Analysis of antiplatelet therapy adherence in patients with ischemic cerebral stroke.

ABSTRACT

BACKGROUND: The related factors affecting the adherence of ischemic cerebral stroke (ICS) patients to antiplatelet therapy have attracted much attention. METHODS: Patients with ICS (confirmed by CT or MRI) were enrolled from January 2020 to July 2021. The demographic data were retrospectively investigated and analyzed. The adherence calculation was as follows Adherence = number of tablets taken/number of tablets needed to be taken. Adherence < 100% was defined as nonadherence. Severe nonadherence is defined as adherence ≤ 75%. RESULTS: A total of 229 patients with ICS were enrolled. We found no significant difference in the proportion of patients with nonadherence, while the proportion of severe nonadherence in the aspirin group was significantly higher (p < .001). Multivariable analysis indicated that medical insurance (odds ratio [OR] = 0.071, p < .001) and regular exercise (OR = 0.438, p = .015) were independent factors associated with adherence. In addition, only medical insurance (OR = 5.475, p < .001) and aspirin treatment (OR = 0.228, p < .001) were independent risk factors associated with severe nonadherence. We therefore constructed a nomogram plot and a model as follows Adherence risk score = 3 × medical insurance + regular exercise. Patients were divided into low-risk and high-risk groups for adherence based on the median model score. A total of 13.3% of patients in the low-risk group were nonadherent patients compared with 53.4% in the high-risk group (p < .001). Similarly, 8.4% of patients in the low-risk group had severe nonadherence compared with 19.9% in the high-risk group (p = .022). Moreover, in low-risk patients, no significant difference was observed. In patients with high risk, aspirin-treated patients showed significantly decreased adherence compared with the other two groups. CONCLUSION: Medical insurance and regular exercise were independent factors for antiplatelet therapy adherence. For patients with high model scores, timely intervention is necessary.