GI Psychiatry

Article information
Title: Specific Depressive Symptoms are Associated with Subtypes of Fatigue in Inflammatory Bowel Disease
PubMed ID: 42084792
First Author Last Name: Duarte

Abstract:
Fatigue is common in inflammatory bowel disease (IBD) yet firm understanding of its management remains unclear. While psychosocial factors influence fatigue, less is known about how these factors differ among fatigue subtypes. This study examined the contributions of self-reported disease activity and psychosocial factors to five fatigue subtypes. Patients with Crohn's disease or ulcerative colitis were recruited from an academic medical center. Participants completed online surveys including the Multidimensional Fatigue Inventory (MFI-20) and measures of depression, anxiety, pain interference, pain catastrophizing, and insomnia. In 312 participants, depression was most associated with subtypes of fatigue. Percent variance attributable to fatigue subtypes: general (17%), physical (12%), mental (25%), and reduced motivation (17%). Anxiety was most strongly associated with activity avoidance (17% variance). Factors associated to a lesser degree were pain interference and insomnia. The contribution of psychosocial factors to fatigue was as, if not more substantial than patient-reported disease activity. Symptom-level analysis found cognitive and behavioral symptoms of depression to be most associated with fatigue. While subtypes of fatigue differ in their association to psychosocial factors, depression was a consistent and strongly associated factor. Results highlight the importance of screening for depression and point to specific treatments that may be beneficial for fatigue management.

Annotation

The finding:
This study aimed to examine the relative contributions of self-reported disease activity and modifiable psychosocial factors commonly targeted within behavioural therapies for IBD to five subtypes of fatigue: general fatigue, physical fatigue, mental fatigue, reduced motivation, and activity avoidance. Among psychosocial factors analysed, depression and pain interference emerged as the most consistent and strongly associated factors in each subtype of fatigue. Interestingly, the association of psychosocial factors with fatigue was as, if not more substantial than patient-reported disease activity. Finally, an item-level analysis of depressive and anxious symptoms provided insights into specific clinical presentations that may be associated with fatigue in IBD

Strength and weaknesses:
The study found that the relationship between mental fatigue and depression was particularly strong, with depression accounting for around 25% of the variance in this factor. Within physical fatigue, mental fatigue, and reduced motivation, depression accounted for more variance than patient-reported disease activity or patient demographics. This study was the first to examine a broad set of psychosocial factors in relation to five subtypes of fatigue measured by the MFI. Results add to the growing literature supporting the need to include depression screening and treatment in the management of fatigue in IBD care. However, due to a highly homogeneous sample, results may not be generalizable to the general population with regard to race and ethnicity. Moreover, the cross-sectional nature of the study hinders ability to make sense of directionality between depression and fatigue. Another limitation to the current study was the use of self-report measure of disease activity in the absence of objective measure of IBD. Although patient-reported outcome measures are related and increasingly used as a proxy to objective measures of disease activity, an objective measure is still preferred.

Relevance:
Results add to the growing literature supporting the need to include depression screening
and treatment in the management of fatigue in IBD care.

Article information
Title: A Multiagent Large Language Model-Based System to Simulate the Liver Transplant Selection Committee: A Retrospective Cohort Study
PubMed ID: 41951492
First Author Last Name: Hasjim

Abstract:
Background:
Transplantation is one of the few areas in medicine in which the definitive treatment is rationed. Subjective decision making poses challenges for the transplantation selection process. Large language models (LLMs), through autonomous artificial intelligence (AI) agents, have implications in objective decision making for complex problems; however, there are gaps in the literature regarding their clinical applications. We aimed to examine the performance of a multidisciplinary committee of AI agents in the liver transplantation selection process as a proof of concept towards improving the objectivity of decision making.

Methods:
This retrospective, hybrid cohort study incorporated both real-world observational data and artificially generated data derived from the Scientific Registry of Transplant Recipients (SRTR) database in the USA. We retrospectively analysed adult patients (aged ≥18 years) who received liver transplants from Jan 1, 2004 to June 1, 2024, and generated a hypothetical cohort of patients with standard absolute contraindications to liver transplantation from a random subset of the cohort. An AI selection committee was configured, consisting of four LLM-based AI agents using GPT-4o-2024-08-06 prompted with specialised roles: transplant hepatologist, transplant surgeon, cardiologist, and social worker. This committee was instructed to assess clinical vignettes generated from the SRTR tabular data (eg, demographics, social determinants of health, end-stage liver disease, comorbidities, and laboratory measurements), placing candidates on the transplantation waitlist if liver transplantation would likely offer a 6-month or 1-year survival benefit or rejecting candidates for transplantation if contraindications were present or if such a survival benefit was unlikely. The primary objective was to assess the accuracy, sensitivity, and specificity of the AI selection committee in predicting 1-year post-transplantation survival. The secondary objectives were the accuracy, sensitivity, and specificity of identifying contraindications to liver transplantation and predicting 6-month post-transplantation survival, and the numbers of false-negative cases (ie, patients who were rejected by the AI selection committee but did receive a transplant) and false-positive cases (ie, patients who were placed on the waitlist by the AI selection committee but had assigned contraindications to transplantation).

Findings:
Of 8412 patients, 7033 (83·6%) were placed on the transplantation waitlist and 1379 (16·4%) were assigned to the hypothetical cohort and had contraindications to liver transplantation. For the prediction of 1-year post-transplantation survival, the AI selection committee had an accuracy of 92·00% (95% CI 91·43–92·58), a sensitivity of 1·00 (0·99–1·00), and a specificity of 0·66 (0·64–0·68). For identifying absolute contraindications to liver transplantation and predicting 6-month survival, accuracies were 98·19% (97·90–98·44) and 94·88% (94·37–95·29), respectively; sensitivities were 1·00 (0·99–1·00) in both cases; and specificities were 0·91 (0·89–0·92) and 0·75 (0·73–0·77), respectively. The most common reason for the AI selection committee to reject patients in false-negative cases was hepatocellular carcinoma that did not meet the Milan criteria (16 [62%] of 26 as a contraindication to transplantation, 16 [62%] of 26 rejected on the basis of 6-month survival prediction, and 14 [61%] of 23 rejected on the basis of 1-year survival prediction); the most common cause of death in false-positive cases was malignancy at 1 year (137 [28%] of 481).

Interpretation:
LLMs can be leveraged through a multiagent AI system to simulate discussions of a liver transplantation selection committee and provide accurate, objective insights on patients who might or might not benefit from a liver transplant. Lessons learned from this study are a step towards making the liver transplantation selection process more equitable and objective."

Annotation
The Finding:
This study developed and evaluated a novel multiagent AI system designed to simulate a liver transplant selection committee. The system consisted of four GPT-4o-based agents assigned the roles of transplant hepatologist, transplant surgeon, cardiologist, and social worker. Using over 8,400 liver transplant cases from the Scientific Registry of Transplant Recipients, the AI committee demonstrated high accuracy in identifying absolute contraindications to transplantation (98.2%), predicting 6-month survival (94.9%), and predicting 1-year survival (92.0%). The authors conclude that multiagent LLM systems may be capable of providing objective and reproducible assessments of transplant candidacy and could potentially serve as clinical decision-support tools to reduce variability in transplant selection decisions.

Strengths and Weaknesses:
A major strength of this study is its innovative design and large sample size, incorporating 8,412 transplant recipients from a national registry and creating a multidisciplinary AI committee that mirrors real-world transplant selection processes. The investigators evaluated clinically meaningful outcomes, including post-transplant survival and recognition of contraindications, and examined fairness metrics, bias, and decision-making patterns among the individual AI agents. The study is also notable for moving beyond simple chatbot question-answering tasks and evaluating AI performance in a complex, high-stakes clinical decision-making environment.
The study also has important limitations. It was retrospective and relied on registry data rather than prospective real-world clinical implementation. The investigators created a hypothetical cohort by artificially assigning contraindications to transplant candidates, which may not fully reflect the complexity of actual patients who are denied transplantation. In addition, the AI committee was evaluated against post-transplant outcomes among patients who had already been selected for transplantation, introducing potential selection bias. The model's specificity was only moderate, particularly for 1-year survival prediction (66%), and the study does not address how AI recommendations would affect actual committee deliberations, patient autonomy, or legal and ethical accountability.

Relevance:
This article is highly relevant to consultation-liaison psychiatry because it explores the use of AI to replicate multidisciplinary decision-making processes that incorporate both medical and psychosocial variables. The inclusion of a dedicated social worker agent is particularly noteworthy, as psychosocial assessments, treatment adherence, social support, and substance use are critical components of transplant psychiatry evaluations. The study raises important questions about the future role of AI in resource allocation, capacity assessments, risk stratification, and other ethically complex decisions that are central to CL psychiatry. At the same time, it highlights concerns regarding bias, transparency, explainability, and the risk of delegating value-laden clinical judgments to algorithmic systems. For psychiatrists working within transplant programs, this study provides an early glimpse into how AI may eventually function as a decision-support tool within transplant selection committees while underscoring the continued need for human clinical judgment and ethical oversight.