Digital health intervention to <i>opt</i> imise <i>h</i> eart <i>f</i> ailure management after hospital discharge in Brazil (OPT-HF): a randomised clinical trial protocol
Bibliographic record
Abstract
Introduction Guideline-directed medical therapy (GDMT) for heart failure (HF) reduces adverse events, but is underused. Global barriers to GDMT optimisation include low frequency of visits, clinician inertia and poor patient knowledge, which may be mitigated by digital health interventions (DHI). In Brazil, low digital literacy and reduced access to technology may compromise these potential DHI’s beneficial effects. Our objective is to develop and test the effectiveness of a DHI to optimise GDMT in patients recently hospitalised for HF in the Brazilian public health system ( Sistema Único de Saúde (SUS)). Methods and analysis This is a randomised, controlled, multicentre, parallel-group, clinical trial in which 154 patients being discharged from an HF-related hospitalisation will be randomised. Inclusion criteria are ≥18 years of age, reduced ejection fraction HF (EF<50%) and medication optimisation gaps (at least one GDMT class not started or two among those with prescribed dosage≤50% of the target dose). All participants will receive a written booklet and SUS usual care. Randomisation will be stratified by site. The intervention includes a mobile application (app) to engage patients, developed through a human-centred design. The app’s main features are a check-in page for daily collection of participants’ health status, vital signs and weight; a remote educational programme; a chat function during working hours and longitudinal graphical representations of participants’ data. The participants’ data will be managed daily by a nurse, linked to a cardiologist for teleconsultations. Predefined clinical decision trees will guide actions, including alarm signs and GDMT optimisation. The primary outcome will be changes in GDMT from baseline to end of follow-up in 90 days. Secondary outcomes will include all-cause readmission, HF-related rehospitalisation, change in health status and HF knowledge, and implementation outcomes based on the RE-AIM framework. The analysis of outcomes will follow the intention-to-treat principle. Ethics and dissemination This study was approved by the Universidade Federal de Minas Gerais. Recruitment started in November 2023, and patients involved will sign an informed consent form. Results will be presented at scientific meetings and published in scientific journals in 2025, and will be disclosed in social media and presented to public health stakeholders. Trial registration number Universal Trial Number U1111-1295-1864 Brazilian Clinical Trials Registry ( https://ensaiosclinicos.gov.br/rg/RBR-10vpf9bm ).
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".