged group had public insurance. The Hb SS genotype was more common in the less disadvantaged (90.9%) than the more disadvantaged (74.4%), where Hb SC was overrepresented (24.4%). Greatest prevalence of mental health disease and hospital complications, including acute chest syndrome, avascular necrosis, and pneumonia, were observed in the more disadvantaged. Additionally, the more disadvantaged group included a greater number of patients with scheduled follow-up (61.1%) and shorter time to follow-up (median: 31 days). For treatment and management, both groups had high numbers of prescriptions for hydroxyurea (HU) and opiates, with slightly higher numbers in the less disadvantaged group (82% HU; 100% opioids) compared to the more disadvantaged (61% HU; 81% opioids). Length of hospital-stay and pain scores were similar across groups. The 7-day readmission rate was 9% for the less disadvantaged group and 14% for the more disadvantaged group.Conclusions: We applied a validated measure of socioeconomic disadvantage to a group of pediatric patients with SCD and VOE. Our cohort consists of more disadvantaged patients across ADI scales. Readmission rate was low for both groups and there was no relationship between greater ADI scores and hospital readmission. Within the more disadvantaged group, the higher prevalence of mental health illness as well as associated disease complications may have been countered by protective factors including established follow-up, shorter time to follow-up, and high number of prescriptions for hydroxyurea and opiates. Our results are consistent with the protective effect of ADI on recurrent acute chest syndrome in pediatric SCD patients (Alishlash et al. Pediatr. Blood Cancer. 2021) highlighting the importance of preventative care within the SCD medical home. Further research is warranted on identifying key protective factors that may reduce acute care utilization and social disparity in this population. [Display omitted] DisclosuresNovelli: Novartis Pharmaceuticals: Consultancy.

Readmission for cardiac and non-cardiac causes among adults with epilepsy or multiple sclerosis – A nationwide analysis

PurposeThe aim of this study was to determine proportions of 30-day cardiac readmissions in adults with epilepsy compared to multiple sclerosis (MS) or those with neither condition. Predictors and causes of readmissions were also examined. MethodsWe used the 2014 Nationwide Readmissions Database and ICD-9-CM codes to identify people with epilepsy, MS, and without epilepsy or MS. Multinomial logistic regressions were fitted to: (1) examine association between 30-day readmissions and epilepsy, MS or neither, and (2) to describe causes and predictors of 30-day readmission for cardiac readmissions in epilepsy. ResultsOut of 6,870,508 adults admitted in 2014, 202,938 (2.98%) had epilepsy and 29,556 (0.45%) had MS. The proportion of 30-day readmission for epilepsy and MS were, respectively: (1) due to cardiac causes (0.17% vs. 0.13%); (2) due to other causes (13.89% vs. 10.61%). The odds of 30-day cardiac readmission in those with epilepsy and MS were lower compared to those without either condition (OR = 0.64, 95% CI 0.57–0.73, p < 0.0001; OR = 0.60, 95% CI 0.43–0.84, p = 0.003). Among those with epilepsy, increasing age (OR = 1.03, 95% CI 1.02–1.04, p < 0.0001) and a Charlson comorbidity index ≥1 (OR = 1.79, 95% CI 1.24–2.60, p = 0.002) were associated with higher odds of 30-day cardiac readmission. A higher proportion of those with epilepsy readmitted within 30-days due to cardiac causes died in hospital (10.09%) compared to those with MS (not reportable due to cell frequency <10) or without epilepsy or MS (5.61%). ConclusionThose admitted to a hospital and living with epilepsy had a higher proportion of cardiac readmissions and death in hospital when compared to those living with MS, and the determinants are likely multifactorial. These findings are important and need to be further explored to identify strategies to prevent readmissions due to any cause and treatments that could reduce mortality.

68. PROMIS physical function and pain interference are independent predictors of unplanned readmission after instrumented lumbar spinal arthrodesis

<h3>BACKGROUND CONTEXT</h3> Risk stratification is essential for informed decision-making and patient counseling. The PROMIS physical function computer adaptive test (PFCAT) and pain interference (PI) scales directly evaluate physical function and limitation due to pain with low question burden and may be clinically relevant predictors of adverse outcomes in spine patients. <h3>PURPOSE</h3> To evaluate whether preoperative PROMIS PFCAT or PI scores are important predictors of unplanned readmission in adults undergoing lumbar arthrodesis. <h3>STUDY DESIGN/SETTING</h3> Retrospective analysis of a spine patient registry at a Level I academic center. <h3>PATIENT SAMPLE</h3> A total of 1,782 patients who underwent lumbar arthrodesis between 2015 and 2020 were included. <h3>OUTCOME MEASURES</h3> Primary outcome measures was unplanned hospital readmission. <h3>METHODS</h3> All cases with unplanned hospital readmission were adjudicated by manual chart review. We performed random forest prediction modeling to identify variables with the greatest predictive importance to unplanned hospital readmission risk using the mean decrease in Gini index. <h3>RESULTS</h3> Mean patient age was 62.8 (12.8) years. Mean BMI was 30.2 (6.4) kg/m2. Mean preoperative PFCAT was 33.8 (6.6) and mean PI was 66.5 (6.5). Preop CCI mean was 2.0 (2.5). Of 1,782 patients included, 146 patients (8.19%) had unplanned readmissions after surgery. In both model PFCAT and model pain interference, BMI had the greatest importance (68.9, 63.3, respectively). In model PFCAT, PFCAT had the second highest predictive importance (61.3), while in model pain interference PROMIS PI had the second greatest predictive importance (50.1). In both models, age (49.5, 46.7, respectively) and CCI (32.0, 28.7) were the third and fourth most important predictive variables for unplanned readmission, respectively. <h3>CONCLUSIONS</h3> Random forest modeling is a more flexible modeling technique than linear regression techniques, and therefore, can identify under-explored risk factors. Our analysis using random forest modeling identified BMI as the most important predictive variable for unplanned readmission, with PROMIS PFCAT and PROMIS PI demonstrating the next greatest predictive importance in respective models. Both models also included age and CCI as predictive variables of lesser importance. While traditionally used to assess post-treatment outcomes, PROMIS PFCAT and PI are stronger independent predictors of postoperative readmission than age or CCI. Further research to develop risk-stratification tools incorporating PROMIS measures and random forest models is warranted. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

The biomedical, socioeconomic and demographic predictors of heart failure readmissions

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Sydney Local Health District Introduction Heart failure hospitalisations and readmissions pose a significant clinical and economic burden internationally. Current studies support a range of biomedical and sociodemographic factors as predictors of HF readmissions; however, there are a paucity of studies synthesising the research on this topic. Purpose The purpose of this systematic review was to identify biomedical, socioeconomic and demographic predictors of heart failure-related readmissions in adults with heart failure. Methods Electronic databases (Medline, Embase and Scopus) were searched using a structured search strategy. The particular risk factors of interest were left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) function class, brain natriuretic peptide (BNP), heart rate, blood pressure, employment status, income, age, sex, ethnicity, indigenous status, marital status, and socioeconomic indexes for areas (SEIFA) scores. After the removal of duplicates, the article selection process was conducted by two independent authors according to an inclusion and exclusion criteria. The articles were first filtered by title and abstract against the inclusion and exclusion criteria. The remaining articles were then assessed by reviewing the studies in full text. The relevant Critical Appraisal Skills Programme (CASP) tools were then used to appraise the final articles included for review. Results In total 1,516 papers were retrieved from the search. Of the 1,516 papers, 666 were from Medline, 567 were from CINAHL, 282 were from Embase, and one paper was from an independent search. After the removal of duplicates, 1296 papers were filtered according to title and abstract and 1183 papers were excluded from this process. 95 papers were assessed by reading them in full and of the 95 papers, 77 were excluded. This resulted in eighteen studies being included for review. Unemployment was the only socioeconomic factor predictive of HF readmissions (HR = 1.09; 95%CI= 1.05-1.14; p = 0.03) and SEIFA scores did not predict HF readmissions in adults with HF (p &amp;gt; 0.05). Indigenous status was a risk factor of HF readmissions according to one study (p &amp;lt; 0.05). Age or sex did not affect the risk of HF readmissions in adults with HF (p &amp;gt; 0.05). NYHA function class, BNP levels, and heart rate predicted HF readmissions in the population of interest. LVEF and blood pressure were non-significant predictors of HF readmissions (p &amp;gt; 0.05). Conclusions Current HF guidelines have a strong biomedical focus in the management of HF patients. This review however, shows there are sociodemographic factors that are predictive of HF readmissions in addition to the biomedical predictors that have been more extensively studied. Future studies should examine their combined effect on readmission risk and the early interventions to prevent readmissions.

Rates and Predictors of 30-Day Readmission in Adults with Type 1 Diabetes Hospitalized for Diabetic Ketoacidosis in the US: A Nationwide Study

Introduction: Diabetic ketoacidosis (DKA) is an endocrinological emergency that may be associated with inpatient mortality. This hyperglycemic state is more common in type 1 diabetics (T1DM) as insulin deficiency promotes lipolysis and ketogenesis. Due to the difference in epidemiologic and comorbidity profiles between T1DM and other types of diabetes, it is important to investigate factors affecting this unique group. This study assessed the rate and predictors of readmission in adults with T1DM hospitalized for DKA.Methods: The National Readmission Database for 2017 was searched for hospitalizations involving persons 18 years or older with T1DM who were principally admitted for DKA from January 1 to November 30, 2017. Patients admitted electively or traumatic admissions were excluded from the analysis. Outcomes assessed were 30-day readmission rates, mortality, length of stay (LOS) and hospitalization charges (THC). Multivariate cox regression model was adapted to identify independent predictors of readmission.Results: A total of 91,625 hospitalizations involved adult with T1DM for DKA with 91,401 discharged alive. Of the patients discharged alive, the rate of 30-day readmission was 20.2%, involving 18,553 patients. A majority of patients had a principal readmission for DKA. Compared to the index admission, a 30-day readmission was associated with over two times the mortality rate (Risk ratio 2.06, 95% CI: 1.74 - 2.43, p<0.001). Readmission was associated with increased mean LOS in days (1.0, 95% CI: 0.9 - 1.2, p<0.001) and THC in USD (8217, 95% CI: 6940 - 9492, p<0.001). Independent predictors of readmission included female sex (Hazard Ratio [HR]: 1.14, 95% CI: 1.74 - 1.20, p<0.001), discharge against medical advice (HR: 1.54, 95% CI: 1.43 - 1.66, p<0.001), hypertension (HR: 1.28, 95% CI: 1.20 - 1.35, p<0.001), chronic kidney disease (HR: 1.13, 95% CI: 1.04 - 1.22, p<0.001) and anemia (HR: 1.42, 95% CI: 1.34 - 1.51, p<0.001). However, obesity (HR: 0.70, 95% CI: 0.62 - 0.79, p<0.001) and hyperlipidemia (HR: 0.92, 95% CI: 0.87 - 0.98, p=0.007) were associated with lower rate of readmission for DKA.Conclusion: About a fifth of T1DM patients hospitalized for DKA are readmitted within 30 days of discharge. A 30-day readmission following hospitalization for DKA is associated with higher inpatient mortality in this group. Hypertension, chronic kidney disease, and anemia are amongst the predictors of readmission in patients with DKA. However, comorbid obesity and hyperlipidemia were associated with lower rates of readmissions. Healthcare utilization costs are significantly higher in readmitted patients compared to index admissions for DKA. Efforts should be channeled towards identifying these predictors in hospitalized patients as well as proper discharge planning to decrease the burden of readmissions.