Remote patient monitoring and digital therapeutics in heart failure: lessons from the Continuum pilot study
Bibliographic record
Abstract
Abstract Introduction The increasing use of digital health solutions to monitor heart failure (HF) outpatients has been driven by the COVID-19 pandemic. An ideal technology should answer the specific needs of a public healthcare system: easy integration and proof of clinical benefit to justify investment in its long-term use. Through a consortium bringing together patients, physicians, industry, and hospital organizations, we developed a digital solution called “Continuum,” targeting patients with HF and other comorbidities. Hypothesis A digital health solution combining remote patient monitoring (RPM) and digital therapeutics (DTx) was developed to ensure a better follow-up of patients and to rapidly optimize their medication and subsequently avoid future severe adverse events. Methods A pilot intervention/control study with a three-month follow-up was conducted. Patients in the intervention group (remote patient monitoring group, RPM + ) had a smartphone or tablet and entered in their mobile app their vital signs, weight, and HF symptoms daily. HF patients who either did not have a mobile device or the skills to use the app were enrolled in the control group (RPM - ). The HealthCare Professionals (HCPs) used a web-based dashboard to follow the RPM + patients. They could access the results of a DTx solution to help them optimize the HF treatment according to Canadian guidelines. Results 52 HF patients were enrolled in this study, 32 in the RPM + : 69±9y age, 75% male, ejection fraction 42 ± 14%. In the RPM - group, more patients had at least one hospitalization (all-cause) compared to the RPM + group (35% versus 6% respectively; p=0.008). Similarly, the number of patients with at least one HF hospitalization was more significant in the RPM + group compared to the RPM - (25% versus 6%, p=0.054). Finally, the intervention showed a medium effect on HF treatment optimization (w=0.26) and quality of life for the most compliant patients to the intervention (g=0.48). Conclusion The results of this pilot study demonstrated the feasibility of an intervention combining RPM and DTx solutions for HF patients. Preliminary results suggest promising impacts on quality of life, hospitalizations, and patients’ medication optimization. However, they need to be confirmed in a more extensive study.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".