scholarly journals Machine-learning models to predict tacrolimus dosage in liver transplant recipients

2020 ◽  
Vol 34 (1) ◽  
pp. S147-S147
Author(s):  
Jeong-Moo Lee ◽  
Soo Bin Yoon ◽  
Hyung-Chul Lee ◽  
Chul-Woo Jung ◽  
Suk Kyun Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplant ◽  
Transplant Recipients ◽  
Learning Models ◽  
Tacrolimus Dosage ◽  
Liver Transplant Recipients ◽  
Machine Learning Models

Nifedipine Interaction with Tacrolimus in Liver Transplant Recipients

1997 ◽  
Vol 31 (5) ◽  
pp. 571-575 ◽  
Author(s):  
Raafat A Seifeldin ◽  
Amadeo Marcos-Alvarez ◽  
Fredric D Gordon ◽  
W David Lewis ◽  
Roger L Jenkins
Keyword(s):  
Drug Interaction ◽  
Liver Transplant ◽  
Transplant Recipients ◽  
Blood Concentrations ◽  
Cumulative Dosage ◽  
Tacrolimus Dosage ◽  
Dosage Requirement ◽  
Significant Difference ◽  
Trough Concentrations ◽  
Liver Transplant Recipients

OBJECTIVE: To examine the possible drug interaction between nifedipine and tacrolimus in liver transplant recipients. STUDY DESIGN: A retrospective study was done comparing two groups of liver transplant recipients. The starting time for comparison was the same after transplant. One group (n = 22) consisted of hypertensive patients who were treated with nifedipine; the other group (n = 28) did not receive nifedipine. The two groups were compared over 1 year. The effect of nifedipine on tacrolimus was measured in terms of tacrolimus whole blood trough concentrations, daily tacrolimus dosages, and cumulative tacrolimus dosages at 1, 3, 6, and 12 months. All patient charts were reviewed with regard to concurrent medication that could affect the metabolism of tacrolimus and eventually affect tacrolimus concentrations and dosages. DATA COLLECTION: All required information was retrieved from medical records. RESULTS: There was a statistically significant difference between daily dosage requirements of tacrolimus at 90 (p = 0.03), 180 (p = 0.004), and 365 (p = 0.0004) days between the nifedipine and no-nifedipine groups. The tacrolimus daily dosage in the nifedipine group was decreased by 26%, 29%, and 38% at 3, 6, and 12 months, respectively, compared with the dosage of the no-nifedipine group. Statistically significant differences in cumulative dosages of tacrolimus were observed at 180 (p = 0.02) and 365 (p = 0.003) days between the nifedipine and no-nifedipine groups, with cumulative dosage reduction of 25% and 31% by 6 and 12 months, respectively, in the nifedipine group compared with the no-nifedipine group. CONCLUSIONS: Nifedipine decreased the daily and cumulative dosage requirement of tacrolimus. The interaction observed between nifedipine and tacrolimus is the first reported in humans and is clinically important. As a result of this drug interaction, it is recommended that blood concentrations of tacrolimus be monitored during coadministration of these drugs and that the tacrolimus dosage be adjusted accordingly.

REmote moBile Outpatient mOnitoring in Transplant (Reboot) 2.0: a randomized control trial protocol (Preprint)

2020 ◽  
Author(s):  
Kevin R Murray ◽  
Farid Foroutan ◽  
Juan Duero Posada ◽  
Stella Kozuszko ◽  
Joseph Duhamel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Survival Rate ◽  
Liver Transplant ◽  
Hospital Readmission ◽  
Randomized Control Trial ◽  
Standard Of Care ◽  
Transplant Recipients ◽  
Adult Heart ◽  
Randomized Control ◽  
Liver Transplant Recipients

BACKGROUND The number of solid organ transplants (SOT) 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 hospital appear to be preventable. Mobile health (mHealth) technologies hold promise to reduce admission to hospital and improve patient outcomes as they allow real-time monitoring and timely clinical intervention. OBJECTIVE To determine whether an innovative mHealth intervention can reduce hospital readmission and unscheduled visits to the emergency department (ED) or transplant clinic. Our second objective is to assess the use clinical and continuous ambulatory physiologic data to develop machine learning algorithms to predict 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 (UHN) in Toronto, Canada. Phase 1 will consist of a 1-year concealed randomized control trial of 400 adult heart, kidney, and liver transplant recipients. Participants will be randomized to receive either personalized communication using a mHealth application in addition to standard of care phone communication (intervention group), or standard of care communication only (control group). In phase two, the prior collected dataset will be utilized to develop machine learning (ML) algorithms to identify early markers of rejection, infection, and graft dysfunction post-transplantation. The primary outcome will be a composite of any unscheduled hospital admission, visits to the ED or transplant clinic following discharge from the index admission. Secondary outcomes will include: 1) patient-reported outcomes using validated self-administered questionnaires; 2) 1-year graft survival rate; 3) 1-year patient survival rate; and 4) number of standard of care phone voice messages. RESULTS At the time of this manuscript’s completion, no results are available. CONCLUSIONS Building from previous work, this project will aim to leverage an innovative mHealth application to improve outcomes and reduce hospital readmission in adult SOT recipients. Additionally, the development of ML algorithms to better predict adverse health outcomes will allow for personalized medicine to tailor clinician-patient interactions, and mitigate the healthcare burden of a growing patient population.

Hyperhomocysteinemia in liver transplant recipients

2001 ◽  
Vol 120 (5) ◽  
pp. A562-A562
Author(s):  
A HABIB ◽  
B BACON ◽  
S RAMRAKHIANI
Keyword(s):  
Liver Transplant ◽  
Transplant Recipients ◽  
Liver Transplant Recipients

Persistent hyperglycemia in pediatric liver transplant recipients

2001 ◽  
Vol 120 (5) ◽  
pp. A562-A562
Author(s):  
R ROMERO ◽  
K MELDE ◽  
T PILLEN ◽  
G SMALLWOOD ◽  
C ONEILL ◽  
...  
Keyword(s):  
Liver Transplant ◽  
Transplant Recipients ◽  
Pediatric Liver ◽  
Liver Transplant Recipients ◽  
Pediatric Liver Transplant ◽  
Persistent Hyperglycemia
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