What Are the Burden, Causes, and Costs of Early Hospital Readmissions After Kidney Transplantation?
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ABSTRACT: Introduction
Kidney transplant recipients are at risk for complications resulting in early hospital readmission. This study sought to determine the incidences, risk factors, causes, and financial costs of early readmissions.Design
This single-centre cohort study included 1461 kidney recipients from 1 Jul 2004 to 31 Dec 2012, with at least 1-year follow-up. Early readmission was defined as hospitalization within 30 or 90-days postdischarge from transplant admission. Associations between various parameters and 30 and 90-days posttransplant were determined using multivariable Cox proportional hazards models. The hospital-associated costs of were assessed.Results
The rates of early readmission were 19.4% at 30 days and 26.8% at 90 days posttransplant. Mean cost per 30-day readmission was 11 606 CAD. Infectious complications were the most common reasons and resulted in the greatest cost burden. Factors associated with 30 and 90-days in multivariable models were recipient history of chronic lung disease (hazard ratio or HR 1.78 [95%CI: 1.14, 2.76] and HR 1.68 [1.14, 2.48], respectively), median time on dialysis (HR 1.07 [95% CI: 1.01, 1.13]and HR 1.06 [95% CI: 1.01, 1.11], respectively), being transplanted preemptively (HR 1.75 [95% CI: 1.07, 2.88] and HR 1.66 [95% CI: 1.07, 2.57], respectively), and having a transplant hospitalization lasting of and more than 11 days (HR 1.52 [95% CI: 1.01, 2.27] and HR 1.65 [95% CI: 1.16, 2.34], respectively).Discussion
Early hospital readmission after transplantation was common and costly. Strategies to reduce the burden of early hospital readmissions are needed for all patients.
SUBMITTER: Famure O
PROVIDER: S-EPMC8182333 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundEarly hospital readmission (EHR) within 30 days after kidney transplantation is a significant quality indicator of transplant centers and patient care. This meta-analysis aims to evaluate the incidence, predictors, and outcomes of EHR after kidney transplantation.MethodsWe comprehensively searched the databases, including PubMed, Cochrane CENTRAL, and Embase, from inception until December 2021 to identify studies that assessed incidence, risk factors, and outcome of EHR. The outcomes included death-censored graft failure and mortality. Data from each study were combined using the random effect to calculate the pooled incidence, mean difference (MD), odds ratio (OR), and hazard ratio (HR) with 95% confidence interval (CI).ResultsA total of 17 studies were included. The pooled EHR incidence after kidney transplant was 24.4% (95% CI 21.7-27.3). Meta-analysis showed that recipient characteristics, including older recipient age (MD 2.05; 95% CI 0.90-3.20), Black race (OR 1.31; 95% CI 1.11, 1.55), diabetes (OR 1.32; 95% CI 1.22-1.43), and longer dialysis duration (MD 0.85; 95% CI 0.41, 1.29), donor characteristics, including older donor age (MD 2.02; 95% CI 0.93-3.11), and transplant characteristics, including delayed graft function (OR 1.75; 95% CI 1.42-2.16) and longer length of hospital stay during transplantation (MD 1.93; 95% CI 0.59-3.27), were significantly associated with the increased risk of EHR. EHR was significantly associated with the increased risk of death-censored graft failure (HR 1.70; 95% CI 1.43-2.02) and mortality (HR 1.46; 95% CI 1.27-1.67) within the first year after transplantation.ConclusionAlmost one-fourth of kidney transplant recipients had EHR within 30 days after transplant, and they had worse post-transplant outcomes. Several risk factors for EHR were identified. This calls for future research to develop and implement for management strategies to reduce EHR in high-risk patients.
| S-EPMC9672339 | biostudies-literature
Project description:Thirty percent of kidney transplant recipients are readmitted in the first month posttransplantation. Those with donor-specific antibody requiring desensitization and incompatible live donor kidney transplantation (ILDKT) constitute a unique subpopulation that might be at higher readmission risk. Drawing on a 22-center cohort, 379 ILDKTs with Medicare primary insurance were matched to compatible transplant-matched controls and to waitlist-only matched controls on panel reactive antibody, age, blood group, renal replacement time, prior kidney transplantation, race, gender, diabetes, and transplant date/waitlisting date. Readmission risk was determined using multilevel, mixed-effects Poisson regression. In the first month, ILDKTs had a 1.28-fold higher readmission risk than compatible controls (95% confidence interval [CI] 1.13-1.46; P < .001). Risk peaked at 6-12 months (relative risk [RR] 1.67, 95% CI 1.49-1.87; P < .001), attenuating by 24-36 months (RR 1.24, 95% CI 1.10-1.40; P < .001). ILDKTs had a 5.86-fold higher readmission risk (95% CI 4.96-6.92; P < .001) in the first month compared to waitlist-only controls. At 12-24 (RR 0.85, 95% CI 0.77-0.95; P = .002) and 24-36 months (RR 0.74, 95% CI 0.66-0.84; P < .001), ILDKTs had a lower risk than waitlist-only controls. These findings of ILDKTs having a higher readmission risk than compatible controls, but a lower readmission risk after the first year than waitlist-only controls should be considered in regulatory/payment schemas and planning clinical care.
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Project description:PurposeWe compared the timing, causes, hospital costs and perioperative outcomes of index vs nonindex hospital readmissions after radical cystectomy.Materials and methodsThe 2013 Nationwide Readmissions Database was queried for patients with bladder cancer undergoing cystectomy. Sociodemographic characteristics, hospital costs and causes of readmission were compared among index and nonindex readmitted patients. Univariable and multivariable logistic regression models were used to identify predictors of nonindex readmissions, mortality during the first readmission and subsequent readmission.ResultsAmong 4,991 patients identified 29% (1,447) and 11% (571) experienced an index and nonindex readmission, respectively. Compared to index readmissions, nonindex readmissions were more likely late readmissions (p <0.001) of older patients (p=0.047) who underwent cystectomy at higher volume hospitals (p=0.02) and were readmitted to hospitals located in less populated areas (p <0.001). Compared to index readmissions the percentage of nonindex readmissions for cardiovascular complications was higher (7.6% vs 2.9%, p=0.003), while the percentage of nonindex readmissions for gastrointestinal (6.0% vs 11.0%, p=0.04) and wound (5.3% vs 16.7%, p=0.0001) complications was lower. Predictors of nonindex readmission included longer length of stay (OR 1.02; 95% CI 1.001, 1.04), patient location in less populated areas, nonteaching hospital (OR 0.52; 95% CI 0.31, 0.86) and discharge to facility (OR 2.82; 95% CI 1.75, 4.55) or with home health (OR 1.49; 95% CI 1.05, 2.10). Nonindex readmissions had comparable mean readmission hospital costs ($14,147 vs $15,102, p=0.7), in-hospital mortality (OR 1.11; 95% CI 0.42, 2.87) and subsequent readmission (OR 1.32; 95% CI 0.87, 2.00) to index readmissions.ConclusionsThis nationally representative study of patients undergoing radical cystectomy demonstrated comparable perioperative outcomes and hospital costs between index and nonindex readmitted patients, which supports the continued regionalization of cystectomy care.
| S-EPMC5241219 | biostudies-literature
Project description:BackgroundHospital readmission (HR) after surgery is considered a quality metric.MethodsData on 2371 first-time adult kidney transplant (KT) recipients were collected to analyze the "early" (≤30 days) and "late" (31-365 days) HR patterns after KT at a single center over a 12-year time span (2002-2013).Results30-day, 90-day, and 1-year HR were 31%, 41%, and 53%, respectively. Risk factors for HR included age >50, female sex, black race, BMI >30, transplant LOS >5 days, and pre-transplant time on dialysis >765 days. Indications for early (n = 749) and late (n = 508) HR were similar. Early HR (OR: 3.80, P = .007) and black race (OR: 2.38, P = .009) were associated with higher odds of 1-year graft failure while frequency (1-2, 3-4, 5+) of HR (ORs: 4.68, 8.36, 9.44, P < .001) and age > 50 (OR: 2.11, P = .007) were associated with higher odds of 1-year mortality. Transplant LOS > 5 days increased both odds of 1-year graft failure (OR: 3.51, P = .001) and mortality (OR: 2.05, P = .006). One-year graft and recipient survival were 96.7% and 94.8%, respectively.ConclusionsHospital readmission was associated with reduced graft and patient survival; however, despite a relatively high and consistent HR rate after KT, overall 1-year graft and patient survival was high.
| S-EPMC9270717 | biostudies-literature
Project description:Concepts to ameliorate the continued mismatch between demand for liver allografts and supply include the acceptance of allografts that meet extended donor criteria (ECD). ECD grafts are generally associated with an increased rate of complications such as early allograft dysfunction (EAD). The costs of liver transplantation for the health care system with respect to specific risk factors remain unclear and are subject to change. We analyzed 317 liver transplant recipients from 2013 to 2018 for outcome after liver transplantation and hospital costs in a German transplant center. In our study period, 1-year survival after transplantation was 80.1% (95% confidence interval: 75.8%-84.6%) and median hospital stay was 33 days (interquartile rage: 24), with mean hospital costs of €115,924 (SD €113,347). There was a positive correlation between costs and laboratory Model for End-Stage Liver Disease score (rs = 0.48, P < 0.001), and the development of EAD increased hospital costs by €26,229. ECD grafts were not associated with a higher risk of EAD in our cohort. When adjusting for recipient-associated risk factors such as laboratory Model for End-Stage Liver Disease score, recipient age, and split liver transplantation with propensity score matching, only EAD and cold ischemia increased total costs. Conclusion: Our data show that EAD leads to significantly higher hospital costs for liver transplantation, which are primarily attributed to recipient health status. Strategies to reduce the incidence of EAD are needed to control costs in liver transplantation.
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Project description:BACKGROUND AND OBJECTIVES:The US Centers for Medicare and Medicaid Services have mandated reducing early (30-day) hospital readmissions to improve patient care and reduce costs. Patients with ESKD have elevated early readmission rates, due in part to complex medical regimens but also cognitive impairment, literacy difficulties, low social support, and mood problems. We developed a brief family consultation intervention to address these risk factors and tested whether it would reduce early readmissions. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:One hundred twenty hospitalized adults with ESKD (mean age=58 years; 50% men; 86% black, 14% white) were recruited from an urban, inpatient nephrology unit. Patients were randomized to the family consultation (n=60) or treatment-as-usual control (n=60) condition. Family consultations, conducted before discharge at bedside or via telephone, educated the family about the patient's cognitive and behavioral risk factors for readmission, particularly cognitive impairment, and how to compensate for them. Blinded medical record reviews were conducted 30 days later to determine readmission status (primary outcome) and any hospital return visit (readmission, emergency department, or observation; secondary outcome). Logistic regressions tested the effects of the consultation versus control on these outcomes. RESULTS:Primary analyses were intent-to-treat. The risk of a 30-day readmission after family consultation (n=12, 20%) was 0.54 compared with treatment-as-usual controls (n=19, 32%), although this effect was not statistically significant (odds ratio, 0.54; 95% confidence interval, 0.23 to 1.24; P=0.15). A similar magnitude, nonsignificant result was observed for any 30-day hospital return visit: family consultation (n=19, 32%) versus controls (n=28, 47%; odds ratio, 0.53; 95% confidence interval, 0.25 to 1.1; P=0.09). Per protocol analyses (excluding three patients who did not receive the assigned consultation) revealed similar results. CONCLUSIONS:A brief consultation with family members about the patient's cognitive and psychosocial risk factors had no significant effect on 30-day hospital readmission in patients with ESKD.
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Project description:Online crowdfunding platforms such as GoFundMe are used to raise funds for health-related expenses associated with medical conditions such as organ transplantation. By investigating crowdfunding in Canadian organ transplantation, this study aimed to increase understanding of the motivations and outcomes of organ transplantation crowdfunding. Canadian liver and kidney transplantation campaigns posted to GoFundMe between May 30 & 31 2018 were identified and after exclusion, 258 kidney and 171 liver campaigns were included in study. These campaigns were coded for: worthiness of the campaign recipient, requested financial and non-monetary contributions, how monetary donations would be spent, and comments on the Canadian health system, among others. Results suggest Canadian organ donors, transplant candidates, recipients, and their families and caregivers experience significant financial difficulties not addressed by the public health system. Living and medication costs, transportation and relocation expenses, and income loss were the expenses most commonly highlighted by campaigners. Liver campaigns raised nearly half their goal while kidney campaigns received 11.5% of their requested amount. Findings highlight disease burden and the use of crowdfunding as a response to the extraordinary costs associated with organ transplantation. Although crowdfunding reduces some financial burden, it does not do so equitably and raises ethical concerns.
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Project description:RationaleUnderstanding the causes and factors related to readmission for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) within a nationwide database including all payers and ages can provide valuable input for the development of generalizable readmission reduction strategies.ObjectivesTo determine the rates, causes, and predictors for early (3-, 7-, and 30-d) readmission in patients hospitalized with AECOPD in the United States using the Nationwide Readmission Database after the initiation of the Hospital Readmissions Reduction Program, but before its expansion to COPD.MethodsWe conducted an analysis of the Nationwide Readmission Database from 2013 to 2014. Index admissions and readmissions for an AECOPD were defined consistent with Hospital Readmissions Reduction Program guidelines. We investigated the percentage of 30-day readmissions occurring each day after discharge and the most common readmission diagnoses at different time periods after hospitalization. The relationship between predictors (categorized as patient, clinical, and hospital factors) and early readmission were evaluated using a hierarchical two-level logistic model. To examine covariate effects on early-day readmission, predictors for 3-, 7-, and 30-day readmissions were modeled separately.ResultsThere were 202,300 30-day readmissions after 1,055,830 index AECOPD admissions, a rate of 19.2%. The highest readmission rates (4.2-5.5%) were within the first 72 hours of discharge, and 58% of readmissions were within the first 15 days. Respiratory-based diseases were the most common reasons for readmission (52.4%), and COPD was the most common diagnosis (28.4%). Readmission diagnoses were similar at different time periods after discharge. Early readmission was associated with patient (Medicaid payer status, lower household income, and higher comorbidity burden) and clinical factors (longer length of stay and discharge to a skilled nursing facility). Predictors did not vary substantially by time of readmission after discharge within the 30-day window.ConclusionsThirty-day readmissions after an AECOPD remain a major healthcare burden, and are characterized by a similar spectrum of readmission diagnoses. Predictors associated with readmission include both patient and clinical factors. Development of a COPD-specific risk stratification algorithm based on these factors may be necessary to better predict patients with AECOPD at high risk of early readmission.
| S-EPMC6207114 | biostudies-literature
Project description:BackgroundCurrent readmission models use administrative data supplemented with clinical information. However, the majority of these result in poor predictive performance (area under the curve (AUC)<0.70).ObjectiveTo develop an administrative claim-based algorithm to predict 30-day readmission using standardized billing codes and basic admission characteristics available before discharge.Materials and methodsThe algorithm works by exploiting high-dimensional information in administrative claims data and automatically selecting empirical risk factors. We applied the algorithm to index admissions in two types of hospitalized patient: (1) medical patients and (2) patients with chronic pancreatitis (CP). We trained the models on 26,091 medical admissions and 3218 CP admissions from The Johns Hopkins Hospital (a tertiary research medical center) and tested them on 16,194 medical admissions and 706 CP admissions from Johns Hopkins Bayview Medical Center (a hospital that serves a more general patient population), and vice versa. Performance metrics included AUC, sensitivity, specificity, positive predictive values, negative predictive values, and F-measure.ResultsFrom a pool of up to 5665 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnoses, 599 ICD-9-CM procedures, and 1815 Current Procedural Terminology codes observed, the algorithm learned a model consisting of 18 attributes from the medical patient cohort and five attributes from the CP cohort. Within-site and across-site validations had an AUC?0.75 for the medical patient cohort and an AUC?0.65 for the CP cohort.ConclusionsWe have created an algorithm that is widely applicable to various patient cohorts and portable across institutions. The algorithm performed similarly to state-of-the-art readmission models that require clinical data.
| S-EPMC3932470 | biostudies-literature
Project description:BackgroundThe management of Clostridium difficile infection (CDI) among hospitalized patients is costly, and ongoing payment reform is compelling hospitals to reduce its burden. To assess the impact of CDI on mortality, hospital costs, healthcare use, and Medicare payments for beneficiaries who were discharged with CDI listed as a secondary International Classification of Diseases, Ninth Revision, Clinical Modification claim diagnosis.MethodsData were analyzed from the 2009 to 2010 5% random sample Medicare Standard Analytic Files of beneficiary claims. Patients with index hospitalizations with CDI as a secondary diagnosis and no previous hospitalization within 30 days were identified. Outcomes included inpatient and 30-day mortality, inpatient costs, index hospital payments, all-provider payments, net hospital losses, payment to cost ratio, length of stay (LOS), and 30-day readmission; outcomes were each risk adjusted using propensity score matching and regression modeling techniques.ResultsA total of 3262 patients with CDI were identified after matching to patients without a CDI diagnosis. After risk adjustment, secondary CDI was associated with statistically significantly (all P < 0.05) greater inpatient mortality (3.1% vs. 1.7%), 30-day mortality (4.1% vs. 2.2%), longer LOS (7.0 days vs. 3.8 days), higher rates of 30-day hospital readmissions (14.8% vs. 10.4%), and greater hospital costs ($16,184 vs. $13,954) compared with the non-CDI cohort. The risk-adjusted payment-to-cost ratio was shown to be lower for patients with CDI than those without (0.76 vs. 0.85).ConclusionsSecondary CDI is associated with greater adjusted mortality, costs, LOS, and hospital readmissions, while receiving similar hospital reimbursement compared with patients without CDI in a Medicare population.
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