Alarming rate of 30-day hospital readmissions in patients with liver cirrhosis.
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ABSTRACT:
SUBMITTER: Ebhohon E
PROVIDER: S-EPMC8640909 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
Publications
Alarming rate of 30-day hospital readmissions in patients with liver cirrhosis.
Ebhohon Ebehiwele E Ogundipe Olumuyiwa Akinbolaji OA Adejumo Adeyinka Charles AC
Annals of translational medicine 20211101 21
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Project description:BackgroundCirrhosis is associated with substantial inpatient morbidity and mortality. This study aimed to determine the trends in 30-day hospital readmission rates among patients with cirrhosis and identify factors associated with these readmissions.MethodsWe conducted a retrospective analysis of data retrieved from the Nationwide Readmissions Database to determine trends in 30-day readmission for patients discharged with a diagnosis of cirrhosis in 2010 through 2014. Multivariate logistic regression analysis was used to identify predictors of readmission.ResultsAmong 303,346 patients identified from the database, the 30-day readmission rate for patients with a discharge diagnosis of cirrhosis was 31.4% (n=95,298). The trends in the readmission rates remained steady during the study period. On multivariate analysis, female sex, age 45 years or older, esophagogastroduodenoscopy (EGD) during admission, and disposition to a short-term care facility or skilled nursing facility protected against readmissions. In contrast, coverage by Medicaid insurance, admission during a weekend, nonalcoholic cause of cirrhosis, and history of hepatic encephalopathy and ascites were associated with readmission.ConclusionsWe found an exceptionally high 30-day readmission rate in patients with cirrhosis, although it remained stable during the study period. This study identified some modifiable factors such as disposition to a short-term care facility or skilled nursing facility and patients' attendance of alcohol rehabilitation facilities that could decrease the likelihood of readmission and could inform local and national healthcare policymakers.
| S-EPMC8339830 | biostudies-literature
Project description:Hospital readmission rates are increasingly used to assess quality. Little is known, however, about potential preventability of readmissions among children. Our objective was to evaluate potential preventability of 30-day readmissions using medical record review and interviews.A cross-sectional study in 305 children (<18 years old) readmitted within 30 days to a freestanding children's hospital between December 2012 and February 2013. Interviews (N = 1459) were conducted with parents/guardians, patients (if ≥13 years old), inpatient clinicians, and primary care providers. Reviewers evaluated medical records, interview summaries, and transcripts, and then rated potential preventability. Multivariate regression analysis was used to identify factors associated with potentially preventable readmission. Adjusted event curves were generated to model days to readmission.Of readmissions, 29.5% were potentially preventable. Potentially preventable readmissions occurred sooner after discharge than non-potentially preventable readmissions (5 vs 9 median days; P < .001). The odds of a readmission being potentially preventable were greatest when the index admission and readmission were causally related (adjusted odds ratio [AOR]: 2.6; 95% confidence interval [CI]: 1.0-6.8) and when hospital (AOR: 16.3; 95% CI: 5.9-44.8) or patient (AOR: 7.1; 95% CI: 2.5-20.5) factors were identified. Interviews provided new information about the readmission in 31.2% of cases.Nearly 30% of 30-day readmissions to a children's hospital may be potentially preventable. Hospital and patient factors are associated with potential preventability and may provide targets for quality improvement efforts. Interviews contribute important information and should be considered when evaluating readmissions.
| S-EPMC5557411 | biostudies-other
Project description:A focus of health care reform has been on reducing 30-day hospital readmissions. Patients with ESRD are at high risk for hospital readmission. It is unknown whether more monitoring by outpatient providers can reduce hospital readmissions in patients receiving hemodialysis. In nationally representative cohorts of patients in the United States receiving in-center hemodialysis between 2004 and 2009, we used a quasi-experimental (instrumental variable) approach to assess the relationship between frequency of visits to patients receiving hemodialysis following hospital discharge and the probability of rehospitalization. We then used a multivariable regression model and published hospitalization data to estimate the cost savings and number of hospitalizations that could be prevented annually with additional provider visits to patients in the month following hospitalization. In the main cohort (n=26,613), one additional provider visit in the month following hospital discharge was estimated to reduce the absolute probability of 30-day hospital readmission by 3.5% (95% confidence interval, 1.6% to 5.3%). The reduction in 30-day hospital readmission ranged from 0.5% to 4.9% in an additional four cohorts tested, depending on population density around facilities, facility profit status, and patient Medicaid eligibility. At current Medicare reimbursement rates, the effort to visit patients one additional time in the month following hospital discharge could lead to 31,370 fewer hospitalizations per year, and $240 million per year saved. In conclusion, more frequent physician visits following hospital discharge are estimated to reduce rehospitalizations in patients undergoing hemodialysis. Incentives for closer outpatient monitoring following hospital discharge could lead to substantial cost savings.
| S-EPMC4147977 | biostudies-literature
Project description:Objective:To compare patients' and providers' views on contributors to 30-day hospital readmissions. Design:Analysis of a qualitative interview survey between 18 May-30 June 2015. Setting:Interviews were conducted during the 30-day readmission hospitalisation at a single tertiary care academic hospital. Participants:We conducted 178 interviews of readmitted patients. Measures:We queried opinions of what factors patients believed contributed to their rehospitalisation and compared this with the perspective of the index admission provider. The primary outcome was the view that the readmission was preventable. A review by a RN (nurse) case manager also provided an assessment based on patient report, provider report and chart review. Results:Patients were more likely to view a readmission as preventable compared with physicians (p<0.0001). Patients identified system issues (defined as factors controlled by the hospital discharge process) as contributors to their readmission in 58% (103/178) of cases while providers identified system issues as the contributor to a patients' readmission in 2% (2/101) of cases. Patients with poor functional status were more likely to feel the cause of their readmission was due to system issues than patients with better functional status (p=0.03). A RN case manager review determined that in 48% (86/178) of cases the system had some amount of contribution to a patient's readmission. There was no significant difference in belief that the readmission was preventable between the RN case manager and the patient (p=0.47). Conclusions:Readmitted patients often feel that the hospital system contributed to their readmission. Providers did not recognise patient and RN case manager identified issues as contributors to hospital readmissions.
| S-EPMC6327873 | biostudies-literature
Project description:New tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown.Retrospective cohort study.Six geographically diverse medical centers.All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients.A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions.Among the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above.The HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions.
| S-EPMC5309170 | biostudies-other
Project description:STUDY OBJECTIVES:The primary objective of this study was to determine if nonadherence to continuous positive airway pressure (CPAP) is associated with increased 30-day all-cause, cardiovascular-cause, and pulmonary-cause hospital readmissions. METHODS:Retrospective cohort study at a Veterans Affairs hospital of patients with obstructive sleep apnea (OSA) who were hospitalized from January 1, 2007 to December 31, 2015. Odds ratio of 30-day readmission was calculated for all-cause, cardiovascular-cause, and pulmonary-cause readmissions. Logistic regression model was used to evaluate odds of nonadherent versus adherent group while adjusting for age, sex, body mass index, living situation, race, comorbidities, and medication adherence. RESULTS:Out of 2,077 records reviewed, 345 patients (183 adherent and 162 nonadherent) met our inclusion criteria. The adherent group had a total of 215 initial admissions, and the nonadherent group had a total of 268 index admissions. Thirty-day all-cause readmission rate was significantly higher in the nonadherent group, with an adjusted odds ratio (OR) of 3.52 (95% confidence interval [CI], 2.04-6.08, P < .001). Thirty-day cardiovascular-cause readmission rate was significantly higher in the nonadherent group, with an adjusted OR of 2.31 (95% CI, 1.11-4.78, P = .024). Difference in 30-day pulmonary-cause readmissions was not statistically significant, with an adjusted OR of 3.66 (95% CI, 0.41-32.76, P = .25). CONCLUSIONS:Nonadherence to CPAP is associated with increased 30-day all-cause and cardiovascular-cause readmission in patients with OSA. Ensuring CPAP adherence is crucial in addressing general and cardiovascular-related healthcare utilization and morbidity in patients with OSA. COMMENTARY:A commentary on this article appears in this issue on page 161.
| S-EPMC5786836 | biostudies-literature
Project description:IntroductionNew financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions.Materials and methodsThis study leverages NSQIP data for 722,101 surgeries to develop predictive and prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two prescriptive methods to recommend pre-operative blood transfusions to increase the patient's hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models.ResultsPredictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve-AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic <30). The prediction accuracy of the proposed models exceeds results in the literature.ConclusionsThis study is among the first to develop a methodology for making specific, data-driven, personalized treatment recommendations to reduce the 30-day readmission rate. The reported predicted reduction in readmissions can lead to more than $20 million in savings in the U.S. annually.
| S-EPMC7480861 | biostudies-literature
Project description:ImportanceIdentification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.ObjectiveTo externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.Design, setting, and participantsInternational retrospective cohort study of 117 065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.ExposuresThe HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.Main outcomes and measures30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.ResultsOverall, 117 065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16 992 of 117 065 (14.5%) were followed by a 30-day readmission, and 11 307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n = 73 031 [62.4%]), intermediate (n = 27 612 [23.6%]), and high risk (n = 16 422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson χ2 test P = .89).Conclusions and relevanceThe HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.
| S-EPMC5070968 | biostudies-literature
Project description: Not available
| S-EPMC11632700 | biostudies-literature
Project description:Hospital readmission is a key metric of hospital quality, such as for comparing Veterans Affairs (VA) hospitals to private sector hospitals. To calculate readmission rates, one must first identify individual hospitalizations. However, in the VA Corporate Data Warehouse (CDW), data are organized by "bedded stays," that is, any stay in a healthcare facility where a patient is provided a bed, not hospitalizations. Thus, CDW data poses several challenges to identifying hospitalizations including: (1) bedded stays include both non-acute inpatient stays (i.e. nursing home, mental health) and acute inpatient hospital care; (2) transfers between VA facilities appear as separate bedded stays; and (3) VA care may also be fragmented by non-VA care. Thus, we sought to develop a rigorous method to identify acute hospitalizations using the VA CDW. We examined all VA bedded stays with an admission date in 2009. Non-acute portions of a stay were dropped. VA to VA transfers were merged when consecutive discharge and admission dates were within one calendar day. Finally, hospitalizations that occurred in a non-VA facility were merged. The 30-day readmission rate was calculated at each step of the algorithm to demonstrate the impact. The total number of VA medical hospitalizations in 2009 with live discharges was 505,861. The 30-day readmission rate after adjusting for VA to VA transfers and incorporating non-VA care was 18.3% (95% confidence interval (CI): 18.2, 18.4%).
| S-EPMC9518959 | biostudies-literature