Bruce Kaplan M.D.

Budhiraja, P., Kaplan, B., Kalot, M., Alayli, A. E., Dimassi, A., Chakkera, H. A., Heilman, R., Edwards, A. S. and Mustafa, R. A. (2022). “Current State of Evidence on Kidney Transplantation: How Fragile Are the Results?” Transplantation 106(2): 248-256.

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BACKGROUND: The study aims is to use the fragility index (FI) to examine the strength of evidence of randomized controlled trials (RCTs) published in the last decade on kidney transplantation. METHODS: We searched MEDLINE for studies on kidney transplantation. We included the RCTs that compared 2 groups with 1:1 randomization and reported significant P values (<0.05) for a dichotomous outcome and were published in the top 10 transplant journals. We calculated the FI; a calculation used to determine the minimum number of subjects needed to change from a nonevent to an event to make the study results nonsignificant (P ≥ 0.05). RESULTS: Fifty-seven RCTs met our inclusion criteria. The median sample size was 100 participants in each arm, the median number of events was 16 (interquartile range, 8-30) in the intervention group. Among the included trials, 79% were industry-funded, 93% involved medications, and the majority were open label. The median FI was 3 (interquartile range, 1-11). In 43% of the trials, the number of patients reported lost to follow-up was higher than or equal to the FI. Only 4% of the RCTs imputed a value for the missing dichotomous outcome. Furthermore, the median number of subjects who discontinued the trial because of adverse effects was 21, which was greater than the FI in 60% of the RCTs. CONCLUSIONS: The arbitrary classification of results into "significant" and "nonsignificant" based on P value <0.05 should perhaps be interpreted with the help of other statistical parameters and FI is one of them.

Budhiraja, P., M. A. Kalot, A. E. Alayli, A. Dimassi, B. Kaplan, H. A. Chakkera and R. A. Mustafa (2021). “Reporting and Handling of Missing Participant Data in Systematic Reviews of Kidney Transplant Studies.” Transplantation 105(8): 1708-1717.

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BACKGROUND: Participant withdrawal from clinical trials occurs for various reasons, predominantly adverse effects or intervention inefficacy. Because these missing participant data can have implications for the validity, reproducibility, and generalizability of study results, when conducting a systematic review, it is important to collect and appropriately analyze missing data information to assess its effects on the robustness of the study results. METHODS: In this methodologic survey of missing participant data reporting and handling in systematic reviews, we included meta-analyses that provided pooled estimates of at least 1 dichotomous intervention outcome of a randomized controlled trial performed in adult kidney transplant subjects. RESULTS: Eighty-three systematic reviews (17 Cochrane and 66 non-Cochrane reviews) met the inclusion criteria. The most common intervention was drugs (80%), with the majority involving immunosuppressant drugs 55% (n = 46), followed by surgery in 14% (n = 12). The median follow-up duration was 12 months (maximum, 240 mo). Intention-to-treat or modified intention-to-treat analysis was reported in 24% (n = 20) of the reviews (76% of Cochrane and 10% of non-Cochrane). Overall, the majority of systematic reviews did not quantify (90% [n = 60] non-Cochrane and 29% [n = 5] Cochrane) or include the reasons for missing participant data (88% [n = 58] non-Cochrane and 24% [n = 4] Cochrane). Eleven percent (n = 9) handled missing participant data, 5% (n = 4) justified the analytical method(s) used to handle it, and 2% (n = 2) performed a sensitivity analysis for it. CONCLUSIONS: Systematic reviews of kidney transplantation provide inadequate information on missing participant data and usually do not handle or discuss the associated risk of bias with it.

Budhiraja, P., Kaplan, B., Kalot, M., Alayli, A.E., Dimassi, A., Chakkera, H.A., Heilman, R., Edwards, A.S. and Mustafa, R.A. (2021). “Current State of Evidence on Kidney Transplantation: How Fragile Are the Results?” Transplantation May 6. [Epub ahead of print].

Full text of this article.

BACKGROUND: The study aims is to use the fragility index (FI) to examine the strength of evidence of randomized controlled trials (RCTs) published in the last decade on kidney transplantation. METHODS: We searched MEDLINE for studies on kidney transplantation. We included the RCTs that compared 2 groups with 1:1 randomization and reported significant P-values (<0.05) for a dichotomous outcome and were published in the top 10 transplant journals. We calculated the FI; a calculation used to determine the minimum number of subjects needed to change from a nonevent to an event to make the study results nonsignificant (P-value >0.05). RESULTS: 57 RCTs met our inclusion criteria. The median sample size was 100 participants in each arm, the median number of events was 16(IQR 8-30) in the intervention group. Among the included trials, 79% were industry-funded, 93% involved medications, and the majority were open-label. The median FI was 3 (IQR 1-11). In 43% of the trials, the number of patients reported lost to follow-up was higher than or equal to the FI. Only 4% of the RCTs imputed a value for the missing dichotomous outcome. Furthermore, the median number of subjects who discontinued the trial due to adverse effects was 21, which was greater than the FI in 60% of the RCTs. CONCLUSION: The arbitrary classification of results into “significant” and “nonsignificant” based on p-value <0.05 should perhaps be interpreted with the help of other statistical parameters and FI is one of them.

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 ).

Park, S.H., Mazumder, N.R., Mehrotra, S., Ho, B., Kaplan, B. and Ladner, D.P. (2021). “Artificial Intelligence-related Literature in Transplantation: A Practical Guide.” Transplantation 105(4): 704-708.

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Since John McCarthy introduced the term “artificial intelligence (AI)” in 1955,1 AI research has been growing. “AI” is an umbrella term that encompasses a vast degree of computer technologies (eg, expert systems, computer vision, robotics, and machine learning) (Figure 1A) as well as a concept of a machine-imitating human intelligence2,3 (Table 1). Modern AI is defined as a system’s ability to (1) perceive the current world, ie, data; (2) to cause and compare different approaches to achieve specific goals based on given data; (3) to tune their performance and apply to unseen data; and (4) to repeat the previous processes multiple times and update the previous learning.4 When reviewing results from AI models, it is therefore critical to understand whether they are appropriately developed and validated (Figure 1B). [No abstract; excerpt from article].