Abstract 15964: Improving the Care of Frail or Vulnerable Patients Undergoing Cardiac Surgery
Bibliographic record
Abstract
Background: Increasingly frail/ vulnerable patients are undergoing cardiac surgery. The purpose of this study was, Phase 1: Create a hospital record-based Frailty Index (FI) to better quantify frailty in cardiac surgery &, Phase 2: Implement a novel Telehealth Home monitoring Enhanced- Frailty After Cardiac Surgery (THE-FACS) intervention to improve outcomes. Methods: Phase 1: A 21clinical deficit-based retrospective FI was created using New Brunswick Heart Centre registry, patients grouped into terciles & evaluated for prolonged hospitalization, discharge disposition. Phase 2: Vulnerable patients were recruited prospectively to test the applicability of THE-FACS, which used a tablet device to monitor patient responses daily for 30 days. Trained cardiac surgery follow-up (F/U) nurses monitored the data & contacted patients only if the algorithm triggered an alert. Primary outcomes of interest were prolonged hospitalization, non-home discharge & hospital readmission. Results: Phase 1: A FI was constructed with records from 3463 cardiac surgery patients. The most frail patients (n= 898, 26 %) had prolonged hospitalization (7 days vs. 5 days; p< 0.001), non-home discharge (49 % vs. 17 %; p< 0.001), higher 30 day readmission (18 % vs. 10 %; p< 0.001) & mortality (4.8 % vs. 0.7 %; p< 0.001). Multivariable analysis showed that FI was an independent predictor of composite outcome. Phase 2: Vulnerable patients (64 from 86 approached) were prospectively recruited, representing 34 % of potential surgeries (86/ 254). Several patients required prolonged hospitalization (15/ 64) or non-home discharge (12/ 64). THE-FACS was used in the remaining 35/ 64 patients. 21/ 35 patients completed the 30 day F/U, largely due to technical difficulties with the system. There were few ER visits (10 %) & no readmission, with THE-FACS being easy (100 %), satisfactory (95 %) & amenable to re-use (67 %). Conclusions: Our study highlights that frailty, affecting ~1/3 rd of patients undergoing cardiac surgery, significantly impacts outcomes. Findings from our pilot, THE-FACS, support the feasibility of targeted interventions in some vulnerable patients. However, our results suggest frequent technical challenges & inability to use early in many patients due to delayed discharge.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".