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BACKGROUND: Hypertension is the most common reason for postpartum hospital readmission. Better prediction of postpartum readmission will improve the health care of patients. These models will allow better use of resources and decrease health care costs. OBJECTIVE: This study aimed to evaluate clinical predictors of postpartum readmission for hypertension using a novel machine learning (ML) model that can effectively predict readmissions and balance treatment costs. We examined whether blood pressure and other measures during labor, not just postpartum measures, would be important predictors of readmission. METHODS: We conducted a retrospective cohort study from the PeriData website data set from a single midwestern academic center of all women who delivered from 2009 to 2018. This study consists of 2 data sets; 1 spanning the years 2009-2015 and the other spanning the years 2016-2018. A total of 47 clinical and demographic variables were collected including blood pressure measurements during labor and post partum, laboratory values, and medication administration. Hospital readmissions were verified by patient chart review. In total, 32,645 were considered in the study. For our analysis, we trained several cost-sensitive ML models to predict the primary outcome of hypertension-related postpartum readmission within 42 days post partum. Models were evaluated using cross-validation and on independent data sets (models trained on data from 2009 to 2015 were validated on the data from 2016 to 2018). To assess clinical viability, a cost analysis of the models was performed to see how their recommendations could affect treatment costs. RESULTS: Of the 32,645 patients included in the study, 170 were readmitted due to a hypertension-related diagnosis. A cost-sensitive random forest method was found to be the most effective with a balanced accuracy of 76.61% for predicting readmission. Using a feature importance and area under the curve analysis, the most important variables for predicting readmission were blood pressures in labor and 24-48 hours post partum increasing the area under the curve of the model from 0.69 (SD 0.06) to 0.81 (SD 0.06), (P=.05). Cost analysis showed that the resulting model could have reduced associated readmission costs by US $6000 against comparable models with similar F1-score and balanced accuracy. The most effective model was then implemented as a risk calculator that is publicly available. The code for this calculator and the model is also publicly available at a GitHub repository. CONCLUSIONS: Blood pressure measurements during labor through 48 hours post partum can be combined with other variables to predict women at risk for postpartum readmission. Using ML techniques in conjunction with these data have the potential to improve health outcomes and reduce associated costs. The use of the calculator can greatly assist clinicians in providing care to patients and improve medical decision-making.

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BACKGROUND: Patients with respiratory or cardiovascular diseases often experience higher rates of hospital readmission due to compromised heart-lung function and significant clinical symptoms. Effective measures such as discharge planning, case management, home telemonitoring follow-up, and patient education can significantly mitigate hospital readmissions. OBJECTIVE: This study aimed to determine the efficacy of home telemonitoring follow-up in reducing hospital readmissions, emergency department (ED) visits, and total hospital days for high-risk postdischarge patients. METHODS: This prospective cohort study was conducted between July and October 2021. High-risk patients were screened for eligibility and enrolled in the study. The intervention involved implementing home digital monitoring to track patient health metrics after discharge, with the aim of reducing hospital readmissions and ED visits. High-risk patients or their primary caregivers received education on using communication measurement tools and recording and uploading data. Before discharge, patients were familiarized with these tools, which they continued to use for 4 weeks after discharge. A project manager monitored the daily uploaded health data, while a weekly video appointment with the program coordinator monitored the heart and breathing sounds of the patients, tracked health status changes, and gathered relevant data. Care guidance and medical advice were provided based on symptoms and physiological signals. The primary outcomes of this study were the number of hospital readmissions and ED visits within 3 and 6 months after intervention. The secondary outcomes included the total number of hospital days and patient adherence to the home monitoring protocol. RESULTS: Among 41 eligible patients, 93% (n=38) were male, and 46% (n=19) were aged 41-60 years, while 46% (n=19) were aged 60 years or older. The study revealed that home digital monitoring significantly reduced hospitalizations, ED visits, and total hospital stay days at 3 and 6 months after intervention. At 3 months after intervention, average hospitalizations decreased from 0.45 (SD 0.09) to 0.19 (SD 0.09; P=.03), and average ED visits decreased from 0.48 (SD 0.09) to 0.06 (SD 0.04; P<.001). Average hospital days decreased from 6.61 (SD 2.25) to 1.94 (SD 1.15; P=.08). At 6 months after intervention, average hospitalizations decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.01), and average ED visits decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.02). Average hospital days decreased from 7.48 (SD 2.32) to 6.03 (SD 3.12; P=.73). CONCLUSIONS: By integrating home telemonitoring with regular follow-up, our research demonstrates a viable approach to reducing hospital readmissions and ED visits, ultimately improving patient outcomes and reducing health care costs. The practical application of telemonitoring in a real-world setting showcases its potential as a scalable solution for chronic disease management.

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Background Electrical cardioversion (EC) is a procedure that restores normal sinus rhythm in patients with atrial fibrillation (AF). Data on post-EC outcomes relative to the success of inpatient EC is limited. Methods This is a retrospective study of patients admitted for AF who underwent inpatient EC from January 1, 2017, to January 1, 2021. We collected demographics and clinical, biochemical, and echocardiographic parameters that impact the success of EC. Outcome events were 30-day readmissions and mortality. Results Our study included 54 unique patients who either had EC in the emergency room or as part of their hospital admission course for atrial fibrillation. Most patients were men with an average age of 70 years with traditional risk factors for cardiovascular disease including heart failure, coronary artery disease, and chronic kidney disease. The group who had unsuccessful cardioversion was older than those in the ineffective EC. Mortality at 30 days (p < 0.01), 1 year (p < 0.01), and 30-day readmission rate (p < 0.01) were higher in patients with unsuccessful EC. Conclusion A predictive model for successful EC remains difficult to establish. Patients with unsuccessful in-hospital EC are at high risk for mortality and readmission at 30 days and require a comprehensive pre-discharge multidisciplinary approach and prioritized and individualized post-discharge integrated care.

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Introduction This study evaluates the effectiveness of a comprehensive hip fracture protocol, with a focus on specific readmission reasons. Methods A retrospective cohort study of hip fracture patients aged 60 and older who underwent surgery before (control) and after (intervention) implementation of a comprehensive hip fracture program. Objectives included identifying readmission reasons and rates, time to operating room (TOR), length of stay (LOS), reoperation, and mortality rates. Logistic regression was utilized to determine significance. Results One hundred and sixty-three patients (control) vs. 238 patients (intervention) were identified. The intervention group had higher odds of 90-day readmission for a medical reason (OR = 1.735, p = 0.028). Thirty-three out of forty-two patients (79%) in the control group and 68/78 patients (87%) in the intervention group were readmitted secondary to a medical reason (pulmonary etiology being the most common). Surgical-related readmissions (surgical site infections and dislocations are most common) were lower in the intervention group compared with the control group, with 10/78 patients (13%) and 9/42 patients (21%), respectively. Twenty-four-hour TOR was achieved in 125 patients (52.5%) in the intervention group vs. 70 patients (42.9%) in the control group. LOS was shorter by 1.1 days for the intervention group (p = 0.010). Mortality was lower in the intervention group. Discussion A comprehensive hip fracture protocol can reduce LOS, TOR, mortality rate, and even surgical-related readmissions. Readmission rates are mainly due to medical problems, which may be unavoidable and thus may not be an adequate hip fracture effectiveness metric. Potential areas of improvement and additional study may include closer internal medicine oversight and primary care follow-up after discharge.

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Objective: This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS). Methods: Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS. Results: Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.9625 and an area under the precision-recall curve (AUPRC) of 0.8575 for 30-day mortality at discharge and an AUROC of 0.9545 and AUPRC of 0.8419 at admission. For the readmission and PLOS risk, the highest AUROC achieved were 0.8198 and 0.9797, respectively. The tree-based models consistently outperformed the traditional ML models in all 4 prediction tasks. The key predictors were age, derived temporal features, routine laboratory tests, and diagnostic and procedural codes. Conclusion: The study underscores the potential of leveraging medical history for enhanced hospital predictive analytics. We present an accurate and intuitive framework for early warning models that can be easily implemented in the current and developing digital health platforms to predict adverse outcomes accurately.

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Background: The role of high-density lipoprotein cholesterol (HDL-C) in heart failure (HF) outcomes is contentious. We aimed to assess HDL-C's prognostic value in HF patients. Methods: In this retrospective cohort study (2012-2022) at the First Affiliated Hospital of Xinjiang Medical University, we analyzed 4442 patients, categorized by HDL-C quartiles. We applied the Cox proportional hazards model to assess survival and report hazard ratios (HR) with 95% confidence intervals (CI). Results: Over a decade, we recorded 1354 fatalities (42.3%) and 820 readmissions. The third HDL-C quartile (0.93-1.14 mmol/L) showed the lowest mortality rates, with reduced risks in the second and third quartiles compared to the first (Q2 HR=0.809, 95% CI 0.590-1.109; Q3 HR=0.794, 95% CI 0.564-1.118). The fourth quartile presented a lower mortality risk compared to the first (Q4 HR=0.887, 95% CI 0.693-1.134). A significant correlation existed between HDL-C levels and cardiovascular risk (HR=0.85, 95% CI 0.75-0.96, p<0.01). Conclusion: HDL-C levels exhibit a complex association with mortality in HF, indicating the importance of HDL-C in HF prognosis and the need for tailored management strategies.

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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.

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OBJECTIVE: To determine the change in English emergency postnatal maternal readmissions 2007-2017 (pre-COVID-19) and the association with maternal demographics, obstetric risk factors and postnatal length of stay (LOS). DESIGN: National cohort study. SETTING: All English National Health Service hospitals. POPULATION: A total of 6 192 140 women who gave birth in English NHS hospitals from April 2007 to March 2017. METHODS: Statistical analysis using birth and readmission data from routinely collected National Hospital Episode Statistics (HES) database. MAIN OUTCOME MEASURES: Rate of emergency postnatal maternal hospital readmissions related to pregnancy or giving birth within 42 days postpartum, readmission diagnoses and association with maternal demographic factors, obstetric risk factors and postnatal LOS. RESULTS: A significant increase in the rate of emergency postnatal maternal readmissions from 15 128 (2.5%) in 2008 to 20 734 (3.4%) in 2016 (aOR 1.32, 95% CI 1.28-1.37) was found. Risk factors for readmission included minoritised ethnicity (particularly Black or Black British ethnicity: aOR 1.35, 95% CI 1.31-1.39); age < 20 years (aOR 1.09, 95% CI 1.05-1.12); 40+ years (aOR 1.07, 95% CI 1.03-1.10); primiparity (multiparity: aOR 0.92, 95% CI 0.91-0.93); nonspontaneous vaginal birth modes (emergency caesarean: aOR 1.86, 95% CI 1.82-1.90); longer LOS (4+ vs. 0 days: aOR 1.58, 95% CI 1.53-1.64); and obstetric risk factors including urinary retention (aOR 2.34, 95% CI 2.06-2.53) and postnatal wound breakdown (aOR 2.01, 95% CI 1.83-2.21). CONCLUSIONS: The concerning rise in emergency maternal readmissions should be addressed from a health inequalities perspective focusing on women from minoritised ethnic groups; those <20 and ≥40 years old; primiparous women; and those with specified obstetric risk factors.

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BACKGROUND: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals. OBJECTIVE: To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population. DESIGN: Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement. PARTICIPANTS: Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison. INTERVENTIONS: A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021. MAIN MEASURES: Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison. KEY RESULTS: Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036). CONCLUSION: Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.

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Background: The disparities in healthcare access due to varying insurance coverage significantly impact hospital outcomes, yet what is unclear is the role of insurance in providing care once the patient is in the hospital for a preventable admission, particularly in a weak gatekeeping environment. This study aimed to investigate the association between insurance types and readmission rates, healthcare expenditures, and length of hospital stay among patients with chronic ambulatory care sensitive conditions (ACSCs) in China. Methods: This retrospective observational study utilized hospitalization data collected from the Nanhai District, Foshan City, between 2016 and 2020. Generalized linear models (GLMs) were employed to analyze the relationship between medical insurance types and readmission rates, lengths of hospital stay, total medical expenses, out-of-pocket expenses, and insurance-covered expenses. Results: A total of 185,384 records were included. Among these, the participants covered by urban employee basic medical insurance (UEBMI) with 44,415 records and urban and rural resident basic medical insurance (URRBMI) with 80,752 records generally experienced more favorable outcomes compared to self-pay patients. Specifically, they had lower readmission rates (OR = 0.57, 95% CI: 0.36 to 0.90; OR = 0.59, 95% CI: 0.42 to 0.84) and reduced out-of-pocket expenses (ß = -0.54, 95% CI: -0.94 to -0.14; ß = -0.41, 95% CI: -0.78 to -0.05). However, they also experienced slightly longer lengths of hospital stay (IRR = 1.08, 95% CI: 1.03 to 1.14; IRR = 1.11, 95% CI: 1.04 to 1.18) and higher total medical expenses (ß = 0.26, 95% CI: 0.09 to 0.44; ß = 0.25, 95% CI: 0.10 to 0.40). Conclusions: This study found that different types of health insurance were associated with varying clinical outcomes among patients with chronic ambulatory care sensitive conditions (ACSCs) in China. Since the hospitalization of these patients was initially avoidable, disparities in readmission rates, lengths of hospital stay, and medical expenses among avoidable inpatient cases exacerbated the health gap between different insurance types. Addressing the disparities among different types of insurance can help reduce unplanned hospitalizations and promote health equity.

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BACKGROUND: According to data from the Federal Statistical Office, the diagnosis of alcohol use disorder (AUD) (F 10) is the second most common main diagnosis for hospital treatment. Those affected by this disorder are often repeatedly hospitalized at short intervals due to relapses; however, little is known about the factors that influence readmission rates after initial treatment. AIM OF THE STUDY: The aim of this retrospective analysis is to analyze the effects of treatment type (qualified withdrawal treatment (QE) versus physical detoxification) and discharge mode on the probability of readmission in alcohol-dependent patients after inpatient treatment. MATERIAL AND METHODS: Data from 981 male and female alcohol-dependent patients who completed either qualified withdrawal treatment (QE) (68% men; mean age 47.6 years) or inpatient detoxification (74% men; mean age 48.0 years) were analyzed. Predictors of regular discharge were determined separately for both types of treatment using stepwise logistic regression. RESULTS: Patients who had completed a qualified withdrawal treatment were significantly more likely to be regularly discharged. Regular completion of the qualified withdrawal treatment (QE) led to a relative reduction in the readmission rate of 25.64% within 1 year compared to a physical detoxification. CONCLUSION: In order to prevent readmission and chronic courses of alcohol use disorder (AUD), qualified withdrawal treatment should always be recommended to affected patients instead of physical detoxification. Aktuelle Daten des Statistischen Bundesamtes für das Jahr 2022 zeigen, dass die Diagnose "Psychische und Verhaltensstörungen durch Alkohol (F 10.X)" die zweithäufigste Hauptdiagnose bei Krankenhausbehandlungen darstellt [13]. Im Gesundheitssystem entstehen durch dieses Erkrankungsbild und seine somatischen und psychischen Folgeerkrankungen jährlich ca. 10 Mrd. € direkte Kosten [13]. Dieser Sachverhalt wird dadurch kontrastiert, dass die Krankenkassen die qualifizierte Entzugsbehandlung (QE) als leitliniengerechte Goldstandardtherapie [4] wiederholt infrage stellen [10].

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BACKGROUND: Adults with cerebral palsy (CP) are a largely understudied, growing population with unique health care requirements. We sought to establish a deeper understanding of the surgical risk in adults with CP undergoing a common general surgical procedure: cholecystectomy. METHODS: Data were obtained from the State Inpatient Database developed for the Healthcare Cost and Utilization Project. Inclusion criteria included patients ≥ 18 years with CP and a primary ICD-9 procedure code indicating open or laparoscopic cholecystectomy. Demographics, procedure-related factors, and comorbid conditions were analyzed, and unplanned 30 and 90 day readmission rates calculated for each variable. Reasons for readmission based on ICD-9 diagnosis codes were grouped into relevant categories. Univariate analysis identified factors significantly associated with readmission rates. RESULTS: A total of 802 patients with CP met the inclusion criteria. Unplanned 30 and 90 day readmission rates after laparoscopic cholecystectomy were 11.4% and 18.1%, respectively. Average length of stay (LOS) after laparoscopic cholecystectomy was 7.1 days. After open cholecystectomy, 30 and 90 day readmission rates were 16.9% and 30.3% with an average LOS of 14.6 days. Infection was the most common cause for 30 and 90 day readmission. Factors associated with 30 day readmission included type of cholecystectomy, LOS, discharge to skilled nursing facility, and comorbid diabetes and malnutrition. Factors associated with 90 day readmission included type of cholecystectomy, LOS, discharge to skilled nursing facility, and comorbid heart failure, renal disease, epilepsy, and malnutrition. CONCLUSION: Unplanned readmission rates after open and laparoscopic cholecystectomy in adult patients with CP are much higher than previously demonstrated rates in the general population. These patients frequently suffer multiple comorbid conditions that significantly complicate their surgical care. As more and more of these patients live longer into adulthood, further study is warranted to grasp the perioperative risk of simple and complex surgical procedures.

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Background: Identification of prognostic variables for poor outcomes following open reduction internal fixation (ORIF) of displaced proximal humerus fractures have been limited to singular, linear factors and subjective clinical intuition. Machine learning (ML) has the capability to objectively segregate patients based on various outcome metrics and reports the connectivity of variables resulting in the optimal outcome. Therefore, the purpose of this study was to (1) use unsupervised ML to stratify patients to high-risk and low-risk clusters based on postoperative events, (2) compare the ML clusters to the American Society of Anesthesiologists (ASA) classification for assessment of risk, and (3) determine the variables that were associated with high-risk patients after proximal humerus ORIF. Methods: The American College of Surgeons-National Surgical Quality Improvement Program database was retrospectively queried for patients undergoing ORIF for proximal humerus fractures between 2005 and 2018. Four unsupervised ML clustering algorithms were evaluated to partition subjects into "high-risk" and "low-risk" subgroups based on combinations of observed outcomes. Demographic, clinical, and treatment variables were compared between these groups using descriptive statistics. A supervised ML algorithm was generated to identify patients who were likely to be "high risk" and were compared to ASA classification. A game-theory-based explanation algorithm was used to illustrate predictors of "high-risk" status. Results: Overall, 4670 patients were included, of which 202 were partitioned into the "high-risk" cluster, while the remaining (4468 patients) were partitioned into the "low-risk" cluster. Patients in the "high-risk" cluster demonstrated significantly increased rates of the following complications: 30-day mortality, 30-day readmission rates, 30-day reoperation rates, nonroutine discharge rates, length of stay, and rates of all surgical and medical complications assessed with the exception of urinary tract infection (P < .001). The best performing supervised machine learning algorithm for preoperatively identifying "high-risk" patients was the extreme-gradient boost (XGBoost), which achieved an area under the receiver operating characteristics curve of 76.8%, while ASA classification had an area under the receiver operating characteristics curve of 61.7%. Shapley values identified the following predictors of "high-risk" status: greater body mass index, increasing age, ASA class 3, increased operative time, male gender, diabetes, and smoking history. Conclusion: Unsupervised ML identified that "high-risk" patients have a higher risk of complications (8.9%) than "low-risk" groups (0.4%) with respect to 30-day complication rate. A supervised ML model selected greater body mass index, increasing age, ASA class 3, increased operative time, male gender, diabetes, and smoking history to effectively predict "high-risk" patients.

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Objective: Firstly, the study explores the association between timely initiation of rehabilitation and 90-day and 365-day all-cause acute readmission and secondly, 90-day and 365-day all-cause mortality in a cohort of Odense Municipality residents. Methods: The registry-based observational cohort study investigates acute contacts at Odense University Hospital from 2015 to 2020. Descriptive statistics, Cox regression and cumulative incidence rates were used for analysis. Subjects: The study utilizes initiated rehabilitation referrals within 60 days from Odense Municipality residents. Results: In total, 7,377 rehabilitation plans were initiated, including 5051 (68.5%) within the legal timeframe. Overall, timely initiation of rehabilitation within the legal timeframe was associated with a significantly reduced risk of 90-day all-cause acute readmission (Adjusted HR 0.82, 95% CI 0.74-0.90).In the adjusted analysis, timely initiation was also significantly associated with reduced risk in 365-day all-cause acute readmission (HR 0.90, 95% CI 0.83-0.97). Each week of delay in initiation of rehabilitation was associated with an increased risk of readmission (HR 1.05, 95% CI 1.02-1.07). Further, timely initiation of rehabilitation was associated with a significant reduction in the risk of 365-day all-cause mortality (HR 0.74, 95% CI 0.61-0.89). Conclusion: Timely initiation of rehabilitation within the legal timeframe of 7 or 14 days was associated with significantly reduced risk of 90-day and 365-day all-cause acute readmission. Timely initiation of rehabilitation was also associated with significant reduction in the risk of 365-day all-cause mortality.

Most patients can benefit from rehabilitation after hospital admission. Early rehabilitation has shown to be useful for specific patient groups. This study explores the association between timely rehabilitation and readmission and early mortality in a cohort of Odense Municipality residents. Findings show that timely initiation of rehabilitation is associated with fewer acute readmissions. For each week the rehabilitation is delayed the risk of readmission increases. Furthermore, timely initiation of rehabilitation was significantly associated with a reduction of all-cause mortality within 365 days.

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OBJECTIVE: To evaluate predictive validity of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition Indicators to diagnose pediatric malnutrition (AAIMp) and the Screening Tool for Risk on Nutritional Status and Growth (STRONGkids) in regard to pediatric patient outcomes in US hospitals. STUDY DESIGN: A prospective cohort study (Clinical Trial Registry: NCT03928548) was completed from August 2019 through January 2023 with 27 pediatric hospitals or units from 18 US states and Washington DC. RESULTS: Three hundred and forty-five children were enrolled in the cohort (n=188 in the AAIMp validation subgroup). There were no significant differences in the incidence of emergency department (ED) visits and hospital readmissions, hospital length of stay (LOS), or healthcare resource utilization for children diagnosed with mild, moderate, or severe malnutrition using the AAIMp tool compared with children with no malnutrition diagnosis. The STRONGkids tool significantly predicted more ED visits and hospital readmissions for children at moderate and high malnutrition risk (moderate risk - incidence rate ratio [IRR] 1.65, 95% confidence interval [CI]: 1.09, 2.49, p = 0.018; high risk - IRR 1.64, 95% CI: 1.05, 2.56, p = 0.028) and longer LOS (43.8% longer LOS, 95% CI: 5.2%, 96.6%, p = 0.023) for children at high risk compared with children at low risk after adjusting for patient characteristics. CONCLUSIONS: Malnutrition risk based on the STRONGkids tool predicted poor medical outcomes in hospitalized US children; the same relationship was not observed for a malnutrition diagnosis based on the AAIMp tool.

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Objective: This study aimed to determine the rate of readmission for trauma patients in ICUs, as well as the factors that predict this outcome. Methods: This retrospective cohort study was conducted at Emtiaz Hospital, a level I referral trauma center (Shiraz, Iran). It analyzed the ICU readmission rates among trauma patients over three years. The required data were extracted from the Iranian Intensive Care Registry (IICUR), which included patient demographics, injury severity, physiological parameters, and clinical outcomes. Statistical analysis was performed using SPSS version 25.0. Descriptive statistics and different statistical tests, such as T-tests, Mann-Whitney tests, Chi-square tests, and logistic binary regression test were utilized. Results: Among the 5273 patients discharged from the ICU during the study period, 195 (3.7%) were readmitted during the same hospitalization. Patients readmitted to the ICU had a significantly higher mean age (54.83±22.73 years) than those who were not readmitted (47.08 years, p<0.001). Lower Glasgow Coma Scale (GCS) scores at admission and discharge were associated with ICU readmission, implying that neurological status and readmission risk were correlated with each other. Furthermore, respiratory challenges were identified as the leading cause of unexpected readmission, including respiratory failure, hypoxic respiratory failure, respiratory distress, and respiratory infections such as pneumonia. Injury patterns analysis revealed a higher frequency of poly-trauma and head and neck injuries among patients readmitted to the ICU. Conclusion: This study underscored the importance of ICU readmission among trauma patients, with a high readmission rate during the same hospitalization. By developing comprehensive guidelines and optimizing discharge processes, healthcare providers could potentially mitigate ICU readmissions and associated complications, ultimately enhancing patient outcomes and resource utilization in trauma ICU settings. This research provided valuable insights to inform evidence-based practices and improve the quality of care delivery for trauma patients in intensive care settings.

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Objective: Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals. Methods: Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk. Results: The gradient boosting model was selected demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.887 in the internal validation set. Further external validation in hold-out test set and three other medical centers upheld the model's robustness with consistent high AUCs, ranging from 0.852 to 0.879. Conclusion: The results endorse the integration of ML algorithms in healthcare to enhance patient risk stratification, potentially optimizing clinical interventions, and diminishing the burden of CCU readmissions.

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BACKGROUND: Community deprivation has been linked to poor health outcomes following primary total knee arthroplasty (pTKA), but few studies have explored revision TKA (rTKA). The present study analyzed implications of neighborhood deprivation on rTKA outcomes by characterizing relationships between Area Deprivation Index (ADI) and (1) non-home discharge disposition (DD), (2) hospital length of stay (LOS), (3) 90-day emergency department (ED) visits, (4) 90-day hospital readmissions, and (5) the effect of race on these healthcare outcomes. METHODS: A total of 1,434 patients who underwent rTKA between January 2016 and June 2022 were analyzed. Associations between the ADI and postoperative healthcare resource utilization outcomes were evaluated using multivariate logistic regression. Mediation effect was estimated using a nonparametric bootstrap resampling method. RESULTS: Greater ADI was associated with non-home DD (p < 0.001), LOS ≥ 3 days (p < 0.001), 90-day ED visits (p = 0.015), and 90-day hospital readmission (p = 0.002). Although there was no significant difference in ADI between septic and aseptic patients, septic patients undergoing rTKA were more likely to experience non-home discharge (p < 0.001), prolonged LOS (p < 0.001), and 90-day hospital readmission (p = 0.001). The effect of race on non-home DD was found to be mediated via ADI (p = 0.038). Similarly, results showed the effect of race on prolonged LOS was mediated via ADI (p = 0.01). CONCLUSION: A higher ADI was associated with non-home discharge, prolonged LOS, 90-day ED visits, and 90-day hospital readmissions. The impacts of patient race on both non-home discharge and prolonged LOS were mediated by ADI. This index allows clinicians to better understand and address disparities in rTKA outcomes.

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Background: Patients with acute heart failure (HF) are at high risk of 30-day readmission. Little is known about the characteristics and associated factors of 30-day readmissions among patients with acute HF in China. Methods: We enrolled consecutive patients hospitalized for acute HF and discharged from 52 hospitals in China from August 2016 to May 2018. We describe the rate of 30-day readmission, the time interval from discharge to readmission, and the causes of readmission. We also analyzed the factors associated with readmission risk by fitting multivariate Cox proportional hazards models. Results: We included 4875 patients with a median age of 67 years (interquartile range, 57-75), 3045 (62.5%) of whom were male. Within 30 days after discharge, 613 (12.6%) patients were readmitted for all causes, with a median from discharge to readmission of 12 (6-21) days. Most readmissions were attributed to cardiovascular causes (71.1%) and 60.0% to HF-related causes. Readmission occurred within 14 days of discharge in more than half of the patients (56.4%). Diabetes (hazard ratio [HR]: 1.25, 95% confidence interval [95% CI]: 1.06-1.50), anemia (HR: 1.26, 95% CI: 1.03-1.53), high New York Heart Association classification (HR: 1.48, 95% CI: 1.08-2.01), elevated N-terminal pro-B type natriuretic peptide (HR: 1.67, 95% CI: 1.24-2.25), and high-sensitivity cardiac troponin T (HR: 1.26, 95% CI: 1.01-1.58) were associated with increased risks of readmission. High systolic blood pressure (HR: 0.56, 95% CI: 0.38-0.81) and Kansas City Cardiomyopathy Questionnaire-12 scores (HR: 0.64, 95% CI: 0.44-0.94) were associated with decreased risk of readmission. Conclusions: In China, almost one in eight patients with acute HF were readmitted within 30 days after discharge, mainly due to cardiovascular reasons, and approximately three-fifths of the readmissions occurred in the first 14 days. Both clinical and patient-centered characteristics were associated with readmission.

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INTRODUCTION: Ileostomy, frequently created after colorectal resections, hinders the physiologic function of the colon and can lead to dehydration and acute kidney injury due to high stoma outputs. This study aimed to evaluate the effectiveness of preventive measures on ileostomy-induced dehydration and related readmissions in a high-volume unit. METHODS: In this prospective cohort study at a high-volume colorectal surgery department in Turkiye, the Prospective Ileostomy-induced Dehydration Prevention Bundle Project (PIDBP) was assessed from March 2021 to March 2022. The study enrolled patients undergoing colorectal surgery with ileostomy and involved comprehensive inpatient stoma care, education, and a structured post-discharge follow-up. The follow-up included the "Hydration follow-up scale" to monitor ileostomy output and related complications. The primary outcome was the readmission rate due to dehydration-related complications. The patients receiving the bundle intervention were compared with patients treated in the preceding year, focusing on the effectiveness of interventions such as dietary adjustments, fluid therapy, and pharmacological management. RESULTS: In the study, 104 patients were analyzed, divided into 54 pre-bundle and 50 bundle group patients, with no significant differences in patient characteristics. While the overall readmission rate due to dehydration was 12.5%, a significant reduction in dehydration-related readmissions was observed in the bundle group compared to the pre-bundle group (2% vs. 22%, p = 0.002). Univariate analysis identified high stoma output (> 800 ml/24 h) (p < 0.001), chronic renal failure (CRF) (p = 0.01), postoperative ileus (p = 0.03), higher ASA status (p = 0.04), extended hospital stays (p = 0.03), and small bowel resections (especially in J-pouch patients) (p < 0.001) as significant predictors of readmission. Multivariate analysis revealed that the mean ileostomy output before discharge was the sole significant predictor of dehydration-related readmission (OR 1.01), with an optimal cutoff of 877.5 ml/day identified with an area under the curve (AUC) of 0.947, demonstrating high sensitivity (92.3%) and specificity (86.8%) in predicting readmission risk. CONCLUSION: The Prospective Ileostomy-induced Dehydration Prevention Bundle Project significantly reduced readmission rates after colorectal surgery.

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