Characteristics and Hospital Outcomes of 1403 Patients Hospitalized at Community Hospitals With Ankylosing Spondylitis.

Background: In this study, we aimed to assess the hospital course, outcomes after hospitalization, and predictors of outcomes in patients with ankylosing spondylitis (AS). Methods: We included 1403 patients with AS between 2016 and 2021 who were identified using International Classification of Disease (ICD) codes from a large for-profit healthcare system database. Demographics and clinical characteristics were compared between those who had a favorable outcome, defined as being discharged to home without readmission within 3 months of discharge, versus those who had an unfavorable outcome. A stepwise logistic regression was used to identify demographic and clinical characteristics associated with home discharge and readmission. Results: The mean age for all AS patients was 56.06 ± 17.01 years, which was younger in the favorable outcome group, and 82.47% of patients were discharged to home after the average length of stay of 3.72 ± 4.09 days, also shorter in the favorable outcome group. Of 1403 patients, 37.56% were readmitted within 3 months of discharge, at a lower rate in the group with home discharge. Opioids were the most commonly used medication during hospitalization (67.07%), prescribed at a lower rate in the favorable outcome group. Medical coverage by Medicare and Medicaid, fall at admission, hospital-acquired anemia, steroid, acetaminophen, muscle relaxant use, and an increased dose of morphine milligram equivalent at discharge were significantly associated with decreased odds of home discharge. Surgical procedures during admission, gastrointestinal complications, discharge to inpatient rehabilitation units, and use of benzodiazepine were associated with an increased risk of readmission within 3 months. Conclusion: Recognizing factors that put patients with AS at risk of unfavorable outcomes is useful information to improve patient care during hospitalization.

Moderating Effects of Informal Care on the Relationship Between ADL Limitations and Adverse Outcomes in Stroke Survivors.

BACKGROUND: Stroke survivors with limitations in activities of daily living (ADL) have a greater risk of experiencing falls, hospitalizations, or physical function decline. We examined how informal caregiving received in hours per week by stroke survivors moderated the relationship between ADL limitations and adverse outcomes. METHODS: In this retrospective cohort, community-dwelling participants were extracted from the National Health and Aging Trends Study (2011-2020; n=277) and included if they had at least 1 formal or informal caregiver and reported an incident stroke in the prior year. Participants reported the amount of informal caregiving received in the month prior (low [<5.8], moderate [5.8-27.1], and high [27.2-350.4] hours per week) and their number of ADL limitations (ranging from 0 to 7). Participants were surveyed 1 year later to determine the number of adverse outcomes (ie, falls, hospitalizations, and physical function decline) experienced over the year. Poisson regression coefficients were converted to average marginal effects and estimated the moderating effects of informal caregiving hours per week on the relationship between ADL limitations and adverse outcomes. RESULTS: Stroke survivors were 69.7% White, 54.5% female, with an average age of 80.5 (SD, 7.6) years and 1.2 adverse outcomes at 2 years after the incident stroke. The relationships between informal caregiving hours and adverse outcomes and between ADL limitations and adverse outcomes were positive. The interaction between informal caregiving hours per week and ADL limitations indicated that those who received the lowest amount of informal caregiving had a rate of 0.12 more adverse outcomes per ADL (average marginal effect, 0.12 [95% CI, 0.005-0.23]; P=0.041) than those who received the highest amounts. CONCLUSIONS: Informal caregiving hours moderated the relationship between ADL limitations and adverse outcomes in this sample of community-based stroke survivors. Higher amounts relative to lower amounts of informal caregiving hours per week may be protective by decreasing the rate of adverse outcomes per ADL limitation.

Impact of the method of calculating 30-day readmission rate after hospitalization for heart failure. Data from the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry.

BACKGROUND: Thirty-day readmission rate after heart failure (HF) hospitalization is widely used to evaluate healthcare quality. Methodology may substantially influence estimated rates. We assessed the impact of different definitions on HF and all-cause readmission rates. METHODS: Readmission rates were examined in 1,835 patients discharged following HF hospitalization using 64 unique definitions derived from five methodological factors: (1) ICD-10 codes (broad vs narrow), (2) index admission selection (single admission only first-in-year vs. random sample; or multiple admissions in year with vs. without 30-day blanking period), (3) variable denominator (number alive at discharge vs. number alive at 30-days), (4) follow-up period start (discharge date vs day following discharge), and (5) annual reference-period (calendar vs fiscal). The impact of different factors was assessed using linear-regression. RESULTS: The calculated 30-day readmission rate for HF varied more than 2-fold depending solely on the methodological approach (6.5% to 15.0%). All-cause admission rates exhibited similar variation (18.8% to 29.9%). The highest rates included all consecutive index admissions (HF 11.1-15.0%, all-cause 24.0-29.9%), and lowest only one index admission per patient per year (HF 6.5-11.3%, all-cause 18.8-22.7%). When including multiple index admissions and compared to blanking the 30-days post-discharge, not blanking was associated with 2.3% higher readmission rates. Selecting a single admission per year with a first-in-year approach lowered readmission rates by 1.5%, while random-sampling admissions lowered estimates further by 5.2% (p<0.001). CONCLUSION: Calculated 30-day readmission rates varied more than 2-fold by altering methods. Transparent and consistent methods are needed to ensure reproducible and comparable reporting.

Increasing and sustaining discharges by noon - a multi-year process improvement project.

High hospital occupancy degrades emergency department performance by increasing wait times, decreasing patient satisfaction, and increasing patient morbidity and mortality. Late discharges contribute to high hospital occupancy by increasing emergency department (ED) patient length of stay (LOS). We share our experience with increasing and sustaining early discharges at a 650-bed academic medical center in the United States. Our process improvement project followed the Institute of Medicine Model for Improvement of successive Plan‒Do‒Study‒Act cycles. We implemented multiple iterative interventions over 41 months. As a result, the proportion of discharge orders before 10 am increased from 8.7% at baseline to 22.2% (p < 0.001), and the proportion of discharges by noon (DBN) increased from 9.5% to 26.8% (p < 0.001). There was no increase in balancing metrics because of our interventions. RA-LOS (Risk Adjusted Length Of Stay) decreased from 1.16 to 1.09 (p = 0.01), RA-Mortality decreased from 0.65 to 0.61 (p = 0.62) and RA-Readmissions decreased from 0.92 to 0.74 (p < 0.001). Our study provides a roadmap to large academic facilities to increase and sustain the proportion of patients discharged by noon without negatively impacting LOS, 30-day readmissions, and mortality. Continuous performance evaluation, adaptability to changing resources, multidisciplinary engagement, and institutional buy-in were crucial drivers of our success.

Risk Factors for Readmission in Excisional Hemorrhoidectomy at a Tertiary Teaching Center.

INTRODUCTION: Unplanned readmission is often seen after excisional hemorrhoidectomy. This study aims to explore associations between patient and operative factors, and readmission rates in excisional hemorrhoidectomy. METHODS: We performed a retrospective analysis of all excisional hemorrhoidectomies performed in Capital and Coast District Health Board for an 8-year period from January 1, 2012, to December 31, 2020. The primary outcome measure was 30-day readmissions post hemorrhoidectomy. Univariate and multivariable logistic regression analyses were performed to identify risk factors to readmisson. A decision tree model was designed to further look at the interactions between risk factors. RESULTS: There were a total 370 patients undergoing 389 excisional hemorrhoidectomies over the study period. There were 47 (12.1%) readmissions. The commonest reasons for readmission were pain (48.9%) and bleeding (38%). 17% of readmitted patients required operative intervention. Readmission was associated with the use of advanced energy devices (OR 2.21; P = 0.027). Trainees were more likely to use advance energy devices than consultants (51% versus 38%, P = 0.010). Consultants were involved in more procedures requiring a removal of 3 pedicles or more than trainees (43% versus 30%, P = 0.010). A decision tree model predicts readmission based on primary operator experience, age, advanced energy device use, and patient ethnicity. CONCLUSIONS: Two risk models are presented showing the complex relationship between the factors associated with readmission after hemorrhoidectomy. Advanced energy device use was associated with an increased risk of readmission after hemorrhoidectomy in our population. Future work could involve targeted interventions to patients at increased risk of readmission such as preprocedural and postprocedural information, early interval follow-up and targeted analgesia regimes.

Comparison of Transcatheter Edge-to-Edge Mitral Valve Repair for Primary Mitral Regurgitation Outcomes to Hospital Volumes of Surgical Mitral Valve Repair.

BACKGROUND: Transcatheter edge-to-edge mitral valve (MV) repair (TEER) is an effective treatment for patients with primary mitral regurgitation at prohibitive risk for surgical MV repair (MVr). High-volume MVr centers and high-volume TEER centers have better outcomes than low-volume centers, respectively. However, whether MVr volume predicts TEER outcomes remains unknown. We hypothesized that high-volume MV surgical centers would have superior risk-adjusted outcomes for TEER than low-volume centers. METHODS: We combined data from the American College of Cardiology/Society of Thoracic Surgeons Transcatheter Valve Therapy registry and the Society of Thoracic Surgeons adult cardiac surgery database. MVr was defined as leaflet resection or artificial chords with or without annuloplasty and was evaluated as a continuous variable and as predefined categories (<25, 25-49, and ≥50 MV repairs/year). A generalized linear mixed model was used to evaluate risk-adjusted in-hospital/30-day mortality, 30-day heart failure readmission, and TEER success (mitral regurgitation ≤2+ and gradient <5 mm Hg). RESULTS: The study comprised 41 834 patients from 500 sites of which 332 (66.4%) were low, 102 (20.4%) intermediate, and 66 (13.2%) high-volume surgical centers (P<0.001). TEER success was 54.6% and was not statistically significantly different across MV surgical site volumes (P=0.4271). TEER mortality at 30 days was 3.5% with no significant difference across MVr volume on unadjusted (P=0.141) or adjusted (P=0.071) analysis of volume as a continuous variable. One-year mortality was 15.0% and was lower for higher MVr volume centers when adjusted for clinical and demographic variables (P=0.027). Heart failure readmission at 1 year was 9.4% and was statistically significantly lower in high-volume centers on both unadjusted (P=0.017) or adjusted (P=0.015) analysis. CONCLUSIONS: TEER can be safely performed in centers with low volumes of MV repair. However, 1-year mortality and heart failure readmission are superior at centers with higher MVr volume.

Association of Posttraumatic Stress Disorder and Race on Readmissions After Stroke.

BACKGROUND: There is limited research on outcomes of patients with posttraumatic stress disorder (PTSD) who also develop stroke, particularly regarding racial disparities. Our goal was to determine whether PTSD is associated with the risk of hospital readmission after stroke and whether racial disparities existed. METHODS: The analytical sample consisted of all veterans receiving care in the Veterans Health Administration who were identified as having a new stroke requiring inpatient admission based on the International Classification of Diseases codes. PTSD and comorbidities were identified using the International Classification of Diseases codes and given the date of first occurrence. The retrospective cohort data were obtained from the Veterans Affairs Corporate Data Warehouse. The main outcome was any readmission to Veterans Health Administration with a stroke diagnosis. The hypothesis that PTSD is associated with readmission after stroke was tested using Cox regression adjusted for patient characteristics including age, sex, race, PTSD, smoking status, alcohol use, and comorbidities treated as time-varying covariates. RESULTS: Our final cohort consisted of 93 651 patients with inpatient stroke diagnosis and no prior Veterans Health Administration codes for stroke starting from 1999 with follow-up through August 6, 2022. Of these patients, 12 916 (13.8%) had comorbid PTSD. Of the final cohort, 16 896 patients (18.0%) with stroke were readmitted. Our fully adjusted model for readmission found an interaction between African American veterans and PTSD with a hazard ratio of 1.09 ([95% CI, 1.00-1.20] P=0.047). In stratified models, PTSD has a significant hazard ratio of 1.10 ([95% CI, 1.02-1.18] P=0.01) for African American but not White veterans (1.05 [95% CI, 0.99-1.11]; P=0.10). CONCLUSIONS: Among African American veterans who experienced stroke, preexisting PTSD was associated with increased risk of readmission, which was not significant among White veterans. This study highlights the need to focus on high-risk groups to reduce readmissions after stroke.

Patient Readmission for Ischemic Stroke: Risk Factors and Impact on Mortality.

Patient readmission for ischemic stroke significantly strains the healthcare and medical insurance systems. Current understanding of the risk factors associated with these readmissions, as well as their subsequent impact on mortality within China, remains insufficient. This is particularly evident in the context of comprehensive, contemporary population studies. This 4-year retrospective cohort study included 125 397 hospital admissions for ischemic stroke from 838 hospitals located in 22 regions (13 urban and 9 rural) of a major city in western China, between January 1, 2015 and December 31, 2018. The Chi-square tests were used in univariate analysis. Accounting for intra-subject correlations of patients' readmissions, accelerated failure time (AFT) shared frailty models were used to examine readmission events and pure AFT models for mortality. Risk factors for patient readmission after ischemic stroke include frequent admission history, male gender, employee's insurance, advanced age, residence in urban areas, index hospitalization in low-level hospitals, extended length of stay (LOS) during index hospitalization, specific comorbidities and subtypes of ischemic stroke. Furthermore, our findings indicated that an additional admission for ischemic stroke increased patient mortality by 16.4% (P < .001). Stroke readmission contributed to an increased risk of hospital mortality. Policymakers can establish more effective and targeted policies to reduce readmissions for stroke by controlling these risk factors.

Impact of the COVID-19 pandemic on outcomes of cholecystectomy for acute cholecystitis: a national cohort study.

BACKGROUND: The COVID-19 pandemic was associated with significant disruptions to healthcare provision globally and in Aotearoa New Zealand. It remains unclear how this disruption affected the surgical management of acute cholecystitis and whether there are ongoing impacts. METHODS: We conducted a secondary analysis of two multicentre cohort studies (CHOLECOVID and CHOLENZ) on patients who underwent cholecystectomy for acute cholecystitis. Participants were categorized into pre-pandemic (September-November 2019), pandemic (March-May 2020), and late-pandemic (August-October 2021) phases. Baseline demographics, clinical management, and 30-day postoperative complications were assessed between phases. Multivariable logistic regression was used to explore the impact of timing of operation on rates of hospital readmission and postoperative complications. RESULTS: 517 participants were included, of whom 85 (16%) were in the pre-pandemic-phase, 52 (10%) were in the pandemic phase, and 380 (73%) were in the late-pandemic phase. Pandemic and late-pandemic phase participants were more comorbid and had higher rates of obesity and deranged blood results than pre-pandemic. After multivariable adjustment, there were no differences in rates of hospital readmission or postoperative complications at 30-day follow-up across phases. CONCLUSION: The COVID-19 pandemic had minimal impacts on the provision of cholecystectomy for acute cholecystitis in Aotearoa New Zealand. However, patients managed during the COVID-19 pandemic were more comorbid and had higher rates of obesity and elevated inflammatory markers.

Multi-Trajectories of Symptoms and their Associations with Unplanned 30-day Hospital Readmission among Patients with Heart Failure: A longitudinal study.

AIMS: This study aimed to uncover hidden patterns and predictors of symptom multi-trajectories within 30 days after discharge in patients with heart failure and assess the risk of unplanned 30-day hospital readmission in different patterns. METHODS AND RESULTS: The study was conducted from September 2022 to September 2023 in four third-class hospitals in Tianjin, China. A total of 301 patients with heart failure were enrolled in the cohort, and 248 patients completed a 30-day follow-up after discharge. Three multi-trajectory groups were identified: mild symptom status (24.19%), moderate symptom status (57.26%), and severe symptom status (18.55%). With the mild symptom status group as a reference, physical frailty, psychological frailty, and comorbid renal dysfunction were predictors of the moderate symptom status group. Physical frailty, psychological frailty, resilience, taking diuretics, and comorbid renal dysfunction were predictors of the severe symptom status group. Compared with the mild symptom status group, the severe symptom status group was significantly associated with high unplanned 30-day hospital readmission risks. CONCLUSIONS: This study identified three distinct multi-trajectory groups among patients with heart failure within 30 days after discharge. The severe symptom status group was associated with a significantly increased risk of unplanned 30-day hospital readmission. Common and different factors predicted different symptom multi-trajectories. Healthcare providers should assess the physical and psychological frailty and renal dysfunction of patients with heart failure before discharge. Inpatient care aimed at alleviating physical and psychological frailty and enhancing resilience may be important to improve patients' symptom development post-discharge.

Healthcare Quality Received by Insured Patients in Two Indonesian Regional Public Hospitals.

Background: Hospitals are required to improve the quality of health services provided to patients. Purpose: Evaluating and comparing the healthcare quality received by insured patients hospitalized in two Indonesian regional public hospitals. Methods: Secondary data analysis used the 2019 and 2020 Indonesian National Health Insurance e-claim databases of Hospital A and Hospital B. Descriptive and crosstabs analyses were used to determine INA-CBGs diagnoses that were categorized as high volume, high risk, and high cost. Results: The admissions that caused financial loss at the Hospital A were 21.1% in 2019 and 19.8% in 2020, while 30.3% in 2019 and 27.5% at the Hospital B. More than 60% of these admissions were placed in the 3rd class of inpatient wards of the two hospitals. Of these admissions, < 5% at the Hospital A and >5% at the Hospital B were readmitted within 30 days, although more than 90% were previously discharged based on physicians' approval. Conclusions: Inadequate healthcare quality received by insured patients. Hence, an integrated clinical pathways based professional nursing practice model is highly recommended to increase patient outcomes and decrease 30 days hospital readmission rates.

Racial disparities in short-term spinal fusion outcomes across 4263 consecutive patients.

OBJECTIVE: Race plays a salient role in access to surgical care. However, few investigations have assessed the impact of race within surgical populations after care has been delivered. The objective of this study was to employ an exact matching protocol to a homogenous population of spine surgery patients in order to isolate the relationships between race and short-term postoperative outcomes. METHODS: In total, 4263 consecutive patients who underwent single-level, posterior-only lumbar fusion at a single multihospital academic medical center were retrospectively enrolled. Of these patients, 3406 patients self-identified as White and 857 patients self-identified as non-White. Outcomes were initially compared across all patients via logistic regression. Subsequently, White patients and non-White patients were exactly matched on the basis of key demographic and health characteristics (1520 matched patients). Outcome disparities were evaluated between the exact-matched cohorts. Primary outcomes were readmissions, emergency department (ED) visits, reoperations, mortality, intraoperative complications, and discharge disposition. RESULTS: Before matching, non-White patients were less likely to be discharged home and more likely to be readmitted, evaluated in the ED, and undergo reoperation. After matching, non-White patients experienced higher rates of nonhome discharge, readmissions, and ED visits. Non-White patients did not have more surgical complications either before or after matching. CONCLUSIONS: Between otherwise similar cohorts of spinal fusion cases, non-White patients experienced unfavorable discharge disposition and higher risk of multiple adverse postoperative outcomes. However, these findings were not accounted for by differences in surgical complications, suggesting that structural factors underlie the observed disparities.

Blood neutrophil to lymphocyte ratio is associated with 90-day mortality and 60-day readmission in Gram negative bacteremia: a multi-center cohort study.

INTRODUCTION: The Neutrophil-Lymphocyte Ratio (NLR) in blood has demonstrated its capability to predict bacteremia in emergency departments, and its association with mortality has been established in patients with sepsis in intensive care units. However, its potential concerning mortality and readmission in patients with Gram-negative bacteremia (GNB) is unexplored. METHODS: This retrospective cohort study included patients with GNB between 2018 and 2022 from six hospitals in the Capital Region of Denmark. Patients who were immunosuppressed or had missing NLR values on the day of blood culture were excluded. Logistic regression models were used to analyze the association between NLR levels and 90-day all-cause mortality, while the logit link interpretation of the cumulative incidence function was used to assess the association between NLR levels and 60-day readmission. Associations were quantified as odds ratios (OR) with corresponding 95% confidence intervals (CI). RESULTS: The study included 1763 patients with a median age was 76.8 years and 51.3% were female. The median NLR was 17.3 and 15.8% of patients had a quick sequential organ failure assessment score of two or three. Urinary tract infection (UTI) was the most frequent focus and Escherichia coli the most frequent pathogen. Statistically significant differences in median NLR were found by age group and pathogen, and for patients with or without hypertension, liver disease, chronic obstructive pulmonary disease, dementia, and alcohol abuse. 378 patients (21.4%) died before 90 days. 526 (29.8%) patients were readmitted to the hospital within 60 days. For each doubling of the NLR, the OR for all-cause 90-day mortality was 1.15 (95% CI, 1.04-1.27) and 1.12 (95% CI, 1.02-1.24) for 60-day readmission. Analysis of subgroups did not show statistically significant differences between groups in relation to the association between NLR and mortality. The discriminatory ability of NLR for mortality was limited and comparable to blood neutrophil or lymphocyte count, producing receiver operating characteristic curves with an area under the curve of 0.59 (95% CI, 0.56-0.63), 0.60 (95% CI, 0.56-0.65) and 0.53 (95% CI, 0.49-0.56), respectively. CONCLUSION: Blood neutrophil-lymphocyte ratio was associated with 90-day all-cause mortality and 60-day readmission in patients with GNB. However, the ratio has limited ability in predicting mortality or readmission.

Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.

BACKGROUND: Heart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database. HYPOTHESIS: ML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data. METHODS: Through comprehensive evaluation, the best-performing model was used for prediction. Finally, all the trained models were applied to the test data, which included 20% of the total data. For the final evaluation and comparison of the models, five metrics were used: accuracy, F1-score, sensitivity, specificity and Area Under Curve (AUC). RESULTS: Ten ML algorithms were evaluated. The CatBoost (CAT) algorithm uses a series of decision tree models to create a nonlinear model, and this CAT algorithm performed the best of the 10 models studied. According to the three final outcomes from this study, which involved 2488 participants, 366 (14.7%) of the patients were readmitted to the hospital, 97 (3.9%) of the patients died within 1 month of the follow-up, and 342 (13.7%) of the patients died within 1 year of the follow-up. The most significant variables to predict the events were length of stay in the hospital, hemoglobin level, and family history of MI. CONCLUSIONS: The ML-based risk stratification tool was able to assess the risk of 5-year all-cause mortality and readmission in patients with HF. ML could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of critical features in the model.

Machine Learning-Based 30-Day Readmission Prediction Models for Patients with Heart Failure: A Systematic Review.

AIMS: Heart failure (HF) is one of the most frequent diagnoses for 30-day readmission after hospital discharge. Nurses have role in reducing unplanned readmission and providing quality of care during HF trajectories. This systematic review assessed the quality and significant factors of machine learning (ML)-based 30-day HF readmission prediction models. METHODS AND RESULTS: Eight academic and electronic databases were searched to identify all relevant articles published between 2013 and 2023. Thirteen studies met our inclusion criteria. The sample sizes of the selected studies ranged from 1,778 to 272,778 patients, and patients' average age ranged from 70 to 81 years. Quality appraisal was performed. CONCLUSION: The most commonly used ML approaches were Random Forest and XGBoost. The 30-day HF readmission rates ranged from 1.2% to 39.4%. The area under the receiver operating characteristic curve for models predicting 30-day HF readmission were between 0.51 and 0.93. Significant predictors included sixty variables with nine categories (socio-demographics, vital signs, medical history, therapy, echocardiographic findings, prescribed medications, laboratory results, comorbidities, and hospital performance index). Future studies using ML algorithms should evaluate the predictive quality of the factors associated with 30-day HF readmission presented in this review, considering different healthcare systems and type of HF. More prospective cohort studies with combining structured and unstructured data are required to improve the quality of ML based prediction model, which may help nurses and other healthcare professionals assess early and accurate 30-day HF readmission predictions and plan individualized care after hospital discharge. REGISTRATION: This study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD 42023455584).

Pharmacist-Driven Alcohol Use Disorder Screening May Increase Inpatient Utilization of Extended-Release Naltrexone: A Single Center Pilot Study.

Individuals with mental illness have a high incidence of comorbid substance use, with one of the most prevalent being alcohol use disorder (AUD). Naltrexone, FDA-approved for AUD, decreases reward associated with alcohol-related social cues. This study aimed to determine if a pharmacist-driven screening tool would increase the use of extended-release naltrexone (XR-NTX) in patients with AUD and a comorbid psychiatric condition. Pharmacists screened and recommended XR-NTX for adults admitted to the inpatient psychiatric unit, who had a DSM-5 diagnosis of AUD, a negative urine drug screen for opioids, and were hospitalized for at least 1 day. Endpoints evaluated included the number of XR-NTX doses administered during the screening period to the prescreening period, 30-day readmission rates, recommendation acceptance rates, and reasons for not administering XR-NTX. Pharmacists identified 66 of 641 screened patients who met the inclusion criteria and were candidates for XR-NTX. Compared to the preintervention period, more patients received XR-NTX for AUD (2 vs. 8). Readmission rates were similar between those with AUD who received XR-NTX and those who did not. Pharmacist-driven screening for AUD led to greater administration of XR-NTX when compared to the same 4-month period the year prior to initiating the study.

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan.

AIM: The aim of this study is to analyse the risk factors for unplanned readmissions within 1 month after hospital discharge to develop a seamless support system from discharge to home care. BACKGROUND: With shorter hospital stay lengths, understanding the characteristics of patients with multiple risk factors is important to prevent rehospitalization. DESIGN: This is a single-centre retrospective descriptive study. METHODS: Logistic regression and decision tree analyses were performed using eight items from the records of 3117 patients discharged from a university hospital between April-September 2017 as risk factors. RESULTS: Unplanned readmission risk was significantly associated with emergency hospitalization (odds ratio [OR]: 3.12, 95% confidence interval [CI]: 2.04-4.77), malignancy (OR: 2.16, 95% CI: 1.44-3.24), non-surgical admission (OR: 1.76, 95% CI: 1.07-2.88), hospital stay of ≥ 15 days (OR: 1.66, 95% CI: 1.14-2.43) and decline in activities of daily living owing to hospitalization (OR: 1.68, 95% CI: 1.06-2.64). The highest risk combinations for rehospitalization were as follows: emergency hospitalization and malignancy; emergency admission, non-malignancy and a hospital stay of ≥15 days; and scheduled hospitalization, no surgery and a hospital stay of ≥15 days. CONCLUSIONS: Patients with multiple risks for unplanned readmission should be accurately screened and provided with optimal home care.

Predictors of mortality in heart failure patients with reduced or mildly reduced Ejection Fraction: The CASABLANCA HF Study.

BACKGROUND: Heart failure (HF) poses a significant public health challenge throughout the world and Morocco. Our objective was to delineate the epidemiological characteristics of Moroccan patients living with chronic heart failure and to identify prognostic factors correlated with CHF mortality. RESULTS: A total of 1344 patients participated in this study, with survival rates at 3, 6, and 10 years recorded at 75.2%, 60%, and 34%, respectively. During the follow-up, 886 patients succumbed, representing a mortality rate of 65.9%. A Cox regression model, utilizing baseline candidate variables, was developed to predict cardiovascular (CV) mortality. Predictors all of which had a P value less than 0.05 included age, left ventricular ejection fraction (EF) at commencement (< 35%), hypertension, male sex, anemia, creatinine levels, and the number of hospitalizations due to HF decompensation. Notably, the frequency of hospitalizations emerged as the most potent predictor of mortality, with an HR of 2.5 (95% CI [2-2.9]). Almost 90% of patients with three or more readmissions for HF decompensation experienced mortality by the follow-up's conclusion. CONCLUSIONS: This study offers valuable insights into risk factors and clinical outcomes in HF patients in Morocco. Factors such as male gender, advanced age, a history of hypertension, lower systolic blood pressure, rehospitalizations for HF decompensation, low left ventricular ejection fraction, anemia, and elevated creatinine levels were associated with increased mortality. Medical and health services managers should be aware of the substantial burden and future challenges posed by HF in Morocco, prompting the adoption of multidisciplinary strategies for its management and care.

Evaluation of the Predictive Value of Routinely Collected Health-Related Social Needs Measures.

The objective was to assess the value of routinely collected patient-reported health-related social needs (HRSNs) measures for predicting utilization and health outcomes. The authors identified Mayo Clinic patients with cancer, diabetes, or heart failure. The HRSN measures were collected as part of patient-reported screenings from June to December 2019 and outcomes (hospitalization, 30-day readmission, and death) were ascertained in 2020. For each outcome and disease combination, 4 models were used: gradient boosting machine (GBM), random forest (RF), generalized linear model (GLM), and elastic net (EN). Other predictors included clinical factors, demographics, and area-based HRSN measures-area deprivation index (ADI) and rurality. Predictive performance for models was evaluated with and without the routinely collected HRSN measures as change in area under the curve (AUC). Variable importance was also assessed. The differences in AUC were mixed. Significant improvements existed in 3 models of death for cancer (GBM: 0.0421, RF: 0.0496, EN: 0.0428), 3 models of hospitalization (GBM: 0.0372, RF: 0.0640, EN: 0.0441), and 1 of death (RF: 0.0754) for diabetes, and 1 model of readmissions (GBM: 0.1817), and 3 models of death (GBM: 0.0333, RF: 0.0519, GLM: 0.0489) for heart failure. Age, ADI, and the Charlson comorbidity index were the top 3 in variable importance and were consistently more important than routinely collected HRSN measures. The addition of routinely collected HRSN measures resulted in mixed improvement in the predictive performance of the models. These findings suggest that existing factors and the ADI are more important for prediction in these contexts. More work is needed to identify predictors that consistently improve model performance.