Use of a remote telemonitoring platform significantly improves medication optimisation in heart failure patients
Bibliographic record
Abstract
Abstract Background Guideline directed medical therapy (GDMT) has been shown to reduce morbidity and mortality in patients with heart failure with reduced ejection fraction (HFrEF). Despite this, a large number of eligible patients do not receive these treatments or have prolonged delays in achieving optimal doses. Purpose To determine whether a telemonitoring-supported, remote medication optimisation programme could increase the proportion of HFrEF patients reaching maximum tolerated GDMT, in a shorter period of time compared to usual care. Methods A prospective, randomised controlled trial recruited 108 patients with a diagnosis of HFrEF from the ambulatory heart function clinic of a North American cardiac centre. All patients were enrolled onto a non-invasive remote monitoring platform which allowed daily nurse coordinator-led assessment of patient-reported symptoms and trends in heart rate, blood pressure and weight. In the remote titration intervention group, telemonitoring data were used by treating physicians to make decisions on optimisation of GDMT every two weeks, which was enacted by the patient's nurse coordinator, with no physician visit required. Patients in the control group were reviewed in clinic by their treating physician, where medication doses were optimised as per standard of care. The proportion of patients achieving maximum tolerated GDMT, and the time taken for this were compared between groups. Continuous data are presented as mean±standard deviation and compared with Student's t-test, while categorical data are shown as number (%) and compared using the Chi-squared test. Results 108 patients (69.4% male, mean age 54.1±15.4 years) were recruited with a median follow-up of 740 days. Baseline characteristics and medication prescription were similar between groups (56 randomised to remote titration, RT, 52 to usual care, UC, see Table). There were three withdrawals from the RT group and two from the UC group. Significantly more patients in the RT group 52/53 (98.1%) achieved the primary outcome, reaching maximum tolerated GDMT, compared with 42/50 (84.0%) in the UC group (p=0.01). The RT group achieved GDMT earlier (123±70 vs. 183±136 days, p=0.01) with a 40% reduction in clinic visits (p<0.01). In a time-to-event analysis, time to optimisation was significantly shorter in the intervention group (median 105 vs. 165 days, p[log rank] <0.01, see Figure). There was a similar increase in prescription of GDMT in both groups and no differences in hospitalisation or urgent clinic review suggesting that there was no excess hazard of remote titration. Conclusion Remote titration of GDMT in HFrEF patients resulted in more patients achieving maximum tolerated doses, on average two months earlier, with a reduction in clinic visits and no excess adverse outcomes. Telemonitoring-supported remote GDMT titration is effective, safe and could reduce healthcare costs associated with the management of HFrEF. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): DHB is supported by a post-doctoral fellowship award from TRANSFORM-HF (Ontario, Canada).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".