Bruce Kaplan M.D.

Sapir-Pichhadze, R. and B. Kaplan (2020). “Seeing the Forest for the Trees: Random Forest Models for Predicting Survival in Kidney Transplant Recipients.” Transplantation 104(5): 905-906.

Full text of this article.

Most practice guidelines are geared toward the “average patient.” Machine learning tools can capture the complexity of individual patients’ characteristics and aid transplant clinicians with patient-specific care decisions. As these tools become more prevalent, it is important to develop best practice guidelines and ensure there is regulatory oversight on their development and application. (Excerpt from text; no abstract available.)

Naesens, M., J. Friedewald, V. Mas, B. Kaplan and M. M. Abecassis (2020). “A Practical Guide to the Clinical Implementation of Biomarkers for Subclinical Rejection Following Kidney Transplantation.” Transplantation 104(4): 700-707.

Full text of this article.

Noninvasive biomarkers are needed to monitor stable patients following kidney transplantation (KT), as subclinical rejection, currently detectable only with invasive surveillance biopsies, can lead to chronic rejection and graft loss. Several biomarkers have recently been developed to detect rejection in KT recipients, using different technologies as well as varying clinical monitoring strategies defined as “context of use (COU).” The various metrics utilized to evaluate the performance of each biomarker can also vary, depending on their intended COU. As the use of molecular biomarkers in transplantation represents a new era in patient management, it is important for clinicians to better understand the process by which the incremental value of each biomarkers is evaluated to determine its potential role in clinical practice. This process includes but is not limited to an assessment of clinical validity and utility, but to define these, the clinician must first appreciate the trajectory of a biomarker from bench to bedside as well as the regulatory and other requirements needed to navigate this course successfully. This overview summarizes this process, providing a framework that can be used by clinicians as a practical guide in general, and more specifically in the context of subclinical rejection following KT. In addition, we have reviewed available as well as promising biomarkers for this purpose in terms of the clinical need, COU, assessment of biomarker performance relevant to both the need and COU, assessment of biomarker benefits and risks relevant to the COU, and the evidentiary criteria of the biomarker relevant to the COU compared with the current standard of care. We also provide an insight into the path required to make biomarkers commercially available once they have been developed and validated so that they used by clinicians outside the research context in every day clinical practice.

Harhay, M. N., M. K. Rao, K. J. Woodside, K. L. Johansen, K. L. Lentine, S. G. Tullius, R. F. Parsons, T. Alhamad, J. Berger, X. S. Cheng, J. Lappin, R. Lynch, S. Parajuli, J. C. Tan, D. L. Segev, B. Kaplan, J. Kobashigawa, D. M. Dadhania and M. A. McAdams-DeMarco (2020). “An overview of frailty in kidney transplantation: measurement, management and future considerations.” Nephrol Dial Transplant Mar 19. [Epub ahead of print].

Full text of this article.

The construct of frailty was first developed in gerontology to help identify older adults with increased vulnerability when confronted with a health stressor. This article is a review of studies in which frailty has been applied to pre- and post-kidney transplantation (KT) populations. Although KT is the optimal treatment for end-stage kidney disease (ESKD), KT candidates often must overcome numerous health challenges associated with ESKD before receiving KT. After KT, the impacts of surgery and immunosuppression represent additional health stressors that disproportionately impact individuals with frailty. Frailty metrics could improve the ability to identify KT candidates and recipients at risk for adverse health outcomes and those who could potentially benefit from interventions to improve their frail status. The Physical Frailty Phenotype (PFP) is the most commonly used frailty metric in ESKD research, and KT recipients who are frail at KT (~20% of recipients) are twice as likely to die as nonfrail recipients. In addition to the PFP, many other metrics are currently used to assess pre- and post-KT vulnerability in research and clinical practice, underscoring the need for a disease-specific frailty metric that can be used to monitor KT candidates and recipients. Although frailty is an independent risk factor for post-transplant adverse outcomes, it is not factored into the current transplant program risk-adjustment equations. Future studies are needed to explore pre- and post-KT interventions to improve or prevent frailty.

Fleishman, A., K. Khwaja, J. D. Schold, C. D. Comer, P. Morrissey, J. Whiting, J. Vella, L. K. Kayler, D. Katz, J. Jones, B. Kaplan, M. Pavlakis, D. A. Mandelbrot and J. R. Rodrigue (2020). “Pain expectancy, prevalence, severity, and patterns following donor nephrectomy: Findings from the KDOC Study.” Am J Transplant 2020 Mar 17. [Epub ahead of print].

Full text of this article.

Postoperative pain is an outcome of importance to potential living kidney donors (LKDs). We prospectively characterized the prevalence, severity, and patterns of acute or chronic postoperative pain in 193 LKDs at six transplant programs. Three pain measurements were obtained from donors on postoperative Day (POD) 1, 3, 7, 14, 21, 28, 35, 41, 49, and 56. The median pain rating total was highest on POD1 and declined from each assessment to the next until reaching a median pain-free score of 0 on POD49. In generalized linear mixed-model analysis, the mean pain score decreased at each pain assessment compared to the POD3 assessment. Pre-donation history of mood disorder (adjusted ratio of means [95% confidence interval (CI)]: 1.40 [0.99, 1.98]), reporting “severe” on any POD1 pain descriptors (adjusted ratio of means [95% CI]: 1.47 [1.12, 1.93]) and open nephrectomy (adjusted ratio of means [95% CI]: 2.61 [1.03, 6.62]) were associated with higher pain scores across time. Of the 179 LKDs who completed the final pain assessment, 74 (41%) met criteria for chronic postsurgical pain (CPSP), that is, any donation-related pain on POD56. Study findings have potential implications for LKD education, surgical consent, postdonation care, and outcome measurements.