Unplanned Readmission Of Patients Research Articles

1492P The role of LACE index in predicting 30-day unplanned readmission in patients receiving immunotherapy

1492P The role of LACE index in predicting 30-day unplanned readmission in patients receiving immunotherapy

Institutional experience with a limb salvage quality improvement initiative to reduce length of stay and readmissions

BackgroundThis article aims to outline the process and present preliminary outcomes of a local quality improvement initiative focusing on length of stay (LOS) and 30-day unplanned readmissions in patients undergoing treatment for peripheral arterial disease. MethodsInitiating with internal data acquisition, our clinical informaticists, adept in Epic data mining, were tasked with monthly query of the database for all patients with peripheral arterial disease managed since May 2021. An automated report of crucial variables, eliminating the need for individual chart review, was thus generated. Studying trends in LOS and readmissions, we set target goals of 7 days for LOS and 11% for 30-day unplanned readmission, referencing the top-decile institutions in the Vizient ranking. To enhance our quality improvement initiative, we formed a comprehensive limb salvage committee, comprising a vascular surgeon, hospitalist, podiatrists, nurses, a case management team, and a clinical researcher. This multidisciplinary team met regularly to review monthly reports, identify trends, and propose improvements. After 6 months of observation, the limb salvage committee finalized a set of recommendations, which were then communicated to the vascular surgery faculty for feedback and subsequent implementation. Monthly outcomes were continuously monitored by the limb salvage committee to assess progress and pinpoint areas necessitating further improvement. ResultsBetween May 2021 and March 2023, 985 consecutive procedures were performed. The average postoperative LOS was 7 ± 10 days, with 35% not meeting the goal. Patients not meeting the LOS goal often had a history of coronary artery disease or congestive heart failure and were admitted initially for nonvascular reasons. The readmission rate was 15%. Recommendations from the limb salvage committee included deferring nonurgent revascularization to outpatient care with prehabilitation for at-risk patients, and early follow-up by a nurse practitioner for wound check and pain management. These recommendations were implemented at the end of March 2023. After implementing these recommendations, 272 cases recorded between April and July 2023 saw a decrease in patients not meeting the target LOS to 27%, and unplanned readmissions decreased to 11%. ConclusionsOur limb salvage quality improvement project has proved efficient in monitoring quality metrics and targeting specific areas in need of improvement. Continuous monitoring is necessary to ensure compliance, track outcomes, and identify areas in need of further improvement. Future studies will assess further the effectiveness of this process in improving patient care.

The burden and predictors of 30-day unplanned readmission in patients with acute liver failure: a national representative database study

BackgroundLiver diseases were significant source of early readmission burden. This study aimed to evaluate the 30-day unplanned readmission rates, causes of readmissions, readmission costs, and predictors of readmission in patients with acute liver failure (ALF).MethodsPatients admitted for ALF from 2019 National Readmission Database were enrolled. Weighted multivariable logistic regression models were applied and based on Directed Acyclic Graphs. Incidence, causes, cost, and predictors of 30-day unplanned readmissions were identified.ResultsA total of 3,281 patients with ALF were enrolled, of whom 600 (18.3%) were readmitted within 30 days. The mean time from discharge to early readmission was 12.6 days. The average hospital cost and charge of readmission were $19,629 and $86,228, respectively. The readmissions were mainly due to liver-related events (26.6%), followed by infection (20.9%). The predictive factors independently associated with readmissions were age, male sex (OR 1.227, 95% CI 1.023–1.472; P = 0.028), renal failure (OR 1.401, 95% CI 1.139–1.723; P = 0.001), diabetes with chronic complications (OR 1.327, 95% CI 1.053–1.672; P = 0.017), complicated hypertension (OR 1.436, 95% CI 1.111–1.857; P = 0.006), peritoneal drainage (OR 1.600, 95% CI 1.092–2.345; P = 0.016), etc.ConclusionsPatients with ALF are at relatively high risk of early readmission, which imposes a heavy medical and economic burden on society. We need to increase the emphasis placed on early readmission of patients with ALF and establish clinical strategies for their management.

Associations between postoperative anaemia and unplanned readmission to hospital after major surgery: a retrospective cohort study†.

Anaemia following major surgery may be associated with unplanned readmission to hospital. However, the severity-response relationship between the degree of anaemia at discharge and the risk of unplanned readmission is poorly defined. We aimed to describe the severity-response relationship between haemoglobin concentration at the time of discharge and the risk of unplanned readmission in a cohort of patients undergoing different types of major surgery. We performed a retrospective cohort study in a single tertiary health service, including all patients who underwent major surgery (orthopaedic, abdominal, cardiac or thoracic) between 1 May 2011 and 1 February 2022. The primary outcome was unplanned readmission to hospital in the 90 days following discharge after the index surgical procedure. These complex, non-linear relationships were modelled with restricted cubic splines. We identified 22,134 patients and included 14,635 in the primary analysis, of whom 1804 (12%) experienced at least one unplanned readmission. The odds of unplanned readmission rose when the discharge haemoglobin concentration was < 100 g.l-1 (p < 0.001). On subgroup analysis, the haemoglobin threshold below which odds of readmission began to increase appeared to be higher in patients undergoing emergency surgery (110 g.l-1; p < 0.001) compared with elective surgery. Declining discharge haemoglobin concentration was associated with increased odds ratios (95%CI) of unplanned readmission in patients undergoing orthopaedic (1.08 (1.01-1.15), p = 0.03), abdominal (1.13 (1.07-1.19), p < 0.001) and thoracic (1.12 (1.01-1.24), p = 0.03) procedures, but not cardiac surgery (1.09 (0.99-1.19), p = 0.07). Our findings suggest that a haemoglobin concentration < 100 g.l-1 following elective procedures and < 110 g.l-1 following emergency procedures, at the time of hospital discharge after major surgery, was associated with unplanned readmission. Future interventional trials that aim to treat postoperative anaemia and reduce unplanned readmission should include patients with discharge haemoglobin below these thresholds.

299 Predictors of Unplanned Readmission in Patients Undergoing Intracranial Tumor Resection: A Multi-center Analysis of 31,776 Patients

INTRODUCTION: Although unplanned readmission is a postoperative outcome metric associated with significant morbidity and financial burden, precise assessment tools for its prediction are yet to be developed. The Revised Risk Analysis Index (RAI) has been recently endorsed as a robust frailty index for predicting surgical outcomes. Hence, the RAI could potentially be utilized to help improve prediction unplanned readmission in patients undergoing intracranial tumor resection (ITR). METHODS: Data of patients undergoing ITR were obtained from the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database from the years 2012-2019 using the ICD-9 and ICD-10 codes, indicating ITR. Baseline characteristics, preoperative clinical status, and outcomes were compared between patients with and without unplanned readmission. Frailty, as a measure of baseline physiological reserve, was calculated using the RAI. Univariate and multivariate logistic regression analyses were performed to identify independent associations between unplanned readmissions and patient characteristics. RESULTS: The unplanned readmission rate of this cohort (N = 31,776) was 10.8% (N = 3,420). Among the readmitted patients, 958 required unplanned reoperation. Multiple characteristics were significantly different between the readmitted and non-readmitted patients, including age, BMI, comorbidities, and RAI groups (p &lt; 0.05). The most common causes of unplanned readmission were infection (9.4%), seizures (6%), and pulmonary embolism (4%). Patient characteristics identified as reliable predictors of unplanned readmission, included age, body mass index (BMI), functional status, diabetes, hypertension, hyponatremia, and their RAI score (p &lt; 0.05). In multivariate analysis, RAI “frail” patients had an unplanned readmission odds ratio (OR) of 1.3(95%CI; 1.2-1.4), and RAI “severely frail” patients had an OR of 1.6 (95%CI; 1.3-2.1) (p &lt; 0.001). CONCLUSIONS: The RAI is a reliable pre-operative frailty index for predicting unplanned readmission following ITR. Utilizing the RAI to identify high risk patients may decrease unplanned readmission through implementation of appropriate management guidelines.

Risk Factors for Re-admission in Covid-19 Patients from a Tertiary Care Health System: Quality Control (QC) and Quality Improvement (QI) Project

The COVID-19 pandemic has made a significant impact on public health globally and has unleashed an array of deficiencies that have already crippled our health care system. It also brought out the health care disparities among minorities and thrust health inequity to the limelight. Hospital readmission is one descriptive marker for overall patient clinical prognosis. Prior to SARS-CoV-2 Pandemic (COVID-19), 30-day-all cause readmission rates was highest in patients with heart failure, psychoses, chronic lung disease, and peripheral vascular disease status post-surgical procedure. However, pneumonia is the most common reason for unplanned patient readmission. COVID-19 continues to evolve, from the rising versality in variants to the clinical concerns of longer haulers. This study followed by a review will illustrate COVID-19 post-acute disease role on readmission diagnosis and rates. The aim is to identify most common reason for readmission and overall patient clinical prognosis. Readmitted patients in this study were older.&#x0D; In this QC/QI project the frequent symptom on readmission was respiratory illness and the primary diagnostic codes on readmission were noted to be respiratory failure and “COVID-19 sequelae.” Readmitted patients in this study were older (&gt;61yrs of age), with an equal sex distribution, more likely to have multiple comorbidities, majority were of African American ethnicity, reflecting the disproportionate impact ofCOVID-19 on this group. About 70% of readmissions had a Charlson Comorbidity index of 3 and above reflecting the chronic conditions present in this cohort. Less than 40% of patients were up to date with their COVID 19 immunization and overall mortality rate was close to 12%. Based on our QC/QI analysis, a comprehensive and coordinated approach that addresses important metrics comprising optimal medical management of comorbidities, COVID 19/Pneumococcal immunizations, focus on mental health treatment and homelessness is essential to decrease the risk for readmissions in this population.

The impact of metabolic overweight/obesity phenotypes on unplanned readmission risk in patients with COPD: a retrospective cohort study

Background: There is an inconsistent association between overweight/obesity and chronic obstructive pulmonary disease (COPD). Considering that different metabolic characteristics exist among individuals in the same body mass index (BMI) category, the classification of overweight/obesity based on metabolic status may facilitate the risk assessment of COPD. Our study aimed to explore the relationship between metabolic overweight/obesity phenotypes and unplanned readmission in patients with COPD.Methods: We conducted a retrospective cohort study using the Nationwide Readmissions Database (NRD). According to metabolic overweight/obesity phenotypes, patients were classified into four groups: metabolically healthy non-overweight/obesity (MHNO), metabolically unhealthy non-overweight/obesity (MUNO), metabolically healthy with overweight/obesity (MHO), and metabolically unhealthy with overweight/obesity (MUO). The primary outcome was unplanned readmission to hospital within 30 days of discharge from index hospitalization. Secondary outcomes included in-hospital mortality, length of stay (LOS) and total charges of readmission within 30 days.Results: Among 1,445,890 patients admitted with COPD, 167,156 individuals were unplanned readmitted within 30 days. Patients with the phenotype MUNO [hazard ratio (HR), 1.049; 95%CI, 1.038–1.061; p &amp;lt; 0.001] and MUO (HR, 1.061; 95%CI, 1.045–1.077; p &amp;lt; 0.001) had a higher readmission risk compared with patients with MHNO. But in elders (≥65yr), MHO also had a higher readmission risk (HR, 1.032; 95%CI, 1.002–1.063; p = 0.039). Besides, the readmission risk of COPD patients with hyperglycemia or hypertension regardless of overweight/obesity increased (p &amp;lt; 0.001).Conclusion: In patients with COPD, overweight/obesity alone had little effect on unplanned readmission, whereas metabolic abnormalities regardless of overweight/obesity were associated with an increased risk of unplanned readmission. Among the metabolic abnormalities, particular attention should be paid to hyperglycemia and hypertension. But in elders (≥65yr) overweight/obesity and metabolic abnormalities independently exacerbated the adverse outcomes.

Implication of Peripheral Neuropathy on Unplanned Readmissions in Patients Hospitalized for Complicated Diabetic Foot Disease.

Introduction. Unplanned readmissions are common following discharge in patients after hospitalization for diabetic foot disease (DFD) complications. The aim of this study was to identify factors associated with readmissions in these high-risk patients, treated in a multidisciplinary setting and the implication of measures that could effectively reduce readmission rates. Methods. Patients presenting with DFDs admitted between 2015 and 2017 were studied retrospectively in a single-centre patient database. The demographics and clinical comorbidities were analyzed and comparison was made between 2 groups: patients readmitted within 30 days of discharge and those who did not require readmission. Multivariate analysis was performed to identify risk factors associated with readmissions. Results. In total, 340 patients were included. The unplanned readmission rate was 10.9%. More than half of readmissions (71%) were related to wound deterioration and infection. In the readmission group, the patients had lower body mass index, higher rate of osteomyelitis, lower rate of debridement, and evidence of peripheral vascular disease below the knee in the index admissions but these were not significant. In the multivariate analysis, peripheral neuropathy was the only significant risk associated with unplanned readmissions (odds ratio: 2.78, 95% confidence interval: 1.23-6.29, P = .014). Conclusion. This study demonstrates a significant association between peripheral neuropathy and unplanned readmissions. The implications of this nonmodifiable risk factor in reducing readmissions include all levels of patient care delivery such as adequate preparation for discharge and transition back into the community. Recognition and education in successful long-term offloading of insensate diabetic feet may help reduce rates of unplanned readmission.

Causes and Predictors of Unplanned Readmission in Patients Undergoing Intracranial Tumor Resection: A Multicenter Analysis of 31,776 Patients

Although unplanned readmission is a postoperative outcome metric associated with significant morbidity and financial burden, precise assessment tools for its prediction are yet to be developed. The Risk Analysis Index (RAI) could potentially be utilized to help improve prediction unplanned readmission in patients undergoing intracranial tumor resection (ITR). Here we evaluate the predictive accuracy of frailty on 30-day unplanned readmission following ITR using the RAI. Data were obtained from the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database. Baseline characteristics, preoperative clinical status, and outcomes were compared between patients with and without unplanned readmission. Frailty was calculated using the RAI. Univariate and multivariate logistic regression analyses were performed to identify independent associations between unplanned readmissions and patient characteristics. The unplanned readmission rate of this cohort (N=31,776) was 10.8% (N= 3,420). Among the readmitted patients, 958 required unplanned reoperation. Multiple characteristics were significantly different between the readmitted and non-readmitted patients, including age, BMI, comorbidities, and RAI groups (p<0.05). Common causes of unplanned readmission included infection (9.4%), seizures (6%), and pulmonary embolism (4%). Patient characteristics identified as reliable predictors of unplanned readmission, included age, body mass index (BMI), functional status, diabetes, hypertension, hyponatremia, and their RAI score (p<0.05). Frail status, hyponatremia, leukocytosis, hypertension, and thrombocytosis were significant predictors of unplanned readmission. The RAI is a reliable pre-operative frailty index for predicting unplanned readmission following ITR. Utilizing the RAI may decrease unplanned readmission thorough identifying high risk patients, and enabling future implementation of appropriate management guidelines.

ThTP5.9 Are readmission rates in a new surgical SDEC department acceptable? A service evaluation of a Healthboard’s new department

Abstract Aims Unplanned patient readmission after discharge is a costly and potentially preventable occurrence, which can negatively impact both the patient’s experience and overall outcomes. Readmission rates across the country vary depending on location, population demographics and presenting complaint, but most departments have rates between 4 – 10%. Our health board has recently implemented a new surgical Same Day Emergency Care (SDEC) department in response to rising pressures on beds. There have already been promising drops in patient waiting times since its inception, however patient readmission rates have not been formally evaluated. We aimed to investigate 30 day unplanned patient readmission rates in this new SDEC department. Methods All patients that came in to the SDEC department in October 2022 were placed on a prospective database. We used the healthboard’s Clinical Workstation (CWS) database application to track whether they had re-presented to the Emergency or surgical departments within 30 days of their discharge from SDEC. Results 344 patients were seen in SDEC over the course of 31 days. Of these 238 were discharged, 34 were planned readmissions and 71 were admitted. Of the discharged patients 13 came back as unplanned readmissions within 30 days. This gives an unplanned readmission rate of 5.5%. Conclusion SDEC is providing an excellent surgical service, with low unplanned readmission rates. SDEC has proven to be an effective way to divert patients from the Surgical Assessment Unit and ease the acute on call surgical take pressures.

Incidence, causes, and risk factors for unplanned readmission in patients admitted with pulmonary tuberculosis in China.

To investigate the incidence, causes, and risk factors for unplanned readmission within 30 days of discharge in patients with pulmonary tuberculosis (TB). The clinical data of 1,062 patients with confirmed pulmonary TB who were admitted to our hospital from October 2018 to October 2021 were analysed retrospectively. The subjects were divided into a readmission group (354 cases) and a non-readmission group (708 cases) according to whether there was an unplanned admission within 30 days of discharge. We analysed the risk factors for unplanned readmission within 30 days after discharge with pulmonary TB. The incidence of unplanned readmission in patients with pulmonary TB was 5.2%. Being female (OR = 0.63, 95% CI: 0.434-0.942) and living in cities (OR = 0.218, 95% CI: 0.151-0.315) were protective factors for the readmission of patients with TB (p < 0.05). However, being ≥ 65 years old (OR = 2.574, 95% CI: 1.709-3.870), being a smoker (OR = 2.773, 95% CI: 1.751-4.390), having chronic obstructive pulmonary disease (COPD) (OR = 3.373, 95% CI: 1.708-6.660), having viral hepatitis (OR= 2.079, 95% CI: 1.067-4.052), receiving non-standard treatment (OR = 15.620, 95% CI: 10.413-23.431), having medical side effects (OR = 6.138, 95% CI: 3.798-9.922) and l unauthorised discharge (OR = 2.570, 95% CI: 1.509-4.376) were risk factors for the readmission to hospital of patients with TB (p < 0.05). Gender, age, place of residence, smoking, COPD, hepatitis, non-standard treatment, adverse drug reactions and unauthorised discharge were risk factors of TB for unplanned readmission.

Thirty-Day Unplanned Hospital Readmissions in Patients With Cancer and the Impact of Social Determinants of Health: A Machine Learning Approach.

Develop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors. The initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve. We included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable. Key drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission.

Profile of and risk factors for early unplanned readmissions in patients with acute necrotizing pancreatitis

IntroductionAcute necrotizing pancreatitis (ANP) complicates up to 15% of acute pancreatitis cases. ANP has historically been associated with a significant risk for readmission, but there are currently no studies exploring factors that associate with risk for unplanned, early (<30-day) readmissions in this patient population. MethodsWe performed a retrospective review of all consecutive patients presenting to hospitals in the Indiana University (IU) Health system with pancreatic necrosis between December 2016 and June 2020. Patients younger than 18 years of age, without confirmed pancreatic necrosis and those that suffered in-hospital mortality were excluded. Logistic regression was performed to identify potential predictors of early readmission in this group of patients. ResultsOne hundred and sixty-two patients met study criteria. 27.7% of the cohort was readmitted within 30-days of index discharge. The median time to readmission was 10 days (IQR 5–17 days). The most frequent reason for readmission was abdominal pain (75.6%), followed by nausea and vomiting in (35.6%). Discharge to home was associated with 93% lower odds of readmission. We found no additional clinical factors that predicted early readmission. ConclusionPatients with ANP have a significant risk for early (<30 days) readmission. Direct discharge to home, rather than short or long-term rehabilitation facilities, is associated with lower odds of early readmission. Analysis was otherwise negative for independent, clinical predictors of early unplanned readmissions in ANP.

Unplanned 30-day readmissions, comorbidity and impact on one-year mortality following incident heart failure hospitalisation in Western Australia, 2001–2015

BackgroundReadmissions within 30 days after heart failure (HF) hospitalisation is considered an important healthcare quality metric, but their impact on medium-term mortality is unclear within an Australian setting. We determined the frequency, risk predictors and relative mortality risk of 30-day unplanned readmission in patients following an incident HF hospitalisation.MethodsFrom the Western Australian Hospitalisation Morbidity Data Collection we identified patients aged 25–94 years with an incident (first-ever) HF hospitalisation as a principal diagnosis between 2001 and 2015, and who survived to 30-days post discharge. Unplanned 30-day readmissions were categorised by principal diagnosis. Logistic and Cox regression analysis determined the independent predictors of unplanned readmissions in 30-day survivors and the multivariable-adjusted hazard ratio (HR) of readmission on mortality within the subsequent year.ResultsThe cohort comprised 18,241 patients, mean age 74.3 ± 13.6 (SD) years, 53.5% males, and one-third had a modified Charlson Comorbidity Index score of ≥ 3. Among 30-day survivors, 15.5% experienced one or more unplanned 30-day readmission, of which 53.9% were due to cardiovascular causes; predominantly HF (31.4%). The unadjusted 1-year mortality was 15.9%, and the adjusted mortality HR in patients with 1 and ≥ 2 cardiovascular or non-cardiovascular readmissions (versus none) was 1.96 (95% confidence interval (CI) 1.80–2.14) and 3.04 (95% CI, 2.51–3.68) respectively. Coexistent comorbidities, including ischaemic heart disease/myocardial infarction, peripheral arterial disease, pneumonia, chronic kidney disease, and anaemia, were independent predictors of both 30-day unplanned readmission and 1-year mortality.ConclusionUnplanned 30-day readmissions and medium-term mortality remain high among patients who survived to 30 days after incident HF hospitalisation. Any cardiovascular or non-cardiovascular readmission was associated with a two to three-fold higher adjusted HR for death over the following year, and various coexistent comorbidities were important associates of readmission and mortality risk. Our findings support the need to optimize multidisciplinary HF and multimorbidity management to potentially reduce repeat hospitalisation and improve survival.

Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease.

Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.

Characteristics of Lymphoma Patients Experiencing Unplanned Readmissions at a Large Academic Medical Center

Background: Patients with lymphoma frequently experience hospital readmissions. Yet, limited data exist describing hospital readmissions in the lymphoma population. In this study, we sought to identify additional patient-specific characteristics associated with unplanned readmissions in patients with lymphoma at our institution. Methods: We conducted a retrospective, single-center study of patients with lymphoma who were age >18 and discharged from medical oncology wards from January-December 2019 (index admission). We categorized patients as having high-risk medications per the Institute for Safe Medication Practices, potentially inappropriate medications (PIMs) for older patients (age 65+) per the American Geriatric Society Beers Criteria, and polypharmacy and major polypharmacy as 5 or more and 10 or more scheduled medications on the discharge medication list, respectively. We performed univariate logistic regression to assess the association of with having an unplanned readmission within 30 days of discharge from index admission. The following factors, selected for clinical relevance, were assessed: age; gender; race; marital status; Charlson comorbidity score; lymphoma diagnosis and related parameters (stage, LDH, prior treatment ECOG PS); index hospitalization reason, duration, discharge disposition and prior healthcare utilization; activities of daily living (ADL) status; Morse fall score; laboratory values at time of discharge; discharge medication list; 30-day readmission status and readmission reason if applicable. Results: We included 207 patients (mean age 63.9 years, 38.6% female, 83.1% white race, 56.8% partnered), 65 (31.4%) of whom experienced a 30-day unplanned readmission. Patients experiencing a 30-day unplanned readmission were more likely to have ECOG PS >2 (36.9% vs 14.2%, p<0.001), higher age-adjusted IPI score (score 0, 3.3% vs 7.8%; score 1, 23.3% vs 35.7%; score 2, 55.0% vs 47.8%; score > 3, 18.3% vs 8.7%; p=0.012), received CAR T-cell therapy (9.2% vs 2.1%, p=0.032), index length of stay >5 days (55.4% vs 33.1%, p=0.003), been discharged to a skilled nursing facility (12.3% vs 3.5%, p=0.023), albumin <3.5 g/dL (78.5% vs 52.1%, p<0.001), and sodium <135 mEq/L (18.5% vs 8.5%, p=0.041). Patients discharged home were less likely to have a 30-day unplanned readmission (56.9% vs 76.8%, p=0.004). Age, sex, marital status, Charlson comorbidity score, aggressive versus non-aggressive lymphoma diagnosis, prior lymphoma treatment, discharge home with services or to inpatient rehabilitation, Morse fall score, bowel and/or bladder continence, independence in feeding or bathing, neutropenia (ANC<1.5 k/mcL), and Hgb <10 g/dL were not significantly associated with increased likelihood for unplanned readmission. Among medication-related factors, we found that patients with major polypharmacy on the index admission discharge medication list had a higher likelihood for unplanned readmission (56.9% vs 37.3%, p=0.009). Patients with discharge medication lists with polypharmacy (90.8% vs 81.7%, 00, long-acting opiates (10.8% vs 4.2%, p=0.082), and/or anticoagulants (26.2% vs 15.5%, p=0.071) had a higher likelihood for unplanned readmissions, but these differences did not reach statistical significance. Presence of PIMs was not associated with readmission. Conclusions: In this relatively large cohort of patients with lymphoma, we identified several risk factors for unplanned hospital readmissions. We found associations among unplanned readmissions and impaired performance status, receipt of prior CAR T therapy, higher age-adjusted IPI score, discharge location (home, skilled nursing facility, etc), lower albumin and sodium levels, as well as major polypharmacy on the discharge medication list. Future directions include further efforts to identify lymphoma patients at high risk for hospital readmissions and developing readmission prevention strategies in those patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Real-world outcomes of telehealth initiative to reduce readmissions for patients with cancer.

387 Background: Unplanned readmissions in patients with cancer increase both financial toxicity and mortality. It adversely impacts their quality of life by increasing distress for the patient and caregivers. Improving the transition from inpatient to outpatient care has shown to reduce readmissions. The goal of our study is to reduce hospital readmission rate for patients with solid malignancies by implementing a Telehealth transitional care visit for patients with a high EPIC readmission score at the time of discharge. Methods: A pilot study of patients with thoracic and gastrointestinal malignancies who had a high (≥23%) EPIC readmission score at discharge home between March 2021 and May 2022 were included. Patients who were discharged to a skilled nursing facility/rehabilitation center and patients who were admitted for scheduled chemotherapy were excluded. Telehealth visit with an Advanced Practice Provider (APP) was scheduled within seven days after discharge. It included evaluation of medications, addressing reasons for the index admission, review of symptoms, concerns and reminders for follow up appointments with the primary oncologist team. Readmission rates were compared to a control cohort. Results: A total of 44 patients with high readmission score underwent a Telehealth visit following discharge home. The median age was 63.5 years (range 38-83). Of the 44 patients, 36% (16) were male and 64% (28) were female. Fourteen (32%) patients had thoracic malignancy and 30 (68%) had gastrointestinal malignancy. All patients had received at least one line of chemotherapy. Twelve of the 44 patients (27.3%) had a true 30-day readmission which was lower compared to the control group (31.8%, 14/44 patients) who did not have a Telehealth intervention. Conclusions: A timely Telehealth visit within seven days of the index admission can positively impact the true 30-day readmission rate for patients with cancer.

Analysis of Influencing Factors of Unplanned Readmission in Patients With Acute Coronary Syndrome Within 30 Days After PCI.

The purpose of this study is to identify the influencing factors of unplanned readmission in patients with the acute coronary syndrome (ACS) within 30 days after percutaneous coronary intervention (PCI). From November 1, 2018, to October 31, 2019, the clinical data of 1277 patients with acute coronary syndrome and percutaneous coronary intervention retrospectively were collected. After screening by exclusion and rejection criteria, a total of 936 patients finally entered the study. Patients were divided into the readmission group (57 cases) and the non-readmission group (879 cases), according to whether unplanned readmission occurred within 30 days after PCI. To analyze the influence of patients' age, past disease history, medication history, laboratory data, vascular diseases, and other factors on readmission and the clinical characteristics of readmission patients. Fifty-seven patients had unplanned readmission within 30 days, and the readmission rate was 6.09%. The clinical features of readmission patients are older age, longer hospitalization days, more emergency percutaneous coronary intervention, more patients with diabetes history, and more patients diagnosed with ST-segment elevation myocardial infarction and non-ST-segment elevation myocardial infarction. Logistic regression analysis revealed that smoking index, number of diseased vessels, ACEF score, diabetes, and PCI status were the influencing factors of unplanned readmission of ACS patients within 30 days after PCI. Smoking index, number of diseased vessels, ACEF score, diabetes, and PCI status are the influencing factors of unplanned readmission within 30 days after percutaneous coronary intervention for patients with acute coronary syndrome.

Impact of mild cognitive impairment on unplanned readmission in patients with coronary artery disease.

To investigate the effect of mild cognitive impairment (MCI) on unplanned readmission in patients with coronary artery disease (CAD). From 2132 CAD patients, MCI was estimated with the Japanese version of the Montreal Cognitive Assessment (MoCA-J) in 243 non-dementia patients who met the study criteria. The primary outcome was unplanned hospital readmission after discharge. The incidence of MCI in this cohort was 33.3%, and 51 patients (21.0%) had unplanned readmission during a mean follow-up period of 418.6 ± 203.5 days. After adjusting for the covariates, MCI (hazard ratio, 2.28; 95% confidence interval: 1.09-4.76; P = 0.03) was independently associated with unplanned readmission in the multivariable Cox proportional hazard regression analysis. In the Kaplan-Meier analysis, the cumulative incidence of unplanned readmission for the MCI group was significantly higher than that for the non-MCI group (log-rank test, P < 0.001). Even after exclusion of the patients readmitted within 30 days of discharge, the main results did not change (log-rank test, P < 0.001). Mild cognitive impairment was independently associated with unplanned readmission after adjustment for many independent variables in CAD patients. In addition to its short-term effects, the adverse effects of MCI had a persistent, long-term impact on CAD patients. Assessment of cognitive function should be conducted by health professionals prior to hospital discharge and during follow-up. To prevent readmission of CAD patients, it will be necessary to support solutions to the problems that inhibit secondary prevention behaviours based on the assessment of the patients' cognitive function.

Predictors for Recurrent 30-Day Unplanned Readmissions in Patients with Hematologic Malignancies

BackgroundNewly diagnosed hospitalized patients (pts) with hematologic malignancies (HM) frequently have high symptom burden, often need prolonged courses of highly myelosuppressive chemotherapy and consequent protracted transfusion support- factors that predispose these pts to unplanned readmissions (UR). Given the paucity of data on risk factors associated with repeated UR in HM pts, we first analyzed the baseline characteristics of pts with one or more 30-day UR and then evaluated factors that predicted for additional 30-day UR following the first 30-day UR event.MethodsAdult (≥ 18 years) HM pts treated at Cleveland Clinic between January 2011-February 2016 were included in the study. Thirty-day UR was defined as hospitalization within 30 days of index admission for any reason other than planned chemotherapy. A readmission event served as an index admission for the subsequent readmission. Bone marrow transplant pts were excluded. HM included acute leukemias, myelodysplastic syndromes (MDS) and aggressive lymphomas (Diffuse Large B-Cell Lymphoma, Burkitt's). Demographics (age, race, sex, highest education attained, median income, payer status), clinical characteristics (HM type, symptoms at index admission/readmission, disease status, body mass index at discharge, absolute neutrophil count (ANC) at discharge/readmission, and discharge on intravenous antibiotics or narcotics), hospitalization (reason for readmission, length of stay (LOS) at index admission/readmission), and discharge characteristics (discharged to and readmitted from) were collected. Data were analyzed using Fisher's exact test chi-square tests, and t-tests for univariable analyses; and logistic regression for multivariable analyses. Recursive partitioning was used to find cut points for continuous factors.ResultsA total of 259 thirty-day UR were observed in 157 pts - 107 had a single 30-day UR, while 50 (32%) had > 2 UR. The majority of pts were males (59%), with a median age of 66 years (yrs, interquartile range (IQR) 48-75). Acute myeloid leukemia was the most common HM diagnosis (44%) and 50% had relapsed or refractory disease. Pt median income was $51,700 and 86% (IQR, 80-90%) had greater than a high school education, using census tract based data (Table 1). By payer source, 49% of pts were covered by Medicare, 12% by Medicaid, and 36% by private insurance. Discharges following UR were to: home (50%), home with home health (32%), nursing facility (15%) and hospice (3%). Of the discharges after UR, 10% were discharged on intravenous antibiotics, 44% on opioids and 48% on psychotropic drugs. Neutropenic fever with or without a confirmed source was the primary diagnosis (61%) for 30-day UR and 59% had symptoms at presentation. Median ANC at discharge from an UR was 870/mL (IQR 190-3570) and was 940/mL (IQR, 100-3700) at the time of readmission. The median LOS during 30-day UR was 5 days (IQR 3-10) and the median interval from prior discharge to readmission was 11 days (IQR 5-19). The majority of 30-day UR originated from outpatient clinic, emergency department, or pt's home (46%) followed by a non-health care facility (27%) and outside hospital transfers (22%). Table 1 shows univariable analysis of factors significantly associated with 30-day UR. In multivariate analysis, factors that independently predicted for subsequent 30-day UR included pts with the following characteristics - ANC <2000/mL at last discharge (OR 7.5, P=.0002), constitutional symptoms (fevers/chills/sweats/severe fatigue at index admission presentation) (OR 5.84, P=.002), gastrointestinal symptoms (OR 5.11, P=.009), if pt was transferred from an outside facility (OR 3.26, P=.005), febrile neutropenia as reason for index admission (OR 3.18, P=.0001), relapsed/refractory disease (OR 2.94, P=.009), and higher education (OR 2.08, P=.01).ConclusionOur findings suggest directing UR reduction strategies to optimize symptom control (constitutional and gastrointestinal) prior to discharge and design specific measures for the cohort with relapsed/refractory disease who might further benefit with individualized care from a multidisciplinary care team, and closer ambulatory follow up. Increased risk of additional 30-day UR with education may reflect better understanding of care complexities resulting in a lower threshold for seeking care with a change in medical condition, or possibly a surrogate for better health care access. [Display omitted] DisclosuresGerds:Incyte: Consultancy; CTI BioPharma: Consultancy. Advani:Takeda/ Millenium: Research Funding; Pfizer: Consultancy. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees.