Clinical Utility of the Inflammatory Factors Combined With Lipid Markers in the Diagnostic and Prognostic Assessment of Ischemic Stroke: Based on Logistic Regression Models

In this study, we developed novel logistic regression models for the diagnostic and prognostic assessment of ischemic stroke. Methods: A total of 288 ischemic stroke patients and 300 controls admitted to The First Affiliated Hospital of Soochow University were included in the testing group. Two validation groups from The Affiliated Kunshan Hospital of Jiangsu University and The Second Affiliated Hospital of Soochow University were included to assess our novel assessment models. Results: Results from the testing group indicated that the diagnostic assessment model for ischemic stroke prediction was: Logit(P)  = 437.116 − 87.329 (Hypertension) − 89.700 (Smoking history) − 87.427 (Family history of ischemic stroke) − .090 (high-density lipoprotein cholesterol [HDL-C]) − 1.984 (low-density lipoprotein cholesterol [LDL-C]) − 17.005 (Lp(a)) − 15.486 (Apo A/Apo B), and the final prognost ic assessment model of ischemic stroke was: Logit(P) = 458.437−92.343 (Hypertension) − 89.763 (Smoking history) + .251 (NLR) − .088 (HDL-C) − 1.994 (LDL-C) − 2.883 (hs-CRP) − .058 (IL-6) − 6.356 (TNF-α) − 16.485 (Lp(a)) − 17.658 (Apo A/Apo B).
Source: Journal of Stroke and Cerebrovascular Diseases - Category: Neurology Authors: Source Type: research