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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: 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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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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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: Hospitalization of patients with DKA creates a significant burden on the US healthcare system. While previous studies have identified multiple potential contributors, a comprehensive review of the factors leading to DKA readmissions within the US healthcare system has not been done. This scoping review aims to identify how access to care, treatment adherence, socioeconomic status, race, and ethnicity impact DKA readmission-related patient morbidity and mortality and contribute to the socioeconomic burden on the US healthcare system. Additionally, this study aims to integrate current recommendations to address this multifactorial issue, ultimately reducing the burden at both individual and organizational levels. METHODS: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. RESULTS: A total of 15 articles were retained for analysis. Among the various social factors identified, those related to sex/gender (n = 9) and age (n = 9) exhibited the highest frequency. Moreover, race and ethnicity (n = 8) was another recurrent factor that appeared in half of the studies. Economic factors were also identified in this study, with patient insurance type having the highest frequency (n = 11). Patient income had the second highest frequency (n = 6). Multiple studies identified a link between patients of a specific race/ethnicity and decreased access to treatment. Insufficient patient education around DKA treatment was noted to impact treatment accessibility. Certain recommendations for future directions were highlighted as recurrent themes across included studies and encompassed patient education, early identification of DKA risk factors, and the need for a multidisciplinary approach using community partners such as social workers and dieticians to decrease DKA readmission rates in diabetic patients. CONCLUSION: This study can inform future policy decisions to improve the accessibility, affordability, and quality of healthcare through evidence-based interventions for patients with DM following an episode of DKA.

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

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INTRODUCTION: The primary aim of this study was to assess 30-day and one-year rates for unplanned readmission due to implant-associated complications following total hip (THA) or total knee arthroplasty (TKA) in Austria. Secondary endpoints were reasons for readmission and differences in revision risk depending on demographics and hospital size. METHODS: Data on patients receiving THA (n = 18,508) or TKA (n = 15,884) in orthopaedic and trauma units across Austria within a one-year period (January 2021 to December 2021) was retrieved from a government-maintained database. The absolute and relative frequencies of unplanned readmissions were calculated. Risk factors for 30-day and one-year readmission following THA or TKA due to implant-associated complications were investigated. RESULTS: The thirty-day and one-year readmission rates for any implant-associated complication were 1.0% (339 of 34,392) and 3.0% (1,024 of 34,392). Relative to the overall readmission rate for any complication at 30 days (n = 1,952) and one year (n = 12,109), respectively, readmission rates for implant-associated complications were 17.4 and 8.5%. The thirty-day readmission rates were higher in THA (1.2%) than TKA patients (0.8%; P = 0.001), while it was the opposite at one year (THA, 2.7%; TKA, 3.3%; P < 0.001). Mechanical complications (554 of 1,024) were the most common reason for one-year readmission. Prolonged length of in-hospital stay independently associated with increased one-year readmission risk in THA and TKA patients. Treatment at large-sized hospitals was associated with a higher one-year readmission risk in TKA patients. CONCLUSIONS: The thirty-day and one-year readmission rates for implant-associated complications following THA or TKA in Austria are lower than reported in other countries, with similar risk factors and reasons for readmission. Considering that almost 20% of unplanned hospital readmissions following total joint arthroplasty are attributable to implant-associated complications, optimization of in-hospital and post-discharge medical care for these patients is warranted.

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

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

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BACKGROUND: Little is known about the prevalence of malnutrition among patients receiving home care (HC) and ambulatory care (AC) services. Further, the risk of hospital readmission in malnourished patients transitioning from hospital to HC or AC is also not well established. This study aims to address these two gaps. METHODS: A descriptive cohort study of newly referred HC and AC patients between January and December 2019 was conducted. Nutrition status was assessed by clinicians using the Mini Nutritional Assessment-Short Form (MNA-SF). Prevalence of malnutrition and at risk of malnutrition (ARM) was calculated, and a log-binomial regression model was used to estimate the relative risk of hospital readmission within 30 days of discharge for those who were malnourished and referred from hospital. RESULTS: A total of 3704 MNA-SFs were returned, of which 2402 (65%) had complete data. The estimated prevalence of malnutrition and ARM among newly referred HC and AC patients was 21% (95% CI: 19%-22%) and 55% (95% CI: 53%-57%), respectively. The estimated risk of hospital readmission for malnourished patients was 2.7 times higher (95% CI: 1.9%-3.9%) and for ARM patients was 1.9 times higher (95% CI: 1.4%-2.8%) than that of patients with normal nutrition status. CONCLUSION: The prevalence of malnutrition and ARM among HC and AC patients is high. Malnutrition and ARM are correlated with an increased risk of hospital readmission 30 days posthospital discharge.

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BACKGROUND AND AIMS: Alcohol use disorder (AUD) is a persistent public health concern, contributing significantly to mortality and morbidity. This study aims to evaluate the impact of in-hospital extended-release naltrexone (XR-NTX) administration on alcohol-related outcomes. METHODS: This retrospective cohort study, conducted at an academic medical centre, included 141 adult patients with AUD who received XR-NTX between December 2020 and June 2021. Primary and secondary outcomes were assessed 90 days before and after XR-NTX administration to identify number of alcohol-related hospitalisations, emergency department (ED) visits and average length of hospital stay. Subgroup analyses assessed outcomes in high hospital utilisers and marginally housed or unhoused populations. RESULTS: There was a significant decrease in ED visits and length of hospital stay post XR-NTX and no significant difference in the number of rehospitalisations. Subgroup analysis showed significant reduction in hospital readmissions and ED visits among high hospital utilisers. Our sample was a predominantly middle-aged, male and white patient population. CONCLUSIONS: In-hospital initiation of XR-NTX for AUD was associated with a significant decrease in ED visits and length of hospital stay. While no significant impact on the number of hospitalisations was observed overall, there was a substantial reduction in hospital readmissions and ED visits among high utilisers. Our findings suggest the potential benefits of in-hospital XR-NTX, emphasising the need for further research to establish causal relationships, assess cost-effectiveness and explore effectiveness across diverse patient populations. Effective in-hospital interventions, such as XR-NTX, hold promise for improving patient outcomes and reducing the healthcare burden associated with AUD.

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OBJECTIVES: (1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index. DATA SOURCES AND SETTING: We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program. STUDY DESIGN: We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk. DATA EXTRACTION: We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022. PRINCIPAL FINDINGS: The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA. CONCLUSIONS: Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

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Background Congestive heart failure (CHF) is a leading cause of hospitalizations and readmissions, placing a significant burden on the healthcare system. Identifying factors associated with readmission risk is crucial for developing targeted interventions and improving patient outcomes. This study aimed to investigate the impact of socioeconomic and demographic factors on 30-day and 90-day readmission rates in patients primarily admitted for CHF. Methods The study was carried out using a cross-sectional study design, and the data were obtained from the Nationwide Readmissions Database (NRD) from 2016 to 2020. Adult patients with a primary diagnosis of CHF were included. The primary outcomes were 30-day and 90-day all-cause readmission rates. Multivariable logistic regression was used to identify factors independently associated with readmissions, including race, ethnicity, insurance status, income level, and living arrangements. Results A total of 219,904 patients with a primary diagnosis of CHF were used in the study. The overall 30-day and 90-day readmission rates were 17.3% and 23.1%, respectively. In multivariable analysis, factors independently associated with higher 30-day readmission risk included Hispanic ethnicity (OR 1.18, 95% CI 1.03-1.35), African American race (OR 1.15, 95% CI 1.04-1.28), Medicare insurance (OR 1.24, 95% CI 1.12-1.38), and urban residence (OR 1.11, 95% CI 1.02-1.21). Higher income was associated with lower readmission risk (OR 0.87, 95% CI 0.79-0.96 for highest vs. lowest quartile). Similar patterns were observed for 90-day readmissions. Conclusion Socioeconomic and demographic factors, including race, ethnicity, insurance status, income level, and living arrangements, significantly impact 30-day and 90-day readmission rates in patients with CHF. These findings highlight the need for targeted interventions and policies that address social determinants of health and promote health equity in the management of CHF. Future research should focus on developing and evaluating culturally sensitive, community-based strategies to reduce readmissions and improve outcomes for high-risk CHF patients.

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

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INTRODUCTION: Diabetic foot (DF) is part of the natural history of diabetes mellitus, ulceration being a severe complication with a prevalence of approximately 6.3 %, which confers a significant economic burden. Hospital readmission in the first thirty (30) days is considered a measure of quality of healthcare and it's been identified that the most preventable causes are the ones that occur in this period. This study seeks to identify the risk factors associated with readmission of patients with DF. METHODS: A case-control study was done by performing a secondary analysis of a database. Descriptive statistics were used for all variables of interest, bivariate analysis to identify statistically significant variables, and a logistic regression model for multivariate analysis. RESULTS: 575 cases were analyzed (113 cases, 462 controls). A 20 % incidence rate of 30-day readmission was identified. Statistically significant differences were found in relation to the institution of attention (Hospital Universitario de la Samaritana: OR 1.9, p value < 0.01, 95 % CI 1.2-3.0; Hospital Universitario San Ignacio: OR 0.5, p value < 0.01, 95 % CI 0.3-0.8) and the reasons for readmission before 30 days, especially due to surgical site infection (SSI) (OR 7.1, p value < 0.01, 95 % CI 4.1-12.4), sepsis (OR 8.4, p value 0.02, 95 % CI 1.2-94.0), dehiscence in amputation stump (OR 16.4, p value < 0.01, 95 % CI 4.2-93.1) and decompensation of other pathologies (OR 3.5, p value < 0.01, 95 % CI 2.1-5.7). CONCLUSION: The hospital readmission rate before 30 days for our population compares to current literature. Our results were consistent with exacerbation of chronic pathologies, but other relevant variables not mentioned in other studies were the hospital in which patients were taken care of, the presence of SSI, sepsis, and dehiscence of the amputation stump. We consider thoughtful and close screening of patients at risk in an outpatient setting might identify possible readmissions.

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

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

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BACKGROUND: With the increased utilization of Total Shoulder Arthroplasty (TSA) in the outpatient setting, understanding the risk factors associated with complications and hospital readmissions becomes a more significant consideration. Prior developed assessment metrics in the literature either consisted of hard-to-implement tools or relied on postoperative data to guide decision-making. This study aimed to develop a preoperative risk assessment tool to help predict the risk of hospital readmission and other postoperative adverse outcomes. METHODS: We retrospectively evaluated the 2019-2022(Q2) Medicare fee-for-service inpatient and outpatient claims data to identify primary anatomic or reserve TSAs and to predict postoperative adverse outcomes within 90 days postdischarge, including all-cause hospital readmissions, postoperative complications, emergency room visits, and mortality. We screened 108 candidate predictors, including demographics, social determinants of health, TSA indications, prior 12-month hospital, and skilled nursing home admissions, comorbidities measured by hierarchical conditional categories, and prior orthopedic device-related complications. We used two approaches to reduce the number of predictors based on 80% of the data: 1) the Least Absolute Shrinkage and Selection Operator logistic regression and 2) the machine-learning-based cross-validation approach, with the resulting predictor sets being assessed in the remaining 20% of the data. A scoring system was created based on the final regression models' coefficients, and score cutoff points were determined for low, medium, and high-risk patients. RESULTS: A total of 208,634 TSA cases were included. There was a 6.8% hospital readmission rate with 11.2% of cases having at least one postoperative adverse outcome. Fifteen covariates were identified for predicting hospital readmission with the area under the curve of 0.70, and 16 were selected to predict any adverse postoperative outcome (area under the curve = 0.75). The Least Absolute Shrinkage and Selection Operator and machine learning approaches had similar performance. Advanced age and a history of fracture due to orthopedic devices are among the top predictors of hospital readmissions and other adverse outcomes. The score range for hospital readmission and an adverse postoperative outcome was 0 to 48 and 0 to 79, respectively. The cutoff points for the low, medium, and high-risk categories are 0-9, 10-14, ≥15 for hospital readmissions, and 0-11, 12-16, ≥17 for the composite outcome. CONCLUSION: Based on Medicare fee-for-service claims data, this study presents a preoperative risk stratification tool to assess hospital readmission or adverse surgical outcomes following TSA. Further investigation is warranted to validate these tools in a variety of diverse demographic settings and improve their predictive performance.

RESUMEN

Backgroud: Acute myocardial infarction (AMI) has a high morbidity rate, high mortality rate, high readmission rate, high health care costs, and a high symptomatic, psychological, and economic burden on patients. Patients with AMI usually present with multiple symptoms simultaneously, which are manifested as symptom clusters. Symptom clusters have a profound impact on the quality of survival and clinical outcomes of AMI patients. Objective: The purpose of this study was to analyze unplanned hospital readmissions among cluster groups within a 1-year follow-up period, as well as to identify clusters of acute symptoms and the characteristics associated with them that appeared in patients with AMI. Methods: Between October 2021 and October 2022, 261 AMI patients in China were individually questioned for symptoms using a structured questionnaire. Mplus 8.3 software was used to conduct latent class analysis in order to find symptom clusters. Univariate analysis is used to examine characteristics associated with each cluster, and multinomial logistic regression is used to analyze a cluster membership as an independent predictor of hospital readmission after 1-year. Results: Three unique clusters were found among the 11 acute symptoms: the typical chest symptom cluster (64.4%), the multiple symptom cluster (29.5%), and the atypical symptom cluster (6.1%). The cluster of atypical symptoms was more likely to have anemia and the worse values of Killip class compared with other clusters. The results of multiple logistic regression indicated that, in comparison to the typical chest cluster, the atypical symptom cluster substantially predicted a greater probability of 1-year hospital readmission (odd ratio 8.303, 95% confidence interval 2.550-27.031, P < 0.001). Conclusion: Out of the 11 acute symptoms, we have found three clusters: the typical chest symptom, multiple symptom, and atypical symptom clusters. Compared to patients in the other two clusters, those in the atypical symptom cluster-which included anemia and a large percentage of Killip class patients-had worse clinical indicators at hospital readmission during the duration of the 1-year follow-up. Both anemia and high Killip classification suggest that the patient's clinical presentation is poor and therefore the prognosis is worse. Intensive treatment should be considered for anemia and high level of Killip class patients with atypical presentation. Clinicians should focus on patients with atypical symptom clusters, enhance early recognition of symptoms, and develop targeted symptom management strategies to alleviate their discomfort in order to improve symptomatic outcomes.