Bibliographic record
Abstract
Most of us, if we practice long enough, can recall patients who underwent cardiac transplantation who despite few pretransplant comorbidities, excellent support, and a flawless transplant surgery had poor medium-term or long-term outcomes. This, of course, led those of us caring for these patients to wonder what we missed in the pretransplant evaluation that would have predicted the poor outcomes for these patients. Sometimes, the term “failing the eyeball test” would be used to describe these patients, meaning that there was a gestalt about their appearance that suggested that they were sicker than appeared on their transplant evaluation forms. However, there was no quantification or correlation with evidence of poor outcomes using the “eyeball test.” No way of saying how badly these patients “failed the eyeball test.” Macdonald and colleagues1 identify frailty as a measure of decreased physiological reserve and increased vulnerability to poor outcomes from stressors as a potential pretransplant predictor of poor posttransplant outcomes. This concept has become increasingly important in identifying patients at increased risk after ventricular assist devices implantation or cardiac surgery or elective noncardiac surgery.2-4 Frailty can be assessed quantitatively using a simple tool5 and can allow for quantification of the “eyeball test” or as the authors call it, the “end of the bed” assessment. Not surprisingly, given the advanced heart failure patients who are evaluated for transplant, frailty is a not an infrequent occurrence, yet Macdonald and colleagues showed that using a simple assessment (Fried’s Phenotype6 including binary scoring of 5 domains: weak grip strength, slow walking speed, poor appetite, poor physical activity, and exhaustion, they could identify patients with frailty so significant that it predicted worse outcomes after cardiac transplantation.1 Frailty was independent of age, sex heart failure duration, left ventricular ejection fraction or renal function but was associated with New York Heart Association (NYHA) class IV heart failure symptoms, poor invasive hemodynamics, such as decreased cardiac index, increased intracardiac filling pressures, lower body mass index, anemia, hypoalbuminemia, cognitive impairment, and depression which the authors also assessed using standard instruments (Montreal Cognitive Assessment) and the Depression in Medical Illness questionnaire. Given the domains in the frailty assessment instrument, it is not surprising that decreased cardiac index, NYHA class IV symptoms, lower BMI, and hypoalbuminemia are associated with frailty. Decreased appetite and exhaustion are associated with these clinical characteristics. What makes frailty so powerful a predictor is that Macdonald and colleagues showed that this test was a powerful independent predictor of all-cause mortality and reduced survival 1 year after transplantation. Frail patients undergoing transplant had a profoundly diminished 1-year actuarial posttransplant survival of 54.9% compared with 79% for nonfrail patients. These data would suggest that listing frail advanced heart failure patients for cardiac transplantation should be an absolute contraindication given the possibility of wasting a scarce resource which would most likely benefit less frail patients. Although the authors raised the fact that they used a simplified frailty instrument, relying mainly on functional parameters as a limitation of their study, this is in fact a strength because they used a test that can be easily performed at the bedside expeditiously, providing important prognostic information. The evidence of the robustness of this frailty instrument as a way to stratify patients prognostically is so strong, it would make sense for the assessment of frailty using this simple instrument to become a standard part of the cardiac transplant evaluation especially as patients on transplant lists are older.6 One important issue, which was not answered by this study, is whether frailty can be reversed in patients with advanced heart with therapies such as medical therapies and devices such as cardiac resynchronization therapy or VADs used as bridges to transplant. It is known that some factors associated with frailty, such as NYHA Functional Class, cardiac hemodynamics, and exercise tolerance, can be improved with medical and device therapies. Several of the domains in the frailty instrument including appetite, exhaustion, walking speed, and physical activity can be improved with therapy for heart failure although the extent of improvement may vary from patient to patient. Reversal of frailty with therapy has not been shown. Given the increasing percentage of patients bridged with Mechanical Circulatory Support on the cardiac transplant list,7 it will be important to know if bridge-to-transplant VADs can reverse frailty and to see if this improves posttransplant outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".