Assessing atrial fibrillation ablation priority during COVID-19 -does use of patient questionnaires help in stratification above physician assessment?
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Introduction Catheter ablation for atrial fibrillation (AF) is largely offered for symptomatic relief. The ORBIT registry has shown that patients with a higher EHRA class and lower quality of life (QoL) scores (AFEQT score <65.7) are more likely to suffer emergency hospital admissions. To help prevent unplanned AF admissions and to best utilise the reduced capacity for elective work during the COVID-19 pandemic, it has become even more important to prioritise the most symptomatic AF patients for ablation. Purpose: To evaluate the accuracy of a subjective symptom-based clinician prioritisation schema compared to objective patient-completed quality of life (QoL) scores. Methods: In July 2020, all elective cases awaiting AF ablation at our institution were categorised by their cardiologist as either category 1 (C1-urgent), category 2 (C2–priority, procedure to be done during the ongoing COVID-19 pandemic) or category 3 (C3–routine, procedure may be delayed until post pandemic). This categorisation was based on review of clinic letters where EHRA AF symptom class or PROMS are not routinely recorded. All patients in C2 and C3 were then posted an AF specific (AFEQT) and a generic (EQ5D) QoL questionnaire to complete. Physicians were blinded to patient responses on the QoL questionnaires. Results: Details of physician prioritisation and completed questionnaires were available for 85 patients (62 ± 10 years, PAF in 61%, males 66%). The 18 patients that had been categorised in C2 (priority) group were found to have a significantly lower AFEQT score (30.4, IQR 17.2-51.9) compared to the 67 patients classed in C3 (routine) group (56.5, IQR 32.1-74.1; p < 0.01)(Figure 1a). EQ5D scores also tended to be lower in the C2 patients (0.7, IQR 0.4-0.8) compared to C3 (0.8, IQR 0.6-0.9; p = 0.056) (Figure 1c). 16 (89%) patients in C2 had significant AF-related impact on QoL (as defined as AFEQT score <65.7) compared to 42 (63%) of patients in C3. However, there was significant overlap between groups (Figure 1b). 4 patients in C3 had unplanned AF related hospital admissions while awaiting ablation, as compared to none in C2. The median AFEQT score of these 4 patients was 23.3, indicating that they were highly symptomatic despite being classified in C3 by their cardiologist. Conclusion : Physician assessments are moderately accurate in prioritising patients awaiting AF ablation. The addition of formal patient-completed QoL assessment such as with AFEQT, helps to identify the most symptomatic patients at risk of emergency hospital admission, and physicians should consider using these as part of routine assessment, especially during the COVID pandemic. Abstract Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".