Improving cognitive function after cardiac surgery: home-based computerised cognitive training (FACCT study)
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Barts Charity Nurse / Allied Healthcare Professional Clinical Research Fellowship (MRC0229, Improving cognitive health after cardiac surgery) Background Postoperative cognitive dysfunction (POCD) occurs in up to 50% of patients after cardiac surgery. Despite extensive research, the underlying causes and pathophysiology are poorly understood. Further, definitive treatments for POCD are lacking. The focus of this doctoral work was to identify predictors of POCD, examine the effectiveness of existing cognitive interventions on improving cognitive function, and use the findings of both of these systematic reviews to design and develop a cognitive training programme in postoperative cardiac surgery patients. Objectives The aim of this study was to evaluate the feasibility and acceptability of a home-based computerised cognitive training programme in postoperative cardiac surgery patients. Methods This is a single-arm, non-blinded, feasibility and acceptability study involving adult (>18 years of age) patients admitted for first time elective cardiac surgery. Participants were required to complete an 8-week cognitive training programme (40 sessions [20 minutes/day, 5 days/week]), commencing one week postoperatively, and administered using their own computers or tablets. The Montreal Cognitive Assessment (MoCA), a brief test of global cognition, was administered preoperatively and after the training programme. Feasibility outcomes included recruitment and retention rates and adherence to the programme. Acceptability was assessed by the Theoretical Framework of Acceptability Questionnaire (TFA-Q) which was administered post-programme. Results In total, 95 patients were screened, 51 (53.7%) were eligible and approached, and 31 (60.8%) consented to participate. Of these, 29 participants enrolled in the cognitive training programme, 7 (24.1%) were female, 20 (69.0%) were white British, and the mean baseline MoCA was 26.9 (out of 30). Data collection is complete, final analysis will be complete for presentation prior to ACNAP 2023. Interim results suggest that home-based computerised cognitive training (CCT) is feasible in terms of recruitment rate (60.8%, exceeding the target of 50%) and acceptable (with 94.1% of those who completed the TFA-Q identifying CCT as acceptable). Other feasibility indicators measured include retention (58.6%) and adherence (31.0%). Conclusions A trial examining home-based computerised cognitive training appears feasible and acceptable to patients, although strategies to improve retention and adherence will need to be strengthened. If found to be beneficial, such a programme could offer an inexpensive and safe method of improving postoperative cognitive function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".