A nomogram for predicting post-stroke cognitive impairment no dementia in patients with first-ever mild ischemic stroke

Zhenjie Teng^1,2,3,4Jing Feng⁵Xiaohua Xie³Tianyuan Guan³Jing Xu³Xin Jiang³Yanzhong Chang^2,6,7*Peiyuan Lv^1,3,4*

• ¹Postdoctoral Innovation Practice Base of Hebei General Hospital, Shijiazhuang, China
• ²Postdoctoral Research Station of Biology, Hebei Normal University, Shijiazhuang, China
• ³Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei Province, China
• ⁴Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive Disorders, Shijiazhuang, Hebei Province, China
• ⁵Department of Endocrinology, Hebei General Hospital, Shijiazhuang, Hebei Province, China
• ⁶Laboratory of Molecular Iron Metabolism, Key Laboratory of Molecular and Cellular Biology of Ministry of Education, College of Life Sciences, Hebei Normal University, Shijiazhuang, Hebei Province, China
• ⁷Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, Hebei Research Center of the Basic Discipline of Cell Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, Hebei Province, China

To identify significant predictors and construct a validated nomogram for predicting post-stroke cognitive impairment no dementia (PSCIND) risk among first-ever mild ischemic stroke (MIS) patients. Methods: This retrospective cohort study analyzed 242 first-ever MIS patients categorized into normal cognitive (n=137) and PSCIND (n=105) groups. Comprehensive data encompassing demographic characteristics, laboratory parameters, cerebral small vessel disease (CSVD) imaging markers, neuropsychological assessments, and ischemic stroke lesion characteristics were collected. Predictor selection was conducted through least absolute shrinkage and selection operator (LASSO) regression analysis, followed by multivariable logistic regression for nomogram construction. Model performance was assessed through discrimination (area under the curve), calibration (calibration plots, Hosmer-Lemeshow test), and clinical utility (decision curve analysis). Results: Eight independent predictors were identified: age (OR=1.060, 95% CI: 1.016-1.106), education level (OR=0.917, 95% CI: 0.845-0.995), type 2 diabetes mellitus (OR=9.407, 95% CI: 3.761-23.528), superoxide dismutase (OR=0.951, 95% CI: 0.931-0.972), uric acid (OR=1.006, 95% CI: 1.002-1.010), homocysteine (OR=1.058, 95% CI: 1.027-1.091), strategic infarcts (OR=4.566, 95% CI: 2.148-9.707), and severe CSVD burden (OR=3.818, 95% CI: 1.842-7.911). The nomogram exhibited excellent discrimination (AUC=0.886) accompanied by satisfactory calibration P=0.104). Decision curve analysis showed clinical utility across threshold probabilities of 6-100%. Conclusion: This validated nomogram incorporating clinical, biochemical, and neuroimaging biomarkers provides a robust tool for individualized PSCIND risk assessment in first-ever MIS patients, with potential to guide targeted interventions and cognitive monitoring.