BACKGROUND: Inappropriate prescribing (IP) is common in hospitalised older adults with frailty. However, it is not known whether the presence of frailty confers an increased risk of mortality and readmissions from IP nor whether rectifying IP reduces this risk. This review was conducted to determine whether IP increases the risk of adverse outcomes in hospitalised middle-aged and older adults with frailty.
METHODS: A systematic review was conducted on IP in hospitalised middle-aged (45-64 years) and older adults (≥ 65 years) with frailty. This review considered multiple types of IP including potentially inappropriate medicines, prescribing omissions and drug interactions. Both observational and interventional studies were included. The outcomes were mortality and hospital readmissions. The databases searched included MEDLINE, CINAHL, EMBASE, World of Science, SCOPUS and the Cochrane Library. The search was updated to 12 July 2024. Meta-analysis was performed to pool risk estimates using the random effects model.
RESULTS: A total of 569 studies were identified and seven met the inclusion criteria, all focused on the older population. One of the five observational studies found an association between IP and emergency department visits and readmissions at specific time points. Three of the observational studies were amenable to meta-analysis which showed no significant association between IP and hospital readmissions (OR 1.08, 95% CI 0.90-1.31). Meta-analysis of the subgroup assessing Beers criteria medicines demonstrated that there was a 27% increase in the risk of hospital readmissions (OR 1.27, 95% CI 1.03-1.57) with this type of IP. In meta-analysis of the two interventional studies, there was a 37% reduced risk of mortality (OR 0.63, 95% CI 0.40-1.00) with interventions that reduced IP compared to usual care but no difference in hospital readmissions (OR 0.83, 95% CI 0.19-3.67).
CONCLUSIONS: Interventions to reduce IP were associated with reduced risk of mortality, but not readmissions, compared to usual care in older adults with frailty. The use of Beers criteria medicines was associated with hospital readmissions in this group. However, there was limited evidence of an association between IP more broadly and mortality or hospital readmissions. Further high-quality studies are needed to confirm these findings.

UNASSIGNED: Spinal Infection (SI) is associated with various comorbidities. The interaction of these comorbidities and their impact on costs and complexity of care has not been fully assessed.
UNASSIGNED: This is a retrospective cohort study of SI patients in an urban hospital system to characterize comorbidities and outcomes in adult patients with SI. Adult patients in our hospital system who were hospitalized with an initial diagnosis of SI between July 1, 2017 and June 30, 2019 were included. Outcomes measures included length of stay (LOS) of the index hospitalization for SI, charges and payments for the index hospitalization, and hospital readmissions within one year after discharge from the index hospitalization. Data was obtained by querying our Electronic Data Warehouse (EDW) using ICD-10-CM and CPT procedure codes. Spearman\'s correlation was used to summarize the relationships between LOS, charges, and payments. Multivariable linear regression was used to evaluate associations of demographics, comorbidities, and other factors with LOS. Multivariable Cox regression was used to evaluate associations of demographics, comorbidities, and other factors with hospital readmissions.
UNASSIGNED: 403 patients with a first diagnosis of SI were identified. The average number of comorbidities per patient was 1.3. 294 (73%) had at least 1 medical comorbidity, and 54 (13%) had 3 or more comorbidities. The most common medical comorbidities were diabetes mellitus (26%), intravenous drug use (IVDU, 26%), and malnutrition (20%). 112 patients (28%) had a surgical site infection (SSI). DM (p<.001) and SSI (p=.016) were more common among older patients while IVDU was more common among younger patients (p<.001). Median LOS was 12 days. A larger number of medical comorbidities was associated with a longer LOS (p<.001) while the presence of a SSI was associated with a shorter LOS (p=.007) after multivariable adjustment. LOS was positively correlated with both charges (r=0.83) and payments (r=0.61). Among 389 patients discharged after the index hospitalization, 36% had a readmission within 1 year. The rate of readmission was twice as high for patients with three or more comorbidities than patients with zero comorbidities (hazard ratio: 1.95, p=.017).
UNASSIGNED: Patients with SI often have multiple comorbidities, and the specific type of comorbidity is associated with the patient\'s age. The presence of multiple comorbidities correlates with initial LOS, cost of care, and readmission rate. Readmission in the first year post-discharge is high.

UNASSIGNED: Patients on dialysis are twice as likely to have early readmissions. This study aimed to identify risk factors for 30-day unplanned readmission among patients on maintenance dialysis in a tertiary hospital.
UNASSIGNED: We conducted a retrospective, unmatched, case-control study. Data were taken from patients on maintenance hemodialysis admitted in the University of the Philippines-Philippine General Hospital (UP-PGH) between January 2018 and December 2020. Patients with 30-day readmission were included as cases and patients with >30-day readmissions were taken as controls. Multivariable regression with 30-day readmission as the outcome was used to identify significant predictors of early readmission.
UNASSIGNED: The prevalence of 30-day unplanned readmission among patients on dialysis is 36.96%, 95%CI [31.67, 42.48]. In total, 119 cases and 203 controls were analyzed. Two factors were significantly associated with early readmission: the presence of chronic glomerulonephritis [OR 2.35, 95% CI 1.36 to 4.07, p-value=0.002] and number of comorbidities [OR 1.34, 95% CI 1.12 to 1.61, p-value=0.002]. The most common reasons for early readmission are infection, anemia, and uremia/underdialysis.
UNASSIGNED: Patients with chronic glomerulonephritis and multiple comorbidities have significantly increased odds of early readmission. Careful discharge planning and close follow up of these patients may reduce early readmissions.

OBJECTIVE: We explore patient-reported behaviors and activities within 30-days post-stroke hospitalization and their role in reducing death or readmissions within 90-days post-stroke.
METHODS: We constructed the adequate transitions of care (ATOC) composite score, measuring patient-reported participation in eligible behaviors and activities (diet modification, weekly exercise, follow-up medical appointment attendance, medication adherence, therapy use, and toxic habit cessation) within 30 days post-stroke hospital discharge. We analyzed ATOC scores in ischemic and intracerebral hemorrhage stroke patients discharged from the hospital to home or rehabilitation facilities and enrolled in the NIH-funded Transitions of Care Stroke Disparities Study (TCSD-S). We utilized Cox regression analysis, with the progressive adjustment for sociodemographic variables, social determinants of health, and stroke risk factors, to determine the associations between ATOC score within 30-days and death or readmission within 90-days post-stroke.
RESULTS: In our sample of 1239 stroke patients (mean age 64 +/- 14, 58 % male, 22 % Hispanic, 22 % Black, 52 % White, 76 % discharged home), 13 % experienced a readmission or death within 90 days (3 deaths, 160 readmissions, 3 readmissions with subsequent death). Seventy percent of participants accomplished a ≥75 % ATOC score. A 25 % increase in ATOC was associated with a respective 20 % (95 % CI 3-33 %) reduced risk of death or readmission within 90-days.
CONCLUSIONS: ATOC represents modifiable behaviors and activities within 30-days post-stroke that are associated with reduced risk of death or readmission within 90-days post-stroke. The ATOC score should be validated in other populations, but it can serve as a tool for improving transitions of stroke care initiatives and interventions.

UNASSIGNED: The coronavirus pandemic highlighted the need for remote patient monitoring to deliver and provide access to patient care and education. A mobile-based app providing interactive tools for patient education and monitoring was piloted at Thunder Bay Regional Health Sciences Centre (TBRHSC) in November 2020. We aimed to examine the platform\'s impact on postoperative length of stay, hospital readmissions, and emergency department (ED) visits 60 days postsurgery in total hip and knee arthroplasty patients in Northwestern Ontario.
UNASSIGNED: Data were assessed from patients undergoing primary total hip or knee arthroplasties at TBRHSC from March 1, 2020, to February 28, 2022. Patients were divided into 2 cohorts based on enrollment with the mobile-based app (SeamlessMD). Statistical differences in outcomes were determined using Mann-Whitney or χ2 tests. An odds ratio was calculated for ED visits.
UNASSIGNED: Patients enrolled in the mobile-based app had statistically lower length of stay (U = 7779.0, P < .001) and fewer ED visits (χ2 (1,212) = 5.570, P = .018) than patients not enrolled in the program. Patients not enrolled had 2.31 times greater odds of visiting the ED postsurgery (odds ratio = 0.432, 95% confidence interval = 0.213-0.877, P = .022). There were no statistical differences found in readmission rates.
UNASSIGNED: The implementation of the mobile-based app at TBRHSC showed its potential value as a tool to reduce costs in the healthcare system and improve patient outcomes. Consequentially, more formal studies are required to elucidate the magnitude of this effect.

UNASSIGNED: To determine timing and risk factors associated with readmission within 30 days of discharge following noncardiac surgery.
UNASSIGNED: Hospital readmission after noncardiac surgery is costly. Data on the drivers of readmission have largely been derived from single-center studies focused on a single surgical procedure with uncertainty regarding generalizability.
UNASSIGNED: We undertook an international (28 centers, 14 countries) prospective cohort study of a representative sample of adults ≥45 years of age who underwent noncardiac surgery. Risk factors for readmission were assessed using Cox regression (ClinicalTrials.gov, NCT00512109).
UNASSIGNED: Of 36,657 eligible participants, 2744 (7.5%; 95% confidence interval [CI], 7.2-7.8) were readmitted within 30 days of discharge. Rates of readmission were highest in the first 7 days after discharge and declined over the follow-up period. Multivariable analyses demonstrated that 9 baseline characteristics (eg, cancer treatment in past 6 months; adjusted hazard ratio [HR], 1.44; 95% CI, 1.30-1.59), 5 baseline laboratory and physical measures (eg, estimated glomerular filtration rate or on dialysis; HR, 1.47; 95% CI, 1.24-1.75), 7 surgery types (eg, general surgery; HR, 1.86; 95% CI, 1.61-2.16), 5 index hospitalization events (eg, stroke; HR, 2.21; 95% CI, 1.24-3.94), and 3 other factors (eg, discharge to nursing home; HR, 1.61; 95% CI, 1.33-1.95) were associated with readmission.
UNASSIGNED: Readmission following noncardiac surgery is common (1 in 13 patients). We identified perioperative risk factors associated with 30-day readmission that can help frontline clinicians identify which patients are at the highest risk of readmission and target them for preventive measures.

UNASSIGNED: Social determinants of health disparities have been shown to adversely impact outcomes following distal radius fracture (DRF) treatment. Identifying risk factors for increased hospital use following DRF has been historically difficult; however, it is of utmost concern to orthopedic surgeons to improve outcomes and decrease the total cost of care. The effect of social deprivation following DRF has yet to be fully investigated.
UNASSIGNED: This is a retrospective cohort analysis of a single institution\'s experience in treating DRF with either an operative or nonsurgical modality between 2005 and 2020. Patient demographic information and health care utilization (hospital readmission, emergency department [ED] visitation, office visits, and telephone use) were collected from within 90 days of treatment. Area Deprivation Index (ADI) national percentiles were recorded. Patients were stratified into terciles based on their relative level of deprivation, and their outcomes were compared. Secondary analyses included stratifying patients based on treatment modality, race, and legal sex.
UNASSIGNED: In total, 2,149 patients were included. The least, intermediate, and most deprived groups consisted of 552, 1,067, and 530 patients, respectively. Risk factors for hospital readmission included higher levels of relative deprivation. Identifying as Black or African American and nonsurgical management were risk factors for increased ED visitation. No differences in rate of hospital readmission, ED visitation, office visitation, or telephone use were seen based on deprivation level.
UNASSIGNED: High levels of social deprivation, treatment modality, race, and legal sex disparities may influence the amount of hospital resource utilization following DRF treatment. Understanding and identifying risk factors for greater resource utilization can help to mitigate inappropriate use and decrease health care costs. We hope to use these findings to guide clinical decision making, educate patient populations, and optimize outcomes following DRF treatment.
UNASSIGNED: Therapeutic III.

Background: Centrifugal-flow left ventricular assist devices (CF-LVADs) have improved morbidity and mortality for their recipients. Hospital readmissions remain common, negatively impacting quality of life and survival. We sought to identify risk factors associated with hospital readmissions among patients with CF-LVADs. Methods: Consecutive patients receiving a CF-LVAD between February 2011 and March 2021 were retrospectively evaluated using prospectively maintained institutional databases. Hospital readmissions within three years post-LVAD implantation were dichotomized into heart failure (HF)/LVAD-related or non-HF/LVAD-related readmissions. Multivariable Cox regression models augmented using a machine learning algorithm, the least absolute shrinkage and selection operator (LASSO) method, for variable selection were used to estimate associations between HF/LVAD-related readmissions and pre-, intra- and post-operative clinical variables. Results: A total of 204 CF-LVAD recipients were included, of which 138 (67.7%) had at least one HF/LVAD-related readmission. HF/LVAD-related readmissions accounted for 74.4% (436/586) of total readmissions. The main reasons for HF/LVAD-related readmissions were major bleeding, major infection, HF exacerbation, and neurological dysfunction. Using pre-LVAD variables, HF/LVAD-related readmissions were associated with substance use, previous cardiac surgery, HF duration, pre-LVAD inotrope dependence, percutaneous LVAD/VA-ECMO support, LVAD type, and the left ventricular ejection fraction in multivariable analysis (Harrell\'s concordance c-statistic; 0.629). After adding intra- and post-operative variables in the multivariable model, LVAD implant hospitalization length of stay was an additional predictor of readmission. Conclusions: Using machine learning-based techniques, we generated models identifying pre-, intra-, and post-operative variables associated with a higher likelihood of rehospitalizations among patients on CF-LVAD support. These models could provide guidance in identifying patients with increased readmission risk for whom clinical strategies to mitigate this risk may further improve LVAD recipient outcomes.

Several risk stratification systems aid clinicians in classifying pulmonary embolism (PE) severity and prognosis. We compared 2 clinical PE scoring systems, the PESI and sPESI scores, with 2 comorbidity indices, the Charlson Comorbidity Index (CCI) and the val Walraven Elixhauser Comorbidity Index (ECI), to determine the utility of each in predicting mortality and hospital readmission. Information was collected from 436 patients presenting with PE via retrospective chart review. The PESI, sPESI, CCI, and ECI scores were calculated for each patient. Multivariate analysis was used to determine each system\'s ability to predict in-hospital mortality, 90-day mortality, overall mortality, and all-cause hospital readmission. The impact of various demographic and clinical characteristics of each patient on these outcomes was also assessed. The PESI score was found to be an independent predictor of in-hospital mortality and 90-day mortality. The PESI score and the CCI were able to independently predict overall mortality. None of the 4 risk scores independently predicted hospital readmission. Other factors including hypoalbuminemia, serum BNP, coagulopathy, anemia, and diabetes were associated with increased mortality and readmission at various endpoints. The PESI score was the best tool for predicting mortality at any endpoint. The CCI may have utility in predicting long-term outcomes. Further work is needed to better determine the roles of the CCI and ECI in predicting patient outcomes in PE. The potential prognostic implications of low serum albumin and anemia at the time of PE also warrant further investigation.

Retrospective cohort study. To assess the utility of the LACE index for predicting death and readmission in patients with spinal infections (SI). SIs are severe conditions, and their incidence has increased in recent years. The LACE (Length of stay, Acuity of admission, Comorbidities, Emergency department visits) index quantifies the risk of mortality or unplanned readmission. It has not yet been validated for SIs. LACE indices were calculated for all adult patients who underwent surgery for spinal infection between 2012 and 2021. Data were collected from a single academic teaching hospital. Outcome measures included the LACE index, mortality, and readmission rate within 30 and 90 days. In total, 164 patients were analyzed. Mean age was 64.6 (± 15.1) years, 73 (45%) were female. Ten (6.1%) patients died within 30 days and 16 (9.8%) died within 90 days after discharge. Mean LACE indices were 13.4 (± 3.6) and 13.8 (± 3.0) for the deceased patients, compared to 11.0 (± 2.8) and 10.8 (± 2.8) for surviving patients (p = 0.01, p < 0.001), respectively. Thirty-seven (22.6%) patients were readmitted ≤ 30 days and 48 (29.3%) were readmitted ≤ 90 days. Readmitted patients had a significantly higher mean LACE index compared to non-readmitted patients (12.9 ± 2.1 vs. 10.6 ± 2.9, < 0.001 and 12.8 ± 2.3 vs. 10.4 ± 2.8, p < 0.001, respectively). ROC analysis for either death or readmission within 30 days estimated a cut-off LACE index of 12.0 points (area under the curve [AUC] 95% CI, 0.757 [0.681-0.833]) with a sensitivity of 70% and specificity of 69%. Patients with SI had high LACE indices that were associated with high mortality and readmission rates. The LACE index can be applied to this patient population to predict the risk of early death or unplanned readmission.