Higher Risk Of Readmission Research Articles

Repeat self-harm hospitalizations in Canada: a survival analysis

BackgroundRepeat self-harm hospitalizations are associated with a greater risk of suicide and place a substantial burden on the healthcare system. In Canada, despite growing awareness of self-harm as a public heath issue, most existing research has focused on the prevalence of self-harm, with less attention given to repeat admissions. This study aims to assess the risk of repeat self-harm hospitalizations in Canada and identify population subgroups at higher risk.MethodsWe included 74,055 patients discharged between April 2016 and March 2022, with self-harm hospitalizations recorded in the Canadian Institute for Health Information’s Discharge Database and the Ontario Mental Health Reporting System. After an initial self-harm hospitalization, patients were followed for repeat admissions during the study period. The risk of readmission was estimated using Kaplan-Meier survival analysis, while hazard ratios for factors such as sex, age group, method of self-harm and the presence of a mental disorder diagnosis, were calculated using Cox regression models.ResultsAmong patients hospitalized for self-harm, the risk of readmission was 9.3% within one year and 13.0% within three years of the index hospitalization. Three-quarters of readmissions occurred within the first year, and 90% occurred within two years. Females had a higher risk of readmission than males (hazard ratio = 1.32), with the highest risk observed among females aged 10–14 years (19.2% within three years), while patients aged 65 years and older had the lowest risk for both males and females. Females who self-harmed by cutting and patients of both sexes who used substance-related poisoning methods, as well as patients with a mental disorder diagnosis, were also at greater risk of readmissions.ConclusionIn Canada, approximately one in ten patients hospitalized for self-harm were readmitted, with most readmissions occurring within the subsequent first year. Certain subgroups, including females, young girls, individuals who engaged in self-harm through cutting or substance use, and those with a mental disorder, face higher risks. This study provides insights to guide targeted interventions aimed at preventing recurrence, informing resource allocation, and emphasizing the need for comprehensive mental health support to improve outcomes for at-risk individuals.

The Impact of Pharmacist Transitions of Care Interventions in Identifying Medications Errors for Patients Discharging to a Skilled Nursing Facility.

Background: Patients being discharged from acute care facilities have a high risk of hospital readmission due to medication errors. Pharmacist interventions during transitions of care (TOC) may be beneficial in identifying medication errors and improving patient outcomes when discharging to a skilled nursing facility (SNF). Objective The objective of this study was to evaluate the impact of pharmacist interventions in reducing medication errors for patients being discharged from an acute care facility to a SNF. Setting A community hospital that is part of a larger health network in Southern California. Practice Description Clinical pharmacists provide TOC interventions to high-risk patients discharging home. Practice Innovation Over a three-month period, pharmacists provided TOC interventions to patients discharging from a hospital to a SNF. A retrospective chart review evaluated documented pharmacist interventions to identify and categorize medication errors based on the potential for harm. Results Pharmacists saw 324 patients being discharged from the hospital and identified a total of 33 medication errors. A total of 61% of errors were related to incorrect dose, frequency, or route of administration, while 51.5% had a capacity to cause temporary harm. Only 1 error could have necessitated intervention to sustain life. Ultimately, 76% of pharmacist interventions were accepted by the patients' physicians or health care teams. Discussion Pharmacists' interventions, in addition to communication with the health care team, were able to prevent medication errors with potential to cause harm as patients transitioned from a hospital to a SNF. Conclusion Pharmacists can support safe transitions for patients discharging from the hospital to the SNF.

Demand analysis of transitional care for patients undergoing minimally invasive cardiac interventions with AI-driven solutions: a mixed-methods approach

AimsMinimally invasive cardiac intervention (MICI) patients remain at high risk of readmission and mortality during their post-discharge phase, with 30-day readmission rates of up to 10%. Although technological innovations, especially AI-driven solutions, hold promise for improving outcomes, there is a pressing need to clarify the full spectrum of patient demands during the transition from hospital to home. This study aimed to systematically identify these demands to guide the development of AI-driven solutions that reduce readmission rates and improve clinical outcomes.Methods and resultsA convergent parallel mixed-methods design was employed to systematically identify patient demands and inform the development of AI-driven interventions in transitional care. Quantitative and qualitative data were collected from 137 MICI patients recruited from four hospitals (June–August 2024). Quantitatively, a 23-item survey was analyzed using the Kano model, revealing no “must-be” demands—indicating that patients were accustomed to a lack of guidance post-discharge. However, health monitoring, medication guidance, symptom management, and personalized exercise plans were identified as “one-dimensional” demands that significantly impact patient satisfaction. Additionally, continuous exercise monitoring and dietary planning emerged as “attractive” features that could enhance care quality without negatively affecting satisfaction if absent. Qualitative interviews uncovered the importance of comorbidity management, psychological support and financial transparency, which were not fully captured in the survey data. The integration of these findings underscores the need for AI-driven personalized health monitoring systems and knowledge-based AI tools to revolutionize the transitional care process for MICI patients.ConclusionThis integrated analysis highlights the significant care demands of MICI patients during the transition from hospital to home. Key recommendations include: (1) deploying AI-driven health monitoring, medication guidance, and symptom management systems, (2) designing personalized exercise and dietary tools, and (3) creating accessible, knowledge-based platforms for reliable medical information. In addition, comorbidity management, psychological support and financial transparency are areas that call for our attention. By aligning with these patient-centered demands and leveraging AI’s capabilities, future transitional care interventions—particularly in China have the potential to address healthcare staffing constraints and improve patient outcomes. However, due to the limitations of our study, these insights require further validation and exploration.

The Clinical significance of Peripheral Blood-related Inflammatory Markers in patients with AECOPD.

The Clinical significance of Peripheral Blood-related Inflammatory Markers in patients with AECOPD.

Cholecystitis Treatment and Outcomes Among People Living With Dementia.

Cholecystectomy is considered the definitive treatment option for cholecystitis, but the effect of different treatment options among people living with dementia (PLWD) has not been elucidated. This study compares outcomes following cholecystectomy, cholecystostomy tube, and medical management of cholecystitis among this high-risk group. We conducted a retrospective analysis of Medicare claims data 1/1/2016 to 12/31/2020. The cohort comprised Medicare PLWD aged 66+ admitted to acute care facilities with a new primary diagnosis of cholecystitis. We used inverse propensity weighting regression to adjust for confounding by indication. We compared outcomes during index admission, readmissions, and mortality. Eight thousand and seven hundred and seventy four individuals met inclusion criteria; 7% open cholecystectomy, 49% minimally invasive (MIS) cholecystectomy, 13% cholecystostomy tube, 31% managed medically. After adjustment, PLWD undergoing open or MIS cholecystectomy had a greater risk of intensive interventions (Open OR 3.3, p < 0.001; MIS OR 1.3, p = 0.02) and surgical complications (Open OR 10.6, p < 0.001; MIS OR 3.3, p < 0.001) during the index admission, but a lower risk of readmission (Open HR 0.9, p = 0.009; MIS HR 0.9, p < 0.001) and lower mortality (Open HR 0.6, p < 0.001; MIS 0.6, p < 0.001) compared with PLWD managed medically. PLWD managed with cholecystostomy tube had no difference in intensive interventions or surgical complications during the index admission, but a higher risk of readmission (HR 1.1, p = 0.01), cholecystectomy during readmission (HR 1.8, p < 0.001) and no difference in mortality compared to those managed medically. Over half of PLWD experiencing acute cholecystitis received definitive surgical treatment during the index admission. Open and MIS cholecystectomy were associated with worse outcomes during the index admission, but reduced mortality and readmissions in the 2 years following index admission. Cholecystostomy tube was associated with a greater likelihood of readmission and subsequent cholecystectomy, and no difference in mortality. These findings should be interpreted within the context of administrative data, which has the potential for selection bias and unmeasured confounding.

Assessing Inequities in Hospital Outcomes for Australian Children From Underserved Populations.

Inequity in health outcomes for children and young people (CYP) from underserved populations (Indigenous, culturally and linguistically diverse, refugee and/or asylum seeking, out-of-home care backgrounds, and National Disability Insurance Scheme participants) persists. We quantify baseline inequities in health outcomes to measure the effectiveness of equity interventions. We analyzed electronic medical records on CYP from the Sydney Children's Hospitals Network between 2015 and 2019. The primary outcome measures were high-acuity presentations, potentially preventable hospitalizations (PPH), chronic condition hospitalizations, discharge against medical advice (DAMA), ward and critical care admission, readmission, and extended length of stay (LOS). We used generalized estimating equation models to examine the relationship between underserved population status and outcomes. One third of 253 934 inpatient and 446 924 emergency department (ED) encounters were underserved CYP. Compared with nonunderserved populations, there was increased risk of PPH (relative risk [RR], 1.25; 95% CI, 1.23-1.27), chronic conditions (RR, 1.09; 95% CI, 1.07-1.10), DAMA (RR, 1.33; 95% CI, 1.19-1.49), ward admission (RR, 1.16; 95% CI, 1.15-1.18), readmission (RR, 1.48; 95% CI, 1.42-1.53), extended inpatient LOS (RR, 1.21; 95% CI, 1.18-1.24), and ED LOS (RR, 1.11; 95% CI, 1.10-1.12). As an example of cumulative risk, Indigenous CYP living with a disability had a 239% higher risk of readmission than CYP without these risk factors (RR, 3.39; 95% CI, 2.92-3.93). Interventions are required to reduce health inequities for underserved CYP. We present strategies that include improved patient identification, enhanced service access, and system-wide culture change within an equity learning health system.

Association Between Nursing Workload and Intensive Care Unit Readmissions: A Prospective Cohort Study.

The aim of this study was to assess the relationship between nursing workload at the time of intensive care unit discharge and the likelihood of intensive care unit readmission. This single-center prospective cohort study was conducted at a Belgian academic hospital and included all intensive care unit admissions from June 1, 2021 to May 31, 2022. The Nursing Activities Score was documented by the nurse responsible for each patient during every shift. Adult patients (≥ 18 years) with intensive care unit stay exceeding 24 h during the study period were eligible for inclusion. Those discharged to another hospital, a nursing home, or their own home were excluded due to the inability to ensure follow-up. Among the 1293 eligible admissions recorded during the study period, 133 patients (10.3%) experienced readmission. Readmitted patients exhibited a higher prevalence of medical reasons for intensive care unit admission, significantly increased mortality rates, and longer hospital length of stay compared to non-readmitted patients. The average daily Nursing Activities Score did not differ significantly between the two groups. The Nursing Activities Score at intensive care unit discharge was notably higher in readmitted patients, and those with a score above the median at discharge demonstrated an increased risk of readmission within 30 days. In multivariable analysis, a high Nursing Activities Score at intensive care unit discharge was an independent predictor of readmission. An elevated nursing workload, as indicated by the Nursing Activities Score recorded at intensive care unit discharge, was significantly associated with a higher risk of readmission. The study examines the relationship between nursing workload at the time of ICU discharge and the likelihood of unplanned readmission. The results highlight the critical role of nursing workload assessment at ICU discharge in capturing the complexity of care requirements patients face at discharge. The results emphasise the importance of revising discharge planning processes, identifying nursing workload as a critical factor in unplanned readmissions. STROBE guidelines were used for this study. Not applicable.

Association of Low BMI, Elevated Model for End-Stage Liver Disease Score, and Poor Functional Status With Increased 30-Day Readmission After Orthotopic Liver Transplant: A Retrospective Cohort Study.

Background Liver transplantation is the ultimate treatment for end-stage liver disease. However, post-transplant management is very complex, with the need for meticulous immunosuppression regimens and multidisciplinary coordinated specialist care. This can be complicated by a higher risk of readmissions after transplant. Readmission rates are now being used as a measure of a facility's efficacy post-transplant. In this study, we investigated recipient characteristics that may place recipients at higher risk for 30-day readmission. Method Chi-square and independent t-test analyses of six variables were performed accordingly on liver transplant recipient data extracted from the Standard Transplant Analysis and Research (STAR) data. The variables were Model for End-Stage Liver Disease (MELD) score, body mass index (BMI), age, diabetes status, hepatitis C status, and functional status. The association between these variables and 30-day readmission rates was investigated. Results We observed six recipient risk factors, including elevated MELD score, positive diabetes mellitus status, positive hepatitis C status, and lower functional status, which increase hospitalization post-transplant. Of the six examined characteristics, lower BMI, elevated MELD score, and lower functional status were significantly associated with 30-day readmission. The t values and P values were as follows: t(38,180) = 4.080, P = 4.514E-05 for BMI; t(38,180) = 4.080, P = 4.514E-05 for MELD score; and t(38,180) = 2.729, P = 6.356E-03 for functional status. Conclusion Our studyshows that liver transplant recipients with lower BMI, higher average MELD score, and lower functional status can be identified as high-risk recipients for readmission within 30 days after liver transplant. These findings might help transplant centers anticipate higher complication rates and possibly implement better nutritional optimization prior to transplant and closer follow-up after transplant. Further research could identify specific thresholds for these characteristics that are associated with significantly worse outcomes.

Total hip arthroplasty: 30 days readmission at a tertiary care hospital.

To assess 30-day readmission at a tertiary care hospital following Total Hip Arthroplasty (THA) and to assess complications for which the patients were readmitted along with associated factors. This prospective observational study examined patients undergoing THA at The Aga Khan University Hospital from February 2023 to December 2023. Patients were followed after THA up to 30 days. They were followed using an electronic medical record number system. Data on patient demographics and procedure was collected, along with reason for re-admission within 30-days. Statistical analysis was done using STATA version 15.1, where in descriptive statistics median and interquartile range, and frequencies and percentages were determined followed by Fisher's exact test. A total of 67 patients were included in the study. Four male patients above 55 years were readmitted within 30-days of THA. Underlying diagnosis was a significant factor for re-admission (p=0.05). Although, OA and AVN were commonly reported in patients >55 years, two of the readmitted patients had an underlying diagnosis of femoral neck fracture. Diagnosis at the time of admission for THA is a substantial risk factor for readmission that warrants further investigation. In addition, it underscores the need for targeted post-operative monitoring to reduce readmissions especially for patients who are at high risk of readmission.

Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness.

Identifying factors that increase the risk of hospital readmission will help determine high-risk patients and decrease the socioeconomic burden. Pneumonia is associated with high readmission rates. Although residential greenness has been reported to have beneficial health effects, no studies have investigated its importance in predicting readmission in patients with pneumonia. This study aimed to build prediction models for 30-day readmission in patients with pneumonia and to analyze the importance of risk factors for readmission, mainly residential greenness. Data on 47 risk factors were collected from 22,600 patients diagnosed with pneumonia. Residential greenness was quantified as the mean of normalized difference vegetation index of the district in which the patient resides. Prediction models were built using logistic regression, support vector machine, random forest, and extreme gradient boosting. Residential greenness was selected from the top 21 risk factors after feature selection. The area under the curves of the four models were 0.6919, 0.6931, 0.7117, and 0.7044. Age, red blood cell distribution width, and history of cancer were the top three risk factors affecting readmission prediction. Residential greenness was the 15th important factor. We constructed prediction models for 30-day readmission of patients with pneumonia by incorporating residential greenness as a risk factor. The models demonstrated sufficient performance, and residential greenness was significant in predicting readmission. Incorporating residential greenness into the identification of groups at high risk for readmission can complement the possible loss of information when using data from electronic health records.

Brief Report: Trends in Hospital Readmissions Among Adults With and Without HIV in the United States, 2010-2020.

Although 30-day hospital readmission is a widely followed quality measure, there are limited US nationwide data to evaluate its trends among people with HIV (PWH) and the sex disparity over time. We describe the 30-day all-cause unplanned readmission trends among PWH and people without HIV (PWoH) in the United States. Adult participants in the 2010-2020 Nationwide Readmissions Database, which weighted represents all US hospitalizations each year. We defined index admission and unplanned readmission using the US Centers for Medicare & Medicaid Services criteria. Overall and sex-specific readmission risks were tabulated among the index admissions from adult PWH and PWoH each year in the 2010-2020 Nationwide Readmissions Database. Random effect linear and Poisson regressions were used to estimate risk difference and annual percentage change of the trend. We added a spline in 2015 and additionally stratified the analysis by age and patient's zip code median household income. All analyses were weighted to generate national estimates. Approximately 140,000 index admissions from PWH and 25 million from PWoH were included each year. For PWoH between 2010 and 2020, annual readmission risk was stable at ∼12%. For PWH, readmission risk was stable at ∼22% during 2010-2015 and decreased from 22.0% in 2016 to 20.1% in 2020 (RD= -1.60 [95% CI: -2.24,-0.95]). Nonpregnant female PWH continued to have higher readmission risk than male PWH for all subgroups and all years. Nonpregnant female PWH <40 years had no reduction in readmission risk between 2016 and 2020 (RD= -0.45 [95% CI: -2.43, 1.53]). There remains strong need for readmission reduction interventions focusing on PWH, especially for young female PWH.

Influence of smoking on shoulder arthroplasty outcomes: A meta-analysis of postoperative complications.

Although advancements in surgical techniques and postoperative management have improved outcomes, the impact of smoking on shoulder arthroplasty outcomes remains controversial. This study aimed to evaluate the influence of smoking on shoulder arthroplasty outcomes and provide a clearer perspective on the controversy surrounding the impact of smoking on medical and surgical complications. A systematic search was conducted using four Library databases. PROSPERO (CRD42023444819). The quality of the studies was assessed using the Methodological Index for Non-randomized Studies. Odds ratios (OR) and mean differences (MD) with 95% confidence intervals (CI) were calculated for the dichotomous and continuous variables. This study used the PICOS strategy to identify studies with patients undergoing shoulder arthroplasty surgery, with smoking as the intervention, non-smoking as the comparison, and postoperative complications as the outcome. The meta-analysis included eight studies with 227,329 patients. The smoking group had a higher risk of readmission (OR: 1.11, 95% CI [1.05-1.17]), revision (OR: 2.32, 95% CI [1.28-4.23]), periprosthetic fracture (OR: 1.38, 95% CI [1.24-1.53]), and surgical site infection (OR: 2.09, 95% CI [1.77-2.47]), but no significant differences were found in wound problems or thromboembolic events. The smoking group had a higher risk of sepsis (OR: 1.31, 95% CI [1.07-1.60]). There were no significant differences in renal complications, urinary tract infections, pulmonary complications, or myocardial infarctions between the two groups. Smoking is a modifiable risk factor that should be addressed to improve outcomes and reduce the costs associated with complications and joint replacement in patients undergoing shoulder arthroplasty. These findings emphasize the need to promote a tobacco-free lifestyle and improve surgical outcomes. Preoperative interventions should include education, counseling, and support, fostering better shoulder arthroplasty results and long-term well-being.

Risk factors and prediction of intensive care unit readmission after oesophagectomy for cancer‡.

Intensive care unit (ICU) readmission has been proposed as a metric for quality of surgical care. The current study investigated potential factors and developed a prediction model for ICU readmission in patients following oesophagectomy for cancer. A total of 3028 patients from January 2019 to December 2022 were retrospectively collated as training cohort, with 829 patients from January 2023 to August 2023 enrolled for validation, respectively. Univariable and multivariable analyses were performed to identify potential factors after which a nomogram based on results from multivariable analysis was constructed and validated. In the training cohort, the rate of ICU readmission was 3.6% (110/3028). Readmitted patients were associated with more reoperations, higher 90-day mortality and prolonged postoperative stay (all P < 0.001). Multivariable analysis demonstrated that older age ≥75 years, neoadjuvant therapy, preoperative albuminaemia, diffusing lung capacity for carbon monoxide (DLCO)%, longer operative duration and retention of endotracheal intubation when entering ICU were independently associated with ICU readmission. Based on these results, a nomogram for predicting readmission was constructed and validated. The Hosmer-Lemeshow test showed the model in the training cohort was well calibrated (χ2 = 5.259, P = 0.73) and area under the receiver operating characteristic curve was 0.739 (95% confidence interval 0.691-0.787). Moreover, the application of the nomogram in the validation cohort showed an improved area under the receiver of 0.780 (95% confidence interval 0.703-0.857). ICU readmission after oesophagectomy although uncommon (3.6%) was associated with prolonged hospitalization and significant mortality. A nomogram based on 6 variables may assist intensivists to early identifying patients at high risk of readmission.

School re‐entry following a mental health crisis

During the school year, psychiatric hospitalization rates for children and adolescents are at their highest (Marshall et al., 2021). A hospitalization may consist of admission to an inpatient unit or a day treatment program, and the length of stay can vary from days to weeks or even months. During hospitalization, youth receive treatment to stabilize symptoms and prepare for a discharge into daily life, including school. However, navigating this transition from hospitalization back to school can be challenging for students, their caregivers, and school staff. Students returning to school often have complex needs that require careful planning by the treatment team to ensure a successful reintegration. This is especially critical given the high risk of readmission during the transition period. This raises an important question: what are the best practices for supporting students returning school after a mental health crisis? In this article, we will explore key strategies and highlight recommended resources for supporting students post‐hospitalization.

Patients' and Health Care Professionals' Expectations of Virtual Therapeutic Agents in Outpatient Aftercare: Qualitative Survey Study.

Depression is a serious mental health condition that can have a profound impact on the individual experiencing the disorder and those providing care. While psychotherapy and medication can be effective, there are gaps in current approaches, particularly in outpatient care. This phase is often associated with a high risk of relapse and readmission, and patients often report a lack of support. Socially interactive agents represent an innovative approach to the provision of assistance. Often powered by artificial intelligence, these virtual agents can interact socially and elicit humanlike emotions. In health care, they are used as virtual therapeutic assistants to fill gaps in outpatient aftercare. We aimed to explore the expectations of patients with depression and health care professionals by conducting a qualitative survey. Our analysis focused on research questions related to the appearance and role of the assistant, the assistant-patient interaction (time of interaction, skills and abilities of the assistant, and modes of interaction) and the therapist-assistant interaction. A 2-part qualitative study was conducted to explore the perspectives of the 2 groups (patients and care providers). In the first step, care providers (n=30) were recruited during a regional offline meeting. After a short presentation, they were given a link and were asked to complete a semistructured web-based questionnaire. Next, patients (n=20) were recruited from a clinic and were interviewed in a semistructured face-to-face interview. The survey findings suggested that the assistant should be a multimodal communicator (voice, facial expressions, and gestures) and counteract negative self-evaluation. Most participants preferred a female assistant or wanted the option to choose the gender. In total, 24 (80%) health care professionals wanted a selectable option, while patients exhibited a marked preference for a female or diverse assistant. Regrading patient-assistant interaction, the assistant was seen as a proactive recipient of information, and the patient as a passive one. Gaps in aftercare could be filled by the unlimited availability of the assistant. However, patients should retain their autonomy to avoid dependency. The monitoring of health status was viewed positively by both groups. A biofeedback function was desired to detect early warning signs of disease. When appropriate to the situation, a sense of humor in the assistant was desirable. The desired skills of the assistant can be summarized as providing structure and emotional support, especially warmth and competence to build trust. Consistency was important for the caregiver to appear authentic. Regarding the assistant-care provider interaction, 3 key areas were identified: objective patient status measurement, emergency suicide prevention, and an information tool and decision support system for health care professionals. Overall, the survey conducted provides innovative guidelines for the development of virtual therapeutic assistants to fill the gaps in patient aftercare.

Influence of physical function and frailty on unplanned readmission in middle-aged and older patients discharged from a hospital: a follow-up study

Background: Unplanned readmissions are associated with increased mortality among older patients. This study investigated the effects of changes in physical function and frailty on unplanned readmissions in middle-aged and older patients after discharge. Methods: This longitudinal study recruited participants through convenience sampling from the general wards of a medical center in northern Taiwan. They were aged 50 years or older and identified as being at high risk for readmission or mortality following discharge. Baseline data were collected through interviews conducted the day before discharged, while follow-up data were obtained through interviews at 1, 2, and 3 months post-discharge. Generalized estimating equation (GEE) was used for statistical analysis, incorporating all tracked variables, including physical function and frailty. Results: A total of 230 participants were recruited, each followed three times after discharge. The unplanned readmission rates at 1, 2, and 3 months post-discharge were 2%, 8%, and 14%, respectively. Participants with poorer physical function (odds ratio [OR] = 1.60 [1.27–2.02]) and more severe frailty symptoms (OR = 3.16 [1.45–6.83]) had significantly higher odds of unplanned readmission. The interaction between the time and frailty indicated a significantly lower likelihood of unplanned readmission over time (OR = 0.73 [0.54–0.98]). Conclusions: Declining physical function and frailty are key risk factors for unplanned readmission in older patients. Effective strategies to reduce this risk include monitoring physical function and frailty symptoms and providing supportive care services.

Using Natural Language Processing in the LACE Index Scoring Tool to Predict Unplanned Trauma and Surgical Readmissions in South Africa.

Unplanned and potentially avoidable readmission within 30days post discharge is a major financial burden. To use text-based electronic patient records to calculate the Charlson Comorbidity Index (CCI) score using a natural language processing technique to establish the feasibility and usefulness of the text-based electronic patient records in identifying patients at risk for unplanned readmission. A retrospective review of electronic patient records for general and trauma surgery in a hospital in South Africa (2012-2022) was conducted using the LACE score. Validated sentiment analysis analyzed free text components of electronic patient records to compute the CCI score and to establish the feasibility and usefulness of the LACE score in identifying patients at risk for unplanned readmission. Trauma surgery patients had a mean LACE score of 5.91 (SD=2.41), with 8.44% scoring 10 or higher and a specificity and sensitivity of 91.63% and 13.81%, respectively. The general surgery patients had a mean LACE score of 7.75 (SD=3.04), with 10.63% scoring 10 or higher and a specificity of 71.47% and a sensitivity of 44.80%, respectively. Logistic regression analysis revealed that LACE scores significantly predicted unplanned readmissions in both trauma (β=0.11, p<0.001; OR=1.112, 95% CI [1.082, 1.143]) and general surgery (β=0.15, p<0.001; OR=1.162, 95% CI [1.130, 1.162]) patients. The LACE score demonstrated the predictive value for readmission in trauma and general surgery patients. The LACE score was relatively effective in identifying patients who were less likely to be readmitted but showed limitations in identifying patients at higher risk of readmission. However, the successful use of natural language processing for data extraction of comorbidities shows promise on addressing the challenges around text-based medical records.

Independent predictors of 90-day readmission in patients with inflammatory bowel disease: a nationwide retrospective study.

There is a paucity of literature that comprehensively investigates risk factors for inflammatory bowel disease (IBD) readmissions on a national scale. In this study, we look to identify independent risk factors for readmission, including psychosocial factors, in patients admitted with a primary diagnosis of ulcerative colitis (UC) or Crohn's disease (CD). We performed a retrospective cohort study using data from the Nationwide Readmissions Database. We identified cohorts of adult patients (n = 28 473) who required inpatient admission for UC or CD in the United States in the year 2020. Multivariate logistic regression models controlling for confounding variables were used to identify independent predictors of 90-day readmission. Patients were identified who required hospitalization for UC (n = 11 476) and CD (n = 16 997). In patients with UC, younger age, male sex, and transfusion requirement during index hospitalization were all independently predictive of increased 90-day readmission (all P < .05). Psychosocial factors predictive of readmission include alcohol use disorder, drug abuse, and poverty (all P < .05). In patients with CD, younger age and chronic pain were both predictive of increased readmissions (all P < .05). Psychosocial factors predictive of readmission include lower income quartile, uninsured status, depression, drug abuse, nicotine dependence, and opioid use disorder (all P < .05). This study identifies several risk factors for readmission in patients with IBD, many of which are potentially modifiable psychosocial factors. Closer follow-up, possibly via virtual modalities, as well as alternative treatment strategies, should be considered in patients with IBD at higher risk of readmission.

Disparities in postdischarge follow-up and risk of readmission between Medicaid and privately insured patients.

Studies have identified higher risk of readmission for patients with Medicaid compared to those with private insurance. Postdischarge follow-up is utilized as an intervention to reduce readmissions in the Medicare population, but it is unclear whether follow-up reduces risk of readmission for patients with Medicaid. To assess whether follow-up within 30 days of discharge reduces risk of readmission and mitigates readmission disparities based upon insurance status. This retrospective cohort study used Cox proportional hazard and competing risk models to estimate associations between sociodemographic and clinical characteristics, follow-up, and readmission. We analyzed data from 4281 patients aged 21-64 years with Medicaid or private insurance who were hospitalized from January 2017 to December 2019 for one of five conditions associated with high risk of readmission. Outpatient follow-up within 30 days of discharge and 30-day all-cause readmission were outcomes. Overall risk of readmission was similar for Medicaid and privately insured patients in this cohort (13.7% and 14.5%, respectively). Patients with Medicaid were 23% points less likely to complete outpatient follow-up within 30 days compared to patients with private insurance (p < .001). However, outpatient follow-up before readmission within 30 days of discharge was not associated with a significant difference in readmission rate (hazard ratio: 1.10, 95% confidence interval: 0.91-1.32) in the overall sample or in analysis stratified by payer. We found similar rates of readmission for Medicaid and privately insured patients despite significant disparities in postdischarge follow-up. Timely follow-up care alone may not be sufficient as an intervention to reduce readmissions.

External validation of the "HOSPITAL simplified" scale as a predictor of 30-day readmission after hospitalisation in OSI Araba Medical Services.

External validation of the "HOSPITAL simplified" scale as a predictor of 30-day readmission after hospitalisation in OSI Araba Medical Services.