Readmission Rates For Patients Research Articles

Risk factors for readmission following esophagectomy and gastrectomy for cancer.

Hospital readmission after surgery is a key quality indicator. This nationwide cohort study aimed to assess readmission rates following esophagectomy and gastrectomy for cancer and identify associated risk factors. Data were extracted from the Dutch Upper GI Cancer Audit (DUCA) for patients with esophagogastric cancer who underwent esophagectomy or gastrectomy with curative intent between January 2011 and June 2016. Logistic regression analysis identified risk factors for 30-day readmission. In total, 5566 patients were included. Readmission within 30days occurred in 483 of 3488 (13.8%) patients after esophagectomy and 243 of 2078 patients (11.7%) after gastrectomy. Both minor (Clavien Dindo 1-2) and major (Clavien Dindo ≥3) postoperative complications were independent predictors of readmission after esophagectomy (OR 2.99; 95%CI 2.23-4.02; p< 0.001 and OR 5.20; 95%CI 3.82-7.09; p< 0.001). Specific complications included pulmonary (OR 1.49; 95%CI 1.20-1.85; p< 0.001), gastrointestinal (OR 2.43; 95%CI 1.94-3.05; p< 0.001), and infectious (OR 2.27; 95%CI 1.60-3.22; p< 0.001). Prolonged length of stay (pLOS) was associated with higher readmission rates in patients without complications following esophagectomy (OR 1.91 95% CI 1.19-3.07; p= 0.008), but lower rates in those with complications (OR 0.65 95% CI 0.51-0.83; p< 0.001). For gastrectomy, postoperative complications were also linked to readmission (OR 3.18; 95%CI 2.30-4.40; p< 0.001), particularly gastrointestinal (OR 2.16; 95%CI 1.40-3.32; p< 0.001), and infectious (OR 3.80; 95%CI 2.53-5.71; p< 0.001). Readmission after esophagogastric resection is common, particularly among patients with both minor and major postoperative complications. Prolonged stay after esophagectomy impacts readmission risk differently based on the presence of complications.

BN O02 - Introducing the Cholecystectomy Urgency Index (CUI) – A novel way to visualise and optimise the waiting list for laparoscopic cholecystectomy at a large district general hospital

Abstract Background Gallstone disease effects approximately 10-15% of the adult population, and laparoscopic cholecystectomy is one of the most common surgical procedures in the UK, with over 60,000 performed each year. Delays to surgery from diagnosis has been found to increase patient morbidity and readmission rate. Waiting lists in the UK have been struck with a significant backlog from the COVID-19 pandemic, and navigating this ever-expanding patient population to determine priority for booking cases is complex. We propose a new system to prioritise and visualise this cohort of patients in real time – the Cholecystectomy Urgency Index. Method At one large district general hospital, we had a waiting list of 148 patients for laparoscopic cholecystectomy. We identified the individual underlying pathology and established the waiting time since diagnosis to allocation of operative date. We used excel to formulate a pairwise scatter plot of the waiting list patients based upon the time waiting. We were able then to colour code them based on pathology (biliary colic/cholecystitis/cholangitis/pancreatitis/obstructive jaundice). We were then able to link this directly to our scheduling to create a live, visual representation of our waiting list, by both days waited and causative pathology. Results We found our median wait times for patients varied based on pathology: Biliary Colic = 185 days, Cholecystitis = 57 days, Pancreatitis, Cholangitis and Obstructive Jaundice (OJ) = 65 days. We found that the pairwise scatter plot provided a rapidly accessible, easily identifiable visual representation of our patient cohort by both days waited and diagnosis. This allows relevant staff, surgeons and schedulers to plan elective cases according to both urgency and time waited. Long waiters are easily identified as outliers on the graph and can therefore be flagged for last minute cancellation slots. Conclusion By using this system, we can optimise our waiting lists, improve productivity and patient outcomes. We propose a system to enable this further when booking patients: to give them a colour coded CUI number of 1-3. 1 being biliary colic, 2 – cholecystitis, 3 – pancreatitis/cholangitis/OJ. This will be directly linked to our scatter graph and in doing so, provides us with a readily available, live snapshot in time of the current wait list. This system allows for both clinical urgency prioritisation and easy visualisation of patient cohorts, thus catalysing the elimination of long waiters by means of clear identification.

Abstract 4127931: Chronic Steroid Use is Associated with Increased Readmission Rates in Patients Admitted with Acute Coronary Syndromes

Introduction: Acute coronary syndrome (ACS) is associated with significant morbidity and mortality. Management of ACS includes percutaneous coronary intervention (PCI) and requires the use of antithrombotic therapy, which increases bleeding risk. Chronic steroid use (CSU) is common for many diseases and is also associated with bleeding risk and impaired wound healing. However, data on outcomes of patients with CSU and AMI are sparse. Methods: Patients admitted for ACS and managed with PCI from 2014 to 2020 with and without CSU were identified using the National Readmissions Database (NRD). In-hospital outcomes include death, arterial thrombosis (composite of ischemic stroke and arterial thromboembolism), and major bleeding (composite of gastrointestinal, intracranial, or post-procedure bleeding or transfusion). Ninety-day readmission outcomes were cardiovascular (CV)-related, bleeding-related and all-cause. Multivariable logistic or Cox proportional hazards were utilized. Results: A total of 1,087,357 patients with ACS were included, of whom, 9,864 (0.9%) had CSU. After multivariable adjustment, there was no significant difference in in-hospital mortality (3.1% vs 3.2%; OR 1.09, 95% CI 0.93-1.26) or risk of arterial thrombosis (3.3% vs 2.8%; OR 0.91, 95% CI 0.81-1.02) between patients with and without CSU. CSU was associated with an increased risk of major bleeding events (8.3% vs 6.0%; OR 1.15, 95% CI 1.06-1.25). CSU was associated with a higher risk of CV-related (10.7% vs 7.5%; HR 1.10, 95% CI 1.02-1.18), bleeding-related (2.3% vs 1.2%; HR 1.28, 95% CI 1.09-1.51), and all-cause 90-day readmissions (23.9% vs 14.9%; HR 1.28, 95% CI, 1.22-1.34). Conclusion: Among patients admitted with ACS who underwent PCI, CSU was associated with increased risk of bleeding during index hospitalization and readmissions for bleeding. Furthermore, CSU was associated with increased risk of 90-day CV and all-cause readmission. Further studies are needed to better characterize this risk.

Abstract 4134692: In-Hospital Mortality Rate and Predictors of 30-Day Readmission in Cancer Patients with MI Undergoing PCI -A Cross Sectional Study From Nationwide Readmission Database

Background and objectives: Data regarding readmission rates and predictors of readmission in cancer patients undergoing PCI are sparce. With the increasing survival rates and prevalence of cardiovascular complications in cancer patients, understanding the patterns and predictors of readmission in this population is paramount for optimizing their outcomes. Cancer patients pose unique clinical challenges due to their combined prothrombotic state and propensity for bleeding. We attempted to identify factors associated with readmission in cancer patients. Methods: We utilized the Nationwide Readmission Database from 2016 to 2020 and included patients more than 18 years of age with primary diagnosis of myocardial infarction(MI) who underwent percutaneous coronary intervention(PCI) and have a preexisting diagnosis of cancer. We used International Classification of Disease, Tenth Revision, Clinical Modification (ICD10 CM) codes to define MI, PCI, and cancer. The primary outcome was the 30-day readmission rate, and secondary outcomes were mortality rates, predictors of readmission, and common causes of readmission. The independent predictors of readmission were analyzed using cox regression analysis. Results: Of the 52,307 cancer patients who underwent PCI, 7,767 were readmitted within a 30-day period. The readmission rate for these patients was 15.70%. The mortality rate was 6.05% for index admission and 6.80% for readmitted cases. Among the readmitted patients in the strongest independent predictor for readmission were leaving against medical advice(AMA), anemia, congestive heart failure, and discharge to a skilled nursing facility or home health. Common causes of readmission within this time included hypertensive heart disease with concomitant CKD stage I-IV and heart failure (6.21%), sepsis (6.12%), NSTEMI (5.60%), hypertensive heart disease with concomitant heart failure (4.62%) and acute kidney injury (1.98%). Conclusions: Thirty-day readmission rate was 15.70%. Independent predictors of readmission were anemia, diabetes mellitus, congestive heart failure, malnutrition, peripheral artery disease, leaving against medical advice, and discharge to facility. Most common cause of readmission was hypertensive heart and kidney disease with heart failure, which comprised 6.21%.

Abstract 4138486: Unplanned Readmissions Due to Post-Acute Myocardial Infarction Complications: Insights from the Nationwide Readmission Database

Introduction: Acute myocardial infarction (MI) is a leading cause of morbidity and mortality worldwide. Despite advances in treatment, readmissions within 30 days remain a significant concern, impacting both patient outcomes and healthcare costs. This study aims to analyze trends in 30-day readmission rates (30-dr) for patients discharged after an acute MI. Methods: We analyzed the 2016-2020 Nationwide Readmission Database for patients aged ≥ 18 years with initial admission of acute MI and were readmitted within 30 days. Variables were identified using ICD-10 codes. The primary outcome was trends in 30-dr; secondary outcomes included trends in complications, mortality rate, length of stay (LOS), and healthcare costs. Multivariate and descriptive bivariate analyses were conducted, with p-values &lt;0.05 considered statistically significant. Results: There was a notable decrease in the acute MI index admissions total number from 507,608 in 2016 to 471,292, p trend &lt; 0.01. The index admission mean age throughout the study years was 66.9 ± 13.5 years, with 62.7% male patient. Index admission inpatient mortality dropped reaching OR 0.92 (CI 0.88-0.96) by 2020 with a drop of 0.08 OR from 2016 with (p trend &lt; 0.01) using logistic regression. The index admission LOS remained consistent with allover average 4.64 days (p trend = 0.25). The mean healthcare costs saw a significant increase from $22,456 in 2016 to $27,249 in 2020 (p trend &lt; 0.01). The 30-dr trend decreased from 9.41% in 2016 to 7.8% in 2020 (p trend &lt; 0.01). The top five readmission reasons in order included heart failure (14.94%of total readmission), myocardial reinfarction (13.59%), sepsis (4.49%), Acute kidney injury(2.4%), and chest pain(2.1%). The risk of readmission went up 1% for each year increase of age and 12% for female gender (using OR,logistic regression), despite initial admission was more male dominant. Conclusion: Our study demonstrates a declining initial admission, its mortality rate,and 30-dr. There was readmission healthcare costs increase. We noticed four top complications-driven readmission falling under cardiovascular diseases category with the top one as heart failure and myocardial reinfarction. This study tries to highlight relevant data to target interventions to reduce readmissions and complications, ultimately improving patient outcomes and reducing the healthcare economic burden.

Utilization of Psychiatric Hospital Services Following Intensive Home Treatment: A Nonrandomized Clinical Trial.

Home treatment (HT) has been associated with fewer inpatient treatment (IT) readmission days but lacks evidence on reducing combined psychiatric hospital service use (IT, HT, day clinic). To assess the association of intensive home treatment (IHT) compared with IT regarding readmission rate, social outcomes, and clinical outcomes. This quasi-experimental, nonrandomized trial was conducted from 2020 to 2022 in 10 psychiatric hospitals in Germany. Propensity score (PS) matching was used to compare both treatment models at the 12-month follow-up using standardized instruments and routine hospital data. All patients were screened until the target sample size was reached, based on these criteria: stable residence with privacy for sessions, no child welfare risk, primary diagnosis within International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes F0X to F6X, residence in the catchment area, no commitment order, no acute suicidality or severe aggression requiring hospitalization, capacity to consent, not participating in other interventional studies, sufficient German language skills, no substantial cognitive deficits or intellectual impairment, and no more than 7 days in IHT or IT before recruitment. Statistical analysis was performed from February to November 2023. IHT provided daily acute psychiatric treatment at home, while IT was psychiatric inpatient treatment as usual. The mean treatment duration of the index treatment was 37.2 days for IHT and 28.2 days for IT. The inpatient readmission rate was the primary outcome. Secondary outcomes were combined readmission rate, total inpatient days, job integration, quality of life, psychosocial functioning, symptom severity, and recovery. Of 1396 individuals, 200 patients receiving IHT and 200 patients receiving IT were included (264 female [65%]; mean [SD] age, 45.45 [15.83] years [range, 18-88 years]). Baseline sociodemographic and psychometric characteristics did not differ significantly between the groups. At 12-month follow-up, patients in the IHT group had lower inpatient readmission rate (IHT vs IT: 31.12% vs 49.74% IT; mean difference, 18% [95% CI, 9%-28%; P < .001), combined readmission rate (mean difference, 13% [95% CI, 4%-24%; P < .001), and fewer inpatient days (mean difference, 6.82 days; P < .001) than the IT group. This nonrandomized clinical trial found that patients receiving IHT had a lower likelihood of utilizing hospital-based psychiatric services and spent fewer inpatient days, suggesting that IHT is a viable alternative to IT. ClinicalTrials.gov Identifier: NCT04745507.

The Impact of Socioeconomic Factors on 90-day Postoperative Readmissions and Cost in Shoulder Arthroplasty Patients

Understanding the impact of Social Drivers of Health (SDOH) on shoulder arthroplasty is pivotal for the development of equitable value-based payment models that enhance the quality of patient care. This investigation aims to understand the influence of Area Deprivation Index (ADI) on hospital admissions, readmissions, and associated costs post-shoulder arthroplasty. We conducted an analysis using US Medicare claims data from 2019 to 2021, identifying patients who received shoulder arthroplasty in either an inpatient or outpatient setting using Current Procedural Terminology (CPT) codes. Our primary outcomes were 90-day unplanned readmissions and post-procedure 90-day care costs. The ADI was our primary exposure variable, calculated at the census block level. Our analysis adjusted for multiple factors using a stepwise modeling approach including patient demographics, 29 Elixhauser comorbid conditions, Medicaid-dual eligibility, and end-stage renal disease status using logistic regression models. A total of 145,435 patients were included in our study. The average age of patients in our cohort was 74.5 years old, with 59.5% being female. Patients in the high ADI group had a higher readmission rate than the lowest ADI group (8.5% vs. 6.0%, p<0.001).When controlling for confounding factors, there was an indepdentent association between high ADI and readmission (OR: 1.28 [95% CI 1.12, 1.46]). Overall, we saw a dose-dependent relationship between ADI and readmission, with the association growing stronger as ADI increased. Conversely, we found a negative association with ADI and high-costs. (High ADI Group OR: 0.80 [95% CI 0.70, 0.91]). Patients in the high SES group had higher healthcare contact days during the 90-day follow-up period, with a median of 16 visits (IQR 8, 23), compared to 13 visits (IQR 6, 22) in the middle SES group and 10 visits (IQR 5, 20) in the low SES group (p<0.001). Socioeconomic disparities significantly influence the outcomes of primary shoulder arthroplasty, as indicated by higher readmission rates for low SES patients. Notably, our analysis shows a strong, independent association between ADI and readmission. Moreover, patients with higher SES incurred greater costs within a 90-day period potentially due to increased resource utilization.

Indication-based analysis of laser interstitial thermal therapy: a propensity score-matched comparison of outcomes for brain tumor versus epilepsy indications.

Laser interstitial thermal therapy (LITT) is a minimally invasive procedure used to ablate abnormal tissue in a targeted fashion. It is most commonly used to treat epileptic foci, brain tumors, and radiation necrosis. This study aimed to compare immediate postoperative outcomes between these indications. This study analyzed clinical data from the Nationwide Readmissions Database (NRD) from 2016 to 2019 and identified 2234 patients who underwent LITT procedures using ICD-10 codes. The authors analyzed patient demographics, complications, discharge disposition, readmission rates, and mortality. Following propensity score matching, 317 patients treated for epilepsy and 323 patients treated for brain tumors were compared. The mean ages were similar (epilepsy: 45.7 vs tumor: 49.0 years, p = 0.55), as were the proportions of female patients (epilepsy: 45.4% vs tumor: 52.9%, p = 0.83), all-payer costs (p = 0.81), income quartiles (p = 0.58), insurance types (p = 0.70), frailty rates (p = 0.85), and comorbid disease burdens as assessed by ECI score (p = 0.73). No significant differences were observed in rates of hemorrhage (p = 0.1), pulmonary embolism (p = 0.32), or infection (p = 0.16). However, the tumor cohort had higher rates of deep vein thrombosis (3.4% vs < 3.15%, p = 0.045), nonroutine discharge (26.6% vs 16.4%, p = 0.04), and 1-year hospital readmission (32.5% vs 18.6%, p = 0.006). One-year mortality rates were similar (tumor: 3.4% vs epilepsy: < 3.15%, p = 0.08). While postoperative complications and 1-year mortality rates were similar among patients undergoing LITT for epilepsy and brain tumors, the tumor cohort experienced higher rates of deep vein thrombosis, nonroutine discharge, and 1-year hospital readmission.

Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.

Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital. We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks. Our work focuses on addressing two key challenges in the medical fields, data imbalance and the variety of data types, in order to boost the performance of machine learning algorithms. Furthermore, we also employ SHapley Additive Explanations (SHAP) value visualization to interpret the model predictions and identify both the key data features and disease codes associated with readmission risks, identifying a specific set of diagnosis codes that are significant predictors of readmission within 30 days. Through extensive benchmarking and the application of a variety of feature engineering techniques, we successfully improved the area under the curve (AUROC) score from 0.628 to 0.7 across our models on the test dataset. We also revealed that specific diagnoses, including cancer, COPD, and certain social factors, are significant predictors of 30-day readmission risk. Conversely, bacterial carrier status appeared to have minimal impact due to lower case frequencies. Our study demonstrates how we effectively utilize routinely collected hospital data to forecast patient readmission through the use of conventional machine learning while applying explainable AI techniques to explore the correlation between data features and patient readmission rate.

Explainable machine learning and multi-modal data for predicting readmission in patients with heart failure

Abstract Background Cardiovascular diseases (CVDs) are among the leading causes of mortality and morbidity globally, with an estimated 1 million hospital admissions for CVD in England in 2019/20, leading to 5.5 million bed days. In addition, patients with CVDs have a notably high rate of hospital readmission, with approximately one in four patients experiencing heart failure (HF) being readmitted within one month of discharge. We explored whether machine learning models trained on electrocardiogram (ECG) and patient electronic health records (EHRs) can support decision-making and risk management and ultimately improve health outcomes for HF patients. Purpose The project aims to develop a machine learning model to predict health outcomes for HF patients. To test the hypothesis, we trained and tested an explainable machine learning model to predict the risk of mortality and hospital readmission rate for HF patients. Methods We used EHR data and tabulated ECG records to train an XGBoost model to predict the risk of 30-day mortality and 30-day hospital readmission for HF patients. EHR data from a cohort of 2,868 patients with their ECG records were used for model development. We extracted a total of 78 features from the ECG data, such as heart rate variability (HRV), P-wave durations, and QT intervals. These ECG features were combined with coded and free-text EHR data (e.g., demographic information, diagnosis, medication history, and lab results) and used as tabular inputs to the XGBoost model. Of the 2,868 patients, an internal set of 574 patients stratified by age, sex, and health outcomes (30-day mortality and 30-day hospital readmission) were held out for testing. Results Among 2,868 HF patients with 10-year follow-up, 1065 patients were reported for mortality or hospital readmission within 30 days of discharge. When the machine learning model was trained on ECG data in addition to EHR data, the model was able to predict patient mortality and hospital readmission rate in the test set with an area under the curve (AUC) of 0.91 and 0.70, respectively. For mortality prediction, the model achieved a sensitivity and specificity of 0.81 and 0.82, respectively, when employing an operating point with minimum difference between the sensitivity and specificity. When the threshold was adjusted to favor sensitivity at 0.91, the model specificity decreased to 0.74. The machine learning model also identified high sensitive troponin levels, QT dispersion, and lactate dehydrogenase levels as features of high importance for predicting the outcomes for HF patients. Conclusions Our developed machine learning model was able to predict mortality and readmission risks in HF patients using multi-modal data. Upon further validation, the machine learning model has the potential to be tested for supporting clinical decision-making and risk management for HF patients.ROC CurveFeature Importance

30-Day Readmissions and the Need for Emergency Surgery Following Non-Operative Management of Perforated Diverticulitis.

Background: Limited data are available on the evaluation and outcomes of patients with perforated diverticulitis who were treated without surgery. Aims: This retrospective review was aimed at investigating the 30-day non-elective re-admission rates for patients hospitalized with perforated diverticular disease who were treated without surgery, rates of patients requiring surgery on re-admission, and the independent predictors of re-admission. Methods: A total of 143,546 patients from the National Readmission Database, between 2016 and 2020, who were admitted with perforated diverticulitis and treated non-operatively were reviewed. Re-admitted patients were compared with those not re-admitted. Comparisons for continuous and categoric variables were made using the student t-test and chi-squared test, respectively. A logistic regression model was used to determine independent factors associated with re-admission. All analyses were done with SAS 9.4; p values <0.05 identified significance. Results: Among patients with perforated diverticulitis who were treated non-operatively, 17,868 (12.4%) were re-admitted within 30 days and 4,924 (27.6%) of patients re-admitted required surgical intervention. The greatest independent predictors of re-admission include patient insurance status, index length of stay, undergoing a drainage procedure, and patient disposition. Comorbidities predicting re-admission include renal failure, chronic pulmonary disease, diabetes mellitus, fluid and electrolyte disorders, and hypertension. Hospital total charges were greater at the index admission for patients requiring re-admission. Conclusion: Non-operative management of perforated diverticulitis is safe for many patients, but the risks for re-admission and subsequent need for emergency surgery require special consideration.

The total joint arthroplasty care patterns in China during the COVID-19 pandemic: a multicenter cohort study.

The COVID-19 pandemic has profoundly affected the care practices of total joint arthroplasty (TJA) throughout the world. However, the impact of the pandemic on TJA care practices has not yet been studied in China. This retrospective multicenter cohort included patients aged 18 years or older who underwent TJA between January 2019 and December 2019 (prepandemic period) and January 2020 to December 2021 (pandemic period). Data were obtained from the medical records of 17 Chinese hospitals. Interrupted time series (ITS) analysis was used to estimate differences in monthly TJA volume, hospitalization proportion of TJA, preoperative characteristics, postoperative complications, 30-day readmissions, length of stay (LOS), and costs in inpatients undergoing TJA between the prepandemic and pandemic periods. Multivariate regression and propensity score matching (PSM) analyses were used to assess the impact of the COVID-19 pandemic on hospital complications, readmissions at 30 days, LOS, and costs at the patient level. A total of 752,477 inpatients undergoing TJA in the prepandemic period, 1,291,248 in the pandemic period, with an average 13.1% yearly decrease in the volume of TJA during the pandemic. No significant changes were observed in the proportion of hospitalizations for TJA. ITS analyses showed increases in the proportion of comorbidities (8.5%, 95% CI: 3.4-14.0%) and the number of comorbidities (15.6%, 95% CI: 7.7-24.1%) in TJA cases during the pandemic, without increasing LOS, costs, complications, and readmission rates. Multivariate and PSM analyses showed 6% and 26% reductions in costs and readmission rates during the pandemic, respectively. The COVID-19 pandemic was associated with more severe preoperative conditions and decreased volume, costs, and readmission rates in patients undergoing TJA in China. These findings demonstrate that the COVID-19 pandemic did not have a dramatic impact on the TJA care pattern in China, which may have resulted from active and strict strategies in combating COVID-19 as well as a rapid response in hospital management.

Application of the Clinical Outcomes, Healthcare Resource Utilization, and Related Costs Model in Chronic Obstructive Pulmonary Disease Patients

Introduction: The change in prevalence and management of chronic obstructive pulmonary disease (COPD) led to changes in outcomes and costs. We aimed to assess current clinical outcomes, resource utilisation, and costs in COPD. Methods: Retrospective, observational study of a cohort of consecutive COPD patients who visited the emergency department (ED) of a large tertiary hospital in 2018. The study measured baseline characteristics, 30-day and 1-year mortality, readmission, re-ED visit rates, and costs using the Clinical Outcomes, HEalthcare REsource utilisatioN, and relaTed costs (COHERENT) model, validated for heart failure. This model, featuring a colour graphic system, tracks time spent in different clinical situations (home, ED, hospital), considering vital status, healthcare resource use, and related costs. Results: In 2018, 2,384 patients with a primary COPD diagnosis visited the ED. The average age was 76 years, with 40% women. Observed mortality rates were 7.6% in-hospital, 8.5% at 30 days, and 23.4% at 1 year. The readmission rates were 9.9% and 36.1%, respectively. The cohort’s 1-year cost was approximately EUR 14.6 million (USD 15.95 million), with a median cost per patient of EUR 3,298 (USD 3,603.96). Hospitalisation incurred the highest costs, with initial hospitalisation and readmissions accounting for 44.7% and 42.6% of expenditures, respectively. Conclusion: One-year mortality and readmission rates for patients with COPD visiting the ED remain high with a significant economic impact on the health system. This burden justifies special programs to improve their care.

The 30-day readmission rate of patients with an overlap of probable sarcopenia and malnutrition undergoing major oncological surgery.

Overlapping sarcopenia and malnutrition may increase the risk of readmission in surgical oncology. Overlapping probable sarcopenia/malnutrition was found in 4.6% of 238 patients and the 30-day unplanned readmission rate was 9.0%. In multivariate analysis, the overlap of probable sarcopenia and malnutrition was a significant predictor for the 30-day unplanned readmission (OR= 8.10, 95%CI= 1.20-0.55; p=0.032). ■ Probable sarcopenia plus malnutrition was significantly associated with unplanned readmission. ■ Overlap of probable sarcopenia and malnutrition was an independent risk factor for readmission. ■ Certification of whether the patient is malnourished and/or sarcopenic preoperatively is necessary. ■ SARC-F and subjective global assessment can effectively and easily assess sarcopenia and malnutrition at admission. To assess the 30-day unplanned readmission rate and its association with overlapping probable sarcopenia and malnutrition after major oncological surgery. A prospective bicentric observational cohort study performed with adult oncological patients undergoing major surgery. The primary outcome was unplanned readmission within 30 days after discharge and the association with probable sarcopenia and malnutrition. Nutritional status and probable sarcopenia were assessed just prior to surgery. Patients classified using subjective global assessment, as B and C were malnourished. Probable sarcopenia was defined using SARC-F (strength, assistance with walking, rise from a chair, climb stairs, falls) questionnaire ≥4 points and low HGS (handgrip strength) <27kg for males and <16kg for females. Two hundred and thirty-eight patients (51.7% female) with a median age of 60 years were included. The 30-day readmission rate was 9.0% (n=20). Univariate analysis showed an association of malnutrition (odds ratio (OR) = 4.84; p=0.024) and probable sarcopenia (OR = 4.94; p=0.049) with 30-day readmission. Furthermore, when both conditions were present, the patient was almost nine times more likely to be readmitted (OR = 8.9; p=0.017). Multivariable logistic regression analysis showed that overlapping probable sarcopenia and malnutrition was an independent predictor of 30-day unplanned readmission (OR = 8.10, 95% confidence interval (95%CI) 1.20-0.55; p=0.032). The 30-day unplanned readmission rate was 9.0%, and the overlap of probable sarcopenia and malnutrition is an independent predictor for the 30-day unplanned readmission after major oncologic surgery.

8152 Benefits of Inpatient Diabetes Management by a Specialized Diabetes Team

Abstract Disclosure: M. Bojarian: None. A. Ohri: None. C. Buettner: None. Background: Inpatient dysglycemia is associated with poor clinical outcomes such as prolonged length of stay (LOS), increased risk of ICU admission, and in-hospital mortality. The benefits of managing diabetic patients in the hospital with a dedicated diabetes team, as opposed to standard care on episodes of hypoglycemia or hyperglycemia, reduction in LOS, and 30-day readmission rates, remain uncertain. A potential confounder is the likelihood that patients receiving a diabetes consult may be sicker than those without a consult. Given the growing evidence supporting the role of specialized diabetes teams in improving care for diabetes patients, we sought to examine these benefits at our hospital. We plan to address confounding variables through propensity score matching. Objective: To assess whether diabetes patients experience reduced LOS, hypoglycemia, hyperglycemia, and 30-day readmission rates when managed by a specialized diabetes team compared to inpatients managed by a hospitalist team. Material and Methods: This quality improvement study compared LOS, hypoglycemia (glucose &amp;lt;70 mg/dL), hyperglycemia (glucose &amp;gt;180 mg/dL), and 30-day readmission rates in diabetes patients managed by a specialized diabetes team versus a hospitalist team at our hospital between June 2022 and December 2022. Results: 5676 patients with diabetes were admitted between June and December 2022 at our hospital, and a specialized diabetes team managed 417 patients (7.34%). In a preliminary analysis in which no propensity matching was performed yet, the 30-day readmission rate was significantly lower (87,16.6%) in patients who received a diabetes consult compared to patients who did not receive a diabetes consult (320,17.9%). The rate of hypoglycemia was higher (24.4%) in the specialized diabetes consult group than in the non-diabetes consults group (19.9%). Additionally, LOS was longer in patients with a specialized diabetes consult (7.1 vs. 3.1 days), and hyperglycemia (glucose &amp;gt;180 mg/dL) was more prevalent in the specialized diabetes consult group (78.9%) compared to the non-diabetes consult group (39.2%). Conclusion: In this retrospective analysis, patients managed by a specialized diabetes team showed lower readmission rates despite longer hospitalization and increased dysglycemic episodes. This could be attributed to sicker patients receiving diabetes consults, influencing dysglycemia rates. To account for such confounders, ongoing propensity score matching aims to provide a clearer understanding. Reduced readmission rates suggest improved care transitions, potentially resulting in significant cost savings for the healthcare system. Presentation: 6/1/2024

Risk factors for early readmission to hospital in patients with malignancy-related ascites: a retrospective cohort study.

Malignancy-related ascites (MRA) is a common serious complication of many advanced malignant tumors with high morbidity and mortality. The high hospital expenditures induced by unplanned readmission in patients with MRA have become an urgent issue to the public. We aimed to overall assess the unplanned early readmission rate of patients with MRA and explore the potential risk factors for such readmission. A retrospective cohort study based on 2018 Nationwide Readmissions Database was performed and patients with MRA were recruited into the analysis. The primary outcome was unplanned 30-day readmission rate and inpatient outcomes. The multivariate logistic regression analysis was performed to evaluate the potential risk factors for such early readmission. Data obtained from 32,457 patients with MRA were analyzed, and of these 7,799 individuals (24.03%) were unplanned readmitted within 30-day follow-up. The mortality rate in the readmitted population was 15.15%. Patients at younger age were at a higher risk of readmission. The morbidities including hypertension (OR=1.117, 95%CI: 1.054-1.184), hyperlipemia (OR=1.075, 95%CI: 1.009-1.146) and diabetes (OR=1.118, 95%CI: 1.053-1.188), gastrointestinal malignancies and peritoneal procedure significantly increased the risk of 30-day readmission in patients with MRA. More than one in five patients with MRA was unplanned readmitted within 30-day follow-up. The above risk factors should be timely intervened and the corresponding medical care should be strengthened in patients with MRA to lessen the unplanned readmission and improve the readmission outcomes.

Community paramedicine program and outcomes of referred coronary artery bypass grafting patients.

Community paramedicine is a field in its infancy. The use of community paramedics has expanded in recent years as an alternative or adjunct to home health in the continued drive to decrease health disparities and complications. In current practice, they function in a position like a home healthcare nurse with an expanded scope of practice, such as providing specialized follow-up care, for example with postoperative care for patients who have undergone major surgery or recent hospitalization. This study assesses if community paramedics are a valid option in reducing rehospitalization of patients who underwent a coronary artery bypass grafting (CABG) procedure. A retrospective chart review between 2021 and 2022 was performed on all patients who underwent CABG in Bismarck, North Dakota, along with obtaining a referral for the community paramedics spanning urban and rural areas. A comparison was made between individuals who saw the community paramedics in their post-care versus those who continued with the standard of care. There were 80 participants and 38 location-matched controls. All variables were found to be statistically insignificant except for the number of walk-in visits (urgent care), in which 7 out of 38 sought medical attention in the controls and 4 out of 80 sought medical attention in the participants. The proportions of inpatient readmission rates and emergency department (ED) visits were similar. Given that paramedicine is in its infancy, the emergence of other variations of the community paramedic certification has brought a discussion of their scope of practice. While walk-in visits, even with the limitations, showed significant improvement with the addition of community paramedics, more research is still needed to show their effectiveness in reducing readmission to hospital. Additionally, the patients who sought help from community paramedics may be more likely than the controls to seek help from medical professionals. This study provided a novel look into the effect that community paramedics can have on patients in urban and rural areas in regard to reducing postoperative complications and minimizing unnecessary advanced healthcare utilization.

Bouncing Back: Hospital Reliance On Transitional Care Beds – A Disservice To Patients? A Review Of Readmission Rates

Abstract Background The Health Service Executive (HSE) developed an Urgent and Emergency Care (UEC) Operational Plan in 2023. One UEC action was an aim to transfer “clinically appropriate” patients to alternative care settings e.g. Transitional Care Beds (TCBs) to relieve pressure caused by high occupancy rates in acute hospitals. Inclusion criteria for TCB use is ill defined and we sought to evaluate their usage. Methods Retrospective analysis of characteristics and readmission rates (RAR) of patients discharged from a tertiary hospital to TCB from Oct 1st, 2023 - Jan 1st, 2024. Results 158 patients were discharged from hospital to TCBs. 49% (n=77) were female, mean [SD] age 77.82 [10.09] years. The median length of stay (LOS) was 15 days. 50% (n=79) were discharged from surgical services, 40.5% (n=64) from medical teams, 8% (n=13) from oncology services, 7% (n=11) from geriatric medicine and 1% (n=2) from ED. 27% (n=43) had a “fall” documented as their discharge diagnosis. 32% (n=51) of patients were readmitted within 90 days, 11% (n=18) within 30 days and 4% (n=6) within 14 days. 30% (n=15) were readmitted directly from TCB. Average time between discharge and readmission was 44.7 days. Median LOS on readmission was 10 (IQR 15.5 days). 9% (n=14) had an eventual discharge to long term care (LTC) either from a subsequent admission or transitioned directly from TCB. 6% (n=10) of patients died. Conclusion Of those readmitted, one third were directly from TCBs suggesting discharge to TCB may have been premature and patient selection inappropriate subsequently resulting costly readmissions. Although interventions are needed to relieve capacity pressure in hospitals, greater emphasis needs to be placed on access for patients to designated rehabilitation programmes. We should aim to support older adults following acute hospital admissions by carrying out comprehensive geriatric assessment to maximise independence and reduce likelihood of hospital readmission or transition to LTC.

Building capacity and capability for quality improvement: insights from a nascent regional health system

ObjectivesQuality improvement (QI) is critical in facilitating advancements in patient outcomes, system efficiency and professional growth. This paper aimed to elucidate the underlying rationale and framework guiding JurongHealth Campus (JHC),...

The effect of telemedicine employing telemonitoring instruments on readmissions of patients with heart failure and/or COPD: a systematic review.

Hospital readmissions pose a challenge for modern healthcare systems. Our aim was to assess the efficacy of telemedicine incorporating telemonitoring of patients' vital signs in decreasing readmissions with a focus on a specific patient population particularly prone to rehospitalization: patients with heart failure (HF) and/or chronic obstructive pulmonary disease (COPD) through a comparative effectiveness systematic review. Three major electronic databases, including PubMed, Scopus, and ProQuest's ABI/INFORM, were searched for English-language articles published between 2012 and 2023. The studies included in the review employed telemedicine incorporating telemonitoring technologies and quantified the effect on hospital readmissions in the HF and/or COPD populations. Thirty scientific articles referencing twenty-nine clinical studies were identified (total of 4,326 patients) and were assessed for risk of bias using the RoB2 (nine moderate risk, six serious risk) and ROBINS-I tools (two moderate risk, two serious risk), and the Newcastle-Ottawa Scale (three good-quality, four fair-quality, two poor-quality). Regarding the primary outcome of our study which was readmissions: the readmission-related outcome most studied was all-cause readmissions followed by HF and acute exacerbation of COPD readmissions. Fourteen studies suggested that telemedicine using telemonitoring decreases the readmission-related burden, while most of the remaining studies suggested that it had a neutral effect on hospital readmissions. Examination of prospective studies focusing on all-cause readmission resulted in the observation of a clearer association in the reduction of all-cause readmissions in patients with COPD compared to patients with HF (100% vs. 8%). This systematic review suggests that current telemedicine interventions employing telemonitoring instruments can decrease the readmission rates of patients with COPD, but most likely do not impact the readmission-related burden of the HF population. Implementation of novel telemonitoring technologies and conduct of more high-quality studies as well as studies of populations with ≥2 chronic disease are necessary to draw definitive conclusions. This study is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), identifier (INPLASY202460097).