Increased Incident Ischemic Stroke Risk in Advanced Kidney Disease: A Large-Scale Real-World Data Study

Maeda, Tosihki; Nishi, Takumi; Tada, Kazuhiro; Tsuji, Masayoshi; Satoh, Atsushi; Kawazoe, Miki; Yoshimura, Chikara; Arima, Hisatomi

doi:10.1159/000509567

Cited by 2 publications

(1 citation statement)

References 43 publications

(53 reference statements)

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“…For example, age, smoking, hypertension, and diabetes were included in all previous Japanese stroke risk models [18][19][20][21][22][23][24]. CKD was shown to be associated with stroke in Japanese patients even after adjustment for traditional cardiovascular risk factors, including hypertension [33][34][35][36]. AF was included in the JALS stroke risk model [24] and was found in other Japanese studies to be associated with the increased risk of stroke [25,26].…”

Section: Discussionmentioning

confidence: 99%

Developing a Stroke Risk Prediction Model Using Cardiovascular Risk Factors: The Suita Study

et al. 2021

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Introduction: Stroke remains a major cause of death and disability in Japan and worldwide. Detecting individuals at high risk for stroke to apply preventive approaches is recommended. This study aimed to develop a stroke risk prediction model among urban Japanese using cardiovascular risk factors. Methods: We followed 6,641 participants aged 30–79 years with neither a history of stroke nor coronary heart disease. The Cox proportional hazard model estimated the risk of stroke incidence adjusted for potential confounders at the baseline survey. The model’s performance was assessed using the receiver operating characteristic curve and the Hosmer-Lemeshow statistics. The internal validity of the risk model was tested using derivation and validation samples. Regression coefficients were used for score calculation. Results: During a median follow-up duration of 17.1 years, 372 participants developed stroke. A risk model including older age, current smoking, increased blood pressure, impaired fasting blood glucose and diabetes, chronic kidney disease, and atrial fibrillation predicted stroke incidence with an area under the curve = 0.76 and p value of the goodness of fit = 0.21. This risk model was shown to be internally valid (p value of the goodness of fit in the validation sample = 0.64). On a risk score from 0 to 26, the incidence of stroke for the categories 0–5, 6–7, 8–9, 10–11, 12–13, 14–15, and 16–26 was 1.1%, 2.1%, 5.4%, 8.2%, 9.0%, 13.5%, and 18.6%, respectively. Conclusion: We developed a new stroke risk model for the urban general population in Japan. Further research to determine the clinical practicality of this model is required.

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