Frailty in Liver Transplant Recipients: A Serious Issue That Would Benefit From a Redefinition of “Successful” Intervention
Bibliographic record
Abstract
In this issue of Transplantation, Thuluvath et al1 reported their study of a home-based, personalized exercise program designed to combat the frailty prevalent in patients awaiting liver transplantation (LT). Eligibility criteria included patients waitlisted for LT who had access to the Internet and a tablet, computer, or smartphone. Physical therapists assessed the patients and assigned condition-appropriate exercises and the patients were educated on how to access their online programs at home. They demonstrated overall improvement in frailty scores, with the largest improvement seen in frail and adherent patients. A comparable standard of care group without exercise intervention showed no improvement in frailty measures. The authors acknowledge that this study is not a randomized controlled clinical trial. However, it does have significant potential. Its value lies in the development of a protocol that is cost-effective, relatively easy to implement, and has demonstrated reduced frailty in the population most at risk for poor outcomes during both the waitlist and posttransplant period. An interrogation of PubMed using the search terms “frailty” and “liver transplantation” yields 175 results between 2010 and 2024. The majority were published after 2018, with 33 in September 2024. They described various exercise and nutrition interventions, most of which show some benefit for waitlist and/or posttransplant survival. Most do not achieve the gold standard indicator of clinical research success—significance in a randomized controlled clinical trial. Lack of patient adherence to the protocol was the most commonly cited reason with encephalopathy, lack of formal physical therapy input, cost, and inability to travel to transplant or exercise center listed frequently. However, the Practice Guidance document from the American Association for the Study of Liver Diseases now has a section devoted to the management of frailty and malnutrition that includes exercise recommendations—thus tacitly acknowledging the importance of exercise to effectively deal with this condition.2 The majority of published studies regarding frailty were conducted in the United States or Canada. Recently, assessments of impact of frailty on LT outcomes have been conducted in European centers. In contrast to many of the North American studies, a study conducted in Spain3 reported that frailty did not influence pretransplant mortality or delisting but did predict higher rates of posttransplant complications and increased length of hospitalization, including intensive care unit stays. The major identifiable difference between the North American and Spanish studies was the relatively short pretransplant wait time (median = 42 d) in Spain compared with that in the United States (median = 5.6 mo) during the same time period. The finding that frailty negatively affects transplant outcomes even when wait times are short reinforces the value of developing successful treatment regimens and initiating them as early as possible. Frailty in LT patients is often accompanied by some degree of sarcopenia, but the conditions do not always develop at the same time or to the same degree.4 Sarcopenia has been associated with a significantly increased risk of mortality in both men (70% increased risk) and women (182%). Sarcopenia may be assessed using several methods including computed tomography (CT). This is potentially very useful for transplant centers planning to implement a program to reduce frailty, as CT surveillance is part of most pretransplant protocols. This would allow existing CT examinations to be analyzed to provide sarcopenia assessment without increasing the patient’s radiation exposure beyond a center’s existing protocol5,6 Myosteatosis is a common finding in cirrhotic patients awaiting transplant. It is believed to result from the differentiation of muscle stem cells into adipocytes rather than myocytes, a process that is facilitated by increased ammonia levels and mitochondrial dysfunction seen in cirrhotic patients. It results in varying degrees of poor muscle function and frank sarcopenia. Ebadi et al7 reported that approximately 52% of patients with cirrhosis meet the criteria for some degree of myosteatosis. They reported that myosteatosis, sarcopenia, Model for End Stage Liver Disease, and hepatic encephalopathy were independently associated with mortality in patients with cirrhosis undergoing LT evaluation. There currently is no universally accepted pharmacologic treatment for myosteatosis. Although patient compliance was suboptimal, there are some reports of exercise-related improvement, and more work in this area is needed. Randomized controlled trials remain the gold standard for the assessment of most therapeutic interventions. This is unlikely to change when one is assessing the efficacy of a pharmacologic or surgical intervention. However, a different perspective should be applied when judging the success or failure of outpatient-based exercise (or nutrition) regimens in patients with significant disease burden. The literature consistently documents improved results in patients who adhere to the regimen. Improvement in adherence would likely improve overall outcomes, but behavioral change is notoriously difficult to attain. One possible solution would be to introduce a “buddy system” in which a family member or friend would engage in the activity with the patient or oversee it. Engagement and encouragement from a trusted individual have been associated with positive results.8 Pretransplant evaluations include an assessment of support systems available to prospective candidates throughout the transplant process. These are usually focused on immediate posttransplant care, but compliance for pre/posthabilitation programs would likely be improved if these trusted individuals became involved. Exercise ± nutritional supplementation is not a panacea for pretransplant frailty or sarcopenia, but it is a low-cost, readily available (and individually modifiable) option shown to improve outcomes in patients in need of LT. The varying degrees of improvement appear to be related to the level of patient adherence. The process outlined by Thuluvath et al1 described one strategy with the potential to improve patient adherence by removing many of the barriers associated with institutional-based programs. The final proof of its worth will rest on its performance in the general transplant population. It is, however, a good beginning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".