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.

Cerebrospinal fluid (CSF) leak is a complication of endoscopic endonasal pituitary adenoma resection. Previous studies examining complications of pituitary adenoma resection have not examined associations of an exhaustive list of clinical and financial variables with CSF leak. We designed a retrospective analysis of 334 consecutive patients that underwent endoscopic endonasal pituitary adenoma resection at a single institution over 5 years, analyzing associations between CSF leak and demographic data, operative data, comorbidities, clinical complications and outcomes, costs, charges, and payments. Of the 20 preoperative variables studied, none were positively associated with CSF leak in between-groups comparison, although multivariate analysis revealed an association with a history of radiation to the skull base (odds ratio [OR], 8.67; 95% confidence interval [CI], 0.94–57.03; p < 0.05). CSF leak was associated with a significantly higher rate of postoperative diabetes insipidus (Δ = 33.4%, p = 0.040) and increased length of stay after operation in between-groups comparison. Multivariate analysis on postoperative variables revealed significant associations between CSF leak and intracerebral hemorrhage (OR, 17.44; 95% CI, 0.65–275.3; p < 0.05) and postoperative intracranial infection (OR, 28.73; 95% CI, 2.04–438.7; p < 0.05). Also, CSF leak was associated with significantly higher costs (Δ = $15,643, p < 0.05) and hospital charges (Δ = $46,026, p < 0.05). Operating room time, room and board, and supplies and implants were the strongest cost drivers. This study highlights the difficulty of utilizing preoperative variables to predict CSF leak, the clinical complications and outcomes of leak, and the financial subcategories that drive the costs, charges, and payments associated with this complication.

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