Development of machine learning models for predicting unfavorable functional outcomes from preoperative data in patients with chronic subdural hematomas

Efficacy of intraoperative irrigation with artificial cerebrospinal fluid in chronic subdural hematoma surgery: study protocol for a multicenter randomized controlled trial

BACKGROUND

The surgical techniques for treatment of chronic subdural hematoma (CSDH), a common neurosurgical condition, have been discussed in a lot of clinical literature. However, the recurrence proportion after CSDH surgery remains high, ranging from 10 to 20%. The standard surgical procedure for CSDH involves a craniostomy to evacuate the hematoma, but irrigating the hematoma cavity during the procedure is debatable. The authors hypothesized that the choice of irrigation fluid might be a key factor affecting the outcomes of surgery. This multicenter randomized controlled trial aims to investigate whether intraoperative irrigation using artificial cerebrospinal fluid (ACF) followed by the placement of a subdural drain would yield superior results compared to the placement of a subdural drain alone for CSDH.

METHODS

The study will be conducted across 19 neurosurgical departments in Japan. The 1186 eligible patients will be randomly allocated to two groups: irrigation using ACF or not. In either group, a subdural drain is to be placed for at least 12 h postoperatively. Similar to what was done in previous studies, we set the proportion of patients that meet the criteria for ipsilateral reoperation at 7% in the irrigation group and 12% in the non-irrigation group. The primary endpoint is the proportion of patients who meet the criteria for ipsilateral reoperation within 6 months of surgery (clinical worsening of symptoms and increased hematoma on imaging compared with the postoperative state). The secondary endpoints are the proportion of reoperations within 6 months, the proportion being stratified by preoperative hematoma architecture by computed tomography (CT) scan, neurological symptoms, patient condition, mortality at 6 months, complications associated with surgery, length of hospital stay from surgery to discharge, and time of the surgical procedure.

DISCUSSION

We present the study protocol for a multicenter randomized controlled trial to investigate our hypothesis that intraoperative irrigation with ACF reduces the recurrence proportion after the removal of chronic subdural hematomas compared with no irrigation.

TRIAL REGISTRATION

ClinicalTrials.gov jRCT1041220124. Registered on January 13, 2023.

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model's predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p < 0.0001). The present results showed good performance in predicting functional and clinical outcomes after microsurgical therapy of UIAs in the internal data set, especially for the main outcome parameters, mRS and permanent neurological deficit. The external validation showed poor discrimination with ROC-AUC values of 0.61, 0.53 and 0.58 respectively for predicting a postoperative mRS > 2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future.