Use of a remote telemonitoring platform significantly improves medication optimisation in heart failure patients
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».