OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

OBJECTIVECurrent understanding of how the pediatric craniocervical junction develops remains incomplete. Measurements of anatomical relationships at the craniocervical junction can influence clinical and surgical decision-making. The purpose of this analysis was to quantitatively define clinically relevant craniocervical junction measurements in a population of children with CT scans that show normal anatomy.METHODSA total of 1458 eligible patients were identified from children between 1 and 18 years of age who underwent cervical spine CT scanning at a single institution. Patients were separated by both sex and age in years into 34 groups. Following this, patients within each group were randomly selected for inclusion until a target of 15 patients in each group had been reached. Each patient underwent measurement of the occipital condyle–C1 interval (CCI), pB–C2, atlantodental interval (ADI), basion-dens interval (BDI), basion-opisthion diameter (BOD), basion-axial interval (BAI), dens angulation, and canal diameter at C1. Mean values were calculated in each group. Each measurement was performed by two teams and compared for intraclass correlation coefficient (ICC).RESULTSThe data showed that CCI, ADI, BDI, and dens angulation decrease in magnitude throughout childhood, while pB–C2, PADI, BAI, and BOD increase throughout childhood, with an ICC of fair to good (range 0.413–0.912). Notably, CCI decreases continuously on coronal CT scans, whereas on parasagittal CT scans, CCI does not decrease until after age 9, when it shows a continuous decline similar to measurements on coronal CT scans.CONCLUSIONSThese morphometric analyses establish parameters for normal pediatric craniocervical spine growth for each year of life up to 18 years. The data should be considered when evaluating children for potential surgical intervention.

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