Predictive value of intracranial high-density areas in neurological function

Zhi-Juan Lu1, Jin-Xing Lai1, Jing-Ru Huang1

  • 1Department of Neurology, Ganzhou People's Hospital, Ganzhou 341000, Jiangxi Province, China.

PubMed

Insights

Intracranial high-density areas (HDAs) detected on CT scans after endovascular mechanical thrombectomy (EMT) indicate a poorer prognosis for acute ischemic stroke (AIS) patients. These findings also correlate with increased neurological deficits and severe depression and anxiety symptoms.

Area of Science:

  • Neurology
  • Radiology
  • Stroke Medicine

Background:

  • Intracranial high-density areas (HDAs) are increasingly recognized markers in stroke imaging.
  • Their predictive value for post-treatment neurological function and mental health remains incompletely understood.
  • Further investigation is crucial to clarify the prognostic significance of HDAs.

Purpose of the Study:

  • To evaluate the predictive capability of intracranial HDAs for neurological outcomes and mental health.
  • To assess the association between HDAs and prognosis following endovascular treatment for acute ischemic stroke (AIS).

Main Methods:

  • A prospective study included 96 patients undergoing endovascular mechanical thrombectomy (EMT) for AIS.
  • Cranial computed tomography (CT) scans were performed within 24 hours post-EMT.
  • Neurological function (NIHSS), functional outcome (mRS), depression (SDS), and anxiety (SAS) were assessed.

Main Results:

  • Patients with HDAs showed more severe neurological deficits and higher rates of disability (mRS).
  • HDAs were associated with significantly higher scores for depression (SDS) and anxiety (SAS).
  • Multivariate analysis confirmed HDAs as independent negative prognostic factors.

Conclusions:

  • Intracranial HDAs on CT imaging predict a poor prognosis in AIS patients treated with EMT.
  • HDAs are linked to worse neurological function and increased depressive and anxiety symptoms.
  • This highlights the importance of identifying HDAs for risk stratification and patient management.
Abstract