ObjectiveAnterior cervical microforaminotomy (ACMF) is a motion-preserving surgical procedure. The purpose of this study is to assess radiologic changes of operated and adjacent segments after ACMF.MethodsWe retrospectively reviewed 52 patients who underwent ACMF between 1998 and 2008. From X-ray film-based changes, disc height and sagittal range of motion (ROM) of operated and adjacent segments were compared at preoperative and last follow-up periods. Radiological degeneration of both segments was analyzed as well.ResultsThe mean follow-up period was 48.2 months. There were 78 operated, 52 upper adjacent, and 38 lower adjacent segments. There were statistically significant differences in the ROM and disc height of operated segment between preoperative and last follow-up periods. However, there were no statistically significant differences in the ROM and disc height of adjacent segment between both periods. Radiological degenerative changes of operated segments were observed in 30%. That of adjacent segments was observed in 11 and 11% at upper and lower segments, respectively.ConclusionAfter mean 4-year follow-up periods, there were degenerative changes of operated segments. However, ACMF preserved motion and prevented degenerative changes of adjacent segments.

BACKGROUND Coronavirus disease 2019 (COVID-19) infection is a risk factor for delirium that must be predicted and prevented to avoid adverse outcomes. OBJECTIVE We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19, and to identify modifiable factors to prevent delirium. METHODS The ML model was developed using training data from 757 patients at three medical centers and externally validated in 121 patients from a fourth medical center. The extreme gradient boosting (XGBoost) algorithm was used. A stratified K-fold approach was used to select model hyperparameters and predictor variables. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was selected as the evaluation metric. RESULTS The incidence of in-hospital delirium was 6.9% in the training cohort. Selected predictor variables for delirium were age, mechanical ventilation, medication (opioids, sedatives, antipsychotics, ambroxol, ceftriaxone, and piperacillin/tazobactam), sodium ion concentration, and white blood cell count (all p < 0.05). The stratified 5-fold AUC values for the training and test cohorts were 0.856 (95% confidence interval [CI] = 0.804–0.908) and 0.998 (CI = 0.989–1.000), respectively. CONCLUSIONS We developed and externally validated the ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.