Abstract 2: The Changing Face of Cardiac Surgery: Frailty, Age, and Adverse Outcomes Create a Mandate for Shared Decision Making
Bibliographic record
Abstract
OBJECTIVES The literature shows that comprehension of risks, benefits, and alternatives of treatment options is poor among patients referred for cardiac interventions. We have previously demonstrated that frail, elderly patients undergoing cardiac surgery require complex procedures and are at markedly increased risk of postoperative death and prolonged institutional care. An effective informed consent process is critical in this population. We sought to determine the proportion of frail, elderly patients referred for cardiac surgery over time and their associated outcomes. METHODS Patient demographics and utilization rates from 2001 to 2010 were examined at a single cardiac surgery center. Patients were stratified by age (≤70, 71-79, 80+) and frailty (defined as any loss of independence in activities of daily living (Katz Index), ambulation, or a documented history of dementia; frailty data available since 2006). We examined case complexity (CABG, Valve, combined CABG+Valve) and in-hospital outcomes: major adverse cardiac events (MACE, defined as one or more of mortality, stroke, infection or renal failure), as well as ICU stay >7 days, and discharge (DC) to an institution. RESULTS In the past decade the proportion of patients 80+ has increased from 7% (75/1005) to 13% (105/824) while the proportion <70 has decreased from 63% (632/1005) to 57% (466/824) (p<0.0005). Isolated CABG surgery declined from (79% (789/1005) to 60% (497/824) while CABG+Valve cases increased from 9% (91/1005) to 12% (104/824) (p<0.05). CABG+Valve surgery was performed on 26% (228/893) of patients 80+ vs. 6% (348/5614) of patients <70 (p<0.0005). In the past five years the proportion of frail patients referred for surgery has increased from 3.8% (33/871) to 9.3% (77/824). Adverse outcomes were significantly higher for frail patients (p<0.0001) as shown in figure 1, as well as for older patients (p<0.0001). CONCLUSIONS We have identified increasing rates of elderly and frail patients with high-risk profiles referred for cardiac surgery. These patients experience higher rates of mortality and prolonged institutional care. We suggest this vulnerable patient population may benefit from the institution of a formalized shared decision making process to effectively communicate risks, benefits and alternatives to the planned procedure.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".