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Preoperative embolization has traditionally been regarded as a safe and effective adjunct to microsurgical treatment of brain arteriovenous malformations (bAVM). However, there is currently no high-level evidence to ascertain this presumption.To compare the outcomes of microsurgery (MS) vs microsurgery with preoperative embolization (E + MS) in patients with bAVM through systematic review.We searched MEDLINE, PubMed, and Embase. The primary outcome was bAVM obliteration. Secondary outcomes were intraoperative bleeding (mL), complications, worsened modified Rankin Scale (mRS), and mortality. The pooled proportions of outcomes were calculated through the logit transformation method. The odds ratio (OR) of categorical data and mean difference of continuous data were estimated through the Mantel-Haenszel and the inverse variance methods, respectively.Thirty-two studies met the eligibility criteria. One thousand eight hundred twenty-eight patients were treated by microsurgery alone, and 1088 were treated by microsurgery with preoperative embolization, respectively. The meta-analysis revealed no significant difference in AVM obliteration (94.1% vs 95.6%, OR = 1.15 [0.63-2.11], P = .65), mortality (1.7% vs 2%, OR = 0.88 [0.30-2.58], P = .82), procedural complications (18.2% vs 27.2%, OR = 0.47 [0.19-1.17], P = .10), worsened mRS (21.2% vs 18.5%, OR = 1.08 [0.33-3.54], P = .9), and intraoperative blood loss (mean difference = 182.89 [-87.76, 453.55], P = .19).The meta-analysis showed no significant difference in AVM obliteration, mortality, complications, worse mRS, and intraoperative blood loss between MS and E + MS groups. For AVMs where MS alone has acceptable results, it is reasonable to bypass unnecessary preoperative embolization given higher postoperative complication risk.

Interest in machine learning (ML)-based predictive modeling has led to the development of models predicting outcomes after aneurysmal subarachnoid hemorrhage (aSAH), including the Nijmegen acute subarachnoid hemorrhage calculator (Nutshell). Generalizability of such models to external data remains unclear.To externally validate the performance of the Nutshell tool while comparing it with the conventional Subarachnoid Hemorrhage International Trialists (SAHIT) models and to review the ML literature on outcome prediction after aSAH and aneurysm treatment.A prospectively maintained database of patients with aSAH presenting consecutively to our institution in the 2013 to 2018 period was used. The web-based Nutshell and SAHIT calculators were used to derive the risks of poor long-term (12-18 months) outcomes and 30-day mortality. Discrimination was evaluated using the area under the curve (AUC), and calibration was investigated using calibration plots. The literature on relevant ML models was surveyed for a synopsis.In 269 patients with aSAH, the SAHIT models outperformed the Nutshell tool (AUC: 0.786 vs 0.689, P = .025) in predicting long-term functional outcomes. A logistic regression model of the Nutshell variables derived from our data achieved adequate discrimination (AUC = 0.759) of poor outcomes. The SAHIT models outperformed the Nutshell tool in predicting 30-day mortality (AUC: 0.810 vs 0.636, P.001). Calibration properties were more favorable for the SAHIT models. Most published aneurysm-related ML-based outcome models lack external validation and usable testing platforms.The Nutshell tool demonstrated limited performance on external validation in comparison with the SAHIT models. External validation and the dissemination of testing platforms for ML models must be emphasized.