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Study Design Level III retrospective database study. Objectives The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF). Methods The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier’s effectiveness in distinguishing cases. Results In total, 54 502 patients met the study criteria. Of these patients, .51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between .52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation. Conclusions The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation.

Mnk kinases (Mnk1 and 2) are downstream effectors of Map kinase pathways and regulate phosphorylation of eukaryotic initiation factor 4E. Engagement of the Mnk pathway is critical in acute myeloid leukemia (AML) leukemogenesis and Mnk inhibitors have potent antileukemic properties in vitro and in vivo, suggesting that targeting Mnk kinases may provide a novel approach for treating AML. Here, we report the development and application of a mutation‐based induced‐fit in silico screen to identify novel Mnk inhibitors. The Mnk1 structure was modeled by temporarily mutating an amino acid that obstructs the ATP‐binding site in the Mnk1 crystal structure while carrying out docking simulations of known inhibitors. The hit compounds display activity in Mnk biochemical and cellular assays, including acute myeloid leukemia progenitors. This approach will enable further rational structure‐based drug design of new Mnk inhibitors and potentially novel ways of therapeutically targeting this kinase.