Lifetime quality of life and cost consequences of delays in endovascular treatment for acute ischaemic stroke: a cost-effectiveness analysis from a Singapore healthcare perspective
Bibliographic record
Abstract
OBJECTIVES: Endovascular therapy (EVT) significantly improves clinical outcomes in patients with acute ischaemic stroke (AIS), while the time of EVT initiation after stroke onset influences both patient clinical outcomes and healthcare costs. This study determined the impact of EVT treatment delay on cost effectiveness of EVT in the Singapore healthcare setting. DESIGN: A short-term decision tree and long-term Markov health state transition model was constructed. For each time window of symptom onset to EVT, the probability of receiving EVT or non-EVT treatment was varied, thereby varying clinical outcomes (modified Rankin Scale scores), short-term costs and long-term modelled (lifetime) costs; all of which were used in calculating an incremental cost-effectiveness ratio of EVT vs non-EVT treatment. Clinical outcomes and cost data were derived from clinical trials, literature, expert opinion, electronic medical records and community-based surveys from Singapore. Deterministic one-way and probabilistic sensitivity analyses were performed to assess the uncertainty of the model. The willingness to pay for per quality-adjusted life-year (QALY) was set to Singapore $50 000 (US$36 500). SETTING: Singapore healthcare perspective. PARTICIPANTS: The model included patients with AIS in Singapore. INTERVENTIONS: EVT performed within 6 hours of stroke onset. OUTCOME MEASURES: The model estimated incremental cost-effectiveness ratios (ICERs) and net monetary benefits (NMB) for EVT versus non-EVT treatment, varied by time from symptom onset to time of treatment. RESULTS: EVT performed between 61 min and 120 min after the stroke onset was most cost-effective time window to perform EVT in the Singapore population, with an ICER of Singapore $7197 per QALY (US$5254) for performing EVT at 61-120 min versus 121-180 min. The resulting incremental NMB associated with receipt of EVT at the earlier time point is Singapore $39 827 (US$29 074) per patient at the willingness-to-pay threshold of Singapore $50 000. Each hour delay in EVT resulted in an average loss of 0.54 QALYs and 195.35 healthy days, with an average net monetary loss of Singapore $26 255 (US$19 166). CONCLUSIONS: From the Singapore healthcare perspective, although EVT is more expensive than alternative treatments in the short term, the lifetime ICER is below the willingness-to-pay threshold. Thus, healthcare policies and procedures should aim to improve efficiency of pre-hospital and in-hospital workflow processes to reduce the onset-to-puncture duration.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".