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

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.

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).

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 (Hosmer-Lemeshow χ ² = 14.542, p = 0.104). Decision curve analysis showed clinical utility across threshold probabilities of 6-100%.

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.