Risk Of Unplanned Readmission Research Articles

Discharge against medical advice in trauma patients: Trends, risk factors, and implications for health care management strategies

ObjectiveGiven the nonelective nature of most trauma admissions, patients who experience trauma are at a particular risk of discharge against medical advice. Despite the risk of unplanned readmission and financial burden on the health care system, discharge against medical advice among hospitalized patients continues to rise. The present study aimed to assess evolving trends and outcomes associated in patients with discharge against medical advice among patients hospitalized for traumatic injury. MethodsThe 2016–2020 Nationwide Readmissions Database was queried to identify all hospitalizations for traumatic injuries. The patient cohort was stratified into those who had discharge against medical advice and those who did not. Temporal trends of discharge against medical advice and associated costs over time were evaluated using nonparametric tests. Multivariable regression models were developed to assess factors associated with discharge against medical advice. Associations of discharge against medical advice with length of stay, hospitalization costs, and unplanned 30-day readmission were subsequently evaluated. ResultsOf an estimated 4,969,717 patients, 65,354 (1.3%) had discharge against medical advice after hospitalization for traumatic injury. Over the study period, the incidence of discharge against medical advice increased (nptrend <0.001). After risk adjustment, older age (adjusted odds ratio, 0.98/per year; 95% confidence interval, 0.97–0.98), female sex (adjusted odds ratio, 0.65; 95% confidence interval, 0.64–0.67), and management at high-volume trauma center (adjusted odds ratio, 0.71; 95% confidence interval, 0.69–0.74) were associated with lower odds of discharge against medical advice. Compared with others, discharge against medical advice was associated with decrements in length of stay by 1.3 days (95% confidence interval, 1.1–1.5 days) and index hospitalization costs by $2,200 (5% confidence interval, $1,600–2,900), while having a greater risk of unplanned 30-day readmission (adjusted odds ratio, 2.21; 95% confidence interval, 2.06–2.36). ConclusionThe incidence of discharge against medical advice and its associated cost burden have increased in recent years. Community-level interventions and institutional efforts to mitigate discharge against medical advice may improve the quality of care and resource allocation for patients with traumatic injuries.

Hospital readmissions among adults living with and without HIV in the US: findings from the Nationwide Readmissions Database

Thirty-day hospital readmission measures quality of care, but there are limited data among people with HIV (PWH) and people without HIV (PWoH) in the era of universal recommendation for antiretroviral therapy. We descriptively compared 30-day all-cause, unplanned readmission risk between PWH and PWoH. A retrospective cohort study was conducted using the 2019 Nationwide Readmissions Database (2019/01/01-2019/12/31), an all-payer database that represents all US hospitalizations. Index (initial) admissions and readmissions were determined using US Centers for Medicare & Medicaid Services definitions. Crude and age-adjusted risk ratios (aRR) comparing the 30-day all-cause, unplanned readmission risk between PWH to PWoH were estimated using random effect logistic regressions and predicted marginal estimates. Survey weights were applied to all analyses. We included 24,338,782 index admissions from 18,240,176 individuals. The median age was 52(IQR=40-60) years for PWH and 61(IQR=38-74) years for PWoH. The readmission risk was 20.9% for PWH and 12.2% for PWoH (age-adjusted-RR:1.88 [95%CI=1.84-1.92]). Stratified by age and sex, young female (age 18-29 and30-39 years) PWH had a higher readmission risk than young female PWoH (aRR=3.50 [95%CI=3.11-3.88] and aRR=4.00 [95%CI=3.67-4.32], respectively). While the readmission risk increased with age among PWoH, the readmission risk was persistently high across all age groups among PWH. The readmission risk exceeded 30% for PWH admitted for hypertensive heart disease, heart failure, and chronic kidney disease. PWH have a disproportionately higher risk of readmission than PWoH, which is concerning given the aging profile of PWH. More efforts are needed to address readmissions among PWH. US National Institutes of Health.

547 ADHD and Burn Injuries: The Hidden Readmission Risk in Pediatric Patients

Abstract Introduction Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with the hallmark characteristics of poor impulse control, inattention, hyperactivity, and other cognitive dysfunction; as a consequence, children with ADHD are at an increased risk of burn injury. We hypothesized that pediatric burn patients with ADHD have a higher rate of unplanned readmission compared to patients without a diagnosis of ADHD. Methods We analyzed the Nationwide Readmission Database (01/2018 - 10/2018) to compare 60-days readmission rates in children (&amp;lt; 18 yo) with and without a diagnosis of ADHD who were treated for burn injuries on their index admission. Weighted multiple logistic regression was performed to obtain population estimates of odds ratios (OR), adjusting for patients’ demographics, burn severity and location, comorbidities, socioeconomic status, other psychiatric illnesses, and burn treatment on index admission. Results Our cohort included 1,879 children with burn injury. Of these, 79 (4.2%) had a diagnosis of ADHD. ADHD patients were more likely to be male (86.1% vs 59.8%, p=0.0002 ) and older (11.6 ± 3.7 vs 5.4 ± 5.3 years, p=0.0001). Children with ADHD were more likely to have other psychiatric diagnoses (26.6% vs 3.8%), specifically obsessive compulsive disorder (2.5% vs 0%, p=0.004), and other developmental disorders (15.2% vs 1.6%, p=0.0002). Patients with ADHD were more likely to be readmitted within 60 days of their index admission (15.2% vs. 4.8%, p=0.002). On weighted multiple regression analysis, ADHD had a higher adjusted odds ratio of readmission (OR: 2.66, 95%CI: 1.13-6.29). Conclusions Children with ADHD have a higher adjusted risk of readmission following thermal injury. This is potentially due to higher barriers to compliance with wound care, physical therapy, and scar management. Targeted discharge planning for patients with ADHD, including scheduled short-term follow-up appointments, home nursing assistance, and post-discharge phone calls, may potentially lower readmission risks in this vulnerable population. Applicability of Research to Practice Pediatric patients with ADHD are at higher than average risk for unplanned readmission within 60 days of discharge. Pediatric burn centers have the opportunity to adjust their post-discharge practice with these high-risk patients to decrease this risk. Interventions could include: more frequent clinic follow-up, longer duration with outpatient therapy, and additional coaching for caretakers on how to improve patient compliance.

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.

Factors associated with unplanned readmissions in pediatric surgical oncology patients

PurposePediatric oncology patients are at increased risk of unplanned readmissions, but factors associated with readmissions are largely unknown. We aimed to identify patients at increased risk for readmission and characterize unplanned readmissions for pediatric surgical oncology patients. MethodsPatients < 20 years with a first primary solid organ cancer who underwent definitive oncologic surgery from 2005–2017 were identified in the California Cancer Registry linked to statewide hospitalization data. Unplanned 30-day readmissions from their definitive surgery were defined as acute medical problems and/or surgical complications not related to planned admissions for chemotherapy, radiation, or rehabilitation. Multivariable logistic regression identified factors associated with unplanned 30-day readmission. Results2507 pediatric oncology patients were identified. Median age was 10 years. 49.2% had a 30-day readmission (n = 1233), and 36.7% (n = 452) of these readmissions were unplanned. In multivariable models, those at highest risk of unplanned readmission were < 1 year old (OR 2.72, CI 1.72–4.29) and 1–5 years (OR 1.64, CI 1.20–2.24) vs. ages 13–19; had metastatic disease at diagnosis (OR 1.6, CI 1.1–2.1); and had central nervous system (CNS) tumors (OR 2.5, CI 1.6–3.9), hepatic tumors (OR 2.3, 95% CI 1.2–4.2), or soft tissue/extraosseous sarcomas (OR 2.2, CI 1.3–3.9). Longer initial hospitalizations were associated with a higher likelihood of unplanned readmission (10 days vs. 7 days, p < 0.0001). ConclusionUnplanned readmissions after surgery for pediatric oncology patients are prevalent. Younger children and those with more advanced/complex disease are at highest risk of unplanned readmissions. Interventions should focus on preventing readmissions in these patients, specifically.

Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study.

Reducing preventable hospitalization for congestive heart failure (CHF) patients is a challenge for health systems worldwide. CHF patients who also have a recent or ongoing mental disorder may have worse health outcomes compared with CHF patients with no mental disorders. This study examined the impact of mental disorders on 28day unplanned readmissions of CHF patients. This retrospective cohort study used population-level linked public and private hospitalization and death data of adults aged ≥18years who had a CHF admission in New South Wales, Australia, between 1 January 2014 and 31 December 2020. Individuals' mental disorder diagnosis and Charlson comorbidity and hospital frailty index scores were derived from admission records. Competing risk and cause-specific risk analyses were conducted to examine the impact of having a mental disorder diagnosis on all-cause hospital readmission. Of the 65861 adults with index CHF admission discharged alive (mean age: 78.6±12.1; 48% female), 19.2% (12675) had at least one unplanned readmission within 28days following discharge. Adults with CHF with a mental disorder diagnosis within 12months had a higher risk of 28day all-cause unplanned readmission [hazard ratio (HR): 1.21, 95% confidence interval (CI): 1.15-1.27, P-value<0.001], particularly those with anxiety disorder (HR: 1.49, 95% CI: 1.35-1.65, P-value<0.001). CHF patients aged ≥85years (HR: 1.19, 95% CI: 1.11-1.28), having ≥3 other comorbidities (HR: 1.35, 95% CI: 1.25-1.46), and having an intermediate (HR: 1.34, 95% CI: 1.28-1.40) or high (HR: 1.37, 95% CI: 1.27-1.47) frailty score on admission had a higher risk of unplanned readmission. CHF patients with a mental disorder who have ≥3 other comorbidities and an intermediate frailty score had the highest probability of unplanned readmission (29.84%, 95% CI: 24.68-35.73%) after considering other patient-level factors and competing events. CHF patients who had a mental disorder diagnosis in the past 12months are more likely to be readmitted compared with those without a mental disorder diagnosis. CHF patients with frailty and a mental disorder have the highest probability of readmission. Addressing mental health care services in CHF patient's discharge plan could potentially assist reduce unplanned readmissions.

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan.

The aim of this study is to analyse the risk factors for unplanned readmissions within 1 month after hospital discharge to develop a seamless support system from discharge to home care. With shorter hospital stay lengths, understanding the characteristics of patients with multiple risk factors is important to prevent rehospitalization. This is a single-centre retrospective descriptive study. Logistic regression and decision tree analyses were performed using eight items from the records of 3117 patients discharged from a university hospital between April-September 2017 as risk factors. Unplanned readmission risk was significantly associated with emergency hospitalization (odds ratio [OR]: 3.12, 95% confidence interval [CI]: 2.04-4.77), malignancy (OR: 2.16, 95% CI: 1.44-3.24), non-surgical admission (OR: 1.76, 95% CI: 1.07-2.88), hospital stay of ≥ 15 days (OR: 1.66, 95% CI: 1.14-2.43) and decline in activities of daily living owing to hospitalization (OR: 1.68, 95% CI: 1.06-2.64). The highest risk combinations for rehospitalization were as follows: emergency hospitalization and malignancy; emergency admission, non-malignancy and a hospital stay of ≥15 days; and scheduled hospitalization, no surgery and a hospital stay of ≥15 days. Patients with multiple risks for unplanned readmission should be accurately screened and provided with optimal home care.

A data-informed timeline for hospital discharge with a reasonably low risk of unplanned readmission after open and minimally invasive liver resections.

Postoperative complications following liver resection remain high, ranging from 20% to 50%. Patients are hospitalized for a certain period of time following liver resection because of the risk of postoperative complications. We hypothesized that the risk of complications decreases with each complication-free postoperative day after open and minimally invasive liver resections and can be stratified using a recently reported three-level complexity classification. Patients undergoing first liver resection without concomitant other organ resections between 2006 and 2019 were included. The three-level complexity classification was used to categorize liver resection procedures into grades I-III. We assessed the rate of cumulative postoperative complications from the time of liver resection to the time of post-hepatectomy complications (≥ Clavien-Dindo grade II). Of the 911 patients included, 200 underwent resection of grade I procedures, 185 underwent resection of grade II procedures, and 526 underwent resection of grade III procedures. The risks of post-hepatectomy complications changed over time and were stratified by surgical complexity. For patients at the time of liver resection, the estimated 30-day complication rate was 21.8% for open grade I resection, 26.7% for open grade II resection, 38.4% for open grade III resection, 8.6% for laparoscopic grade I resection, and 12.5% for laparoscopic grade II resection. For patients without complications at 7 days, the estimated 30-day complication rate decreased to 2.1% for open grade I, 9.2% for open grade II, 17.6% for open grade III, 1.3% for laparoscopic grade I, and 4.5% for laparascopic grade II. The post-hepatectomy complication risks were stratified by surgical complexity, liver resection approach, and the period without complication after liver resection.

The Impact of Inadequate Energy Intake on Readmission Burden of Patients With Heart Failure.

Adequate energy intake is essential for good clinical outcomes. The association between energy intake and readmission burden of patients with heart failure (HF) still needs to be clarified. In this study, our aim was to determine the association between energy intake and readmission in patients with HF. A total of 311 inpatients with HF were recruited. Demographic and clinical information were collected during hospitalization; the daily diets of the participants were collected in the second week after discharge using the 3-day diet record, and the energy intake was calculated using a standardized nutrition calculator. The inadequate energy intake was defined as <70% × 25 kcal/kg of ideal body weight. The participants were followed up for 12 weeks after discharge. The number, reasons, and length of stay of unplanned readmissions were collected. Regression analyses were used to evaluate the associations between inadequate energy intake, and readmission rate and readmission days. The median of the energy intake of participants was 1032 (interquartile range, 809-1266) kcal/d. The prevalence of inadequate energy intake was 40%. Patients with inadequate energy intake had a higher risk of unplanned readmission (odds ratio, 5.616; 95% confidence interval, 3.015-10.462; P < .001) and more readmission days (incidence rate ratio, 5.226; 95% confidence interval, 3.829-7.134, P < .001) after adjusting for potential confounders. Patients with HF had a high incidence of inadequate dietary energy intake, and it increases the burden of readmission.

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.

Association between obesity and risk of delayed discharge and unplanned readmission for day surgery: A systematic review and meta-analysis.

This work aims to investigate the association between obesity and risk of delayed discharge and unplanned readmission in day surgery patients. Day surgeries are well received and developing rapidly. Associations between obesity and delayed discharge and unplanned readmission, which are clinically relevant outcomes in day surgeries, are complex. A systematic review and meta-analysis was conducted. The PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, VIP, and Wan Fang databases were comprehensively searched from inception until January 2021. Two independent reviewers assessed the studies and extracted data. Pooled estimates were obtained using a random-effects model. Eleven articles published between 2007 and 2020 were finally included. Obesity appeared not to increase the risk of delayed discharge. However, morbid obesity seemed to be associated with a higher risk of delayed discharge. The meta-analysis revealed no relationship between higher body mass index (BMI) and unplanned readmission for day surgery patients. Obesity appeared not to increase the risk of delayed discharge except in patients with morbid obesity. Additionally, a higher BMI was not associated with increased risk of unplanned readmission after day surgery. Future studies are required to address this issue further in different types of surgery and areas.

Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach

Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017–2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model’s performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.

Obesity Metabolic Phenotypes and Unplanned Readmission Risk in Diabetic Kidney Disease: An Observational Study from the Nationwide Readmission Database

Obesity is a potentially modifiable factor for reducing readmissions, with heterogeneity that varies according to the metabolic status. Our objective was to examine the independent or mutual relationship between obesity and metabolic abnormalities and diabetic kidney disease (DKD)-related hospitalizations. 493,570 subjects with DKD were enrolled in the 2018 Nationwide Readmission Database (NRD, United States). The at-risk population was reclassified into refined obesity subtypes based on the body mass index (BMI) classification of metabolic abnormalities (hypertension and/or dyslipidemia) to investigate the 180 d readmission risk and hospitalization costs related to DKD. The overall readmission rate was 34.1%. Patients with metabolic abnormalities, regardless of obesity, had a significantly higher risk of readmission compared to non-obese counterparts (adjusted HR, 1.11 [95% CI, 1.07-1.14]; 1.12 [95% CI, 1.08-1.15]). Hypertension appeared to be the only metabolic factor associated with readmission among individuals with DKD. Obesity without metabolic abnormalities was independently associated with readmission (adjusted HR,1.08 [1.01,1.14]), especially among males and those >65 years (adjusted HR,1.10 [1.01-1.21]; 1.20 [1.10-1.31]). Women or those ≤65 years with metabolic abnormalities (all p <0.050) had elevated readmission rates, regardless of obesity; however, no such trend was observed in obese subjects without metabolic abnormalities (adjusted HR, 1.06 [0.98,1.16]). Additionally, obesity and metabolic abnormalities were associated with elevated hospitalization costs (all p <0.0001). Increased BMI and hypertension are positively associated with readmissions and related costs among patients with DKD, which should be considered in future studies.

The Utility of Inflammatory Serum Markers in the Assessment of Perioperative Morbidity after Radical Cystectomy for Bladder Cancer.

Background and Objectives: To date, sparse evidence exists about the impact of inflammatory serum markers in predicting perioperative complications after radical cystectomy (RC) for bladder cancer (BC). Here, we evaluated the role of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), C-reactive protein (CRP), and plasma fibrinogen in predicting perioperative morbidity and unplanned 30-days readmission after RC for BC. Materials and methods: We relied on a collaborative database of 271 patients who underwent open RC for cT1-4a N0 M0 BC between January 2012 and December 2022. Univariable and multivariable binomial logistic regression analyses were performed to assess the odds ratio (OR) with 95% confidence intervals (CI) testing the ability of each serum marker to predict postoperative complications (any-grade and major complications), and 30-days unplanned readmission. Results: The median age at RC was 73 yr (IQR 67-79). A total of 182 (67.2%) patients were male and the median BMI was 25.2 (IQR 23.2-28.4). Overall, 172 (63.5%) patients had a Charlson Comorbidity Index (CCI) greater than 2 points and 98 (36.2%) were current smokers at the time of RC. Overall, 233 (86.0%) patients experienced at least one complication after RC. Of these, 171 (63.1%) patients had minor complications (Clavien-Dindo grade 1-2) while 100 (36.9%) experienced major complications (Clavien-Dindo grade ≥ 3). According to multivariable analysis, current smoking status, high plasma fibrinogen, and preoperative anemia were independently associated with major complications (OR 2.10, 95%CI 1.15-4.90, p = 0.02), (OR 1.51, 95%CI 1.26-1.98, p = 0.09), and (OR 1.35, 95%CI 1.17-2.57, p = 0.03), respectively. Overall, 56 (20.7%) patients experienced a 30-days unplanned readmission. According to univariable analysis, high preoperative CRP and hyperfibrinogenemia were significantly associated with an increased risk of unplanned readmission (OR 2.15, 95%CI 1.15-4.16, p = 0.02; OR 2.18, 95%CI 1.13-4.44, p = 0.02, respectively). Conclusions: In our study, the preoperative immune-inflammation signature described by NLR, PLR, LMR, SII, and CRP showed a low reliability in predicting perioperative course after RC. Preoperative anemia and hyperfibrinogenemia were independent predictors of major complications. Further studies are pending in order to draw definitive conclusions.

Effects of a Multimodal Transitional Care Intervention in Patients at High Risk of Readmission

Hospital readmissions are frequent, costly, and sometimes preventable. Although these issues have been well publicized and incentives to reduce them introduced, the best interventions for reducing readmissions remain unclear. To evaluate the effects of a multimodal transitional care intervention targeting patients at high risk of hospital readmission on the composite outcome of 30-day unplanned readmission or death. A single-blinded, multicenter randomized clinical trial was conducted from April 2018 to January 2020, with a 30-day follow-up in 4 medium-to-large-sized teaching hospitals in Switzerland. Participants were consecutive patients discharged from general internal medicine wards and at higher risk of unplanned readmission based on their simplified HOSPITAL score (≥4 points). Data were analyzed between April and September 2022. The intervention group underwent systematic medication reconciliation, a 15-minute patient education session with teach-back, a planned first follow-up visit with their primary care physician, and postdischarge follow-up telephone calls from the study team at 3 and 14 days. The control group received usual care from their hospitalist, plus a 1-page standard study information sheet. Thirty-day postdischarge unplanned readmission or death. A total of 1386 patients were included with a mean (SD) age of 72 (14) years; 712 (51%) were male. The composite outcome of 30-day unplanned readmission or death was 21% (95% CI, 18% to 24%) in the intervention group and 19% (95% CI, 17% to 22%) in the control group. The intention-to-treat analysis risk difference was 1.7% (95% CI, -2.5% to 5.9%; P = .44). There was no evidence of any intervention effects on time to unplanned readmission or death, postdischarge health care use, patient satisfaction with the quality of their care transition, or readmission costs. In this randomized clinical trial, use of a standardized multimodal care transition intervention targeting higher-risk patients did not significantly decrease the risks of 30-day postdischarge unplanned readmission or death; it demonstrated the difficulties in preventing hospital readmissions, even when multimodal interventions specifically target higher-risk patients. ClinicalTrials.gov Identifier: NCT03496896.

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ∼0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making.

Comparison of 30-day planned and unplanned readmissions in a tertiary teaching hospital in China

PurposeThe purpose of this study was to analyze and compare the clinical characteristics of patients with 30-day planned and unplanned readmissions and to identify patients at high risk for unplanned readmissions. This will facilitate a better understanding of these readmissions and improve and optimize resource utilization for this patient population.MethodsA retrospective cohort descriptive study was conducted at the West China Hospital (WCH), Sichuan University from January 1, 2015, to December 31, 2020. Discharged patients (≥ 18 years old) were divided into unplanned readmission and planned readmission groups according to 30-day readmission status. Demographic and related information was collected for each patient. Logistic regression analysis was used to assess the association between unplanned patient characteristics and the risk of readmission.ResultsWe identified 1,118,437 patients from 1,242,496 discharged patients, including 74,494 (6.7%) 30-day planned readmissions and 9,895 (0.9%) unplanned readmissions. The most common diseases of planned readmissions were antineoplastic chemotherapy (62,756/177,749; 35.3%), radiotherapy sessions for malignancy (919/8,229; 11.2%), and systemic lupus erythematosus (607/4,620; 13.1%). The most common diseases of unplanned readmissions were antineoplastic chemotherapy (2038/177,747; 1.1%), age-related cataract (1061/21,255; 5.0%), and unspecified disorder of refraction (544/5,134; 10.6%). There were statistically significant differences between planned and unplanned readmissions in terms of patient sex, marital status, age, length of initial stay, the time between discharge, ICU stay, surgery, and health insurance.ConclusionAccurate information on 30-day planned and unplanned readmissions facilitates effective planning of healthcare resource allocation. Identifying risk factors for 30-day unplanned readmissions can help develop interventions to reduce readmission rates.

Effectiveness and cost-effectiveness of a digital health intervention to support patients with colorectal cancer prepare for and recover from surgery: study protocol of the RecoverEsupport randomised controlled trial

IntroductionSurgery is the most common treatment for colorectal cancer (CRC) and can cause relative long average length of stay (LOS) and high risks of unplanned readmissions and complications. Enhanced Recovery...

Abstract P369: Using Machine Learning to Predict Unplanned Readmission Due to Cardiovascular Disease Among Hospitalized Patients With Cancer

Background: Cardiovascular disease (CVD) morbidity and mortality rates have risen in cancer patients. We aimed to demonstrate the feasibility of a using machine learning technique in predicting the risk of unplanned readmission due to CVD among hospitalized cancer patients. Methods: We included hospitalized patients with a primary diagnosis code for any cancer for the index hospitalization using the 2017-2018 Nationwide Readmissions Database. The outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. The original data was separated into training (80%) and testing (20%) data. Decision tree (DT) and three ensemble algorithms, including random forest (RF), extreme gradient boost (XGBoost), and AdaBoost, were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model’s performance. Each algorithm's feature importance was obtained. Results: Among 358,629 hospitalized patients with cancer, 5.86% (n=21,021) experienced unplanned readmission due to any CVD. The XGBoost indicated the highest AUC at 0.76, followed by the AdaBoost (AUC=0.75) and RF (AUC=0.75), while the DT had the lowest score at 0.72. Similarly, the XGBoost had the highest F2 score of 0.36, followed by the AdaBoost (F2=0.35) and RF (F2=0.35), whereas the DT had the lowest score at 0.33. The AdaBoost demonstrated the highest accuracy and precision while having the lowest recall (Figure). The feature importance plot of the XGBoost identified Medicare enrollment, mortality risk, private insurance, and cancer surgery as top importance features for unplanned readmission due to CVD. Conclusions: Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance.

Evaluating the Impact of Obesity and Different Metabolic Statuses on the Prognosis of Hospitalized Patients with Inflammatory Bowel Disease: A Cohort Study

Introduction: Obesity is associated with an increased risk of inflammatory bowel disease (IBD), whereas not all obese individuals have the same effect. In individuals with obesity, the role of metabolic status in the readmission of IBD remains unclear. Our study aimed to evaluate the association between different obesity metabolic phenotypes and the prognosis of IBD patients. Methods: We conducted a longitudinal cohort study using Nationwide Readmissions Database (NRD) (2018 sample). Out of 12,928,231 discharge records, 63,748 records with a discharge diagnosis of IBD were identified for analysis. Cox proportional hazard ratio (HR) with 95% confidence interval (CI) was calculated, adjusting for potential confounders. Results: During a 180-day follow-up in IBD patients with different obesity metabolic phenotypes, all-cause readmission rate, inpatient mortality rate, unplanned readmission rate, total charge, hospitalized length of stay were statistically different (all p < 0.001). After multivariate Cox regression analysis, IBD patients with metabolically unhealthy nonobese (MUNO) had higher risk of readmission (all-cause and unplanned) (HR 1.04, 95% CI: 1.00–1.08 and HR 1.06, 95% CI: 1.02–1.10), and those with metabolically unhealthy obesity (MUO) had higher risk of unplanned readmission (HR 1.08, 95% CI: 1.02–1.15). In subgroup analysis, both the MUNO group and MUO group had higher risk of readmission (all-cause and unplanned) in the ulcerative colitis (UC) subgroup, but only the MUNO group had higher risk of readmission (all-cause and unplanned) (HR 1.05, 95% CI: 1.00–1.10 and HR 1.06, 95% CI: 1.01–1.12) in the Crohn’s disease (CD) subgroup. Conclusion: Metabolic abnormalities were associated with an increased risk of readmission in patients with IBD, regardless of obesity.