Hidden risk patterns among acute ischemic stroke patients identified by latent class analysis

Area of Science:

• Neurology
• Clinical Medicine
• Biostatistics

Background:

• Acute ischemic stroke (AIS) patient outcomes are influenced by multiple risk factors, yet comprehensive studies are limited.
• Understanding these risk patterns is crucial for effective patient management and treatment strategies.

Purpose of the Study:

• To identify distinct patient subgroups among AIS patients based on clinical characteristics and risk factor patterns.
• To investigate the association between identified risk patterns and functional outcomes at 3 months post-stroke.
• To inform individualized treatment plans for AIS patients.

Main Methods:

• Latent class analysis (LCA) was employed using five risk factors to categorize 448 AIS patients.
• Cox proportional hazard regression analysis was utilized to assess the relationship between risk patterns and 3-month functional outcomes.
• Clinical follow-up data was collected within 72 hours of admission.

Main Results:

• Two distinct patient subgroups (classes) were identified: Class 1 ('Elderly with low lymphocytes') and Class 2 ('Low neutrophils and high lymphocytes').
• Class 1 patients (47.8%) were predominantly elderly with lower lymphocyte levels.
• Class 1 patients exhibited a significantly higher risk of adverse functional outcomes compared to Class 2 patients (adjusted Hazard Ratio: 3.21; p < 0.001).

Conclusions:

• Latent class analysis successfully identified two distinct subgroups of AIS patients with unique clinical and laboratory features.
• The identified risk patterns are significantly associated with functional outcomes, highlighting the need for tailored therapeutic approaches.
• These findings provide valuable insights for the health management and therapy of AIS patients.