Value of mesenchymal stem cell therapy for patients with septic shock: an early health economic evaluation
Bibliographic record
Abstract
BACKGROUND.: This study estimates the maximum price at which mesenchymal stem cell (MSC) therapy is deemed cost-effective for septic shock patients and identifies parameters that are most important in making treatment decisions. METHODS: We developed a probabilistic Markov model according to the sepsis care trajectory to simulate costs and quality-adjusted life years (QALYs) of septic shock patients receiving either MSC therapy or usual care over their lifetime. We calculated the therapeutic headroom by multiplying the gains attributable to MSCs with willingness-to-pay (WTP) threshold and derived the maximum reimbursable price (MRP) from the expected net monetary benefit and savings attributable to MSCs. We performed scenario analyses to assess the impact of changes to assumptions on the study findings. A value of information analysis is performed to identify parameters with greatest impact on the uncertainty around the cost-effectiveness of MSC therapy. RESULTS: At a WTP threshold of $50,000 per QALY, the therapeutic headroom and MRP of MSC therapy were $20,941 and $16,748, respectively; these estimates increased with the larger WTP values and the greater impact of MSCs on in-hospital mortality and hospital discharge rates. The parameters with greatest information value were MSC's impact on in-hospital mortality and the baseline septic shock in-hospital mortality. CONCLUSION: At a common WTP of $50,000/QALY, MSC therapy is deemed to be economically attractive if its unit cost does not exceed $16,748. This ceiling price can be increased to $101,450 if the therapy significantly reduces both in-hospital mortality and increases hospital discharge rates.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".