RESUMO
BACKGROUND: 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)â¯=â¯437.116â¯-â¯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 prognostic assessment model of ischemic stroke was: Logit(P)â¯=â¯458.437-92.343 (Hypertension) - 89.763 (Smoking history)â¯+â¯.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). In the validation groups, our novel diagnostic assessment model showed good identification (with 87.5% sensitivity and 84.2% specificity in The Affiliated Kunshan Hospital of Jiangsu University, with 85.5% sensitivity and 89.0% specificity in The Second Affiliated Hospital of Soochow University). Moreover, our novel prognostic assessment model has a high value in identifying poor prognosis patients in the validation groups from The Affiliated Kunshan Hospital of Jiangsu University (χ2â¯=â¯8.461, Pâ¯=â¯.004), and The Second Affiliated Hospital of Soochow University (χ2â¯=â¯7.844, Pâ¯=â¯.005). CONCLUSIONS: The diagnostic and prognostic assessment models we have established are of great value in the diagnosis and prognostic evaluation of ischemic stroke.
