Factors Influencing Anxiety and Depression Symptoms in Patients With Acute Ischaemic Stroke and the Development and Validation of a Risk Prediction Model

Abstract

Background: To analyse the factors influencing anxiety and depression symptoms in patients with acute ischaemic stroke (AIS) and construct a nomogram model for predicting the risk of developing anxiety and depression.

Methods: A total of 354 patients with AIS admitted to the Anhui Provincial Corps Hospital of Chinese People’s Armed Police Force between May 2023 and April 2025 were selected. They were divided into anxiety and depression (n = 180) and nonanxiety and depression (n = 174) groups on the basis of the presence of anxiety (Hamilton Anxiety Rating Scale ≥ 7 points) and depression (Hamilton Depression Rating Scale ≥ 8 points). Baseline patient data, laboratory indicators and anxiety–depression status were extracted from medical records. Logistic multivariable analysis was employed to identify factors influencing the occurrence of anxiety and depression in patients with AIS, establishing a nomogram prediction model. Internal validation was conducted by using receiver operating characteristic (ROC) and calibration curves and decision curve analysis.

Results: Comparative analysis revealed statistically significant differences between the two patient groups in terms of age, neutrophil count, angiogenin-like protein 4 (ANGPTL4), silencing information regulator protein 1 (SIRT1), Krüppel-like transcription factor 2 (KLF2), retinol-binding protein (RBP), lipoprotein (a) levels and plaque stability (p < 0.05). Multivariate logistic regression analysis revealed that age (odds ratio [OR] = 1.311, 95% confidence interval [95%CI]: 1.031–1.667), ANGPTL4 (OR = 0.057, 95%CI: 0.023–0.144), SIRT1 (OR = 0.096, 95%CI: 0.016–0.554), KLF2 (OR = 0.001, 95%CI: 0.000– 0.401), RBP (OR = 1.476, 95%CI: 1.068–2.040), lipoprotein (a) (OR = 1.130, 95%CI: 1.024–1.247) and plaque stability (OR = 23.941, 95%CI: 5.178–32.186) were factorsinfluencing anxiety and depression symptoms in patients with AIS (p < 0.05). The established regression model equation is log(P) = 0.271 × age − 0.044 × ANGPTL4 − 2.348 × SIRT1 − 7.453 × KLF2 + 0.390 × RBP + 0.122 × lipoprotein (a) + 3.176 × plaque stability. The area under the ROC curve was 0.901 (95%CI: 0.867–0.934). Internal validation using the bootstrap method demonstrated high concordance between the predictive and standard model curves. 

Conclusions: Age, ANGPTL4, SIRT1, KLF2, RBP, lipoprotein (a) and plaque stability are factors influencing the occurrence of anxiety and depression symptoms in patients with AIS. The nomogram model constructed on the basis of these factors demonstrates predictive validity.