Cost-Effectiveness of Internet of Things–Based Management of Home Noninvasive Positive Pressure Ventilation in Patients With Chronic Obstructive Pulmonary Disease and Hypercapnic Chronic Respiratory Failure: Trial-Based Economic Evaluation
Bibliographic record
Abstract
Abstract Background The management of chronic obstructive pulmonary disease (COPD) places a significant burden on health care systems worldwide. Home noninvasive positive pressure ventilation (NPPV) is an established treatment option associated with significant benefits for patients with COPD and hypercapnic chronic respiratory failure. Internet of Things (IoT)–based management may improve communication between patients and physicians and strengthen the integration and comprehensiveness of home NPPV telemonitoring. However, the economic value of such systems remains insufficiently understood. Objective This study aimed to assess the cost-effectiveness of IoT-based management versus standard management of home NPPV in patients with COPD and hypercapnic chronic respiratory failure. Methods A Markov decision analytic model was developed to simulate real-world COPD progression and predict health outcomes and costs associated with IoT-based and standard management of home NPPV. COPD progression consisted of 4 health states: stable period, nonserious exacerbation period, serious exacerbation period, and death. Efficacy and cost inputs were primarily sourced from a published multicenter, prospective, randomized controlled trial and supplemented by official Chinese databases where necessary. Quality-adjusted life years (QALYs) were used as effect indicators for this model, which were derived from COPD Assessment Test scores. The discounted lifetime cost per QALY gained was calculated from the Chinese health care payer perspective, and sensitivity analyses were conducted to test the robustness of model results across different assumptions. Results Compared with standard NPPV, IoT-based NPPV increased costs by ¥3607.26 and improved QALYs by 0.24 per person across the lifetime horizon, resulting in an incremental cost-effectiveness ratio of ¥15,030.25 per QALY, with a 93.6% probability of being cost-effective at the given willingness-to-pay threshold (currency conversion to US dollars was based on the average exchange rate in 2019 [US $1=¥6.9]). Base case results were also robust to multiple one-way sensitivity analyses, with the main drivers being hospitalization costs for the standard and IoT-based NPPV groups during the serious exacerbation period. Conclusions IoT-based NPPV was cost-effective compared with standard NPPV for patients with COPD and hypercapnic chronic respiratory failure.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| 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 | 0.002 |
| 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".