This was a retrospective observational cross-sectional study from two medical centers that together provide health care for a total population of more than 20 million individuals in the Shandong province of China. The first institute was the Department of Neurology at Weifang Brain Hospital, which was enrolled in the present study from January 2015 to October 2019, and the second was the Department of Neurology at the Affiliated Hospital of Qingdao University, which was enrolled in the present study from January 2014 to December 2016. Our study was approved by Affiliated Hospital of Shandong Second Medical University Ethics Committee. The approval no. of the Ethics Committee was wyfy-2024-ky-495.

Herein, we investigated the relationship between DWI infarction patterns and MES lasting 60 min during TCD monitoring within 72 h after the onset of acute stroke Patients with consecutive acute ischemic stroke within the middle cerebral artery (MCA) territory. Stroke was diagnosed based on the imaging characteristics obtained via magnetic resonance imaging (MRI) and neurological deficits lasting for longer than 24 h. The requirement for informed consent was waived. The patients’ general data, relevant medical histories, treatments, and laboratory examinations were evaluated and recorded by a neurologist.

The exclusion criteria for candidate patients were as follows: (1) younger than 40 years old; (2) carotid artery occlusion or middle cerebral artery occlusion; (3) absence of a temporal acoustic window for TCD monitoring; (4) bilateral anterior infarctions and/or anterior-and posterior-circulation infarctions; (5) cardioembolic stroke, or strokes with etiologies differing from circulation ischemic stroke; (6) severe nephritis or liver disease, or definitive/suspected cancer; (7) no enduring MES for 60 min during TCD monitoring; or (8) a history of carotid endarterectomy or a carotid artery stent.

MES was detected via TCD monitoring (Delica EMS-9A), the specific details of which are described in our previous study (22).

The DWI infarction pattern in the MCA was divided into the territorial, internal border zone (BZ), cortical, and subcortical infarcts, and a correlation was analyzed between the DWI infarction pattern and MES microembolic signals. BZ infarcts were divided into internal and cortical BZ infarcts; of which the latter encompassed infarcts in the Frontal cortex (between the anterior cerebral arteries and middle cerebral arteries) and Occipital cortex (between the middle cerebral arteries and posterior cerebral arteries) (23–25). Based on previous study (26, 27), we set the standards of small subcortical infarction into two levels, as <10 or < 5 mm in diameter (28). The lesion pattern in a single patient may involve the formation of more than one lesion.

SPSS (version 22.0; Chicago, IL, United States) and Free Statistics (version 1.7.1) software were used for data analysis. Quantitative data are expressed as the mean ± standard deviation, while qualitative data are expressed as frequencies and percentages. After testing for normality, intergroup comparisons of quantitative data were performed using t-tests, and qualitative or categorical data were compared using χ² or Fisher’s exact texts. Statistical significant factors (p < 0.05) were analyzed for collinearity before regression analysis. Statistically significant factors in the univariate analyses were included in a stepwise forward logistic regression analysis to identify the independent factors for MES. Odds ratios (ORs) and their 95% CIs were used to evaluate the independent contributions of significant factors. The Hosmer-Lemeshow test was applied to estimate the appropriateness of the model.

A nomogram graph was built using Free Statistics software. Receiver operating characteristic (ROC) curve, concordance index (C-index), and calibration curve analyses were performed to evaluate the discrimination and calibration of the model.

During the study period, 1,132 consecutive patients with acute stroke were deemed eligible. After excluding patients who met the exclusion criteria, 230 patients (149 from the Affiliated Hospital of Qingdao University and 81 from Weifang Brain Hospital)with acute MCA stroke were enrolled in the study (Figure 1). MES occurred in 38 of the 230 cases, with a positivity rate of 16.5%. Male sex, stenosis, and white blood cell counts were significantly higher in the MES+ group than in the MES-group. There were no significant differences in terms of hypertension, diabetes mellitus, ischemic heart disease, stroke history, smoking, drinking, dual antiplatelet, C-reactive platelets, or platelets between MES+ and MES− patients. The general clinical characteristics of the patients are shown in Table 1.

There were 40 territorial infarction (TI), 55 cases BZ cases, 154 SCI cases, and 64 cortical infarction (CI) cases. Among 154 patients with SCI, 78 and 33 had SCIs with a diameter < 10 mm and < 5 mm, respectively.

The variance inflation factors (VIF) of BZ, SCI < 10 mm, CI, stenosis, WBC count, and sex were all less than 2. Multicollinearity was considered nonexistent. BZ, SCI < 10 mm, CI, stenosis, WBC count, and sex were all therefore entered into the logistic regression analysis. Multivariate analysis revealed that the BZ level, CI, and SCI < 10 mm were independent correlation factors for MES. Other factors were not included in this equation (Table 2).

As SCI < 5 mm was found to be significantly different between the two groups in the univariate analysis, we replaced SCI < 10 mm with SCI < 5 mm, and found that SCI < 5 mm was also associated with MES, similar to SCI < 10 mm. The results of the binary logistic regression revealed that SCI < 5 mm was not included in the regression equation, p > 0.05.

Based on the multivariate logistic regression analyses, three independent correlation factors were used to explore the nomogram (Figure 2), which was assessed using area under the curve (AUC), concordance index (C-index), and calibration curve analyses. The AUC for predicting MES+ was 0.826 (95%CI 0.764 to 0.889) (Figure 3A). The accuracy of the model was assessed using a calibration curve, and the slope of the calibration curve was close to the ideal values (Figure 3B). Leave-one-out cross-validation was further performed to evaluate the model (Figure 3C); the corrected C-index was 0.826 and the calibration slope was 1.0. Figure 3D shows the results of the decision curve analysis. This analysis revealed that patients could benefit from the model when the threshold probabilities were approximately 0.1–0.5.

The present study showed that a small SCI was associated with embolism in two medical center patients. Furthermore, by combining cortical and border-zone infarctions, the study employed small SCI as a factor to build a predictive nomogram model for MES, and these parameters were easily acquired. Third, MES is an important marker of recurrent stroke (7), and this model could provide early clues for stroke recurrence.

Nevertheless, this study has several limitations. First, MES detection was the only method used to identify microemboli, and has some limitations. For example, 1 h of MES-TCD monitoring cannot avoid false-negative bias. Second, the sample size did not satisfy the requirements for external model validation. Finally, this was a retrospective cross-sectional study, and our findings therefore need to be validated in future cohort studies.