RESUMO
Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods: This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results: Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions: Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration: ClinicalTrials.gov NCT05042804.
RESUMO
Widespread antibiotic resistance has led to the reappraisal of abandoned antibiotics including chloramphenicol. However, enzyme(s) underlying one form of chloramphenicol resistance, nitroreduction, have eluded identification. Here we demonstrate that expression of the Haemophilus influenzae nitroreductase gene nfsB confers chloramphenicol resistance in Escherichia coli. We characterized the enzymatic product of H. influenzae NfsB acting on chloramphenicol and found it to be amino-chloramphenicol. Kinetic analysis revealed reduction of diverse substrates including the incomplete reduction of 5-nitro antibiotics metronidazole and nitrofurantoin, likely resulting in activation of these antibiotic pro-drugs to their cytotoxic forms. We observed that expression of the H. influenzae nfsB gene in E. coli results in significantly increased susceptibility to metronidazole. Finally, we found that in this strain metronidazole attenuates chloramphenicol resistance synergistically, and in vitro metronidazole weakly inhibits chloramphenicol reduction by NfsB. Our findings reveal the underpinnings of a chloramphenicol resistance mechanism nearly 70 years after its description.
