Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010–2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985–0.999), have Medicare insurance (1.013, 1.005–1.020), and have hypertension (1.014, 1.005–1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

Ampicillin Plus Ceftriaxone Combined Therapy for Enterococcus faecalis Infective Endocarditis in OPAT

Ampicillin plus ceftriaxone (AC) is a well-recognized inpatient regimen for Enterococcus faecalis infective endocarditis (IE). In this regimen, ceftriaxone is usually administered 2 g every 2 h (AC12). The administration of AC in outpatient parenteral antibiotic treatment (OPAT) programs is challenging because multiple daily doses are required. AC regimens useful for OPAT programs include once-daily high-dose administration of ceftriaxone (AC24) or AC co-diluted and jointly administered in bolus every 4 h (ACjoined). In this retrospective analysis of prospectively collected cases, we aimed to assess the clinical effectivity and safety of three AC regimens for the treatment of E. faecalis IE. Fifty-nine patients were treated with AC combinations (AC12 n = 32, AC24 n = 17, and ACjoined n = 10). Six relapses occurred in the whole cohort: five (29.4%) treated with AC24 regimen and one (10.0%) with ACjoined. Patients were cured in 30 (93.3%), 16 (94.1%), and eight (80.0%) cases in the AC12, AC24 and ACjoined groups, respectively. Unplanned readmission occurred in eight (25.0%), six (35.3%), and two (20.0%) patients in the AC12, AC24 and ACjoined groups, respectively. The outcome of patients with E. faecalis IE treated with AC in OPAT programs relies on an optimization of the delivery of the combination. AC24 exhibit an unexpected rate of failures, however, ACjoined might be an effective alternative which clinical results should corroborate in further studies.

Age Based Trends for Unplanned Pediatric Rehospitalizations in the United States

BACKGROUND AND OBJECTIVES: Hospital readmission rate helps to highlight the effectiveness of post- discharge care. There remains a paucity of plausible age based categorization especially for ages below one year for hospital readmission rates. METHODS: Data from 2017 Healthcare cost and utilization project National readmissions database was analyzed for ages 0-18 years. Logistic regression analysis was performed to identify predictors for unplanned early readmissions. RESULTS: We identified 5,529,389 inpatient pediatric encounters which were further divided into age group cohorts. The overall rate of readmissions was identified at 3.2%. Beyond infancy, the readmission rate was found to be 6.7%. Across all age groups, the major predictors of unplanned readmission were cancers, diseases affecting transplant recipients and sickle cell patients. It was determined that reflux, milk protein allergy, hepatitis and inflammatory bowel diseases were significant comorbidities leading to readmission. Anxiety, depression and suicidal ideation depicted higher readmission rates in those older than 13 years. Across ages 1-4 yrs, dehydration, asthma and bronchitis were negative predictors of unplanned readmission. CONCLUSIONS: Thirty-day unplanned readmissions remain a problem leading to billions of tax-payer-dollars lost per annum. Effective strategies for mandatory outpatient follow-up may help the financial aspect of care while also enhancing the quality of care.

External validation of EPIC’s Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

Introduction Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC’s Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC’s Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator. Methods A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1st of January 2018 and the 31st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X2 goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models. Results At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676–0.708), 0.703 (95% CI 0.687–0.719) and 0.705 (95% CI 0.690–0.720). The Hosmer-Lemeshow X2 tests had values of p<0.001. Conclusion In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability—model updating is warranted.

Factors associated with unplanned readmissions and costs following resection of brain metastases in the United States

The purpose of this study was to critically analyze the risk of unplanned readmission following resection of brain metastasis and to identify key risk factors to allow for early intervention strategies in high-risk patients. We analyzed data from the Nationwide Readmissions Database (NRD) from 2010–2014, and included patients who underwent craniotomy for brain metastasis, identified using ICD-9-CM diagnosis (198.3) and procedure (01.59) codes. The primary outcome of the study was unplanned 30-day all-cause readmission rate. Secondary outcomes included reasons and costs of readmissions. Hierarchical logistic regression model was used to identify the factors associated with 30-day readmission following craniotomy for brain metastasis. During the study period, 44,846 index hospitalizations occurred for patients who underwent resection of brain metastasis. In this cohort, 17.8% (n = 7,965) had unplanned readmissions within the first 30 days after discharge from the index hospitalization. The readmission rate did not change significantly during the five-year study period (p-trend = 0.286). The median per-patient cost for 30-day unplanned readmission was $11,109 and this amounted to a total of $26.4 million per year, which extrapolates to a national expenditure of $269.6 million. Increasing age, male sex, insurance status, Elixhauser comorbidity index, length of stay, teaching status of the hospital, neurological complications and infectious complications were associated with 30-day readmission following discharge after an index admission for craniotomy for brain metastasis. Unplanned readmission rates after resection of brain metastasis remain high and involve substantial healthcare expenditures. Developing tools and interventions to prevent avoidable readmissions could focus on the high-risk patients as a future strategy to decrease substantial healthcare expense.

232. Safety and Effectiveness of Intravenous to Oral De-escalation Compared to Continued Vancomycin Therapy in Orthopedic Infections

Background The Oral versus Intravenous Antibiotics for Bone and Joint Infection (OVIVA) trial determined oral antibiotics administered during the first six weeks of therapy were non-inferior to parenteral antibiotics. There was no difference in the incidence of serious adverse effects. The objective of this study was to evaluate the safety and effectiveness of de-escalating to oral therapy compared to continuing parenteral vancomycin therapy in patients with orthopedic infections in a real-world setting. Methods We conducted a single-center, retrospective cohort study of patients discharged between April 1, 2018 and April 1, 2020 with an orthopedic infection, a prescription for at least four weeks of parenteral vancomycin, and documented follow-up. The primary outcome was incidence of adverse events defined as provider documentation of the event and changes to therapy. The secondary outcome was incidence of 6-month treatment failure defined as repeat surgical intervention or therapy escalation. Results One hundred fifty-seven patients were included. Twenty-nine (18.5%) patients were de-escalated to oral therapy. Three (10%) patients in the oral therapy group had an adverse event compared to 35 (27%) in the vancomycin group (p=0.058). Of the 35 patients with an adverse event in the vancomycin group, eight were due to parenteral access-related complications. Treatment failure occurred in three (10%) patients in the oral therapy group compared to 27 (21%) patients in the vancomycin group (p=0.29). Three (10%) patients in the oral therapy group had an unplanned readmission compared to 25 (20%) patients in the vancomycin group (p=0.24). Baseline Characteristics, Unplanned Readmission Rates, and Incidence of Adverse Events and 6-Month Treatment Failure Conclusion Patients de-escalated to oral therapy had fewer adverse events and similar incidences of treatment failure compared to patients maintained on parenteral vancomycin. Switching to oral therapy avoids some adverse events related to parenteral access. Our results in an uncontrolled, real-world setting are consistent with the OVIVA trial. Though limited by sample size, our data indicate switching to oral therapy in patients with an orthopedic infection improves safety outcomes without compromising effectiveness compared to continued parenteral vancomycin therapy. Disclosures Russell J. Benefield, PharmD, Paratek Pharmaceuticals (Grant/Research Support)

P.221 Frailty is an Important Predictor of 30-day Morbidity in Patients Treated for Lumbar Spondylolisthesis Using a Posterior Surgical Approach

Background: A non-operative approach has been favoured for elderly patients with lumbar spondylolisthesis due to a perceived higher risk with surgery. However, most studies have used an arbitrary age cut-off to define “elderly.” We hypothesized that frailty is an independent predictor of morbidity after surgery for lumbar spondylolisthesis. Methods: The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database for years 2010 to 2018 was used. Patients who received posterior lumbar spine decompression with or without posterior fusion instrumented fusion for degenerative lumbar spondylolisthesis were included. The primary outcome was major complication. Secondary outcomes were readmission, reoperation, and discharge to location other than home. Logistic regression analysis was done to investigate the association between outcomes and frailty. Results: There were 15 658 patients in this study. The mean age was 62.5 years (SD 12.2). Frailty, as measured by the Modified Frailty Index-5 was significantly associated with increased risk of major complication, unplanned readmission, reoperation, and non-home discharge. Increasing frailty was associated with increasing risk of morbidity. Conclusions: Frailty is independently associated with higher risk of morbidity after posterior surgery in patients with lumbar spondylolisthesis. These data are of significance to clinicians in planning treatment for these patients.

Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms

Background Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions. Methods We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital’s medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision–recall curve (AUPRC). Results In total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711). Conclusions Our models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.