Activities of daily living limitation and functional decline during hospitalization predict 180-day readmission and mortality in older patients with pneumonia: A single-center, retrospective cohort study.

BACKGROUND: The role of activities of daily living (ADL) as a predictor of adverse outcomes in patients with pneumonia is unclear. This study aimed to assess the association between ADL, including physical and cognitive function, and death or readmission in older inpatients with pneumonia. METHODS: This retrospective, single-center, observational study included consecutive older inpatients with pneumonia between October 2018 and December 2019. ADL was assessed using the Functional Independence Measure (FIM). Functional decline during hospitalization was defined as a decrease of at least 1 point in FIM at discharge from admission. The primary outcome was the time to composite 180-day mortality and readmission from any cause after discharge. RESULTS: In total, 363 patients (median [interquartile range] age: 80 [73-86] years, male: 68 %) were divided according to the median FIM scores (≥100, n = 183 and < 100, n = 180). Among the patients, 25 experienced functional decline during hospitalization, 69 were readmitted, and 17 died. In the Kaplan-Meier analysis, both the lower FIM group and the functional decline group had significantly lower event-free rates than the higher FIM groups and the non-functional decline groups (log-rank test, p < 0.001), respectively. After multivariate analysis, both the lower FIM (adjusted HR, 2.11; 95 % CI, 1.24-3.58; p = 0.006) and functional decline (adjusted HR, 3.18; 95 % CI, 1.44-7.05; p = 0.005) were significantly associated with the primary outcome. CONCLUSIONS: In older patients hospitalized with pneumonia, ADL limitations at discharge and a decline in ADL were associated with poor outcomes.

Rate and Risk Factors of Reinfection, Recurrence, and Hospital Readmission Among SARS-Cov-2 Hospitalized Patients; a National Cohort Study.

Introduction: Reinfection and hospital readmission due to COVID-19 were significant and costly during the pandemic. This study aimed to assess the rate and risk factors of SARS-Cov-2 reinfection, recurrence, and hospital readmission, by analyzing the national data registry in Iran. Methods: This study was a retrospective cohort conducted from March 2020 to May 2021. A census method was used to consider all of the possible information in the national Medical Care Monitoring Center (MCMC) database obtained from the Ministry of Health and Medical Education; the data included information from all confirmed COVID-19 patients who were hospitalized and diagnosed using at least one positive Polymerase Chain Reaction (PCR) test by nasopharyngeal swab specimens. Univariate and multivariable Cox regression analyses were performed to assess the factors related to each studied outcome. Results: After analyzing data from 1,445,441 patients who had been hospitalized due to COVID-19 in Iran, the rates of overall reinfection, reinfection occurring at least 90 days after the initial infection, recurrence, and hospital readmission among hospitalized patients were 67.79, 26.8, 41.61, and 30.53 per 1000 person-years, respectively. Among all cases of hospitalized reinfection (48292 cases), 38.61% occurred more than 90 days from the initial SARS-Cov-2 infection. Getting infected with COVID-19 in the fifth wave of the disease compared to getting infected in the first wave (P<0.001), having cancer (P<0.001), chronic kidney disease (P<0.001), and age over 80 years (P<0.001) were respectively the most important risk factors for overall reinfection. In contrast, age 19-44 years (P<0.001), intubation (P<0.001), fever (P<0.001), and cough (P<0.001) in the initial admission were the most important protective factors of overall reinfection, respectively. Conclusion: Reinfection and recurrence of COVID-19 after recovery and the rate of hospital readmission after discharge were remarkable. Advanced or young age, as well as having underlying conditions like cancer and chronic kidney disease, increase the risk of infection and readmission.

Parâmetros de Strain Diastólico Estão Associados à Mortalidade em Curto Prazo e à Reinternação em Pacientes com Insuficiência Cardíaca Avançada / Diastolic Strain Parameters are Associated with Short Term Mortality and Rehospitalization in Patients with Advanced Heart Failure

Resumo Fundamento A insuficiência cardíaca é uma das principais causas de hospitalização e mortalidade em todo o mundo e representa um grande fardo económico para os sistemas de saúde. A identificação de fatores prognósticos em pacientes com IC é de grande importância para estabelecer estratégias de manejo ideais e evitar procedimentos invasivos e dispendiosos desnecessários em pacientes em estágio terminal. Objetivos No presente estudo, nosso objetivo foi investigar a associação entre parâmetros de strain diastólico, incluindo E/e' SR, e resultados de curto prazo em pacientes com IC avançada. Métodos O estudo populacional incluiu 116 pacientes com insuficiência cardíaca avançada com fração de ejeção reduzida (ICFEr) avançada. Avaliações clínicas, laboratoriais e ecocardiográficas dos pacientes foram realizadas nas primeiras 24 horas de internação. Os pacientes foram acompanhados por um mês e qualquer reinternação por piora dos sintomas de IC e qualquer mortalidade foi registrada. O nível de significância adotado na análise estatística foi de 5%. Resultados A E/e' SR foi significativamente maior no grupo de pacientes em comparação ao grupo controle (p=0,001). Durante o acompanhamento de um mês, 13,8% dos pacientes morreram e 37,1% dos pacientes foram reinternados. NT-ProBNP sérico (p=0,034) e E/e' SR (p=0,033) foram considerados preditores independentes de mortalidade e o uso de IECA (p=0,027) e strain 3C apical (p=0,011) foram considerados independentes preditores de reinternação no grupo de pacientes. Conclusão Os resultados do presente estudo prospectivo demonstram que a E/e' SR medida pela ecocardiografia com speckle tracking é um preditor independente e sensível de mortalidade em curto prazo em pacientes com ICFEr avançada e pode ter um papel na identificação de pacientes com ICFEr em estágio terminal.

Abstract Background Heart failure (HF) is a leading cause of hospitalization and mortality worldwide and places a great economic burden on healthcare systems. Identification of prognostic factors in HF patients is of great importance to establish optimal management strategies and to avoid unnecessary invasive and costly procedures in end-stage patients. Objectives In the current study, we aimed to investigate the association between diastolic strain parameters including E/e' SR, and short-term outcomes in advanced HF patients. Methods The population study included 116 advanced HF with reduced ejection fraction (HFrEF) patients. Clinical, laboratory, and echocardiographic evaluations of the patients were performed within the first 24 hours of hospital admission. Patients were followed for one month and any re-hospitalization due to worsening of HF symptoms and any mortality was recorded. The level of significance adopted in the statistical analysis was 5%. Results E/e' SR was significantly higher in the patient group compared to the control group (p=0.001). During one-month follow-up, 13.8% of patients died and 37.1% of patients were rehospitalized. Serum NT-ProBNP (p=0.034) and E/e' SR (p=0.033) were found to be independent predictors of mortality and ACEİ use (p=0.027) and apical 3C strain (p=0.011) were found to be independent predictors of rehospitalization in the patient group. Conclusion Findings of the current prospective study demonstrate that E/e' SR measured by speckle tracking echocardiography is an independent and sensitive predictor of short-term mortality in advanced HFrEF patients and may have a role in the identification of end-stage HFrEF patients.

Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.

OBJECTIVES: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. METHODS: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. RESULTS: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. CONCLUSIONS: The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

AIMS: Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing predictive models in real-world situations. This study aimed to develop a machine learning model for predicting 90 day readmission for HFpEF patients. METHODS AND RESULTS: Data were extracted from electronic health records from 1 August 2020 to 1 August 2021 and follow-up records of patients with HFpEF within 3 months after discharge. Feature extraction was performed by univariate analysis combined with the least absolute shrinkage and selection operator (LASSO) algorithms. Machine learning models like eXtreme Gradient Boosting (XGBoost), random forest, neural network and logistic regression were adopted to construct models. The discrimination and calibration of each model were compared, and the Shapley Additive exPlanations (SHAP) method was used to explore the interpretability of the model. The cohort included 746 patients, of whom 103 (13.8%) were readmitted within 90 days. XGBoost owned the best performance [area under the curve (AUC) = 0.896, precision-recall area under the curve (PR-AUC) = 0.868, sensitivity = 0.817, specificity = 0.837, balanced accuracy = 0.827]. The Kolmogorov-Smirnov (KS) statistic was 0.694 at 0.468 in the XGBoost model. SHAP identified the top 12 risk features, including activities of daily living (ADL), left atrial dimension (LAD), left ventricular end-diastolic diameter (LVDD), shortness, nitrates, length of stay, nutritional risk, fall risk, accompanied by other symptoms, educational level, anticoagulants and edema. CONCLUSIONS: Our model could help medical agencies achieve the early identification of 90 day readmission risk in HFpEF patients and reveal risk factors that provide valuable insights for treatments.

Post-discharge complications and hospital readmissions are associated with nutritional risk and malnutrition status in a cohort of Canadian pediatric patients.

BACKGROUND: This study constitutes a secondary analysis of a prospective cohort aiming to evaluate the potential correlation between nutritional risk and status at admission with the occurrence of post-discharge complications and hospital readmissions in children receiving care at high resource Centres. METHODS: Data was collected from 5 Canadian tertiary pediatric Centers between 2012 and 2016. Nutritional risk and status were evaluated at hospital admission with validated tools (STRONGkids and Subjective Global Nutrition Assessment [SGNA]) and anthropometric measurements. Thirty days after discharge, occurrence of post-discharge complications and hospital readmission were documented. RESULTS: A total of 360 participants were included in the study (median age, 6.1 years; median length of stay, 5 days). Following discharge, 24.1% experienced complications and 19.5% were readmitted to the hospital. The odds of experiencing complications were nearly tripled for participants with a high nutritional risk compared to a low risk (OR = 2.85; 95% CI [1.08-7.54]; P = 0.035) and those whose caregivers reported having a poor compared to a good appetite (OR = 2.96; 95% CI [1.59-5.50]; P < 0.001). According to SGNA, patients identified as malnourished had significantly higher odds of complications (OR, 1.92; 95% CI, 1.15-3.20; P = 0.013) and hospital readmission (OR, 1.95; 95% CI, 1.12-3.39; P = 0.017) than to those well-nourished. CONCLUSIONS: This study showed that complications and readmission post-discharge are common, and these are more likely to occur in malnourished children compared to their well-nourished counterparts. Enhancing nutritional care during admission, at discharge and in the community may be an area for future outcome optimization.

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study.

BACKGROUND: Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes. OBJECTIVE: This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model. DESIGN: A retrospective observational cohort study. SETTING(S): This study was conducted at Ajou University School of Medicine in South Korea. PARTICIPANTS: Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized. METHODS: Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC. RESULTS: Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model. CONCLUSIONS: This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

Rate and Causes of Unplanned Hospital Returns within 60 Days following Head and Neck Surgery.

Introduction Unplanned hospital returns are frequent and may be preventable. Objective To comprehend the reasons for unplanned hospital readmission and return to the Outpatient Department (OPD) and Emergency Department (ED) within 60 days after discharge following head and neck surgery (HNS) at a tertiary care center in Saudi Arabia. Methods In the present retrospective study, the medical records of all patients who underwent HNS for benign and malignant conditions between January 2015 and June 2022 were reviewed in terms of demographic data, comorbidities, and reasons for hospital return. Results Out of 1,030 cases, 119 (11.55%) returned to the hospital within 60 days after discharge, 19 of which (1.84%) were readmitted. In total, 90 (8.74%) patients returned to the OPD, and 29 (2.82%), to the ED. The common reasons for readmission included infections (26.32%) and neurological symptoms (21.05%). For OPD visits, the common causes were hematoma (20%) and neurological symptoms (14.44%). For ED returns, the frequent causes were neurological symptoms (20.69%) and equipment issues (17.24%). Compared with nonreadmitted patients, readmitted patients had a higher preoperative baseline health burden when examined using the American Society of Anesthesiologists (ASA) score ( p = 0.004) and the Cumulative Illness Rating Scale (CIRS; p = 0.002). Conclusion The 60-day rates of unplanned hospital return to the OPD and ED were of 8.74% and 2.82% respectively, and 1.84% of the patients were readmitted. Hematoma, infections, and neurological symptoms were common causes. Addressing the common reasons may be beneficial to decrease postoperative hospital visits.

Effectiveness of the transitional care model in total knee arthroplasty patients: A randomized controlled trial.

AIM: This study has aimed to assess the effectiveness of the transitional care model (TCM) on functional status, perceived self-efficacy and healthcare utilization in patients undergoing total knee arthroplasty (TKA). METHOD: This randomized controlled study was conducted between February and November 2021 in a public hospital. The study randomly assigned patients to either a 6-week 'TCM' program or usual care. The sample size was n = 70, with each group comprising 35 individuals. Patient outcomes, including self-efficacy, functional status and healthcare service readmission rates, were monitored for TKA patients. RESULTS: Nursing care based on the 'TCM' was found to enhance functional status and increase the level of self-efficacy among TKA patients, leading to a decrease in healthcare service readmissions. CONCLUSIONS: The study recommends preparing patients and their families for the preoperative and postoperative processes. It emphasizes the importance of providing necessary training and consultancy services under the leadership of orthopaedic nurses responsible for TKA patient care, guided by the principles of TCM.

Pharmacist-Led Medication Management in Acute Geriatric Medicine and Its Associations with Rehospitalizations: A Cohort Study.

INTRODUCTION: Hospitalization and discharge in older patients are critical and clinical pharmacists have shown to ameliorate risks. Our objective was to assess their benefit as part of the geriatric team regarding rehospitalizations and related outcomes after discharge focusing on general practitioners' decision to continue or change discharge medication (GPD). METHODS: Prospective implementation study with 6-month follow-up in an acute geriatric clinic. Patients ≥70 years with comorbidities, impairments, and a current drug therapy were consecutively assigned to three groups: control group (CG), implementation group (IG), and wash-out group (WG). CG only received medication reconciliation (MR) at admission; IG and their hospital physicians received a pharmaceutical counseling and medication management; during WG, pharmaceutical counseling except for MR was discontinued. We used a negative-binomial model to calculate rehospitalizations and days spent at home as well as a recurrent events survival model to investigate recurrent rehospitalizations. RESULTS: One hundred thirty-two patients (mean age 82 years, 76 women [57.6%]) finished the project. In most of the models for rehospitalizations, a positive GPD led to fewer events. We also found an effect of pharmaceutical counseling on rehospitalizations and recurrent rehospitalizations in the CG versus WG but not in the CG versus IG models. 95.3% of medication recommendations by the pharmacist in the clinic setting were accepted. While the number of positive GPDs in CG was low (38%), pharmaceutical counseling directly to the GP in IG led to a higher number of positive GPDs (60%). DISCUSSION: Although rehospitalizations were not directly reduced by our intervention in the CG versus IG, the pharmacist's acceptance rate in the hospital was very high and a positive GPD led to fewer rehospitalization in most models.

Is it legitimate to use unplanned hospitalizations as a quality indicator for cancer patients? A retrospective French cohort study with special attention to the influence of social deprivation.

PURPOSE: Readmission indicators are used around the world to assess the quality of hospital care. We aimed to assess the relevance of this type of indicator in oncology, especially for socially deprived patients. Our objectives were (1) to assess the proportion of unplanned hospitalizations (UHs) in cancer patients, (2) to assess the proportion of UHs that were avoidable, i.e., related to poor care quality, and (3) to analyze cancer patients the effect of patients' deprivation level on the type of UH (avoidable UHs vs. unavoidable UHs). METHODS: In a French university hospital, we selected all hospitalizations over a year for a random sample of cancer patients. Based on medical records, we identified those among UHs due to avoidable health problems. We assessed the association between social deprivation, home-to-hospital distance, or home-to-general practitioner with the type of UH (avoidable vs. unavoidable) via a multivariate binary logit estimation. RESULTS: Among 2349 hospitalizations (355 patients), there were 383 UHs (16 %), among which 38% were avoidable. Among UHs, the European Deprivation Index was significantly associated with the risk of avoidable UHs, with a lower risk of avoidable UH for patients with medium or high social deprivation. CONCLUSION: Our results suggest that the use of UHs rate as a quality indicator is questionable in oncology. Indeed, the majority of UHs were not avoidable. Furthermore, within UHs, those involving patients with medium or high social deprivation are more often unavoidable in comparison with other patients.

Machine learning in heart failure diagnosis, prediction, and prognosis: review.

Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations.

Evaluating Quality of Care for Patients with Asthma in the Readmission Penalty Era.

Rationale: Asthma poses a significant burden for U.S. patients and health systems, yet inpatient care quality is understudied. National chronic obstructive lung disease (COPD) readmission policies may affect inpatient asthma care through hospital responses to these policies because of imprecise diagnosis and identification of patients with COPD and asthma. Objectives: Evaluate inpatient care quality for patients hospitalized with asthma and potential collateral effects of the Medicare COPD Hospital Readmissions Reduction Program (HRRP). Methods: This was a retrospective cohort study of patients aged 18-54 years hospitalized for asthma across 924 U.S. hospitals (Premier Healthcare Database). Results: Care quality for patients with asthma was evaluated before HRRP implementation (n = 20,820; January 2010-September 2014) and after HRRP implementation (n = 26,885; October 2014-December 2018) using adherence to inpatient care guidelines (recommended, nonrecommended, and "ideal care" [all recommended with no nonrecommended care]). Between 2010 and 2018, at least 80% of patients received recommended care annually. Recommended care decreased similarly (rate of 0.02%/mo) after versus before HRRP (P = 0.8). Nonrecommended care decreased more rapidly after HRRP (rate of 0.29%/mo) versus before HRRP (rate of 0.17%/mo; P < 0.001), with changes driven largely by decreased antibiotic prescribing. Ideal care increased more rapidly after HRRP (rate of 0.25%/mo) versus before HRRP (rate of 0.17%/mo; P = 0.02), with changes driven largely by nonrecommended care improvements. Conclusions: Post-HRRP trends suggest asthma care improved with increased rates of guideline concordance in nonrecommended and ideal care. Although federal policies (e.g., HRRP) may have had positive collateral effects, such as with asthma care, parallel care efforts, including antibiotic stewardship, likely contributed to these improvements.

Hospital Readmission Rates for Patients Receiving In-Person vs. Telemedicine Discharge Follow-Up Care.

INTRODUCTION: Unplanned readmissions can be avoided by standardizing and improving the coordination of care after discharge. Telemedicine has been increasingly utilized; however, the quality of this care has not been well studied. Standardized measures can provide an objective comparison of care quality. The purpose of our study was to compare quality performance transitions of care management in the office vs telemedicine. METHODS: The Epic SlicerDicer tool was used to compare the percentage of encounters that were completed via telemedicine (video visits); or via in-person for comparison, Chi-squared tests were used. RESULTS: A total of 13,891 patients met the inclusion criteria during the study time frame. There were 12,846 patients in the office and 1,048 in the telemedicine cohort. The office readmission rate was 11.9% with 1,533 patients out of 12,846 compared with telemedicine with the rate of readmission at 12.1% with 126 patients out of 1,045 patients. The P-value for the Chi-squared test between the prepandemic and study time frame was 0.15 and 0.95, respectively. Demographic comparability was seen. DISCUSSION: Our study found a comparable readmission rate between patients seen via in-office and telemedicine for Transitions of Care Management (TCM) encounters. The findings of this study support the growing body of evidence that telemedicine augments quality performance while reducing cost and improving access without negatively impacting HEDIS performance in health care systems. CONCLUSION: Telemedicine poses little threat of negatively impacting HEDIS performance and might be as effective as posthospitalization traditional office care transitions of care management.

Acetazolamide As A Valuable Addition to Acute Heart Failure?

Consider IV Acetazolamide in addition to standard IV loop diuretic therapy in patients hospitalized for acute decompensated heart failure.1.

Nursing diagnoses and hospital readmission of patients with respiratory diseases: Findings from a case-control study.

AIM: The rate of readmission after hospitalisation for respiratory diseases has become a common and challenging clinical problem. Social and functional patient variables could help identify cases at high risk of readmission. The aim was to identify the nursing diagnoses that were associated with readmission after hospitalisation for respiratory disease in Spain. DESIGN: Case-control study within the cohort of patients admitted for respiratory disease during 2016-19 in a tertiary public hospital in Spain (n = 3781). METHODS: Cases were patients who were readmitted within the first 30 days of discharge, and their controls were the remaining patients. All nursing diagnoses (n = 130) were collected from the electronic health record. They were then grouped into 29 informative diagnostic categories. Clinical confounder-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using logistic regression models. RESULTS: The readmission rate was 13.1%. The nursing diagnoses categories 'knowledge deficit' (OR: 1.61; 95%CI: 1.13-2.31), 'impaired skin integrity and risk of ulcer infection' (OR: 1.45; 95%CI: 1.06-1.97) and 'activity intolerance associated with fatigue' (OR: 1.56; 95%CI: 1.21-2.01) were associated with an increased risk of suffering an episode of hospital readmission rate at 30% after hospital discharge, and this was independent of sociodemographic background, care variables and comorbidity. PATIENT OR PUBLIC CONTRIBUTION: The nursing diagnoses assigned as part of the care plan of patients during hospital admission may be useful for predicting readmissions.

Association of Inpatient Occupational Therapy Utilization With Reduced Risk for Psychiatric Readmission Among Veterans.

OBJECTIVE: The authors sought to investigate whether utilization of inpatient occupational therapy (OT) was associated with reduced risk for 30-day psychiatric readmission in the Veterans Health Administration (VHA). METHODS: The authors conducted a secondary analysis of VHA medical record data for veterans who received inpatient psychiatric care from 2015 to 2020 (N=176,889). Mixed-effects logistic regression was used to model psychiatric readmission within 30 days of discharge (yes or no) as a function of inpatient psychiatric OT utilization (none, one, two, three, or four or more encounters) and other care utilization (e.g., previous psychiatric hospitalization), as well as clinical (e.g., primary diagnosis), sociodemographic (e.g., race-ethnicity), and facility (e.g., complexity) characteristics. Sensitivity analyses were conducted to evaluate the robustness of findings (e.g., stratification by discharge disposition). RESULTS: Relatively few veterans received inpatient psychiatric OT (26.2%), and 8.4% were readmitted within 30 days. Compared with veterans who did not receive inpatient psychiatric OT, those with one (OR=0.76), two (OR=0.64), three (OR=0.67), or four or more encounters (OR=0.64) were significantly (p<0.001) less likely to be readmitted within 30 days. These findings were consistent across all sensitivity analyses. CONCLUSIONS: Veterans who received inpatient OT services were less likely to experience psychiatric readmission. A clear dose-response relationship between inpatient psychiatric OT and readmission risk was not identified. These findings suggest that OT services may facilitate high-value inpatient psychiatric care in the VHA by preventing readmissions that stymie recovery and incur high costs. Future research may establish the causality of this relationship, informing policy regarding increased access to inpatient psychiatric OT.

Unplanned Readmissions in Children with Medical Complexity in Saudi Arabia: A Large Multicenter Study.

Background: Children with medical complexity (CMC) account for a substantial proportion of healthcare spending, and one-third of their expenditures are due to readmissions. However, knowledge regarding the healthcare-resource utilization and characteristics of CMC in Saudi Arabia is limited. Objectives: To describe hospitalization patterns and characteristics of Saudi CMC with an unplanned 30-day readmission. Methodology: This retrospective study included Saudi CMC (aged 0-14 years) who had an unplanned 30-day readmission at six tertiary centers in Riyadh, Jeddah, Dammam, Alahsa, and Almadina between January 2016 and December 2020. Hospital-based inclusion criteria focused on CMC with multiple complex chronic conditions (CCCs) and technology assistance (TA) device use. CMC were compared across demographics, clinical characteristics, and hospital-resource utilization. Results: A total of 9139 pediatric patients had unplanned 30-day readmission during the study period, of which 680 (7.4%) met the inclusion criteria. Genetic conditions were the most predominant primary pathology (66.3%), with one-third of cases (33.7%) involving the neuromuscular system. During the index admission, pneumonia was the most common diagnosis (33.1%). Approximately 35.1% of the readmissions were after 2 weeks. Pneumonia accounted for 32.5% of the readmissions. After readmission, 16.9% of patients were diagnosed with another CCC or received a new TA device, and the in-hospital mortality rate was 6.6%. Conclusion: The rate of unplanned 30-day readmissions in children with medical complexity in Saudi Arabia is 7.4%, which is lower than those reported from developed countries. Saudi children with CCCs and TA devices were readmitted approximately within similar post-discharge time and showed distinct hospitalization patterns associated with specific diagnoses. To effectively reduce the risk of 30-day readmissions, targeted measures must be introduced both during the hospitalization period and after discharge.

Community Participation Transition After Stroke (COMPASS) Randomized Controlled Trial: Effect on Adverse Health Events.

OBJECTIVE: To compare adverse health events in intervention versus control group participants in the Community Participation Transition After Stroke trial to reduce barriers to independent living for community-dwelling stroke survivors. DESIGN: Randomized controlled trial. SETTING: Inpatient rehabilitation (IR) to home and community transition. PARTICIPANTS: Stroke survivors aged ≥50 years being discharged from IR who had been independent in activities of daily living prestroke (N=183). INTERVENTIONS: Participants randomized to intervention group (n=85) received home modifications and self-management training from an occupational therapist over 4 visits in the home. Participants randomized to control group (n=98) received the same number of visits consisting of stroke education. MAIN OUTCOME MEASURES: Death, skilled nursing facility (SNF) admission, 30-day rehospitalization, and fall rates after discharge from IR. RESULTS: Time-to-event analysis revealed that the intervention reduced SNF admission (cumulative survival, 87.8%; 95% confidence interval [CI], 78.6%-96.6%) and death (cumulative survival, 100%) compared with the control group (SNF cumulative survival, 78.9%; 95% CI, 70.4%-87.4%; P=.039; death cumulative survival, 87.3%; 95% CI, 79.9%-94.7%; P=.001). Thirty-day rehospitalization also appeared to be lower among intervention participants (cumulative survival, 95.1%; 95% CI, 90.5%-99.8%) than among control participants (cumulative survival, 86.3%; 95% CI, 79.4%-93.2%; P=.050) but was not statistically significant. Fall rates did not significantly differ between the intervention group (5.6 falls per 1000 participant-days; 95% CI, 4.7-6.5) and the control group (7.2 falls per 1000 participant-days; 95% CI, 6.2-8.3; incidence rate ratio, 0.78; 95% CI, 0.46-1.33; P=.361). CONCLUSIONS: A home-based occupational therapist-led intervention that helps stroke survivors transition to home by reducing barriers in the home and improving self-management could decrease the risk of mortality and SNF admission after discharge from rehabilitation.

Fatores Associados aos Custos do Tratamento no Primeiro Ano após Implantes Iniciais ou Trocas de Geradores de Pulsos de Marca-passos Cardíacos / Factors Associated with the Treatment Costs within the First Year after Pacemaker Implantation or Pulse Generator Replacement

Resumo Fundamento O uso de marca-passos cardíacos artificiais tem crescido constantemente, acompanhando o envelhecimento populacional. Objetivos Determinar as taxas de readmissões hospitalares e complicações após implante de marca-passo ou troca de gerador de pulsos e avaliar o impacto desses eventos nos custos anuais do tratamento sob a perspectiva do Sistema Único de Saúde (SUS). Métodos Registro prospectivo, com dados derivados da prática clínica assistencial, coletados na hospitalização índice e durante os primeiros 12 meses após o procedimento cirúrgico. O custo da hospitalização índice, do procedimento e do seguimento clínico foram estimados de acordo com os valores reembolsados pelo SUS e analisados ao nível do paciente. Modelos lineares generalizados foram utilizados para estudar fatores associados ao custo total anual do tratamento, adotando-se um nível de significância de 5%. Resultados No total, 1.223 pacientes consecutivos foram submetidos a implante inicial (n= 634) ou troca do gerador de pulsos (n= 589). Foram observados 70 episódios de complicação em 63 pacientes (5,1%). A incidência de readmissões hospitalares em um ano foi de 16,4% (IC 95% 13,7% - 19,6%) após implantes iniciais e 10,6% (IC 95% 8,3% - 13,4%) após trocas de geradores. Doença renal crônica, histórico de acidente vascular encefálico, tempo de permanência hospitalar, necessidade de cuidados intensivos pós-operatórios, complicações e readmissões hospitalares mostraram um impacto significativo sobre o custo anual total do tratamento. Conclusões Os resultados confirmam a influência da idade, comorbidades, complicações pós-operatórias e readmissões hospitalares como fatores associados ao incremento do custo total anual do tratamento de pacientes com marca-passo.

Abstract Background The use of artificial cardiac pacemakers has grown steadily in line with the aging population. Objectives To determine the rates of hospital readmissions and complications after pacemaker implantation or pulse generator replacement and to assess the impact of these events on annual treatment costs from the perspective of the Unified Health System (SUS). Methods A prospective registry, with data derived from clinical practice, collected during index hospitalization and during the first 12 months after the surgical procedure. The cost of index hospitalization, the procedure, and clinical follow-up were estimated according to the values reimbursed by SUS and analyzed at the patient level. Generalized linear models were used to study factors associated with the total annual treatment cost, adopting a significance level of 5%. Results A total of 1,223 consecutive patients underwent initial implantation (n=634) or pulse generator replacement (n=589). Seventy episodes of complication were observed in 63 patients (5.1%). The incidence of hospital readmissions within one year was 16.4% (95% CI 13.7% - 19.6%) after initial implants and 10.6% (95% CI 8.3% - 13.4%) after generator replacements. Chronic kidney disease, history of stroke, length of hospital stays, need for postoperative intensive care, complications, and hospital readmissions showed a significant impact on the total annual treatment cost. Conclusions The results confirm the influence of age, comorbidities, postoperative complications, and hospital readmissions as factors associated with increased total annual treatment cost for patients with pacemakers.