Actual Cost of Extracorporeal Cardiopulmonary Resuscitation: A Time-Driven Activity-Based Costing Study
Bibliographic record
Abstract
OBJECTIVES: To determine the actual cost and drivers of the cost of an extracorporeal cardiopulmonary resuscitation (E-CPR) care cycle. PERSPECTIVE: A time-driven activity-based costing study conducted from a healthcare provider perspective. SETTING: A quaternary care ICU providing around-the-clock E-CPR service for out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA) in Australia. METHODS: The E-CPR care cycle was defined as the time from initiating E-CPR to hospital discharge or death of the patient. Detailed process maps with discrete steps and probabilistic decision nodes accounting for the complex trajectories of E-CPR patients were developed. Data about clinical and nonclinical resources and timing of activities was collected multiple times for each process . Total direct costs were calculated using the time estimates and unit costs per resource for all clinical and nonclinical resources. The total direct costs were combined with indirect costs to obtain the total cost of E-CPR. RESULTS: From 10 E-CPR care cycles observed during the study period, a minimum of 3 observations were obtained per process. The E-CPR care cycle's mean (95% CI) cost was $75,014 ($66,209-83,222). Initiation of extracorporeal membrane oxygenation (ECMO) and ECMO management constituted 18% of costs. The ICU management (35%) and surgical costs (20%) were the primary cost determinants. IHCA had a higher mean (95% CI) cost than OHCA ($87,940 [75,372-100,570] vs. 62,595 [53,994-71,890], p < 0.01), mainly because of the increased survival and ICU length of stay of patients with IHCA. The mean cost for each E-CPR survivor was $129,503 ($112,422-147,224). CONCLUSIONS: Significant costs are associated with E-CPR for refractory cardiac arrest. The cost of E-CPR for IHCA was higher compared with the cost of E-CPR for OHCA. The major determinants of the E-CPR costs were ICU and surgical costs. These data can inform the cost-effectiveness analysis of E-CPR in the future.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".