Remote monitoring of patients with heart failure during the first national lockdown for COVID-19 in France
Bibliographic record
Abstract
Abstract Aims Multiparametric remote monitoring of patients with heart failure (HF) has the potential to mitigate the health risks of lockdowns for COVID-19. We aimed to compare healthcare use, physiological variables, and HF decompensations during 1 month before and during the first month of the first French national lockdown for COVID-19 among patients undergoing remote monitoring. Methods and results Transmitted vital parameters and data from cardiac implantable electronic devices were analysed in 51 patients. Medical contact was defined as the sum of visits and days of hospitalization. The lockdown was associated with a marked decrease in cardiology medical contact (118 days before vs. 26 days during, −77%, P = 0.003) and overall medical contact (180 days before vs. 79 days during, −58%, P = 0.005). Patient adherence with remote monitoring was 84 ± 21% before and 87 ± 19% during lockdown. The lockdown was not associated with significant changes in various parameters, including physical activity (2 ± 1 to 2 ± 1 h/day), weight (83 ± 16 to 83 ± 16 kg), systolic blood pressure (121 ± 19 to 121 ± 18 mmHg), heart rate (68 ± 10 to 67 ± 10 b.p.m.), heart rate variability (89 ± 44 to 78 ± 46 ms, P = 0.05), atrial fibrillation burden (84 ± 146 vs. 86 ± 146 h/month), or thoracic impedance (66 ± 8 to 66 ± 9 Ω). Seven cases of HF decompensations were observed before lockdown, all but one of which required hospitalization, vs. six during lockdown, all but one of which were managed remotely. Conclusions The lockdown restrictions caused a marked decrease in healthcare use but no significant change in the clinical status of HF patients under multiparametric remote monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".