Impact of Remote Titration Combined With Telemonitoring on the Optimization of Guideline-Directed Medical Therapy for Patients With Heart Failure: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Guideline-directed medical therapy (GDMT), optimized to maximum tolerated doses, has been shown to improve clinical outcomes in patients with heart failure (HF). Timely use and optimization of GDMT can improve HF symptoms, reduce the burden of hospitalization, and increase survival rates, whereas GDMT deferral may worsen the progression of HF, decrease survival rates, and predispose patients to poor outcomes. However, studies indicate that GDMT remains underused, with less than 25% of patients receiving target doses in clinical practice. Telemonitoring is a potential component in the management of HF that can provide reliable and real-time physiological data for clinical decision support and facilitate remote titration of medication. OBJECTIVE: The primary objective of this study is to evaluate the impact of remote titration facilitated by telemonitoring on health care outcomes, with a primary outcome measure being the proportion of patients achieving target doses. The secondary objective is to identify the barriers and facilitators that can affect the implementation and effectiveness of the intervention. METHODS: A mixed methods study of a smartphone-based telemonitoring system is being conducted at the Peter Munk Cardiac Centre (PMCC), University Health Network, Toronto. The study is based on an effectiveness-implementation hybrid design and incorporates process evaluations alongside the assessment of clinical outcomes. The effectiveness research component is assessed by a two-arm randomized controlled trial (RCT) aiming to enroll 108 patients. The RCT compares a remote titration strategy that uses data from a smartphone-based telemonitoring system with a standard titration program consisting of in-office visits. The implementation research component consists of a qualitative study based on semistructured interviews with a purposive sample of clinicians and patients. RESULTS: Patient recruitment began in January 2019 at PMCC, with a total of 76 participants recruited by February 24, 2020 (39 in the intervention group and 37 in the control group). The final analysis is expected to be completed by the winter of 2021. CONCLUSIONS: This study will be among the first to provide evidence on the implementation of remote titration facilitated by telemonitoring and its impact on patient health outcomes. The successful use of telemonitoring for this purpose has the potential to alter the existing approach to titration of HF medication and support the development of a care delivery model that combines clinic visits with virtual follow-ups. TRIAL REGISTRATION: ClinicalTrials.gov NCT04205513; https://clinicaltrials.gov/ct2/show/NCT04205513. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19705.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".