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Objectives: Returns to the Emergency Department (ED) and unplanned readmissions within 90 days of shoulder arthroplasty represent a significant financial burden to healthcare systems. Identifying the reasons and risk factors could potentially reduce their prevalence. Methods: A retrospective review of primary anatomic (aTSA) and reverse shoulder arthroplasty (rTSA) cases from January 2016 through August 2023 was performed. Demographic patient and surgical data, including age, diagnosis of anxiety or depression, body mass index (BMI), smoking status, age-adjusted Charlson Comorbidity Index (ACCI), modified 5-item fragility index (mFI-5), and hospital length of stay (LOS) was collected. Patient visits to the ED within 12 months prior to surgery were recorded. Predictors for return to the ED within 90 days postoperatively and any readmissions were determined. Results: There were 338 cases (167 aTSA and 171 rTSA), of which 225 (67%) were women. Patients with anxiety (OR=2.44, 95% CI 1.11-5.33; P=0.026), surgical postoperative complications (OR=3.22, 95% CI 1.36-7.58; P=0.008), ED visit within 3 months prior to surgery (OR=3.80, 95% CI 1.71-8.45; P=0.001), ED visit 3 to 6 months prior to surgery (OR=2.60, 95% CI 1.12-6.05; P=0.027), and ED visit 6 to 12 months prior to surgery (OR=2.12, 95% CI 1.02-4.41; P=0.045) were more likely to have ED visit within 90 days postoperatively. Patients with prior ipsilateral shoulder surgery (OR=3.32, 95% CI 1.21-9.09; P=0.02), surgical postoperative complications (OR=13.92, 95% CI 5.04-38.42; P<0.001), an ED visit within 3 to 6 months preoperatively (OR=8.47, 95% CI 2.84-25.27; P<0.001), and an mFI-5 ≥2 (OR=3.66, 95% CI 1.35-9.91; P=0.011) were more likely to be readmitted within 90 days. Conclusion: Patients who present to the ED within 12 months prior to shoulder arthroplasty, those with anxiety, those with surgical complications and those with higher fragility should be monitored closely during the early postoperative period to minimize returns to the ED and/or unplanned readmissions.

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PURPOSE: Acetabular fractures are highly complex injuries often resulting from high-energy trauma. The gold standard treatment for these injuries has become open reduction internal fixation (ORIF). The purpose of this study is to further this understanding and investigate how (1) patient demographics and (2) patient-specific risk factors affect 90-day readmission rates. METHODS: A retrospective, nationwide query of private insurance database from January 1st, 2010 to October 31st, 2020 was performed using ICD-9, ICD-10, and CPT codes. Patients who underwent acetabular ORIF and were readmitted within 90 days following index procedure were included, patients who were not readmitted served as controls. Patients were divided by demographics and specific risk factors associated with readmission. RESULTS: The query yielded a total of 3942 patients. Age and sex were found to be non-significant contributing risk factors to 90-day readmissions. Data also showed that statistically significant comorbidities included arrhythmia, cerebrovascular disease, coagulopathy, fluid and electrolyte abnormalities, and pathologic weight loss. CONCLUSION: This study illustrated how several patient-specific risk factors may contribute to increased 90-day readmission risk following acetabular ORIF. A heightened awareness of these comorbidities in patients requiring acetabular ORIF is required to improve patient outcomes and minimize rates of readmission. Further investigation is needed to improve patient outcomes, and increase awareness of potential post-operative complications in these higher-risk patient populations.

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Background: Tobacco carcinogens have adverse effects on bone health and are associated with inferior outcomes following orthopedic procedures. The purpose of this study was to assess the impact tobacco use has on readmission and complication rates following shoulder arthroplasty. Methods: The 2016-2018 National Readmissions Database was queried to identify patients who underwent anatomical, reverse, and hemi-shoulder arthroplasty. ICD-10 codes Z72.0 × (tobacco use disorder) and F17.2 × (nicotine dependence) were used to define "tobacco-users." Demographic, 30-/90-day readmission, surgical complication, and medical complication data were collected. Inferential statistics were used to analyze complications for both the cohort as a whole and for each procedure separately (i.e. anatomical, reverse, and hemiarthroplasty). Results: 164,527 patients were identified (92% nontobacco users). Tobacco users necessitated replacement seven years sooner than nonusers (p < 0.01) and were more likely to be male (52% vs. 43%; p < 0.01). Univariate analysis showed that tobacco users had higher rates of readmission, revisions, shoulder complications, and medical complications overall. In the multivariate analysis for the entire cohort, readmission, revision, and complication rates did not differ based on tobacco usage; however, smokers who underwent reverse shoulder arthroplasty in particular were found to have higher 90-day readmission, dislocation, and prosthetic complication rates compared to nonsmokers. Conclusion: Comparatively, tobacco users required surgical correction earlier in life and had higher rates of readmission, revision, and complications in the short term following their shoulder replacement. However, when controlling for tobacco usage as an independent predictor of adverse outcomes, these aforementioned findings were lost for the cohort as a whole. Overall, these findings indicate that shoulder replacement in general is a viable treatment option regardless of patient tobacco usage at short-term follow-up, but this conclusion may vary depending on the replacement type used.

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BACKGROUND: The present definition of obesity based on body mass index (BMI) is not accurate and effective enough to identify hospitalized patients with a heavier burden, especially for postmenopausal hospitalized patients concomitant with osteoporosis. The link between common concomitant disorders of major chronic diseases such as osteoporosis, obesity, and metabolic syndrome (MS) remains unclear. Here, we aim to evaluate the impact of different metabolic obesity phenotypes on the burden of postmenopausal hospitalized patients concomitant with osteoporosis in view of unplanned readmissions. METHODS: Data was acquired from the National Readmission Database 2018. The study population was classified into metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) patients. We estimated the associations between metabolic obesity phenotypes and 30- and 90-day unplanned readmissions. A multivariate Cox Proportional Hazards (PH) model was used to assess the effect of factors on endpoints, with results expressed as HR and 95% CI. RESULTS: The 30-day and 90-day readmission rates for the MUNO and MUO phenotypes were higher than that of the MHNO group (all p < 0.05), whereas no significant difference was found between the MHNO and MHO groups. For 30-day readmissions, MUNO raised the risk mildly (hazard ratio [HR] = 1.110, p < 0.001), MHO had a higher risk (HR = 1.145, p = 0.002), and MUO further elevated this risk (HR = 1.238, p < 0.001). As for 90-day readmissions, both MUNO and MHO raised the risk slightly (HR = 1.134, p < 0.001; HR = 1.093, p = 0.014, respectively), and MUO had the highest risk (HR = 1.263, p < 0.001). CONCLUSIONS: Metabolic abnormalities were associated with elevated rates and risks of 30- or 90-day readmission among postmenopausal hospitalized women complicated with osteoporosis, whereas obesity did not seem to be innocent, and the combination of these factors led to an additional burden on healthcare systems and individuals. These findings indicate that clinicians and researchers should focus not only on weight management but also metabolism intervention among patients with postmenopausal osteoporosis.

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AIMS: In the CHAMPION (CardioMEMS Heart Sensor Allows Monitoring of Pressure to Improve Outcomes in New York Heart Association Functional Class III Heart Failure Patients) trial, heart failure hospitalization (HFH) rates were lower in patients with ambulatory pulmonary artery pressure (PAP) monitoring guidance. We investigated the effect of ambulatory haemodynamic monitoring on 90 day readmission rates after HFH. METHODS AND RESULTS: We retrospectively analysed patients across the Advocate Aurora Health hospital network who had undergone PAP sensor implantation between 1 October 2015 and 31 October 2019. Patients with a ventricular assist device (VAD) or transplant prior to implantation were excluded. Rates of total HFH and 30 and 90 day all-cause readmission up to 12 months after implantation were collected, while censoring for an endpoint of heart transplantation, VAD, or death. Event rates were compared using Poisson regression. Of 459 patients included, there were 404 HFHs before and 179 after implantation. Compared with pre-implantation, 30 day all-cause readmission [incidence rate ratio (IRR): 0.55 (0.39-0.77), P = 0.0006] and 90 day all cause readmission rates were lower post-implantation [IRR: 0.45 (0.35-0.58), P < 0.0001]. The effect of PAP sensor implantation on 90 day all-cause readmission incidence rates was consistent across multiple subgroups. CONCLUSIONS: Across a large hospital network, ambulatory haemodynamic monitoring was associated with lower HFH rates, as well as 30 and 90 day all-cause readmission rates. This supports the utility of ambulatory PAP monitoring to improve HF management in the era of value-based medicine.

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BACKGROUND: A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty. METHODS: A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis. RESULTS: Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis. CONCLUSION: This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

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OBJECTIVE: Although readmission after surgical procedures has been recognized as a new problem, its association with cholecystectomy has not been solved. We aimed to investigate the rate of unplanned readmission after cholecystectomy and to evaluate the reasons and outcomes in these patients. METHODS: All consecutive patients who underwent open and laparoscopic cholecystectomy were retrospectively evaluated. Hospital readmission within the post-operative first 90 days after the procedure was searched. The rate and reasons for hospital readmission were the primary outcomes. RESULTS: There were 601 patients with a mean age of 53.2 ± 12.4 years. The rate of readmission was 6.16%. Obesity (p = 0.001), number of coexisting disease (p = 0.039), conversion to open surgery (p = 0.002), development of intraoperative complications (p < 0.001), use of drain (p = 0.001), and length of hospital stay > 1 day (p = 0.024) were significantly associated with higher readmission rates. Biliary surgical causes were detected in five patients (12.8%). Non-biliary surgical causes were seen in 34 patients (87.2%). Among these, post-operative pain, nausea, and vomiting were the most common diagnoses in 25 (67.6%) and 5 patients (12.8%). CONCLUSION: The readmission rate after cholecystectomy is low. Significant predictive factors may help physicians to be alerted during the discharge of the patients. Post-operative pain, nausea, and vomiting were the most common diagnoses.

OBJETIVO: Aunque el reingreso hospitalario posterior a la cirugía se reconoció como un problema nuevo, su asociación con la colecistectomía no ha sido resuelta. Nuestro objetivo fue investigar la tasa de reingreso al hospital no planificado después de la colecistectomía y evaluar las razones y los resultados en estos pacientes. MÉTODOS: Todos los pacientes consecutivos que se sometieron a colecistectomía abierta y laparoscópica fueron evaluados retrospectivamente. Se investigó el reingreso al hospital dentro de los primeros 90 días postoperatorios. La tasa y las razones de la readmisión hospitalaria fueron los resultados primarios. RESULTADOS: Se examinaron 601 pacientes con una edad media de 53.2 ± 12.4 años. La tasa de reingreso fue del 6.16%. Obesidad (p = 0.001), número de enfermedades coexistentes (p = 0.039), conversión a cirugía abierta (p = 0.002), desarrollo de complicaciones intraoperatorias (p < 0.001), uso de drenaje (p = 0.001) y longitud de estancia hospitalaria > 1 día (p = 0.024) se asociaron significativamente con tasas más altas de reingreso. Se detectaron causas quirúrgicas biliares en cinco pacientes (12.8%). Se observaron causas quirúrgicas no biliares en 34 pacientes (87.2%). Entre estos, el dolor postoperatorio, las náuseas y los vómitos fueron los diagnósticos más comunes en 25 (67.6%) y 5 pacientes (12.8%). CONCLUSIÓN: La tasa de reingreso después de la colecistectomía es baja. Factores predictivos significativos pueden ayudar a los médicos a estar alertas durante el alta de los pacientes. El dolor postoperatorio, las náuseas y los vómitos fueron los diagnósticos más frecuentes.

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OBJECTIVE: We sought to investigate the effect of preoperative polypharmacy (PP) on the 90-day all-cause readmission rate in older adults undergoing corrective surgery for adult spinal deformity. METHODS: Older adults with a diagnosis of adult spinal deformity undergoing spinal surgery at a quaternary medical center from January 2016 to March 2019 were enrolled in this study. Patients were dichotomized into 2 groups stratified by the number of preoperative prescription medications, with PP defined as 5 or more prescription medications. The primary outcome measure was 90-day all-cause readmission rate. Secondary outcomes included postoperative changes in health-related quality of life measures. RESULTS: Among 161 patients (mean [standard deviation], 69.59 [8.79] years), 97 patients were included in the PP cohort and 64 in the nonpolypharmacy (non-PP) cohort. Both groups were balanced at baseline. Duration of hospital stay (5.82 [1.93] vs. 6.50 [4.00] days), mean number of fusion levels, and duration of surgery were statistically similar between both groups (P > 0.05). There was no difference in the proportion of patients discharged directly home (31.25% vs. 40.42%, P = 0.36). The 90-day all-cause readmission rate was 3-fold higher in the PP cohort compared with the non-PP cohort. After adjusting for preoperative patient optimization, American Society of Anesthesiologists grade, surgical invasiveness, smoking, depression, and baseline functional disability, older adults with PP had a 9.79 increased odds of 90-day all-cause hospital readmission (P = 0.04). Changes in health-related quality of life measures were similar between both groups. CONCLUSION: This study's findings indicate that despite preoperative optimization, older adults exposed to polypharmacy are at a significantly increased risk of hospital readmission within 90 days of surgery.

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BACKGROUND: The incidence of shoulder arthroplasty in the United States continues to increase, and while the risk of intraoperative complications such as fracture remains relatively low, there has been little investigation into whether certain patient-specific risk factors predispose to this complication. This study characterizes the incidence of intraoperative fracture during shoulder arthroplasty and additionally hypothesizes that certain risk factors may exist in addition to potentially leading to worsened near-term outcomes. METHODS: An institutional database of shoulder arthroplasties (N = 1773; 994 anatomic, 779 reverse) was retrospectively reviewed, and the operative reports for each case were examined for documentation of an intraoperative fracture, including during which surgical step the fracture took place. Various preoperative and intraoperative factors were tested for comparative significance (P < .05) using chi-square and Kruskal-Wallis tests as appropriate. Length of stay, 90-day readmission, and discharge to rehabilitation or skilled nursing facility (SNF) were further examined as secondary outcomes. RESULTS: Twenty-one (1.2%) intraoperative fractures were documented, a majority of which occurred in reverse shoulder arthroplasties compared to anatomic procedures (overall incidence: 2.5% vs. 0.2%, P < .001). These most commonly occurred during either stem broaching (33%) or seating (33%) and were most likely to involve the metaphysis (53%) or greater tuberosity (33%). Five fractures occurred during revision arthroplasty, while 16 fractures occurred during primary procedures (overall incidence: 3.0 vs. 1.0%, P = .03). Patient factors reaching statistical significance included female gender and liver disease, while age and smoking history were notably not associated with intraoperative fracture. The fracture cohort had a significantly longer mean length of stay (2.42 vs. 2.17 days, P < .001). While the rates of 90-day readmission and discharge to SNF/rehab were higher in the fracture cohort, these values did not reach statistical significance. CONCLUSION: Intraoperative fractures are a rare complication (1.2%) in shoulder arthroplasty, with reverse shoulder arthroplasty, revision cases, and female gender associated with an elevated overall risk. While these patients had a longer inpatient hospitalization, the substantially higher rates of 90-day readmission and discharge to SNF/rehab did not reach significance in our limited institutional cohort. The aforementioned incidence and risk factors serve as crucial evidence for use during the preoperative counseling process with patients as part of a shared decision-making model.

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OBJECTIVE: Extracranial-intracranial (EC-IC) bypass surgery remains an important treatment option for patients with moyamoya disease (MMD), intracranial arteriosclerotic disease (ICAD) with symptomatic stenosis despite the best medical management, and complex aneurysms. The therapeutic benefit of cerebral bypass surgery depends on optimal patient selection and the minimization of periprocedural complications. The nationwide burden of readmissions and associated complications following EC-IC bypass surgery has not been previously described. Therefore, the authors sought to analyze a nationwide database to describe the national rates, causes, risk factors, complications, and morbidity associated with readmission following EC-IC bypass surgery for MMD, ICAD, and aneurysms. METHODS: The Nationwide Readmissions Database (NRD) was queried for the years 2010-2014 to identify patients who had undergone EC-IC bypass for MMD, medically failed symptomatic ICAD, or unruptured aneurysms. Predictor variables included demographics, preexisting comorbidities, indication for surgery, and hospital bypass case volume. A high-volume center (HVC) was defined as one that performed 10 or more cases/year. Outcome variables included perioperative stroke, discharge disposition, length of stay, total hospital costs, and readmission (30 days, 90 days). Multivariable analysis was used to identify predictors of readmission and to study the effect of treatment at HVCs on quality outcomes. RESULTS: In total, 2500 patients with a mean age of 41 years were treated with EC-IC bypass surgery for MMD (63.1%), ICAD (24.5%), or unruptured aneurysms (12.4%). The 30- and 90-day readmission rates were 7.5% and 14.0%, respectively. Causes of readmission included new stroke (2.5%), wound complications (2.5%), graft failure (1.5%), and other infection (1.3%). In the multivariable analysis, risk factors for readmission included Medicaid/self-pay (OR 1.6, 95% CI 1.1-2.4, vs private insurance), comorbidity score (OR 1.2, 95% CI 1.1-1.4, per additional comorbidity), and treatment at a non-HVC (OR 1.9, 95% CI 1.1-3.0). Treatment at an HVC (17% of patients) was associated with significantly lower rates of nonroutine discharge dispositions (13.4% vs 26.7%, p = 0.004), ischemic stroke within 90 days (0.8% vs 2.9%, p = 0.03), 30-day readmission (3.9% vs 8.2%, p = 0.03), and 90-day readmission (8.6% vs 15.2%, p = 0.01). These findings were confirmed in a multivariable analysis. The authors estimate that centralization to HVCs may result in 333 fewer nonroutine discharges (50% reduction), 12,000 fewer hospital days (44% reduction), 165 fewer readmissions (43%), and a cost savings of $15.3 million (11% reduction). CONCLUSIONS: Readmission rates for patients after EC-IC bypass are comparable with those after other common cranial procedures and are primarily driven by preexisting comorbidities, socioeconomic status, and treatment at low-volume centers. Periprocedural complications, including stroke, graft failure, and wound complications, occurred at the expected rates, consistent with those in prior clinical series. The centralization of care may significantly reduce perioperative complications, readmissions, and hospital resource utilization.

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BACKGROUND: 90-day mortality and rehospitalizations are important hospital quality metrics. Biomarkers that predict these outcomes among malnourished hospitalized patients could identify those at risk and help direct care plans. OBJECTIVES: To identify biomarkers that predict 90-day (primary) and 30-day (secondary) mortality or nonelective rehospitalization. DESIGN AND PARTICIPANTS: An analysis of the ability of biomarkers to predict 90- and 30-day mortality and rehospitalization among malnourished hospitalized patients. SETTING: 52 blood biomarkers were measured in 193 participants in NOURISH, a randomized trial that determined the effects of a nutritional supplement on 90-day readmission and death in patients >65 years. Composite outcomes were defined as readmission or death over 90-days or 30-days. Univariate Cox Proportional Hazards models were used to select best predictors of outcomes. Markers with the strongest association were included in multivariate stepwise regression. Final model of hospital readmission or death was derived using stepwise selection. MEASUREMENTS: Nutritional, inflammatory, hormonal and muscle biomarkers. RESULTS: Mean age was 76 years, 51% were men. In univariate models, 10 biomarkers were significantly associated with 90-day outcomes and 4 biomarkers with 30-day outcomes. In multivariate stepwise selection, glutamate, hydroxyproline, tau-methylhistidine levels, and sex were associated with death and readmission within 90-days. In stepwise selection, age-adjusted model that included sex and these 3 amino-acids demonstrated moderate discriminating ability over 90-days (C-statistic 0.68 (95%CI 0.61, 0.75); age-adjusted model that included sex, hydroxyproline and Charlson Comorbidity Index was predictive of 30-day outcomes (C-statistic 0.76 (95%CI 0.68, 0.85). CONCLUSIONS: Baseline glutamate, hydroxyproline, and tau-methylhistidine levels, along with sex and age, predict risk of 90-day mortality and nonelective readmission in malnourished hospitalized older patients. This biomarker set should be further validated in prospective studies and could be useful in prognostication of malnourished hospitalized patients and guiding in-hospital care.

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STUDY DESIGN: Retrospective review. OBJECTIVES: (1) Identify the 90-day rate of readmission following revision lumbar fusion, (2) identify independent risk factors associated with increased rates of readmission within 90 days, (3) and identify the hospital costs associated with revision lumbar fusion and subsequent readmission within 90 days. METHODS: Utilizing 2014 data from the Nationwide Readmissions Database, patients undergoing elective revision lumbar fusion were identified. With this sample, multivariate logistic regression was utilized to identify independent predictors of readmission within 90 days. An analysis of total hospital costs was also conducted. RESULTS: In 2014, an estimated 14 378 patients underwent elective revision lumbar fusion. The readmission rate at 90 days was 3.1% (n = 446). Diabetes with chronic complications was the only comorbidity found to carry significantly increased odds of readmission. Surgical complications such as deep venous thrombosis, surgical wound disruption, hematoma, and pneumonia (experienced during the index admission) were also independent predictors of readmission. Anterior approaches were associated with increased odds of readmission. The most common related diagnoses on readmission were hardware issues, postoperative infection, and disc herniation. Readmissions were associated with an average of $96 152 in increased hospital costs per patient compared with those not readmitted. CONCLUSION: Relevant patient comorbidities and surgical complications were associated with increased readmission within 90 days. Readmission within 90 days was associated with significant increases in hospital costs.

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The objective of the study is to examine the 90-day readmission rate and identify the predictors for 90-day readmissions at a geriatric ward in a tertiary hospital in Singapore. A secondary analysis of case-control data was performed. Data of patients discharged from a geriatric ward between January 2015 and January 2016 were retrieved from an existing data set. Out of 564 index admissions involving older adults, the 90-day geriatric readmission rate was 10.1%. Activities of daily living dependency (odds ratio [OR]: 0.988, 95% confidence interval [CI]: [0.978, 0.999]) and living with the spouse (OR: 2.988, 95% CI: [1.388, 6.432]) were identified as significant predictors of 90-day geriatric readmissions. The study suggests that rehabilitation to restore the geriatric patient's ability to perform daily activities and adequate caregiver training for the spouse are essential in reducing geriatric readmissions. Also, postdischarge follow-up with both the patient and caregiver can greatly reduce the risk of readmission in geriatric patients.

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Previous studies have shown pharmacists positively impact 30-day readmission rates. However, there is limited data regarding the effect of clinical pharmacist (CP) follow-up on 90-day readmission or evaluation of disease-specific goals after hospitalization. Investigators analyzed the impact of postdischarge extended CP follow-up within a family medicine service (FMS). The primary end point was all-cause 90-day readmission rates. Secondary end points included all-cause 30- and 60-day readmission rates and the achievement of disease-specific goals postdischarge. Retrospective chart review was performed for patients admitted from August 2016 to November 2017 who were seen by a physician within the FMS 14 days postdischarge. Fourteen percent of patients within the CP intervention group were readmitted within 90 days in comparison to 22% in the standard of care group (P = .244). Readmission rates at 30 and 60 days were as follows: intervention group 2%, 10%, and standard of care group 16%, 22% (P = .015, P = .089, respectively). In addition, multiple patients with uncontrolled diabetes who completed CP visits upon hospital discharge met glycemic goals at the end of the study time period. Despite inclusion of the CP in postdischarge care, 90-day readmission rate remained unchanged.

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BACKGROUND: Several heart failure (HF) risk models exist, however, most of them perform poorly when applied to real-world situations. This study aimed to develop a convenient and efficient risk model to identify patients with high readmission risk within 90 days of HF. METHODS: A multivariate logistic regression model was used to predict the risk of 90-day readmission. Data were extracted from electronic medical records from January 1, 2017 to December 31, 2017 and follow-up records of patients with HF within 3 months after discharge. Model performance was evaluated using a receiver operating characteristic curve. All statistical analysis was done using R version 3.5.0. RESULTS: A total of 350 patients met the inclusion criterion of being readmitted within in 90 days. All data sets were randomly divided into derivation and validation cohorts at a 7/3 ratio. The baseline data were fairly consistent among the derivation and validation cohorts. The variables most clearly related to readmission were logarithm of serum N-terminal pro b-type natriuretic peptide (NT-proBNP) level, red cell volume distribution width (RDW-CV), and Charlson comorbidity index (CCI). The model had good discriminatory ability (C-statistic = 0.73). CONCLUSIONS: We developed and validated a multivariate logistic regression model to predict the 90-day readmission risk for Chinese patients with HF. The predictors included in the model are derived from electronic medical record (EMR) admission data, making it easier for physicians and pharmacists to identify high-risk patients and tailor more intensive precautionary strategies.

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OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to identify the ischemic stroke readmission risk factors and establish a 90-day readmission prediction model for first-time ischemic stroke patients. METHODS: The readmission prediction model was developed using the extreme gradient boosting (XGboost) model, which can generate an ensemble of classification trees and assign a predictive risk score to each feature. The patient data were split into a training set (5159) and a validation set (911). The prediction results were evaluated with the receiver operating characteristic (ROC) curve and time-dependent ROC curve, which were compared with the outputs from the logistic regression (LR) model. RESULTS: A total of 6070 adult patients (39.6% female, median age 67 years) without any ischemic attack (IS) history were included, and 520 (8.6%) were readmitted within 90 days. The XGboost-based prediction model achieved a standard area under the curve (AUC) value of .782 (.729-.834), and the best time-dependent AUC value was .808 in 54 days for the validation set. In contrast, the LR model yielded a standard AUC value of .771 (.714-.828) and best time-dependent AUC value of .797. CONCLUSIONS: The XGboost model obtained a better risk prediction for 90-day readmission for first-time ischemic stroke patients than the LR model. This model can also reveal the high risk factors for stroke readmission in first-time ischemic stroke patients.

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BACKGROUND: Over the next 10-15 years, there is expected to be an exponential increase in the number of total joint arthroplasties in the American population. This, combined with rising costs of total joint arthroplasty and more recent changes to the reimbursement payment models, increases the demand to perform quality, cost-effective total joint arthroplasties. The purpose of this study is to build models that could be used to estimate the 30-day and 90-day readmission rates for patients undergoing total joint arthroplasty. METHODS: A retrospective review of patients admitted to a single hospital, over the course of 56 months, for total joint arthroplasty was performed. The goal is to identify patients with readmission in a 30-day or 90-day period following discharge from the hospital. Binary logistic regression was used to build predictive models that estimate the likelihood of readmission based on a patient's risk factors. RESULTS: Of 5732 patients identified for this study, 237 were readmitted within 30 days, while 547 were readmitted within 90 days. Age, body mass index, gender, discharge disposition, occurrence of cardiac dysrhythmias and heart failure, emergency department visits, psychiatric diagnoses, and medication counts were all found to be associated with 30-day admission rates. Similar associations were found at 90 days, with the exclusion of age and psychiatric drug use, and the inclusion of intravenous drug abuse, narcotic medications, and total joint arthroplasty within 12 months. CONCLUSION: There are patient variables, or risk factors, that serve to predict the likelihood of readmission following total joint arthroplasty.

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BACKGROUND: Thirty-day readmissions (30dRAs) and 90-day readmissions (90dRAs) are being increasingly scrutinized as quality metrics for hospital and provider performances. Little information regarding risk factors for 30dRA and 90dRA after elective cerebral aneurysm clipping (CAC) of unruptured cerebral aneurysms is available. We sought to characterize risk factors with a nationally representative administrative database. METHODS: The Nationwide Readmissions Database was used to identify patients who underwent elective CAC between 2010 and 2014. The outcomes of interest were unplanned readmissions occurring within 30 or 90 days of discharge. Binary logistic regression was used to identify variables related to patients' demographics, comorbidities, and index hospital admission that were associated with readmission. A Cochran-Mantel-Haenszel test was used to evaluate for changes in annual readmission rates. RESULTS: A total of 1123 patients met the inclusion criteria for 30dRA analysis and 946 patients were eligible for 90dRA analysis. The 5-year 30dRA and 90dRA readmission rates were 9.1% and 14.9%, respectively. The annual rate of readmission between 2010 and 2014 did not change. Greater Charlson Comorbidity Index (odds ratio [OR], 2.68; 95% confidence interval [CI], 1.14-6.28) and nonroutine discharge after the index admission (OR, 1.81; 95% CI, 1.04-3.14) were associated with greater odds of 30dRA. Charlson Comorbidity Index (OR, 3.45; 95% CI, 1.57-7.56) and treatment at a metropolitan teaching hospital (OR, 2.21; 95% CI, 1.06-4.60) were associated with increased odds of 90dRA. Wound infection was the most common reason for readmission. CONCLUSIONS: Readmission rates after elective CAC remained unchanged between 2010 and 2014, suggesting that improved methods for reducing unplanned readmissions after CAC are needed.

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OBJECTIVE: A fair number of hospital admissions occur after 30 days; thus, the true readmission rate could have been underestimated. Therefore, we hypothesized that the 90-day readmission rate might better characterize the factors contributing to readmission for pediatric patients undergoing spinal tumor resection. METHODS: The Nationwide Readmissions Database was used to study the patient demographic data, comorbidities, admissions, hospital course, spinal tumor behavior (malignant vs. benign), complications, revisions, and 30- and 90-day readmissions. RESULTS: Of the 397 patients included in the 30-day cohort, 43 (10.8%) had been readmitted. In comparison, the 90-day readmission rate was significantly greater; 52 of 325 patients were readmitted (16.0%; P < 0.04). Patients aged 16-20 constituted the largest subgroup. However, the highest readmission rate was observed for patients aged <5 years (30-day, 21.7%; 90-day, 26.4%). Medicaid patients were more likely to be readmitted than were private insurance patients (30-day odds ratio [OR], 3.3 [P < 0.001]; 90-day OR, 2.29 [P < 0.02]). In both cohorts, patients with malignant tumors required readmission more often than did those with benign tumors (30-day OR, 2.78 [P < 0.02]; 90-day OR, 1.92 [P = 0.08]). In the 90-day cohort, the patients had been readmitted 26.4 days after discharge versus 10.6 days in the 30-day cohort. Within the 90-day cohort, 18.6% of the readmissions were for spinal reoperation, 28.3% for chemotherapy or hematologic complications, and 25.6% for other central nervous system disorders. The median charges for each readmission were ∼$50,000 and ∼$40,000 for the 30- and 90-day cohorts, respectively. Medicaid insurance, malignant tumors, and younger age were significant predictors of readmission in the 90-day cohort. CONCLUSIONS: The prevalence and charges associated with unplanned hospital readmissions after spinal tumor resection were remarkably high. Younger age, Medicaid insurance, malignant tumors, and complications during the initial admission were significant predictors of 90-day readmission.

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BACKGROUND: Evolving reimbursement models increasingly compel hospitals to assume costs for 90-day readmission after total joint arthroplasty. Although risk assessment tools exist, none currently reach the predictive performance required to accurately identify high-risk patients and modulate perioperative care accordingly. Although unlikely to perform adequately alone, the Elixhauser index is a set of 31 variables that may lend value in a broader model predicting 90-day readmission. METHODS: Elixhauser comorbidities were examined in 10,022 primary unilateral total joint replacements, of which 4535 were hip replacements and 5487 were knee replacements, all performed between June 2013 and January 2018 at a single tertiary referral center. Data were extracted from electronic medical records using structured query language. After randomizing to derivation (80%) and validation (20%) subgroups, predictive models for 90-day readmission were generated and transformed into a system of weights based on each parameter's relative performance. RESULTS: We observed 497 90-day readmissions (5.0%) during the study period, which demonstrated independent associations with 14 of the 31 Elixhauser comorbidity groups. A score created from the sum of each patient's weighted comorbidities did not lose substantial predictive discrimination (area under the curve: 0.653) compared to a comprehensive multivariable model containing all 31 unweighted Elixhauser parameters (area under the curve: 0.665). Readmission risk ranged from 3% for patients with a score of 0 to 27% for those with a score of 8 or higher. CONCLUSIONS: The Elixhauser comorbidity score already meets or exceeds the predictive discrimination of available risk calculators. Although insufficient by itself, this score represents a valuable summary of patient comorbidities and merits inclusion in any broader model predicting 90-day readmission risk after total joint arthroplasty. LEVEL OF EVIDENCE: III.

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