Bruce Kaplan M.D.

Kastelz, A., Fernhall, B., Wang, E., Tzvetanov, I., Spaggiari, M., Shetty, A., Gallon, L., Hachaj, G., Kaplan, B. and Benedetti, E. (2021). “Personalized physical rehabilitation program and employment in kidney transplant recipients: A randomized trial.” Transpl Int Mar 18. [Epub ahead of print].

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INTRODUCTION: Kidney transplantation is the preferred treatment for kidney failure however after transplant, reduced physical function, poor self-perceptions and unemployment are common concerns that remain. METHODS: This randomized controlled trial compared the effects of a 12 month exercise rehabilitation program (intervention) to standard care alone (control) in kidney transplant recipients. The exercise intervention consisted of a 2 day/week, 60 minute personalized, one-on-one, resistance based exercise trainings. RESULTS: Eighty participants completed the study (52 intervention vs. 28 control). For individuals unemployed at baseline there was a 52.3% increase in employment compared to 13.3 % increase in the control group after 12 months (p=<0.0001). For those already employed at baseline, 100% of individuals maintained employment in both groups after 12 months (p=0.4742). For all comers, there was a positive trend for Global Physical Health (p=0.0034), Global Mental Health (p=0.0064), and Physical Function (p=0.0075), with the intervention group showing greater improvements. DISCUSSION/CONCLUSION: These findings suggest the implementation of an exercise rehabilitation program post kidney transplant can be beneficial to increase employment for individuals previously unemployed, improve self-perceived health, physical function, and mental health, overall contributing to better health outcomes in kidney transplant recipients. (Clinicaltrials.gov number: NCT02409901 ).

Edwards, A.S., Kaplan, B. and Jie, T. (2021). “A Primer on Machine Learning.” Transplantation 105(4): 699-703.

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In transplant medicine, large collections of data from patients and various procedures have been stored and organized in registries and databases. With the increase in data volume, there has been a demand for tools that can handle the challenges presented by so-called “big data.” In recent years, mathematical and statistical tools such as machine learning are being utilized in an increasing number of analyses. In addition, machine learning has been utilized in various other domains in which a large amount of complex data needs to be interrogated (eg, genomics). Although the term “machine learning” has become a term commonly mentioned, the techniques, strengths, and limitations are often not fully understood by readers of transplant literature. This commentary will cover some of the history and basic concepts of machine learning. [No abstract; excerpt from article].

Dutcher, E.G., Green, E.P. and Kaplan, B. (2020). “Using Behavioral Economics to Increase Transplantation Through Commitments to Donate.” Transplantation 104(12): 2467-2468.

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Like many countries, Japan is facing a shortage of deceased kidney donations. In Japan, this is exacerbated by certain cultural and religious objections to the use of deceased donors. Despite efforts to mitigate these objections and increase deceased donor utilization, only 111 deceased donors were performed in Japan in 2017 (while 14 002 patients remained on the waiting list). To increase the number of donors, Hirai et al 1 leverage behavioral economics and test 5 interventions and their impact on commitments to donate in Japan. Using these simple and inexpensive interventions, the authors are able to increase the commitment to donation, which will potentially lead to saving more lives through transplantation. In this commentary, we explore the economic principles that explain the behavior observed in their study and provide further considerations for using behavioral economics to increase deceased kidney donations in light of their findings. [No abstract; excerpt from article.].

Akanit, U., Bozorgmehri, S., Alquadan, K., Nelson, J., Kaplan, B., Ozrazgat-Baslanti, T. and Womer, K.L. (2020). “Improved ability to achieve target trough levels with liquid versus capsule tacrolimus in kidney transplant patients with HIV on protease inhibitor- or cobicistat-based regimens.” Transpl Infect Dis Nov 20;e13517. [Epub ahead of print.].

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HIV + patients are commonly accepted for kidney transplantation. However, patients on protease inhibitor (PI)- or cobicistat (cobi)-based regimens have trouble achieving optimal tacrolimus (Tac) levels. Our study compared the ability to achieve target levels using liquid versus immediate-release capsule Tac in kidney transplant patients with HIV on PI- or cobi-based regimens. The study included four kidney transplant patients who were converted to liquid Tac due to inability to achieve acceptable drug levels on the capsule formulation. Tac trough levels were analyzed retrospectively to compare target levels before and after conversion. The individual patient time in the therapeutic range (TTR) was calculated using Rosendaal’s linear interpolation method, and the difference between before and after conversion TTR was determined. In combined data, 44.63% of all Tac trough levels were within the target range after conversion to liquid Tac compared to 22.07% prior to conversion (P < .001). Furthermore, 3.31% and 7.44% of Tac trough levels were lower than 3 ng/mL or higher than 12 ng/mL, respectively, after conversion compared to 11.72% (P = .0564) and 24.14% (P < .0001) prior to conversion. The overall mean TTR was 45.1% after conversion to liquid Tac compared to 16.2% prior to conversion (P = .097). Finally, the coefficient of variation for Tac trough levels was 42.6 after conversion compared to 56.4 prior to conversion. A significantly improved ability to achieve target trough Tac levels was achieved with liquid Tac extemporaneous versus capsule formulation in kidney transplant patients with HIV taking a PI- or cobi-based regimen.

Edwards, A.S., Kaplan, B. and Jie, T. (2020). “A Primer on Machine Learning.” Transplantation Aug 18. [Epub ahead of print.].

Full text of this article.

In transplant medicine, large collections of data from patients, and various procedures have been stored and organized in registries and databases. With the increase in data volume, there has been a demand for tools that can handle the challenges presented by so called “big data”. In recent years, mathematical and statistical tools such as machine learning are being utilized in an increasing number of analyses. In addition, machine learning has been utilized in various other domains where a large amount of complex data needs to be interrogated (eg, genomics). While the term ‘machine learning’ has become a term commonly mentioned, the techniques, strengths, and limitations are often not fully understood by readers of transplant literature. This commentary will cover some of the history and basic concepts of Machine learning. [No abstract; excerpt from article.].