Abstract 14563: Improving Monitoring Outcomes and Quality of Life for Heart Failure Patients in Telehomecare Program
Bibliographic record
Abstract
Introduction: Introduced in 2007 by the Ontario Telemedicine Network, the Telehomecare program provides monitoring of patient’s parameters and coaching sessions. There is an inconclusive data on the association of participation and patient-level outcomes. Hypothesis: We hypothesized that blood pressure (BP), daily weight fluctuation and quality of life would improve among heart failure patients participating in the Telehomecare. Methods: We applied a time series study design to analyze patients’ blood pressure and weight, transmitted via telemonitoring devices on a daily basis. Longitudinal surveys were conducted on a small sample of prospectively enrolled patients (n=22) to assess quality of life using EQ-5D and SF-12 over the three-month period. Daily weight gain was defined as an increase of ≥2 lbs between two consecutive measurements. The data for the period of July 2012 to March 2015 was analysed by repeated measures with generalized linear mixed model procedures in SAS. Results: Overall 1354 patients with heart failure (52% women) were enrolled with average age of 76.3±11.1. During the first month of enrolment, one third of the patients (n=433) had elevated BP with a monthly average systolic BP of 150.3±9.6 mm Hg and diastolic BP of 76.7±12.8, in comparison with adequately controlled (n=921) patients, who had an average systolic BP of 118.8±13.5 mm Hg and diastolic BP of 66.4±9.6 mm Hg. Over the seven month period, we found significant reduction in systolic (by 11.0 mm Hg; 95% CI 9.3-12.7) and diastolic BP (by 6.0 mm Hg; 95% CI 4.9-7.3) among patients who had elevated values at baseline, adjusted for age and gender. During the first month of enrolment, weight gain of ≥2 lbs was registered for 8% of days. The likelihood of daily weight gain slightly declined over the seven month period (OR 0.86. 95% CI 0.75-0.98). Physical and mental component scores of SF-12 and EQ 5D’s index score did not improve significantly except for EQ 5D visual analogue scale scores. Conclusion: In conclusion, changes observed in the patient monitoring parameters over time pointed out that hypertensive patients might benefit the most from the Telehomecare. The survey evaluations failed to detect any significant impact of the Telehomecare on the patients’ quality of life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.006 | 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".