Abstract 12698: Alert-Driven versus Scheduled Remote Monitoring of Implantable Cardiac Defibrillators: An Economic Analysis From the Trust Randomized Trial
Bibliographic record
Abstract
Introduction: Current implementation of remote patient monitoring (RPM) for implantable cardioverter defibrillators (ICDs) involves a hybrid of remote assessment and in-person evaluation (IPE) every 3 months. Alert-driven RPM or fully virtual care without routine visits may reduce non-actionable patient evaluation, increased clinic workload and inefficient resource allocation. Objective: To conduct a cost-consequence analysis to compare three patient management strategies following ICD implantation: (a) IPE only, (b) RPM-conventional (hybrid of IPE and RPM), and (c) RPM-alert (alert-based ICD follow up). Methods: We constructed a decision-analytic Markov model to estimate the costs and benefits of three strategies of patient management following ICD implantation over a two-year time horizon from the perspective of the US healthcare system. Aggregate and patient-level data from the TRUST (Lumos-T Safely RedUceS RouTine Office Device Follow-up) clinical trial informed clinical effectiveness model inputs. TRUST randomized 1339 patients 2:1 to conventional RPM or IPE alone, and found that RPM was safe and reduced the number of non-actionable encounters. Cost data was obtained from the published literature. The primary outcome was incremental cost. Results: The mean cumulative costs per patient were $12,688 in the IPE group, $12,001 in the RPM-conventional group, and $11,011 in the RPM-alert group. Compared to the IPE group, both the RPM-conventional and RPM-alert groups were associated with lower incremental costs of -$687 (95% confidence interval (CI)-$2,138 to +$638) and -$1,677 (95% CI -$3,134 to -$304), respectively. Of the three groups, the RPM-alert strategy was the most cost-effective strategy with an estimated cost-savings in 99% of simulations. Conclusions: Alert-driven RPM was economically attractive and, if patient outcomes and safety are comparable to conventional RPM, may be the preferred strategy of patient follow up.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".