Prognostic Prediction Model in Patients With Moyamoya Disease Undergoing Revascularization Surgery

Plain-language summary

Moyamoya Disease (MMD) is a rare chronic cerebrovascular disease characterized by progressive occlusion of the internal carotid artery or its major branches, with blood flow compensated by the formation of an abnormal vascular network (smoky). According to the latest national epidemiological survey in 2022, the cumulative number of new cases in the population was 47,443 in two years, with the annual incidence rate increasing year by year. The first symptoms are mainly cerebral infarction, transient ischemic attack, intracranial hemorrhage, and epileptic seizure, and the onset of the disease is concentrated in the age group of 45-54 years old, which is one of the most important causes of new strokes in middle-aged and young people and imposes a heavy medical burden on the society and the family. As one of the major causes of new strokes in young and middle-aged people, it brings a heavy medical burden to society and families. Hemodialysis has been confirmed as a standard treatment for patients with smokers' disease in large sample studies to prevent bleeding and recurrence of hemorrhage; however, there is no better consensus on which anesthetic technique to use for hemodialysis in patients with MMD. Currently, the more perfect prediction model is the postoperative collateral compensation formation prediction model for direct and indirect hemodialysis, which has the advantage of making full use of the patients' preoperative baseline variables and imaging characteristics, but the relatively insufficient inclusion of the sample size and the lack of intraoperative (vital signs, respiratory parameters, local cerebral oxygenation, etc.) and postoperative (postoperative neurological injury markers, etc.) variables included in the model limits the clinical scenarios. The lack of intraoperative (vital signs, respiratory parameters, local cerebral oxygen saturation) and postoperative (postoperative neurologic injury markers) variables limits the clinical application scenarios and is unable to guide the clinical decision-making and prognosis in the important stages of the perioperative period. This study aims to establish a prospective cohort database for MMD hemodialysis that includes perioperative anesthesia management, intraoperative treatment data, and postoperative treatment variables; to integrate preoperative, intraoperative, postoperative, and near- and long-term prognostic data from multiple sources, and to construct a perioperative multi-stage, multi-dimensional prognostic prediction model.

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