Benzodiazepine-Free Cardiac Anesthesia for Reduction of Postoperative Delirium (B-Free): A Protocol for a Multi-centre Randomized Cluster Crossover Trial

Delirium is common after cardiac surgery and is associated with adverse outcomes. Administration of benzodiazepines before and after cardiac surgery is associated with delirium; guidelines recommend minimizing their use. Benzodiazepine administration during cardiac surgery remains common because of its recognized benefits. The Benzodiazepine-Free Cardiac Anesthesia for Reduction of Postoperative Delirium (B-Free) trial is a randomized cluster crossover trial evaluating whether an institutional policy of restricting intraoperative benzodiazepine administration (ie, ≥ 90% of patients do not receive benzodiazepines during cardiac surgery), as compared with a policy of liberal intraoperative benzodiazepine administration (ie, ≥ 90% of patients receive ≥ 0.03 mg/kg midazolam equivalent), reduces delirium. Hospitals performing ≥ 250 cardiac surgeries a year are included if their cardiac anesthesia group agrees to apply both benzodiazepine policies per their randomization, and patients are assessed for postoperative delirium every 12 hours in routine clinical care. Hospitals apply the restricted or liberal benzodiazepine policy during 12 to 18 crossover periods of 4 weeks each. Randomization for all periods takes place in advance of site startup; sites are notified of their allocated policy during the last week of each crossover period. Policies are applied to all patients undergoing cardiac surgery during the trial period. The primary outcome is the incidence of delirium at up to 72 hours after surgery. The B-Free trial will enroll ≥ 18,000 patients undergoing cardiac surgery at 20 hospitals across North America. Delirium is common after cardiac surgery, and benzodiazepines are associated with the occurrence of delirium. The B-Free trial will determine whether an institutional policy restricting the administration of benzodiazepines during cardiac surgery reduces the incidence of delirium after cardiac surgery.Clinicaltrials.gov registration number: NCT03928236 (First registered April 26, 2019). Résumé: L’état confusionnel est fréquent après une chirurgie cardiaque et il est associé à des complications. L’administration de benzodiazépines avant et après une chirurgie cardiaque est associée à l’état confusionnel; dans les lignes directrices, on recommande de réduire leur utilisation au minimum. L’administration de benzodiazépines pendant une chirurgie cardiaque demeure fréquente, en raison des leurs bienfaits reconnus. L’essai B-Free (Benzodiazepine-Free Cardiac Anesthesia for Reduction of Postoperative Delirium ou l’anesthésie sans benzodiazépine en contexte de chirurgie cardiaque pour la réduction de l’état confusionnel postopératoire) est un essai à répartition aléatoire par grappes et avec permutation, visant à évaluer si une politique institutionnelle de restriction de l’administration peropératoire de benzodiazépines (c.-à-d. que ≥ 90 % des patients ne reçoivent pas de benzodiazépines durant une chirurgie cardiaque) réduit l’état confusionnel, comparativement à une politique d’administration peropératoire libérale de benzodiazépines (c.-à-d. que ≥ 90 % des patients reçoivent ≥ 0,03 mg/kg d’équivalent du midazolam). Des hôpitaux effectuant au moins 250 chirurgies cardiaques par année sont inclus dans l’essai si leurs équipes d’anesthésie cardiaque acceptent d’appliquer les deux politiques relatives aux benzodiazépines en vertu de la répartition aléatoire et si les patients sont évalués toutes les 12 heures, en ce qui a trait à l’état confusionnel postopératoire, dans le cadre des soins cliniques habituels. Les hôpitaux mettent en œuvre la politique d’administration restreinte ou libérale de benzodiazépines durant 12 à 18 périodes de permutation de 4 semaines chacune. La répartition aléatoire de l’ensemble des périodes a lieu avant le début de l’essai à l’hôpital; les établissements sont avisés de la politique qui leur est attribuée au cours de la dernière semaine de chaque période de permutation. Les politiques sont appliquées à tous les patients qui subissent une chirurgie cardiaque durant la période de l’essai. Le critère d’évaluation principal est l’incidence de l’état confusionnel dans les 72 heures suivant l’intervention chirurgicale. L’étude B-Free inclura au moins 18 000 patients qui subiront une chirurgie cardiaque dans 20 hôpitaux en l’Amérique du Nord. L’état confusionnel est fréquent après une chirurgie cardiaque, et les benzodiazépines sont associées à la survenue de l’état confusionnel. L’essai B-Free permettra de déterminer si une politique institutionnelle de restriction de l’administration de benzodiazépines durant une chirurgie cardiaque réduit l’incidence de l’état confusionnel après une telle chirurgie.Clinicaltrials.gov registration number: NCT03928236 (First registered April 26, 2019)

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020

Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020