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Enregistrement W4389063580 · doi:10.1097/ea9.0000000000000040

Cost-effectiveness of greenhouse gas emission reductions with desflurane and sevoflurane waste gas recovery

2023· article· en· W4389063580 sur OpenAlexaffabout
Stephan Williams, Gabriel Paquin-Lanthier, Laurelie Perret

Notice bibliographique

RevueEuropean Journal of Anaesthesiology Intensive Care · 2023
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueClimate Change and Health Impacts
Établissements canadiensCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesnon disponible
Mots-clésDesfluraneGreenhouse gasSevofluraneEnvironmental scienceWaste managementNitrous oxideGlobal-warming potentialAnesthesiaMedicineEngineeringGeology

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,057
Tête enseignante GPT0,297
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission2
Résumé présentoui

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