Frontiers in Neurology (Sep 2024)
Logistic regression model for predicting risk factors and contribution of cerebral microbleeds using renal function indicators
Abstract
BackgroundThe brain and kidneys share similar low-resistance microvascular structures, receiving blood at consistently high flow rates and thus, are vulnerable to blood pressure fluctuations. This study investigates the causative factors of cerebral microbleeds (CMBs), aiming to quantify the contribution of each risk factor by constructing a multivariate model via stepwise regression.MethodsA total of 164 hospitalized patients were enrolled from January 2022 to March 2023 in this study, employing magnetic susceptibility-weighted imaging (SWI) to assess the presence of CMBs. The presence of CMBs in patients was determined by SWI, and history, renal function related to CMBs were analyzed.ResultsOut of 164 participants in the safety analysis, 36 (21.96%) exhibited CMBs and 128 (78.04%) did not exhibit CMBs, and the median age of the patients was 66 years (range: 49–86 years). Multivariate logistic regression identified hypertension (OR = 13.95%, 95% CI: 4.52, 50.07%), blood urea nitrogen (BUN) (OR = 1.57, 95% CI: 1.06–2.40), cystatin C (CyC) (OR = 4.90, 95% CI: 1.20–22.16), and urinary β-2 microglobulin, (OR = 2.11, 95% CI: 1.45–3.49) as significant risk factors for CMBs. The marginal R-square (RM2) was 0.25. Among all determinants, hypertension (47.81%) had the highest weight, followed by UN (11.42%). Quasi-curves plotted using the bootstrap method (999 times) showed good agreement between the predictive model and actual observations.ConclusionHypertension, BUN, urinary β-2 microglobulin, CyC were risk factors for CMBs morbidity, and controlling the above indicators within a reasonable range will help to reduce the incidence of CMBs.
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