Tools to improve donor utilization by infectious diseases clinicians
Bibliographic record
Abstract
Transplant infectious disease (TID) is a well-recognized area that evolved from the need to increase safety for solid organ transplant patients and mitigate infection risk. Infectious disease (ID) clinicians often play a key role during the “organ offer” or “donor call,” when a potential donor is identified and organ suitability is determined. Determining organ suitability is a collaborative process involving the Organ Procurement Organization (OPO), transplant surgeon, transplant coordinator, and medical subspecialists. With limited data available and within a narrow time frame, the ID clinician must evaluate the potential donor and recipient, assess the nature, magnitude, and manageability of risks for donor-derived infection, and ultimately advise the transplant team about whether to accept or decline the organ. TID curriculum recommendations from the American Society of Transplantation Infectious Diseases Community of Practice, published over 10 years ago, state that trainees should “Have a clear approach to common donor-related ID and current laboratory evaluation techniques for potentially transmissible infections in donors.” 1 However, approaches and resources used to teach donor evaluation to ID fellows are not documented and lack standardization. Standardization of donor screening by OPOs has improved, and donor evaluation guidelines have been published for some infections2; but there remains variability of practice and decisions on acceptance of organs, even among experienced transplant ID providers, depending on the type of infection.3 For example, respondents were evenly divided on donors with Methicillin-Susceptible Staphylococcus aureus bacteremia, with 50.8% willing to accept organs from such donors. With another new annual record of over 42 800 transplants performed in the United States this past year per the Organ Procurement and Transplantation Network and an ever-growing need for more organs, improved training on donor evaluation may have an important impact on organ availability.4 For these reasons, training during ID fellowship is paramount, and not just for TID clinicians. A prior survey of practicing ID providers “who rarely or never managed solid organ transplant (SOT) patients,” 78% (of 684) had been consulted on urgent donor-derived infection issues and 43% (of 357) asked to comment on donor suitability prior to procurement, and this exposure will likely increase.5 In their recent publication, Sigler and colleagues describe a pilot for teaching the process of organ evaluation during ID training with use of simulation .6 The authors developed six cases to address common concerns for organ suitability: five cases of suspected or known active donor infections (cases 1, 3–6) and one case of latent donor-infection from standard screening (case 2) at time of procurement. For a realistic experience, preceptors took on the role of the transplant coordinator and sent prompts via text page. Trainees were given a 15-min window to request additional information and ultimately decide organ suitability. Trainees subsequently submitted explanations for their decisions, which were assessed using pre-defined rubrics. The authors observed that although most fellows made the correct “accept or decline” decision for organ allocation (73%–100% in cases of common infection concerns versus 43% more rare Naegleria fowleri case), there was a 19% discordance in the underlying clinical reasoning. These results are telling in two ways. First, there is an unmet need for exposure to and practice of TID concepts in fellowship training, particularly to support the development of consistent clinical reasoning. Of the 15 fellows in the donor-call study, representing different levels of training and 7 different programs, 73% expressed interest in a TID career yet 46% had seen five or fewer transplant patients at the time of the simulation.6 This is consistent with a recent large survey of ID fellows (13 transplant, 203 general) in the United States and Canada, where TID training was rated less than ideal or adequate, due to limited frequency of dedicated didactic activities and limited exposure to transplant patient cases during training.7 Second, this pilot study shows proof of concept for the use of donor-call simulation as an evaluation tool—that it can identify deficiencies in the clinical reasoning used during donor evaluation, even when the “accept or decline” response is correct. If in one of five instances the correct decision was reached by fallacious reasoning, in other words by luck or chance, we should be evaluating fellows in this manner. Case-based simulation is not new to medical training. It is commonly used to practice skills for lower frequency, high-stakes situations, such as cardiac arrests, and has been shown to improve patient outcomes.8 Simulation, paired with debriefing, is an active learning technique that incorporates critical reflection on the decision-making process and allows real-time feedback.9 What Sigler and colleagues have not yet been able to evaluate with their pilot study is acquisition of new knowledge and skills through the use of simulations. Still, given the unpredictable nature of organ procurement, opportunities to develop and exercise skills in the clinical environment can be limited and case simulation feels like an enticing fit. This project is timely in relation to a recent survey where fellows expressed desire for new educational tools to augment TID training—including case-based interactive modules, web and mobile applications with access to guidelines, and accessible collections of relevant articles as high yield resources.7 Although this study was directed at ID trainees, the utility of donor call case-simulation may prove beneficial for other trainees who may participate in donor evaluation in their future careers, such as transplant surgery fellows, nephrologists, gastroenterologists, pulmonologists, and others. Sigler et al.’s pilot shows the utility of simulated “donor calls” for assessing clinical reasoning in the donor evaluation and has exciting promise as a potentially effective learning tool. The post-case survey showed that most trainees perceived the cases to be beneficial to their education and 80% felt more prepared for clinical practice; however, it is not known if clinical reasoning improved.6 Further study is warranted, including baseline knowledge assessment followed by simulation exercises at interval periods as both practice and reassessment to monitor progress. This pilot represents an important step toward realizing the long-held goal of the field of TID broadly: to teach approaches to donor evaluation for potentially transmissible infections, and importantly to achieve “clarity” in the procedures and reasoning leading to a decision with such far reaching implications as the acceptance or declination of donor organs. The authors received no specific funding for this work. Data sharing is not applicable—no new data have been generated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".