www.nature.com/scientificreports OPEN Thirty‑day readmission rates, trends and its impact on liver transplantation recipients: a national analysis Khalid Mumtaz 1,2*, Jannel Lee‑Allen1, Kyle Porter 3, Sean Kelly1,2, James Hanje1,2, Lanla F. Conteh1,2, Anthony J. Michaels1,2, Ashraf El‑Hinnawi4, Ken Washburn4, Sylvester M. Black4 & Marwan S. Abougergi5,6 Reduction of early hospital readmissions is a declared goal in the United States economic and quality improvement agenda. A retrospective study was performed using the Nationwide Readmissions Database from 2010 to 2014. Our primary aim was to study the rate of early readmissions and its predictors in liver transplant recipients (LTRs). Our secondary aims were to determine the trends of LT, reasons for readmission, costs and predictors of calendar year mortality. Multivariable logistic regression and Cox proportional hazards models were utilized. The 30‑day readmission rate was 30.6% among a total of 25,054 LTRs. Trends of LT were observed to be increased in patients > 65 years (11.7–17.8%, p < 0.001) and decreased in 40–64 years (78.0–73.5%, p = 0.001) during study period. The majority of 30‑day readmissions were due to post transplant complications, with packed red blood cell transfusions being the most common intervention during readmission. Medicaid or Medicare insurance, surgery at low and medium volume centers, infections, hemodialysis, liver biopsy, and length of stay > 10 days were the predictors of 30‑day readmission. Moreover, number of early readmission, age > 64 years, non‑alcoholic cirrhosis, and length of stay > 10 days were signifcant predictor of calendar year mortality in LTRs. Approximately one third of patients require early admission after LT. Early readmission not only increases burden on healthcare, but is also associated with calendar year mortality. Strategies should be implemented to reduce readmission in patients with high risk of readmission identifed in our study. With the recent advances in surgical technique and medical care, liver transplantation (LT) is now a successful surgery, with resulting improvement in median patient life expectancy from 2 years without LT to approximately 10 years with LT1. However, patient evaluation for LT, the listing process and waiting list as well as the surgery itself is resource intensive and not without challenges. Studies have reported the mortality rate during the initial hospitalization to be 5% -10% with an associated increased total hospitalization costs 2–7. Afer discharge, early hospital readmission has a signifcant contribution to mortality and resource utilization. Several single center studies and one study using the UNOS database have investigated the predictors, healthcare utilization and outcomes of post-LT early readmission2–6. Te reported readmission rates in these studies have ranged from 35 to 50%. Recipient old age, altered mental status, high Model for End-Stage Liver Disease (MELD) score, need for mechanical ventilation, and multiple co-morbidities were identifed as best predictors of post LT early readmission3–5. To date, the study that has included the largest number of patients is that using the UNOS database linked to University Health System Consortium from 2007 to 2011 by Wilson et al.6. In the study, the authors reported a 30-day readmission rate afer LT of 38%. Te independent predictors of readmission were 1Division of Gastroenterology, Hepatology and Nutrition, The Ohio State University Wexner Medical Center, 395 West 12th Ave., 3rd Floor, Columbus, OH 43210, USA. 2Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA. 3Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. 4Division of Transplantation, Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, USA. 5Division of Gastroenterology, Department of Internal Medicine, University of South Carolina, Columbia, SC, USA. 6Catalyst Medical Consulting, Simpsonville, USA. *email: [email protected] Scientifc Reports | (2020) 10:19254 | https://doi.org/10.1038/s41598-020-76396-5 1 Vol.:(0123456789) www.nature.com/scientificreports/ a high MELD score, Diabetes Mellitus at time of LT, renal dialysis dependence at time of LT, a high donor risk index allograf and discharge to a rehabilitation facility. Reduction of hospital readmissions is a declared goal in the United States economic and quality improvement agenda. Te Afordable Care Act (ACA) mandated the public reporting of 30-day hospital readmission rates for several medical and surgical conditions. Tis step is a necessary precursor for the implementation of penalties (up to 3% reduction in reimbursements) to hospitals with elevated readmission rates8. Initially, these penalties applied for certain medical conditions including heart failure, pneumonia and acute myocardial infarction as well as some elective surgeries including total hip and/or knee arthroplasty. More recently, coronary artery bypass grafing has been added to the list8. While LT is not currently a part of the Hospital Readmission Reduction Program (HRRP), there are ofen indirect fnancial consequences to hospitals, as many managed care plans ofer a fxed reimbursement for this procedure3. Most importantly, 30-day readmission is associated with increased morbidity and mortality for patients 3–6. Te last reported LT national readmission rate and its predictors was based on 2007–2011 data. Tis data is based on a very comprehensive set of databases that captured the majority, yet not all, LT recipients. Terefore, we set out to provide updated statistics for early LT readmission rates and its predictors using recent releases of the largest publicly available readmission database in the United States. We also aimed to study the reasons for readmission as well as the impact of early readmission on calendar year mortality and healthcare resource utilization. Methods Database information. We utilized the 2010–2014 Nationwide Readmissions Database (NRD) to study 30-day readmissions among LT recipients. Te NRD is a part of the Healthcare Cost and Utilization Project (HCUP), a government initiated group of databases. It is the nation’s largest database of all-payer, encounter- level hospital care data and is developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ)9. Te NRD is the HCUP database that focuses exclusively on hospital readmissions via verifed, de-identifed, but linked patient data from HCUP state inpatient databases. It is designed as a stratifed probability sample to be representative of all nonfederal acute care inpatient hospi- talizations nationwide. Briefy, hospitals are stratifed according to ownership/control, number of beds, teaching status, urban/rural location, and geographic region. A 20% probability sample of all hospitals within each stratum is then collected. Tose hospital discharges are recorded, and information about patients’ demographics, principal and second- ary diagnoses, vital status at discharge, readmission and resource use including length of stay, procedures per- formed, and total hospitalization costs and charges are entered into the NRD. Each discharge is then weighted (weight = total number of discharges from all acute care hospitals in the United States divided by the number of discharges included in the 20% sample) to make the NRD nationally representative 8. Te NRD contains both patient- and hospital-level information. Up to 25 discharge diagnoses and 15 proce- dures are collected for each patient using the International Classifcation of Diseases, Ninth Revision, Clinical Modifcation (ICD9-CM). Te NRD utilizes the verifed patient linkage numbers in the state inpatient database (SID) to track each patient with any type of insurance or no insurance across hospitals within a state. From 2010 to 2014, the NRD collected data from 13.9 to 14.9 million discharges each year from 1808 to 2048 hospitals across 18 to 22 geographically dispersed states, accounting for over 49% of both the total United States resident population and hospitalizations. Te Ohio State Data and Specimen Policy and Human Subjects Research policy does not require Institutional Review Board (IRB) approval for population based public data set. Per 45 Code of Federal Regulations (CFR 46.101), research using certain publicly available data sets does not involve “human subjects”. Te data contained within these specifc data sets are neither identifable nor private and thus do not meet the federal defnition of “human subject” as defned in 45 CFR 46.102. Terefore these research projects do not need to be reviewed and approved by the IRB". Study population. Patients who underwent deceased donor liver transplantation between January 2010 and November 2014 were included in the study. We excluded (1) patients less than 18 years of age (n = 3777), (2) patients receiving multi-organ transplants (n = 3620), (3) re-liver transplantation (n = 0), (4) those discharged from the hospital during the month of December because of unavailability of a full 30 days of follow-up period to ascertain the occurrence of readmission (n = 3114), and (5) patients who died during the index hospitalization (n = 1431; Fig. 1). Tere is no separate ICD code for re- liver transplantation (re-LT). However, we defned the index admission for each patient as the frst instance of an eligible LT. A subsequent LT would not be included as an index admission. All the ICD-9