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The authors developed a score for predicting the risk of postoperative complicationsThe score was developed from 1,094 patients and validated in 830 patients from six French hospitalsSevere complications occurred in about 11% of each cohortThe positive predictive value was poor, but the negative prediction value was excellent and might be used to identify patients who do not need critical careCraniotomy for brain tumor displays significant morbidity and mortality, and no score is available to discriminate high-risk patients. Our objective was to validate a prediction score for postoperative neurosurgical complications in this setting.Creation of a score in a learning cohort from a prospective specific database of 1,094 patients undergoing elective brain tumor craniotomy in one center from 2008 to 2012. The validation cohort was validated in a prospective multicenter independent cohort of 830 patients from 2013 to 2015 in six university hospitals in France. The primary outcome variable was postoperative neurologic complications requiring in–intensive care unit management (intracranial hypertension, intracranial bleeding, status epilepticus, respiratory failure, impaired consciousness, unexpected motor deficit). The least absolute shrinkage and selection operator method was used for potential risk factor selection with logistic regression.Severe complications occurred in 125 (11.4%) and 90 (10.8%) patients in the learning and validation cohorts, respectively. The independent risk factors for severe complications were related to the patient (Glasgow Coma Score before surgery at or below 14, history of brain tumor surgery), tumor characteristics (greatest diameter, cerebral midline shift at least 3 mm), and perioperative management (transfusion of blood products, maximum and minimal systolic arterial pressure, duration of surgery). The positive predictive value of the score at or below 3% was 12.1%, and the negative predictive value was 100% in the learning cohort. In–intensive care unit mortality was observed in eight (0.7%) and six (0.7%) patients in the learning and validation cohorts, respectively.The validation of prediction scores is the first step toward on-demand intensive care unit admission. Further research is needed to improve the score’s performance before routine use.