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Background: Residual neuromuscular block (NMB) after anesthesia poses significant risk to patients, which can be reduced by adhering to evidence-based practices for the dosing, monitoring, and reversal of NMB. Incorporation of best practices into routine clinical care remains uneven across providers and institutions, prompting the need for effective implementation strategies., Methods: An interdisciplinary quality improvement initiative aimed to optimize NMB reversal practices across a large multi-campus urban medical center. Using the Institute for Healthcare Improvement (IHI) framework, interventions were designed to increase Train-of-Four (TOF) monitoring and promote evidence-based and cost-effective use of the NMB reversal agents. Process and outcome measures were tracked through Plan-Do-Study-Act (PDSA) cycles. Qualitative interviews provided insights into clinician perspectives., Results: The study encompassed 35,198 surgical cases utilizing NMB agents. The interventions led to a sustained increase in TOF monitoring from 42 % to 83 %. Significant increases were also observed in TOF ratio documentation and utilization of sugammadex. Postoperative respiratory complication rates decreased by 41 % (RR 0.59, 95 % CI 0.32-0.96) over the course of the initiative. The most pronounced increases in TOF monitoring were associated with financial incentives for the achievement of department-wide target monitoring rate., Conclusion: This initiative demonstrates successful large-scale integration of quantitative TOF monitoring and evidence based NMB management across a diverse medical center, while highlighting important barriers in implementation. These findings contribute to the broader discussion on translating evidence into practice, offering insights for improving patient care and safety through tailored implementation strategies., Competing Interests: Declaration of competing interest Pavel Goriacko reports financial support was provided by Agency for Healthcare Research and Quality. Pavel Goriacko reports financial support was provided by National Center for Advancing Translational Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Study Objective: A low dynamic driving pressure during mechanical ventilation for general anesthesia has been associated with a lower risk of postoperative respiratory complications (PRC), a key driver of healthcare costs. It is, however, unclear whether maintaining low driving pressure is clinically relevant to measure and contain costs. We hypothesized that a lower dynamic driving pressure is associated with lower costs., Design: Multicenter retrospective cohort study., Setting: Two academic healthcare networks in New York and Massachusetts, USA., Patients: 46,715 adult surgical patients undergoing general anesthesia for non-ambulatory (inpatient and same-day admission) surgery between 2016 and 2021., Interventions: The primary exposure was the median intraoperative dynamic driving pressure., Measurements: The primary outcome was direct perioperative healthcare-associated costs, which were matched with data from the Healthcare Cost and Utilization Project-National Inpatient Sample (HCUP-NIS) to report absolute differences in total costs in United States Dollars (US$). We assessed effect modification by patients' baseline risk of PRC (score for prediction of postoperative respiratory complications [SPORC] ≥ 7) and effect mediation by rates of PRC (including post-extubation saturation < 90%, re-intubation or non-invasive ventilation within 7 days) and other major complications., Main Results: The median intraoperative dynamic driving pressure was 17.2cmH 2 O (IQR 14.0-21.3cmH 2 O). In adjusted analyses, every 5cmH 2 O reduction in dynamic driving pressure was associated with a decrease of -0.7% in direct perioperative healthcare-associated costs (95%CI -1.3 to -0.1%; p = 0.020). When a dynamic driving pressure below 15cmH 2 O was maintained, -US$340 lower total perioperative healthcare-associated costs were observed (95%CI -US$546 to -US$132; p = 0.001). This association was limited to patients at high baseline risk of PRC (n = 4059; -US$1755;97.5%CI -US$2495 to -US$986; p < 0.001), where lower risks of PRC and other major complications mediated 10.7% and 7.2% of this association (p < 0.001 and p = 0.015, respectively)., Conclusions: Intraoperative mechanical ventilation targeting low dynamic driving pressures could be a relevant measure to reduce perioperative healthcare-associated costs in high-risk patients., Competing Interests: Declaration of competing interest Daniel Talmor received speaking fees and grant funds from Hamilton Medical, Inc. and Mindray Medical, outside the submitted work. Matthias Eikermann received grants from Merck & Co and serves as an Associate Editor for the British Journal of Anaesthesia. Maximilian S. Schaefer received funding for investigator-initiated studies from Merck & Co., which do not pertain to this manuscript. He is an associate editor for BMC Anesthesiology. He received honoraria for lectures from Fisher & Paykel Healthcare and Mindray Medical International Limited. He received an unrestricted philanthropic grant from Dr. Jeffrey and Judith Buzen. All other authors have no support from any organization for the submitted work, no financial relationships with any organizations that might have an interest in the submitted work in the previous three years, and no other relationships or activities that could appear to have influenced the submitted work. This work was supported by an unrestricted grant from Dr. Jeffrey and Judith Buzen to Maximilian S. Schaefer. Dr. Jeffrey and Judith Buzen had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication., (Copyright © 2024. Published by Elsevier Inc.)

Objective: We estimated hospital costs associated with postoperative reintubation and tested the hypothesis that prolonged surveillance in the post-anesthesia care unit (PACU) modifies the hospital costs of reintubation., Design: Retrospective observational research study., Setting: Two tertiary care academic healthcare networks in the Bronx, New York and Boston, Massachusetts, USA., Patients: 68,125 adult non-cardiac surgical patients undergoing general anesthesia between 2016 and 2021., Interventions: The exposure variable was unplanned reintubation within 7 days of surgery., Measurements: The primary outcome was direct hospital costs associated with patient care related activities. We used a multivariable generalized linear model based on log-transformed costs data, adjusting for pre- and intraoperative confounders. We matched our data with data from the Healthcare Cost and Utilization Project-National Inpatient Sample (HCUP-NIS). In the key secondary analysis, we examined if prolonged postoperative surveillance, defined as PACU utilization (≥4 h) modifies the association between reintubation and costs of care., Main Results: 1759 (2.6%) of patients were re-intubated within 7 days after surgery. Reintubation was associated with higher direct hospital costs (adjusted model estimate 2.05; 95% CI: 2.00-2.10) relative to no reintubation. In the HCUP-NIS matched cohort, the adjusted absolute difference (AD adj ) in costs amounted to US$ 18,837 (95% CI: 17,921-19,777). The association was modified by the duration of PACU surveillance (p-for-interaction <0.001). In patients with a shorter PACU length of stay, reintubation occurred later (median of 2 days; IQR 1, 5) versus 1 days (IQR 0, 2; p < .001), and was associated with magnified effects on hospital costs compared to patients who stayed in the PACU longer (AD adj of US$ 23,444, 95% CI: 21,217-25,799 versus AD adj of US$ 17,615, 95% CI: 16,350-18,926; p < .001)., Conclusion: Postoperative reintubation is associated with 2-fold higher hospital costs. Prolonged surveillance in the recovery room mitigated this effect. The cost-saving effect of longer PACU length of stay was likely driven by earlier reintubation in patients who needed this intervention., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: We studied the primary hypothesis that the training level of anesthesiology residents (first clinical anesthesia year, CA1 vs CA2/3 residents) is associated with early postoperative desaturation (oxygen saturation < 90%). We also analyzed the change in the rate (trajectory) of desaturation during the resident's development from CA1 to CA2/3 resident, and its effects on postoperative respiratory complications., Design: Retrospective hospital registry study., Setting: Two university-affiliated hospitals networks (MA and NY, USA)., Patients: 140,818 adults undergoing non-cardiac surgery under general anesthesia and extubation in the operating room by residents (n = 378) between 2005 and 2021., Measurements: Multivariate logistic and quantile regression were used in the analyses. The secondary outcome was major respiratory complication within 7 days after surgery., Main Results: In 6.5% and 1.6% of cases, early postoperative desaturation to < 90% and 80% occurred. Compared to CA2/3 residents, CA1 residents had higher odds of experiencing early postoperative desaturation to < 90% and 80% (adjusted odds ratio [ORadj], 1.07; 95%CI 1.03-1.12; p = 0.002, and ORadj 1.10; 95%CI 1.01-1.20; p = 0.037, respectively). The change in postoperative desaturation rate during the transition from CA1 to CA2/3 status varied substantially from ORadj 0.80 (decreased risk) to 1.33 (increased risk). Major respiratory complication did not differ between experience levels (p = 0.52). However, a strong decline in improvement regarding the rate of postoperative desaturation during the transition from CA1 to CA2/3, was paralleled by an increased odds of major respiratory complication for CA2/3 residents (ORadj 1.20; 95%CI 1.02-1.42; p = 0.026, p-for-interaction = 0.056)., Conclusion: Patients treated by CA1 residents have an increased risk of postoperative desaturation. Some residents show an improvement and others a decline in postoperative desaturation rate. Our secondary analysis suggests that there should be more focus on those residents who had a declining performance in postoperative desaturation despite becoming more experienced., Competing Interests: Declaration of Competing Interest Matthias Eikermann and Maximilian S. Schaefer received a grant from Merck not related to this study. Maximilian S. Schaefer is an associate editor for BMC Anesthesiology. He received honoraria for presentations from Fisher & Paykel Healthcare and Mindray Medical International Limited, as well as funds from philanthropic donors Jeffrey and Judith Buzen. All other authors have no conflicts of interest to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Study Objective: Postoperative respiratory failure is a major surgical complication and key quality metric. Existing prediction tools underperform, are limited to specific populations, and necessitate manual calculation. This limits their implementation. We aimed to create an improved, machine learning powered prediction tool with ideal characteristics for automated calculation., Design, Setting, and Patients: We retrospectively reviewed 101,455 anesthetic procedures from 1/2018 to 6/2021. The primary outcome was the Standardized Endpoints in Perioperative Medicine consensus definition for postoperative respiratory failure. Secondary outcomes were respiratory quality metrics from the National Surgery Quality Improvement Sample, Society of Thoracic Surgeons, and CMS. We abstracted from the electronic health record 26 procedural and physiologic variables previously identified as respiratory failure risk factors. We randomly split the cohort and used the Random Forest method to predict the composite outcome in the training cohort. We coined this the RESPIRE model and measured its accuracy in the validation cohort using area under the receiver operating curve (AUROC) analysis, among other measures, and compared this with ARISCAT and SPORC-1, two leading prediction tools. We compared performance in a validation cohort using score cut-offs determined in a separate test cohort., Main Results: The RESPIRE model exhibited superior accuracy with an AUROC of 0.93 (95% CI, 0.92-0.95) compared to 0.82 for both ARISCAT and SPORC-1 (P-for-difference < 0.0001 for both). At comparable 80-90% sensitivities, RESPIRE had higher positive predictive value (11%, 95% CI: 10-12%) and lower false positive rate (12%, 95% CI: 12-13%) compared to 4% and 37% for both ARISCAT and SPORC-1. The RESPIRE model also better predicted the established quality metrics for postoperative respiratory failure., Conclusions: We developed a general-purpose, machine learning powered prediction tool with superior performance for research and quality-based definitions of postoperative respiratory failure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: The ASA physical status (ASA-PS) is determined by an anesthesia provider or surgeon to communicate co-morbidities relevant to perioperative risk. Assigning an ASA-PS is a clinical decision and there is substantial provider-dependent variability. We developed and externally validated a machine learning-derived algorithm to determine ASA-PS (ML-PS) based on data available in the medical record., Design: Retrospective multicenter hospital registry study., Setting: University-affiliated hospital networks., Patients: Patients who received anesthesia at Beth Israel Deaconess Medical Center (Boston, MA, training [n = 361,602] and internal validation cohorts [n = 90,400]) and Montefiore Medical Center (Bronx, NY, external validation cohort [n = 254,412])., Measurements: The ML-PS was created using a supervised random forest model with 35 preoperatively available variables. Its predictive ability for 30-day mortality, postoperative ICU admission, and adverse discharge were determined by logistic regression., Main Results: The anesthesiologist ASA-PS and ML-PS were in agreement in 57.2% of the cases (moderate inter-rater agreement). Compared with anesthesiologist rating, ML-PS assigned more patients into extreme ASA-PS (I and IV), (p < 0.01), and less patients in ASA II and III (p < 0.01). ML-PS and anesthesiologist ASA-PS had excellent predictive values for 30-day mortality, and good predictive values for postoperative ICU admission and adverse discharge. Among the 3594 patients who died within 30 days after surgery, net reclassification improvement analysis revealed that using the ML-PS, 1281 (35.6%) patients were reclassified into the higher clinical risk category compared with anesthesiologist rating. However, in a subgroup of multiple co-morbidity patients, anesthesiologist ASA-PS had a better predictive accuracy than ML-PS., Conclusions: We created and validated a machine learning physical status based on preoperatively available data. The ability to identify patients at high risk early in the preoperative process independent of the provider's decision is a part of the process we use to standardize the stratified preoperative evaluation of patients scheduled for ambulatory surgery., Competing Interests: Declaration of Competing Interest All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: Matthias Eikermann received unrestricted funds from philanthropic donors Jeffrey and Judith Buzen, (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives., Design: Retrospective hospital registry study., Setting: University-affiliated hospitals network (NY, USA)., Patients: 246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort., Measurements: Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery., Main Results: 8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively., Conclusions: We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

Study Objective: To evaluate the effectiveness of preoperative gabapentinoid administration., Design: Retrospective hospital registry study., Setting: Tertiary referral center (Boston, MA)., Patients: 111,008 adult non-emergency, non-cardiac surgical patients between 2014 and 2018., Interventions: Preoperative administration of gabapentinoids (gabapentin or pregabalin)., Measurements: We tested the primary hypothesis that preoperative gabapentinoid use was associated with lower odds of hospital readmission within 30 days. Contingent on this hypothesis, we examined whether lower intraoperative opioid utilization mediated this effect. Secondary outcome was postoperative respiratory complications., Main Results: Gabapentinoid administration was associated with lower odds of readmission (adjusted odds ratio [OR adj ] 0.80 [95% CI, 0.75-0.85]; p < 0.001). This effect was in part mediated by lower intraoperative opioid utilization in patients receiving gabapentinoids (8.2% [2.4-11.5%]; p = 0.012). Readmissions for gastrointestinal disorders (OR adj 0.74 [0.60-0.90]; p = 0.003), neuro-psychiatric complications (OR adj 0.66 [0.49-0.87]; p = 0.004), non-surgical site infections (OR adj 0.68 [0.52-0.88; p = 0.004) and trauma or poisoning (OR adj 0.25 [0.16-0.41]; p < 0.001) occurred less frequently in patients receiving gabapentinoids. The risk of postoperative respiratory complications was lower in patients receiving gabapentinoids (OR adj 0.77 [0.70-0.85]; p < 0.001). Lower doses of pregabalin (< 75 mg) and gabapentin (< 300 mg) compared to both, no and high-dose administration of gabapentinoids, were associated with a lower risk of postoperative respiratory complications (OR adj 0.61 [0.50-0.75]; p < 0.001 and OR adj 0.70 [0.53-0.92]; p = 0.012, respectively). These lower gabapentinoid doses prevented 30-day readmission (OR adj 0.74 [0.65-0.85]; p < 0.001). The results were robust in several sensitivity analyses including surgical procedure defined subgroups and patients undergoing ambulatory surgery., Conclusions: The preoperative use of pregabalin and gabapentin, up to doses of 75 and 300 mg respectively, mitigates the risks of hospital readmission and postoperative respiratory complications which can in part be explained by lower intraoperative opioid use. Further research is warranted to elucidate mechanisms of the preventive action., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Study Objective: Dexmedetomidine, which is commonly used for procedural sedation and as adjunct to general anesthesia for ambulatory procedures, may affect patient discharge from the post-anesthesia care unit (PACU). We hypothesized that intraoperative dexmedetomidine use in ambulatory surgery is associated with delayed discharge from the PACU and that this is modified by surgical duration and anesthesia type., Design: Retrospective cohort study., Setting: Academic medical center., Patients: 130,854 adult patients undergoing ambulatory surgery between 2008 and 2018., Interventions: Intraoperative administration of dexmedetomidine., Measurements: The primary outcome was PACU length of stay. In secondary and exploratory analyses, we examined dose-dependency, effect modification by duration of surgery and anesthesia type, effects of timing of dexmedetomidine administration, and PACU discharge delays., Main Results: Dexmedetomidine was associated with a prolonged PACU length of stay (adjusted absolute difference [AD adj ] 15.0 min; 95%CI 12.7-17.3; p < 0.001). This effect was dose-dependent (p-for-trend < 0.001), magnified in surgeries of less than one hour (AD adj 20.7 min; 95%CI 16.7-24.7; p < 0.001) and in patients undergoing monitored anesthesia care compared to general anesthesia (AD adj 16.8 min; 95%CI 14.1-19.6; p < 0.001). The effect was more pronounced if dexmedetomidine was administered within the last 60 min of surgery (AD adj 18.7 min; 95%CI 15.7-21.7; p < 0.001). Dexmedetomidine was associated with discharge delays due to cardiovascular complications (OR adj 2.27; 95%CI 1.59-3.24; p < 0.001) and over-sedation (OR adj 1.28; 95%CI 1.11-1.48; p < 0.001). In patients who received dexmedetomidine (n = 2901), the use of bolus doses only versus the combination of bolus and infusions, magnified the effects on PACU length of stay (AD adj 29.5 min per μg/kg; 95%CI 17.3-41.8 versus 18.1 min per μg/kg; 95%CI 11.4-24.8; p < 0.001)., Conclusions: The intraoperative administration of dexmedetomidine was dose-dependently associated with a prolonged PACU length of stay. Clinicians should judiciously titrate dexmedetomidine, especially when using this long-acting drug for monitored anesthesia care for shorter procedures., (Copyright © 2021 Elsevier Inc. All rights reserved.)