{
  "abstract": "Objectives Arteriovenous malformation (AVM) rupture can be life-threatening, necessitating accurate predictive methodologies. We developed and validated the normalized stasis index (NSI), derived from quantitative digital subtraction angiography (qDSA), for predicting AVM rupture risk.Methods In this multicenter, retrospective, observational study, we enrolled consecutive AVM patients who underwent DSA examination between 2017 and 2022. qDSA was used to calculate hemodynamic parameters. We performed univariate and multivariate analyses to assess NSI’s association with AVM rupture, evaluated its predictive performance through receiver operating characteristic (ROC) curve analysis, and compared it with existing prediction models using DeLong’s test. An independent cohort provided external validation.Results The study included 663 patients (internal cohort: n=523; external validation cohort: n=140) with 324 ruptured and 339 unruptured AVMs. The ruptured group demonstrated significantly higher NSI values than the unruptured group (median: 9.03 vs 2.11; p<0.001). Across multivariate models, NSI maintained an independent association with AVM rupture after adjusting for vascular structural factors (p=0.009), hemodynamic factors (p<0.001), combined factors (p=0.029), and demographic factors (p=0.027). Integration of NSI into existing R 2eD and VALE predictive models significantly enhanced their performance (R2eD: area under the curve (AUC) 0.768; VALE: AUC 0.792; both p<0.001).Conclusions NSI demonstrates an independent association with AVM rupture and exhibits robust predictive capability for rupture events, providing valuable insights for clinical risk stratification and therapeutic decision-making.",
  "authors": [
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Jiadi Weng"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Chengzhuo Wang"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA"
      ],
      "name": "Tzak S Lau"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Heze Han"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Ruinan Li"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Zhipeng Li"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Qinghui Zhu"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "China National Clinical Research Center for Neurological Diseases, Beijing, China"
      ],
      "name": "Yu Chen"
    },
    {
      "affiliations": [
        "Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "China National Clinical Research Center for Neurological Diseases, Beijing, China"
      ],
      "name": "Xiaolin Chen"
    },
    {
      "affiliations": [],
      "name": "on behalf of Registry of Multimodality Treatment for Brain Arteriovenous Malformation in Mainland China (MATCH)"
    }
  ],
  "title": "Quantitative digital subtraction angiography (qDSA)-derived normalized stasis index for brain arteriovenous malformation rupture assessment",
  "uid": "2b63f883-914b-5f01-a0e8-6c50c7ec3941"
}