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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.

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.

2,6-Dichlorohydroquinone 1,2-dioxygenase (PcpA) from Sphingobium chlorophenolicum ATCC 39723 is a member of a class of Fe(II)-containing hydroquinone dioxygenases that is involved in the mineralization of the pollutant pentachlorophenol. This enzyme has not been extensively characterized, despite its interesting ring-cleaving activity and use of Fe(II), which are reminiscent of the well-known extradiol catechol dioxygenases. On the basis of limited sequence homology to the extradiol catechol dioxygenases, the residues ligating the Fe(II) center were originally proposed to be H159, H227, and E276 (Xu et al. in Biochemistry 38:7659-7669, 1999). However, PcpA has higher sequence homology to a newly reported, crystallographically characterized zinc metalloenzyme that has a similar predicted fold. We generated a homology model of the structure of PcpA based upon the structure of this zinc metalloenzyme. The homology model predicts that the tertiary structure of PcpA differs significantly from that of the extradiol dioxygenases, and that the residues ligating the Fe(II) are H11, H227, and E276. This structural model was tested by mutating each of H11, H159, H227, and E276 to alanine. An additional residue that is predicted to lie near the active site and is conserved among PcpA, its closest homologues, and the extradiol dioxygenases, Y266, was mutated to phenylalanine. Of these mutants, only H159A retained significant activity, thus confirming the active-site location predicted by the homology-based structural model. The model provides an important basis for understanding the origin of the unique function of PcpA.