Readmission and resource utilization after orthotopic heart transplant versus ventricular assist device in the National Readmissions Database, 2010–2014

Mukdad, Laith; Mantha, Aditya; Aguayo, Esteban; Sanaiha, Yas; Juo, Yen-Yi; Ziaeian, Boback; Shemin, Richard J.; Benharash, Peyman

doi:10.1016/j.surg.2018.04.013

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…17 In cardiac surgery, the association between readmission, poor patient outcomes, and increased healthcare expenditures is well established. 18,19 In this nationwide study of readmissions after SAVR, we report the following findings: the national rate of 30-day readmission after SAVR was substantial at 13.7%, which is in keeping with previously reported rates of 7% to 23%. [20][21][22][23][24][25][26] Approximately half of readmissions were due to cardiac complications, most frequently heart failure and arrhythmia.…”

Section: Commentsupporting

confidence: 88%

Readmission After Surgical Aortic Valve Replacement in the United States

Khoury

Ragalie

Sanaiha

et al. 2020

The Annals of Thoracic Surgery

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Section: Commentsupporting

confidence: 88%

Readmission After Surgical Aortic Valve Replacement in the United States

Khoury

Ragalie

Sanaiha

et al. 2020

The Annals of Thoracic Surgery

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“…Key demographic and clinical characteristics were tabulated using the data dictionary provided by OPTN. Within the NRD, hospitalizations involving HT were compiled using relevant International Classification of Diseases, 9 th and 10 th Revision procedure codes (ICD‐9 and ICD‐10 [3751, 02YA0Z0, 02YA0Z1, and 02YA0Z2]) 12,13 . Patients undergoing multi‐organ transplantation or re‐transplantation were excluded from both databases.…”

Section: Methodsmentioning

confidence: 99%

“…Within the NRD, hospitalizations involving HT were compiled using relevant International Classification of Diseases, 9 th and 10 th Revision procedure codes (ICD-9 and ICD-10 [3751, 02YA0Z0, 02YA0Z1, and 02YA0Z2]). 12,13 Patients undergoing multi-organ transplantation or re-transplantation were excluded from both databases. To reduce cohort heterogeneity, records meeting the following criteria were excluded from analysis:…”

Section: Study Population and Endpointsmentioning

confidence: 99%

Association of postoperative length of stay with outcomes following orthotopic heart transplantation—A national analysis

Curry

Bakhtiyar

Kim

et al. 2023

Clinical Transplantation

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Background: In the absence of standardized recovery protocols, there is little evidence to guide postoperative care to ensure optimal in‐hospital and long‐term outcomes following heart transplantation (HT). Using two national databases, we examined the association between postoperative length of stay (LOS) with patient/graft survival, index hospitalization costs, and non‐elective readmissions.Methods: Adult HT recipients from 2010 to 2019 were identified and analyzed within the Organ Procurement and Transplantation Network (OPTN) Database and Nationwide Readmissions Database (NRD). The risk‐adjusted relationship between 1‐year mortality and LOS was assessed with restricted cubic splines and subsequently used to stratify patients into Expedited (7–11 days), Routine (12–16 days), and Delayed (>16) discharge groups. Survival outcomes were analyzed using Restricted Means Survival Time analysis (RMST) and multivariable Cox models.Results: Of 9995 HT recipients within the OPTN, 3777 (38%) were categorized as Expedited, and 3040 (30%) as Routine. After adjustment, expedited discharge was not associated with inferior 90‐day (ΔRMST ‐.01, p = .91) and 1‐year patient survival (ΔRMST ‐.02, p = .53). Additionally, expedited was not associated with increased odds of non‐elective readmission at 90‐days (HR 1.04, CI .77–1.43) relative to Routine discharge. Counterfactual analysis revealed an estimated cost saving of $50 million if all Routine patients received an expedited discharge.Conclusion: Expedited discharge after HT seems to be cost‐effective and is not associated with inferior outcomes. Institutional‐level outcome analyses should be performed to identify patients that would benefit from expedited discharge, and future studies should analyze the feasibility of implementing standardized discharge protocols following HT.

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“…During the transition from pre- to posttransplant, recipients are introduced to a number of new medications and medical issues. In light of this complexity, early hospital readmission is common, ranging from 18% to 47% [ 1 - 3 ]. In addition to an increased burden on the health care system, early hospital readmission is associated with increased patient morbidity and mortality [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0: Protocol for a Randomized Controlled Trial

Murray¹,

Foroutan²,

Amadio³

et al. 2021

JMIR Res Protoc

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Background The number of solid organ transplants in Canada has increased 33% over the past decade. Hospital readmissions are common within the first year after transplant and are linked to increased morbidity and mortality. Nearly half of these admissions to the hospital appear to be preventable. Mobile health (mHealth) technologies hold promise to reduce admission to the hospital and improve patient outcomes, as they allow real-time monitoring and timely clinical intervention. Objective This study aims to determine whether an innovative mHealth intervention can reduce hospital readmission and unscheduled visits to the emergency department or transplant clinic. Our second objective is to assess the use of clinical and continuous ambulatory physiologic data to develop machine learning algorithms to predict the risk of infection, organ rejection, and early mortality in adult heart, kidney, and liver transplant recipients. Methods Remote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0 is a two-phased single-center study to be conducted at the University Health Network in Toronto, Canada. Phase one will consist of a 1-year concealed randomized controlled trial of 400 adult heart, kidney, and liver transplant recipients. Participants will be randomized to receive either personalized communication using an mHealth app in addition to standard of care phone communication (intervention group) or standard of care communication only (control group). In phase two, the prior collected data set will be used to develop machine learning algorithms to identify early markers of rejection, infection, and graft dysfunction posttransplantation. The primary outcome will be a composite of any unscheduled hospital admission, visits to the emergency department or transplant clinic, following discharge from the index admission. Secondary outcomes will include patient-reported outcomes using validated self-administered questionnaires, 1-year graft survival rate, 1-year patient survival rate, and the number of standard of care phone voice messages. Results At the time of this paper’s completion, no results are available. Conclusions Building from previous work, this project will aim to leverage an innovative mHealth app to improve outcomes and reduce hospital readmission in adult solid organ transplant recipients. Additionally, the development of machine learning algorithms to better predict adverse health outcomes will allow for personalized medicine to tailor clinician-patient interactions and mitigate the health care burden of a growing patient population. Trial Registration ClinicalTrials.gov NCT04721288; https://www.clinicaltrials.gov/ct2/show/NCT04721288 International Registered Report Identifier (IRRID) PRR1-10.2196/26816

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Readmission and resource utilization after orthotopic heart transplant versus ventricular assist device in the National Readmissions Database, 2010–2014

Cited by 13 publications

References 28 publications

Readmission After Surgical Aortic Valve Replacement in the United States

Readmission After Surgical Aortic Valve Replacement in the United States

Association of postoperative length of stay with outcomes following orthotopic heart transplantation—A national analysis

Remote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0: Protocol for a Randomized Controlled Trial

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