Abstract 207: Assessing the Clinical Value of Frailty as a Prognostic Indicator to Aid Decision-Making in Cardiac Surgery
Bibliographic record
Abstract
Background: In North America, octogenarians are the fastest growing demographic. Chronological age of a patient is not always the same as their biological age, and their biological status can vary from robust to frail. Frail patients are predisposed to falls, institutionalization, hospitalization, and mortality. In the realm of cardiac surgery, there is little research examining frailty as a prognostic factor for cardiac surgical intervention. Purpose: The objective of the current study is to explore whether frailty provides additional information as a risk factor regarding patient prognosis over and above that of the comprehensive risk analysis scale, EuroSCORE II, used to determine suitability for cardiac surgery. Methods: This non-interventional study uses hospital patient files and questionnaire interviews, which assesses the patient’s frailty using the Frailty Assessment for Care-Planning Tool (FACT). From the documented EuroSCORE II from patient files, predictive modeling was used to consider frailty as a prognostic indicator for three adverse outcomes, discharge to an institution, major adverse cardiac events (MACE), and all-cause mortality + MACE. Furthermore, the sensitivity and specificity of the FACT will be evaluated using the area under the ROC curve for significant models. Results: Prognostic models determined that AUROC values provide improved prediction for two adverse outcomes, MACE and all-cause mortality + MACE. Using a cumulative score that involves all four domains (usual mobility, daily tasks, social function, and memory), higher discrimination with good calibration is achieved. Conclusions: Certain aspects of frailty, as measured by the FACT, have clinical value as prognostic indicators. These models are the first, to our knowledge, to investigate the relationship between the EuroSCORE II and MACE +/- all-cause mortality using the FACT. Traditional risk assessment scores such as the EuroSCORE II will benefit from having frailty included as a risk factor. Implications: This study will assist in educating future heart surgery patients about their possible risks by predicting adverse outcomes with better predictive ability. It is hoped that patients who possess more knowledge about their personal risks will be able to make more informed decisions about their surgery. Strategies to address and reduce frailty by increasing mobility and cognitive function and reducing nutritional deficiencies could use this information to inform future work.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".