A cost-effectiveness analysis for high versus standard (low) dose caffeine for the treatment of apnea in neonatal intensive care unit
Bibliographic record
Abstract
Objective: Preterm babies are prone to experiencing apnea of prematurity (AOP), mostly characterised by a pause in breathing lasting a minimum of 20 seconds. Recent literature supported higher maintenance doses of caffeine, indicating benefits. This study evaluated the cost-effectiveness of high maintenance dose (HD) versus low maintenance dose (LD) caffeine for AOP in neonates. Methods: From the hospital perspective of Hamad Medical Corporation (HMC), Qatar, a cost-effectiveness decision-analytic model was constructed to follow the use of a HD maintenance caffeine of 20 mg/kg/dose versus a LD maintenance caffeine of 10 mg/kg/dose, in a simulated cohort of AOP neonates, over a therapy follow-up duration of six weeks, until neonatal intensive care (NICU) discharge. The clinical inputs were primarily literature-based, while the resource cost and utilisation were locally extracted in HMC. The cost-effectiveness outcome measure was calculated per therapy success, defined as survival with no apnea and successful extubation removal within 72 hours, with or without adverse events. One-way and multivariate sensitivity analyses were performed to confirm the robustness of the results. Results: With 0.23 (95% CI, 0.23-0.23) enhancement in success rate, at United States dollar (US$) 3869 (95% CI, US$ 3823-3915) added infant cost, the HD caffeine was between dominant (34.8%) and cost-effective (63.7%), with an average incremental cost-effectiveness ratio of US $16,895 (95% CI, US$ 15,242-18,549) relative to LD caffeine per additional case of success. The hospitalisation contributed the most to the total infant cost, and the probability of patent ductus arteriosus was the model input that influenced the results most. Conclusion: This is the first literature economic evaluation of caffeine for AOP. Despite increasing the cost of therapy, HD maintenance caffeine seems to be a cost-effective alternative to LD caffeine in Qatar. Our results support the recent global trends of increased use of HD caffeine for AOP in NICU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".