Cost-effectiveness of greenhouse gas emission reductions with desflurane and sevoflurane waste gas recovery
Bibliographic record
Abstract
The climate crisis caused by anthropogenic greenhouse gas (GHG) emissions is the most important global health threat facing humanity in the 21st century, mandating a reduction in GHG emissions to net zero before 2050. To meet this challenge while preserving the quality of care, medical professionals need to lead the transition of healthcare processes away from GHG-producing processes using quantitatively evaluated strategies.1 Anaesthesia is a medical GHG hotspot through the use of halogenated anaesthetic gases (HAGs).2 Over the last decade, the anaesthesia community has proposed several strategies to reduce HAG-related emissions, including intravenous and/or regional anaesthesia, avoidance of desflurane, reduction of fresh gas flows (FGFs) and recovery of waste anaesthetic gas (WAG).3 WAG recovery devices allow the recapture and eventual recycling of WAG from the anaesthesia machine scavenging circuit.4 However, a proportion of HAGs absorbed by patient tissues during anaesthesia are exhaled after disconnection from the anaesthetic circuit, thus evading recovery. This report aims to help clinicians and decision makers faced with the choice of investing in WAG recovery versus other strategies for GHG reduction, by examining cost-effectiveness of this technology in relation to expected reductions in GHG emissions. As no human or animal subjects were involved, ethics board approval was waived. Daily avoided GHG emissions were estimated for sevoflurane and desflurane by simulating four 2-h cases to model a typical anaesthesia day, using a semi-closed circuit, a patient weight of 70 kg, alveolar ventilation at 4 l min−1 and cardiac output at 5 l min−1 (Gas Man 4.3 Med Man Simulations Inc, Chestnut Hill, Massachusetts, USA). Every simulation began by reaching 1.0 ETMAC with a FGF of 2 l min−1 using inspired HAG concentrations (FiHAG) of 18% for desflurane and 8% for sevoflurane, then reducing FGF to 0.5 l min−1. The FiHAG was continually adjusted to maintain the ETMAC within 5% of 1 MAC. After 2 h of anaesthesia, washout was simulated by setting FiHAG to 0% and increasing FGF to 10 l min−1 until ETMAC reached 0.1. HAG remaining in the patient after simulated washout was recorded as ‘nonrecoverable’. HAGs sent to scavenging systems between anaesthetic induction and the end of washout were considered ‘recovered’. Two simulations were performed for each gas to ensure reproducible results. Litres of HAG vapour were converted to kg of CO2 equivalent GHG emissions using published GWP100 values. Hypothetical daily costs were equal to or lower than those of a WAG recovery system currently deployed at the Centre Hospitalier de l’Université de Montréal (Deltasorb, Blue-Zone Technologies, Concord Ontario, Canada), including rental, reprocessing and transport costs. Costs per ton of GHG emissions avoided by WAG recovery were calculated using daily costs of recovery systems, divided by GHG emissions avoided (in CO2eq) during a model anaesthesia day. Estimates for the social costs of damage from GHG emissions were based on recent published scientific and Canadian government evaluations of 185 US$ ton−1.5 A 2-h case at 1 MAC and 0.5 l min−1 produced an estimated total of 2.6 kg CO2eq emissions for sevoflurane and 142.1 kg CO2eq emission for desflurane, of which 50% could be recovered for sevoflurane and 69% for desflurane. Nonrecoverable emissions with desflurane were 34 times (43.6 versus 1.3 kg) greater than those with sevoflurane (Fig. 1). Accordingly, the social cost of nonrecoverable emissions during a typical anaesthesia day was 32.24$ for desflurane, and 0.97$ for sevoflurane. Recovered emissions avoided a daily social cost of 72.89$ with desflurane, and 1.35$ with sevoflurane. The costs per ton of GHG emissions avoided by WAG recovery were 71 times higher with sevoflurane than with desflurane in this scenario (Table 1), as 1429 h of system use was required to recapture one ton of CO2eq with sevoflurane, versus 20 h with desflurane.Fig. 1: Recoverable and nonrecoverable greenhouse gas emissions using either sevoflurane or desflurane for a 2-h case at one minimal alveolar concentration with a 0.5 l min−1 fresh gas flow. Table 1 - Cost per CO2eq ton of avoided greenhouse gas emissions Daily cost per OR of HAG waste gas recovery system ($) Sevoflurane Desflurane 1 178.57$ per ton 2.53$ per ton 2 357.14$ per ton 5.06$ per ton 5 892.86$ per ton 12.65$ per ton 10 1785.70$ per ton 25.30$ per ton CO2eq, equivalent amount of carbon dioxide needed to produce same warming effect; HAG, halogenated anaesthetic gas; OR, operating room. This brief report shows the scale and social cost of GHG emissions resulting from desflurane, even using WAG recovery systems. It also highlights the large difference in both nonrecoverable GHG emissions and cost per ton of recovered GHG emissions that can be expected when implementing WAG recovery into anaesthetic practice using low FGFs and low-solubility HAGs. With sevoflurane, nonrecoverable GHG emissions are relatively low, whereas recovery costs per ton are high; with desflurane, the opposite is true. This study has some limitations. First, the data presented is based on simulations rather than actual measurements. However, end-tidal concentrations of HAGs in real patients are closely correlated with the values predicted by the software used.6 Second, calculations of GHG emissions did not consider HAG life cycle emissions or the processing of WAG recovery. However, the rest of the HAG life cycle generates negligible emissions compared with the effect of agent release in the atmosphere.7 The third limitation is that the present report assumed 100% effective WAG recovery. Although limited evidence shows that WAG recovery technologies appear to be highly effective at recovering HAGs from scavenging systems,4 actual emissions of GHGs and cost per ton of recovered GHGs will be slightly higher. Finally, only desflurane and sevoflurane were modelled; other HAGs could be compared with these agents using a similar methodology. Despite these limitations, the results of our analysis were an important contribution to the decision of anaesthesiologists at our institution to both reduce the use of desflurane anaesthesia and deploy WAG recovery only when desflurane anaesthesia was considered. Quantitative estimates of GHG recovery such as those in the present report can be used to compare this technology to other methods of reducing anaesthesia-related GHG emissions, in order to best allocate limited resources. In conclusion, this report shows that although WAG recovery significantly reduces the environmental footprint of halogenated anaesthesia, WAG recovery is much less effective than choice of agent in reducing GHG emissions, with desflurane emissions remaining 34 times greater than sevoflurane. Furthermore, the cost per ton of recovered GHG emissions is about 70 times higher for sevoflurane than it is for desflurane because of the much smaller amount of GHG emissions associated with sevoflurane anaesthesia. It is hoped that these results will help guide anaesthesiologists in the necessary evolution of our practice towards net zero GHG emissions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".