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Objectives: Intraoperative ultrasonography (IOUS) offers the advantage of providing real-time imaging features, yet it is not generally used. This study aims to discuss the benefits of utilizing IOUS in spinal cord surgery and review related literature. Materials and methods: Patients who underwent spinal cord surgery utilizing IOUS at a single institution were retrospectively collected and analyzed to evaluate the benefits derived from the use of IOUS. Results: A total of 43 consecutive patients were analyzed. Schwannoma was the most common tumor (35%), followed by cavernous angioma (23%) and ependymoma (16%). IOUS confirmed tumor extent and location before dura opening in 42 patients (97.7%). It was particularly helpful for myelotomy in deep-seated intramedullary lesions to minimize neural injury in 13 patients (31.0% of 42 patients). IOUS also detected residual or hidden lesions in 3 patients (7.0%) and verified the absence of hematoma post-tumor removal in 23 patients (53.5%). In 3 patients (7.0%), confirming no intradural lesions after removing extradural tumors avoided additional dural incisions. IOUS identified surrounding blood vessels and detected dural defects in one patient (2.3%) respectively. Conclusions: The IOUS can be a valuable tool for spinal cord surgery in identifying the exact location of the pathologic lesions, confirming the completeness of surgery, and minimizing the risk of neural and vascular injury in a real-time fashion. [ABSTRACT FROM AUTHOR]

Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013–9). External validation was performed using three separate cohorts A–C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908–0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876–0.882), 0.872 (95% CI, 0.870–0.874), and 0.931 (95% CI, 0.925–0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making. [ABSTRACT FROM AUTHOR]