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Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% ( 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.

BackgroundAwake fibreoptic intubation is a complex advanced airway technique used by anaesthesiologists in the management of a difficult airway. The time to setup this important procedure can be significant which may dissuade its use by some providers. In our institution, the awake intubation setup process was highly variable and error prone.MethodsWe deployed Lean methods to improve the efficiency and accuracy of the awake fibreoptic intubation setup process. A 2-day improvement event with a multidisciplinary team addressed the setup process, tested solutions and created standard work documents. Twenty awake fibreoptic intubation simulations were conducted before and after the intervention to quantify gains in setup efficiency and error reduction.ResultsVariability in the setup process, including clinical locations visited, was reduced through creating a standardised process. The average time to for an awake fibreoptic intubation setup was reduced by approximately 50%, from 23 min to 11 min (pConclusionUsing Lean tools, we were able to make the setup of awake fibreoptic intubation not only more efficient, but also more accurate. A similar methodological approach may have value for other complex anaesthesia procedures.

Patients with rheumatoid arthritis (RA) often receive opioid analgesics for pain management. We examined the association between mental health conditions and the risk of chronic opioid therapy.A retrospective cohort of veterans with RA initiating opioid use was assembled using Veterans Health Administration databases (2001-2012). Mental health conditions included anxiety (N = 1108, 12.9%), depression (N = 1912, 22.2%), bipolar disease (N = 131, 1.5%), and post-traumatic stress disorder (N = 768, 8.9%) and were identified by ICD coded diagnoses and use of specific medications. Cohort members were followed from opioid initiation through chronic opioid therapy, defined as the continuous availability of opioids for at least 90 days. Multivariable Cox proportional hazard regression models assessed the association between mental health conditions and chronic opioid therapy accounting for relevant covariates. Subgroup analyses examined whether the strength of the observed association varied by the duration of the initial opioid prescription.We identified 14,767 patients with RA with 22,452 episodes of opioid use initiation. Mental health conditions were identified in 8607 (38.3%) patients. Compared with patients without mental health conditions, patients with mental health conditions have a higher risk of developing chronic opioid therapy (469.3 vs 378.1 per 1000 person-years, adjusted hazard ratio [aHR] 1.18, 95% CI 1.09, 1.29). The increased risk was highest for those with a history of opioid use disorder (aHR 1.94, 95% CI 1.09, 3.46) and also elevated for those with other substance use disorders (aHR 1.35, 95% CI 1.05, 1.73). Duration of the initial opioid prescription was independently associated with chronic opioid therapy, regardless of the estimated opioid daily dose.History of mental health conditions and duration of the initial opioid prescription were associated with an increased risk of chronic opioid therapy among patients with RA.Key Points• Approximately a third of patients with RA are exposed to opioid analgesics.• Patients with RA and history of mental health disease, especially substance use disorders, who initiate opioid use have an increased risk of chronic opioid therapy.• This study provides insight in an underrepresented population of mainly male patients with RA.

OBJECTIVES/SPECIFIC AIMS: Opioid prescribing is common and increasing in certain areas of the country with known risk of misuse and dependence. Our study examined the association of opioid prescription at discharge after hospitalization for acute coronary syndrome (ACS) or acute decompensated heart failure (ADHF) with emergency department (ED) care or all-cause readmission, intended healthcare utilization (follow-up with physician within 30 d of discharge and cardiac rehab participation), and all-cause mortality. METHODS/STUDY POPULATION: The Vanderbilt Inpatient Cohort Study is a prospective cohort of hospitalized patients age >18 enrolled with either ACS or ADHF between 2011 and 2015 (index hospitalization). We then excluded those who died during the index hospitalization, patients with hospitalization p=0.009) compared with those not discharged on opioid medications. Sensitivity analysis of patients who were prescribed prehospital opioids (including prehospital opioids in the exposure group with postdischarge opioids) did not reveal a statistically significant increase in mortality (aHR: 1.09, 95% CI 0.91, 1.31) or unintended healthcare utilization (aHR: 1.12, 95% CI 0.89, 1.41) among opioid users. DISCUSSION/SIGNIFICANCE OF IMPACT: Morbidity and mortality related to opioid use is a public health concern. Our study demonstrates a statistically significant reduction in physician follow-up and participation in cardiac rehab among opioid users, both of which are known to decrease patient mortality. We did not find a statistically significant increase in unplanned healthcare utilization or mortality. Sensitivity analysis combining prehospital and posthospital opioid prescriptions did not reveal a statistically significant association between opioid use, hospital readmissions, or mortality. The hospital provides unique patient interactions where providers can make significant medical changes based on their patient’s clinical status. Continuing to understand the association between opioid use, healthcare utilization, morbidity, and mortality in recently hospitalized cardiac patients will provide data to support reduction in total opioid dose to improve clinical outcomes.

The inability to ventilate a patient after successful intubation is a rare but emergent situation and may be caused by obstruction of the endotracheal tube, bilateral tension pneumothorax, esophageal intubation, severe bronchospasm, or mainstem bronchus intubation. We describe an increase in mean airway pressure, inability to ventilate, and loss of cardiac output secondary to a blood clot acting as a ball valve at the end of an endotracheal tube.