Clinical and Imaging Risk Factors for Early Neurological Deterioration and Long-Term Neurological Disability in Patients with Single Subcortical Small Infarction

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Methods: We retrospectively included SSSI patients hospitalized. Outcomes were defined as modified Rankin Scale (mRS) score >2 at follow-up and the occurrence of END during hospitalization. Multivariate logistic regression identified independent predictors of END and long-term outcomes. Stepwise regression analysis was used to develop a predictive model for poor outcomes. The predictive performance of risk factors and the model was assessed using receiver operating characteristic (ROC) curves. Results: A total of 289 SSSI patients were included. During hospitalization, 18 patients (6.2%) experienced END, and 29 patients (10%) had neurological disability at a median follow-up of 21.4 (16.7–25.2) months. Multivariate analysis showed the National Institutes of Health Stroke Scale (NIHSS) score (OR 1.438, 95% CI 1.182–1.749, P < 0.001), Total cholesterol (TC) (OR 1.545, 95% CI 1.014–2.355, P = 0.043), neutrophil to High density lipoprotein cholesterol ratio (NHR) (OR 1.371, 95% CI 1.074–1.75, P = 0.011), and neutrophil count (OR 1.333, 95% CI 1.025–1.733, P = 0.032) were independently associated with END. Age (OR 1.083, 95% CI 1.008–1.163, P = 0.029), lesion diameter (OR 1.121, 95% CI 1.001–1.255, P = 0.048), NIHSS (OR 1.685, 95% CI 1.33–2.134, P < 0.001), symptomatic intracranial artery stenosis (OR 6.655, 95% CI 1.618–27.38, P = 0.009), lacune grading (OR 3.644, 95% CI 1.468–9.048, P = 0.005), and The degree of brain atrophy (OR 2.232, 95% CI 1.199–4.154, P = 0.011) were independently associated with neurological disability. The predictive model for END (included NIHSS score and NHR level) and long-term neurological disability (included age, NIHSS score, symptomatic intracranial artery stenosis, number of lacunes, and brain atrophy) showed areas under the ROC curve of 0.836 and 0.926, respectively. Conclusion: High NIHSS, TC, NHR, and neutrophil count are independent risk factors for END. Age, NIHSS, lesion size, symptomatic intracranial artery stenosis, the degree of lacunes and brain atrophy are predictors of neurological disability in SSSI patients. Single subcortical small infarction Cerebral Small vessel disease Magnetic resonance imaging Early neurological deterioration Long-term neurological disability Figures Figure 1 Introduction Single small subcortical infarction (SSSI), an infarction in the territory of penetrating arteries[1], accounts for approximately 20% of all ischemic strokes[2]. Due to the small size of the lesions and generally mild symptoms, clinicians may misjudge the prognosis of these patients. However, SSSI can still result in severe long-term neurological disabilities or even death. Additionally, despite adequate antithrombotic and lipid-lowering therapies, some patients with SSSI may experience early neurological deterioration (END) during hospitalization. END is characterized by sudden worsening of symptoms such as limb weakness, speech difficulties, and swallowing issues[3]. END can significantly impact long-term outcomes[4], and previous studies have reported END incidence rates ranging from 3% to 31.1%[5,6]. Admission NIHSS score, history of hypertension, and low-density lipoprotein-cholesterol (LDL-C) levels are predictors of outcomes in SSSI patients[6]. Additionally, studies have suggested that systemic inflammation markers, such as the neutrophil-to-lymphocyte ratio (NLR), are associated with END[7]. However, studies focusing on risk factors for END and long-term neurological disability in SSSI patients is still limited. Some demographic and cardiovascular risk factors have not been thoroughly examined, and Comprehensive models for predicting adverse outcomes are currently unavailable. Currently, SSSI is mostly considered as an important neuroimaging feature of cerebral small vessel disease (CSVD)[8]. Other imaging markers of CSVD include white matter hyperintensities (WMHs), lacunes, enlarged perivascular spaces (ePVS), cerebral microbleeds, and brain atrophy[9]. The occurrence of SSSI is closely associated with CSVD and SSSI lesions may convert to other CSVD imaging markers, such as lacunes and WMHs[10,11]. Previous studies have also demonstrated that the overall burden of CSVD is associated with poor outcomes at discharge in SSSI patients[12,13]. However, there is limited research on the differential impact of CSVD imaging marker types and locations on the END and long-term outcomes of SSSI. It remains unclear which combinations of markers are most predictive of SSSI prognosis. Additionally, the morphology and location characteristics of SSSI lesions may be related to outcomes. Lesion size and specific locations, such as the pons and internal capsule, have been shown to be associated with END in SSSI patients[4,14,15], but overall, the sample sizes in these studies have been limited. Given the current insufficient exploration of factors influencing the occurrence of END during hospitalization and long-term neurological disability in SSSI patients, this study first aimed to evaluate potential risk factors for END and long-term prognosis. Clinical, neuroimaging (including CSVD features and the morphology and location of infarcts) and laboratory indicators were comprehensively considered. Furthermore, we aimed to develop predictive models for adverse outcomes (END and long-term disability) of SSSI to facilitate early identification and intervention for high-risk patients in clinical practice. Methods Study Population We retrospectively included consecutive patients with SSSI who were hospitalized in the Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and completed head MRI scans from April 2021 to June 2022. The inclusion criteria were: 1) admission within 72 hours after symptom onset, 2) maximum lesion diameter 18 years. The exclusion criteria were: 1) receiving endovascular thrombectomy, 2) pre-stroke modified Rankin Scale score (mRS) > 1, 3) concurrent malignancy, vasculitis, systemic immune diseases, or hemorrhagic stroke, 4) multiple infarct lesions, and 5) current or previous cortical infarction and/or cerebellar infarction. The study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20210731). All patient information in this retrospective study was de-identified and anonymized, and written informed consent was obtained from each participant. Outcome Definition Two neurologists who were blinded to the baseline data conducted follow-up assessments of SSSI patients independently. The NIHSS score on admission was used to assess the severity of symptoms of SSSI. END was defined as an increase in the NIHSS score by ≥2 points or in the limb weakness sub-score by ≥1 point after admission[7]. Long-term poor neurological outcomes were determined based on whether the mRS score >2 at follow-up. The mRS scoring criteria range from 0 (no symptoms) to 6 (death). Clinical Assessment We systematically collected demographic information (e.g., gender, age), medical history (e.g., hypertension, diabetes, hyperlipidemia, coronary artery disease, stroke), laboratory test results (e.g., eGFR), time from onset to admission (OTT), diastolic blood pressure (DBP) and systolic blood pressure (SBP) on admission, and TOAST classification. Laboratory samples were collected on the day following admission. Hypertension was defined as a pre-admission or in-hospital systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or current use of antihypertensive medications. Diabetes was defined as a pre-admission or in-hospital fasting blood glucose >7 mmol/L and/or postprandial blood glucose ≥11.1 mmol/L and/or HbA1c ≥6.5%, or current use of hypoglycemic medications. Hyperlipidemia was defined as total cholesterol (TC) ≥5.2 mmol/L and/or low-density lipoprotein-cholesterol (LDL-C) ≥3.4 mmol/L and/or triglycerides (TG) ≥1.7 mmol/L, or current use of lipid-lowering medications. Renal dysfunction was determined by an eGFR <60 ml/min/m² measured during hospitalization. TOAST classification was used to categorize the stroke subtype by causes. Neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-high density lipoprotein cholesterol(HDL-C) ratio (NHR) were calculated by the formulas: NLR = neutrophil count (cells) / lymphocyte count (cells)[16] NHR = neutrophil count (10^9 cells) / HDL-C (mmol/L)[17]. Imaging Evaluation A 3 Tesla brain MRI was performed within 48 hours of admission, including fluid-attenuated inversion recovery imaging (FLAIR), T2-weighted imaging, T1-weighted imaging, and DWI. (FLAIR: repetition time (TR): 8000 ms, echo time (TE): 150 ms, matrix size of 512 × 512. slice thickness: 5 mm, interslice gap:1.5 mm; DWI: TR of 3000 ms, TE: 65.3ms, slice thickness: 5 mm, interslice gap: 1 mm, a b-value: 1000 s/m^2). Two neuroradiologists, blinded to the clinical information and follow-up data, independently performed the imaging evaluations. The assessments included arterial stenosis, infarct lesion diameter, lesion location, degree of suspected vascular-original white matter hyperintensities (WMHs), extent of enlarged perivascular spaces (EPVS), number of chronic lacunar infarctions, and degree of brain atrophy. The severity of arterial stenosis was assessed using digital subtraction angiography (DSA), computed tomography angiography (CTA), or magnetic resonance angiography (MRA). Symptomatic extracranial arterial stenosis was defined as ≥50% stenosis of the cervical arteries (common carotid artery, extracranial internal carotid artery, and extracranial vertebral artery) with ipsilateral acute cerebral infarction or brain ischemic symptoms. Symptomatic intracranial arterial stenosis was defined as ≥50% stenosis of intracranial arteries with downstream acute cerebral infarction or brain ischemic symptoms. SSSI lesion were identified as areas of hyperintensity on DWI, T2, and FLAIR, and hypointensity on apparent diffusion coefficient (ADC) maps. Suspected vascular-original WMHs were identified as hyperintensities in the white matter on FLAIR or T2-weighted images, categorized into periventricular WMHs and subcortical WMHs, and assessed using the Fazekas scale[18], scoring from 0 to 3 at each location. EPVS were identified as small (≤3 mm) round or linear hyperintensities in the basal ganglia and centrum semiovale on T2-weighted images and graded from 0 to 4 based on number: 0 = no EPVS, 1 = 1–10 EPVS, 2 = 11–20 EPVS, 3 = 21–40 EPVS, 4 = >40 EPVS[19]. Lacunes were defined as lesions with cerebrospinal fluid intensity on T2, typically 3–15 mm in diameter, with a hyperintense rim on FLAIR, and were graded into three categories: 0: no lacunes, 1: 1-2 lacunes, 2: >2 lacunes[20]. The degree of brain atrophy was assessed using a 0-3 grading scale based on comparisons of cortical sulcal depth and ventricular size with standard MRI images of age-matched elderly controls: 0 = no atrophy, 1 = mild atrophy (similar to the 95th percentile template image), 2 = moderate atrophy (greater than the 95th percentile but not exceeding twice the ventricular size and sulcal depth of the standard image), 3 = severe atrophy (ventricular size and sulcal depth more than twice the 95th percentile template image)[21]. Statistical Analysis All statistical analyses were performed using SPSS version 26 (SPSS, Inc., Chicago, IL, USA). Continuous variables with a normal distribution were presented as mean ± standard deviation, while non-normally distributed continuous variables and ordinal variables were presented as median (interquartile range). Categorical variables were expressed as counts (percentages). Group comparisons for normally distributed variables were performed using the independent samples t-test, while non-normally distributed variables were analyzed using the Mann-Whitney U test. Categorical variables were compared using the chi-square test, with or without continuity correction and Fisher's exact test. Spearman correlation analysis was used to assess the degree of association between two variables. Binary logistic regression analysis was conducted to identify clinical, imaging, and laboratory factors associated with long-term poor neurological outcomes and END. Variables with P < 0.1 in univariate analysis and those considered potentially influential based on previous studies were included in the multivariate logistic regression model. A stepwise regression method was used to sequentially include variables with P < 0.05 in the multivariate logistic regression model, eliminating irrelevant factors to develop the final prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity, specificity, and predictive value (area under curve, AUC) of risk factors and the final model. The cut-off value was determined based on the maximum Youden's index on the curve. Youden's index was calculated by the formula: Youden’s Index = Sensitivity + Specificity - 1. A two-sided p-value < 0.05 was considered statistically significant. Results Demographic Characteristics of Study Subjects During the study period, a total of 1,329 patients with SSSI were hospitalized. Among them, 304 patients met the inclusion and exclusion criteria. Excluding patients with missing baseline information, loss to follow-up, and incomplete MRI examinations (15/304), a final total of 289 patients with SSSI were included in the analysis. The clinical information of the subjects is summarized in Table 1. The mean age of the patients was 60 years (60.2 ± 10.3), with 216 (74.7%) males. The stroke lesions were primarily located in the brainstem (93/289, 33.2%), thalamus (36/289, 12.5%), basal ganglia (56/289, 19.4%), and corona radiata/centrum semiovale (97/289, 33.6%). During hospitalization, 18 patients (6.2%) experienced early neurological deterioration (END). After a median follow-up of 21.4 (16.7–25.2) months, 29 patients (10%) had a modified Rankin Scale (mRS) score >2, while 260 patients (90%) had an mRS score ≤2. Periventricular white matter hyperintensities (WMHs) were present in 84.4% (244/289) of the patients, and subcortical WMHs were present in 78.5% (227/289). Lacunes were detected in 45.7% (132/289) of the patients, and brain atrophy was observed in 33.7% (97/289), with moderate to severe brain atrophy ( > 1 score) present in 14.2% (41/289) (Table 1). Univariate and Multivariate Analysis of Early Neurological Deterioration (END) Compared to patients without END during hospitalization, those with END had higher DBP and NIHSS scores on admission, larger lesion diameters, and higher values of total cholesterol (TC), neutrophil count, NLR, and NHR. After adjusting for confounding factors, multivariate logistic regression analysis showed that NIHSS score (OR = 1.438, 95% CI = 1.182–1.749, P < 0.001), neutrophil count (OR = 1.333, 95% CI = 1.025–1.733, P = 0.032), TC (OR = 1.545, 95% CI = 1.014–2.355, P = 0.043), and NHR (OR = 1.371, 95% CI = 1.074–1.75, P = 0.011) were independent predictors of END (Table 2). A predictive model for END in SSSI was constructed using stepwise regression, which included NIHSS score and NHR on admission (Table 2), with an area under the ROC curve of 0.836 (95% CI = 0.744–0.927). ROC analysis for the independent predictive factors indicated that the optimal cut-off value for NIHSS score in predicting END was 4.5, with a sensitivity of 83.3% and specificity of 76%. The cut-off value for NHR was 5.28, with a sensitivity of 66.7% and specificity of 65.7% (Table 4, Fig. 1). Univariate and Multivariate Analysis of Poor Neurological Outcomes (mRS > 2) Univariate analysis indicated that age, diastolic blood pressure (DBP) and NIHSS score on admission, responsible intracranial artery stenosis, periventricular and subcortical WMHs, number and grading of lacunes, degree of brain atrophy, eGFR, MCHC, NLR, and NHR were associated with long-term poor outcomes in SSSI patients. Multivariate logistic regression models adjusted for age, gender, imaging evaluations, and laboratory results showed that age (OR = 1.083, 95% CI = 1.008–1.163, P = 0.029), NIHSS score on admission (OR = 1.685, 95% CI = 1.33–2.134, P < 0.001), maximum lesion diameter (OR = 1.121, 95% CI = 1.001–1.255, P = 0.048), grading of lacunes (OR = 3.644, 95% CI = 1.468–9.048, P = 0.005), grading of brain atrophy (OR = 2.232, 95% CI = 1.199–4.154, P = 0.011), MCHC (OR = 0.952, 95% CI = 0.912–0.994, P = 0.026), and symptomatic intracranial artery stenosis (OR = 6.655, 95% CI = 1.618–27.38, P = 0.009) were independently associated with poor outcomes at follow-up (Table 3). After stepwise regression to exclude irrelevant variables, we developed a predictive model for poor prognosis, which consisting of age, NIHSS score at admission, symptomatic intracranial artery stenosis, number of lacunes, and brain atrophy (Table 3). Subsequent ROC curve analysis showed that the model had good predictive performance (AUC 0.926, 95% CI = 0.889–0.964). ROC analysis for the independent predictive factors indicated that the optimal cut-off value for NIHSS in predicting poor outcomes was 3.5, with a sensitivity of 86.2% and specificity of 65%. The cut-off value for age was 67.5 years, with a corresponding sensitivity of 65.5% and specificity of 79.2%. The cut-off value for the number of lacunes was 1.5, with a sensitivity of 55.2% and specificity of 75%. The cut-off value for the degree of brain atrophy was 0.5, with a sensitivity of 72.4% and specificity of 70.8% (Table 4, Fig. 1). Discussion In this study, we systematically analyzed the clinical, imaging, and laboratory factors influencing END and long-term poor outcomes in SSSI patients. Our findings indicate that patients with more severe WMH at baseline are more likely to experience long-term poor outcomes. Independent predictors of long-term poor outcomes included advanced age, lower MCHC, higher admission NIHSS score, larger lesion diameter, greater number of lacunes, higher degree of brain atrophy, and the presence of symptomatic intracranial artery stenosis. Independent predictors of END during hospitalization included higher NIHSS score, TC, neutrophil count, and NHR. Finally, we developed predictive models for END and long-term outcomes, with ROC curve analysis demonstrating good predictive performance. Previous study focusing on minor ischemic stroke (NIHSS≤3) showed that age, NIHSS score, large artery atherosclerosis, non-culprit vessel stenosis, and lesion diameter were associated with poor outcomes at 3 months[22]. Kim et al. reported that a history of diabetes and NIHSS score were independently associated with poor outcomes at 3 months in patients with mild stroke (NIHSS ≤5) undergoing thrombolytic therapy[23]. Studies have identified NIHSS score, culprit artery stenosis, and branch atheromatous disease as independent predictors of END in acute lacunar stroke patients[7,24]. Another study indicated that elevated LDL-C was an independent risk factor for END[4]. Consistent with previous findings, ROC analysis showed that even lower NIHSS scores at admission could predict END and long-term poor outcomes (cut-off values of 4.5 and 3.5, respectively). This suggests that despite the small lesion size and relatively mild symptoms, patients can still develop significant neurological disability, emphasizing the need for adequate clinical attention in these patients. The AUC and Youden index for prediction model of END did not show a substantial improvement over that of the independent variable NIHSS, suggesting that the NIHSS holds dominant predictive value in this model. The statistical significance of lesion diameter was borderline; it lost significance after adjusting for clinical and imaging risk factors but was statistically significant in the final model, possibly due to insufficient sample size. Unlike previous studies, the history of hypertension and diabetes showed insignificant in influenced outcomes. This discrepancy may be due to differences in the study populations; our study had a higher proportion of hypertensive patients and included those with large artery stenosis. Our study also demonstrated that SSSI patients with symptomatic intracranial artery stenosis were more likely to have poor long-term outcomes compared to other etiologies. The sample sizes for patients with extracranial artery stenosis and atrial fibrillation were insufficient to detect differences, further studies are needed to elucidate their relationship with SSSI outcomes. Previous studies have shown that the total burden of CSVD is the risk factor for in-hospital worsening of mRS and lack of improvement in NIHSS scores in patients with acute lacunar infarction[13]. Liu et al. also demonstrated that the total CSVD score is an independent predictor of poor outcomes at three months in stroke patients receiving thrombolytic therapy[25]. Sanjeeva et al. found that WMHs are associated with poor symptom improvement and disability at three months post-discharge in patients with minor stroke (NIHSS < 6)[26]. Helenius et al. conducted a study involving 80 patients with small subcortical infarction (SSI) and found that the severity of WMHs was related to lesion volume and poor outcomes at three months[27], although two studies did not report differences in outcomes based on the location of WMHs. Another study, which included 119 patients with acute ischemic stroke, demonstrated that brain atrophy and lacunes were associated with poor outcomes at three months[28]. Our findings are consistent with these results but with a longer follow-up period. We provided a detailed classification and localization of CSVD imaging characteristics, finding differences in periventricular and subcortical WMHs between different outcome groups, though which were not statistically significant after adjusting for confounding factors. ePVS were not related to outcomes, whereas lacunes and brain atrophy were independent predictors of poor prognosis. The different relationships between CSVD subtypes and outcomes suggest distinct underlying mechanisms and risk factors, highlighting the varying importance of CSVD markers when assessing prognosis in SSSI patients. The extent of CSVD may represent "brain frailty[29]," affecting post-stroke functional recovery by reducing cerebral functional reserve or collateral compensation[28,30,31]. The ROC curve cut-off values for brain atrophy and lacunes suggest that few lacunes and mild brain atrophy can predict adverse outcomes. However, whether the relationship between brain atrophy and outcomes differs based on the vascular or neurodegenerative origin of brain atrophy remains to be further studied. Some studies suggest that neurodegenerative causes may have a more significant impact on outcomes[32]. The lack of susceptibility-weighted imaging in most patients precluded an assessment of cerebral microbleeds, limiting the comprehensiveness of our analysis regarding the impact of CSVD on stroke outcomes. Previous studies have demonstrated that red blood cell-related tests such as hemoglobin, hematocrit, and red cell distribution width are independently associated with ischemic stroke outcomes[33,34]. However, there are few reports on the relationship between MCHC and ischemic stroke prognosis. Huang et al. found that MCHC is independently associated with mortality during hospitalization and within one year post-discharge in acute myocardial infarction patients[35]. Inflammatory responses play a crucial role in the pathogenesis and progression of ischemic stroke. Various inflammatory markers, such as D-dimer, C-reactive protein, and the widely recognized inflammation index NLR, have been shown to be independently associated with ischemic stroke outcome[12,36], which suggested that inflammation might mediate the relationship between MCHC and SSSI by impairing iron metabolism[35]. Additionally, red blood cells may influence cerebral atherosclerosis by regulating vascular function and maintaining vascular integrity[37]. The relationship between red blood cell parameters and ischemic stroke warrants further investigation. Nam et al. reported that NLR is associated with END in a study involving 438 SSSI patients[7]. NHR, a novel inflammation index proposed in recent years, has been supported by several studies to be associated with the incidence of adverse cardiovascular events and stroke[17,38], as well as poor outcomes in acute coronary syndrome and ischemic stroke patients[39,40]. To our knowledge, no studies have yet reported on the relationship between NHR and SSSI outcomes. We observed that both NLR and NHR were elevated in patients with poor outcomes, but NLR lost its statistical significance in predicting END after adjusting for admission NIHSS or lesion diameter. This suggests that, compared to NHR, the association of NLR with END is primarily mediated by stroke severity and lesion size (Spearman correlation analysis: NLR: NIHSS: correlation coefficient 0.208, P < 0.0001; long-axis diameter: correlation coefficient 0.141, P = 0.016; NHR: NIHSS: correlation coefficient 0.157, P = 0.008; long-axis diameter: correlation coefficient 0.013, P = 0.826). NHR may be a better independent marker for predicting in-hospital outcomes in stroke patients, which needs to be validated in further large-scale prospective studies. This study has several strengths: 1. Unlike previous studies with shorter follow-up periods, our study provides insights into the long-term prognosis of SSSI patients. 2.We included a broader range of variables related to SSSI prognosis compared to prior studies, eventually obtained highly efficient model for predicting END and long-term outcomes. However, the limitations must also be point out: 1. The relatively small sample size and outcome events led to wide confidence intervals for some predictive factors and might have masked certain risk factors. 2.The retrospective method of the study may introduce bias due to record inaccuracies and missing information. 3. The long follow-up period means that the health status of patients might have been influenced by other comorbidities. Conclusion Our study reveals that the severity of symptoms at admission, elevated neutrophil counts, TC, and NHR are risk factors for END during hospitalization. the lesion size, severity of symptoms at admission, presence of symptomatic intracranial arterial stenosis, lacune, and brain atrophy are associated with poor long-term prognosis in SSSI patients. This study provides a predictive model for the END and long-term adverse outcomes of SSSI. The findings have potential value for stratifying high-risk patients and evaluating therapeutic efficacy, offering clues for patient and risk factor selection in future prospective studies. Abbreviations Abbreviation Full name ADC Apparent Diffusion Coefficient AUC Area Under Curve CSVD Cerebral Small Vessel Disease CTA Computed Tomography Angiography DBP Diastolic Blood Pressure DSA Digital Subtraction Angiography DWI Diffusion-Weighted Imaging eGFR Estimated Glomerular Filtration Rate END Early Neurological Deterioration ePVS Enlarged Perivascular Spaces FLAIR Fluid-Attenuated Inversion Recovery HDL-C High Density Lipoprotein Cholesterol LDL-C Low-Density Lipoprotein Cholesterol MCA Middle Cerebral Artery MCHC Mean Corpuscular Hemoglobin Concentration mRS Modified Rankin Scale MRA Magnetic Resonance Angiography NHR Neutrophil to High Density Lipoprotein Cholesterol Ratio NIHSS National Institutes of Health Stroke Scale NLR Neutrophil-to-Lymphocyte Ratio OTT Onset to Admission Time ROC Receiver Operating Characteristic SBP Systolic Blood Pressure SSSI Single Subcortical Small Infarction TC Total Cholesterol TE Echo Time TG Triglycerides TIA TIA transient ischemic attack TOAST Trial of Org 10172 in Acute Stroke Treatment TR Repetition Time WMHs White Matter Hyperintensities Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20210731). The patient information in this retrospective study was de-identified and anonymized, and written informed consent was obtained from each participant. Consent for publication: Not applicable. Availability of data and materials Upon reasonable request, the corresponding author will provide access to the datasets generated and/or analyzed during this study. Competing interests The authors have no conflicts of interest to disclose. Funding This work was supported by the Natural Science Foundation of Hubei Province (2021CFB382). Authors’ contributions FX: Responsible for study design, drafting the manuscript, data collection, and data analysis. MH, MT, DJY: Responsible for data collection and analysis. ZWH, XSB: Responsible for study design, data analysis, data interpretation, and manuscript revision. 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Neutrophil to high-density lipoprotein cholesterol ratio, a novel risk factor associated with acute ischemic stroke. Med (Baltim). 2023;102:e34173. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman. R A MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987;149:351–6. Doubal FN, Maclullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010;41:450–4. Chen Y, Xu J, Pan Y, Yan H, Jing J, Yang Y, et al. Association of Trimethylamine N-Oxide and Its Precursor With Cerebral Small Vessel Imaging Markers. Front Neurol. 2021;12:648702. Farrell C, Chappell F, Armitage PA, Keston P, Maclullich A, Shenkin S, et al. Development and initial testing of normal reference MR images for the brain at ages 65–70 and 75–80 years. Eur Radiol. 2009;19:177–83. You W, Li Y, Ouyang J, Li H, Yang S, Hu Q, et al. Predictors of Poor Outcome in Patients with Minor Ischemic Stroke by Using Magnetic Resonance Imaging. J Mol Neurosci. 2019;69:478–84. Kim DH, Lee DS, Nah HW, Cha JK. Clinical and radiological factors associated with unfavorable outcome after intravenous thrombolysis in patients with mild ischemic stroke. BMC Neurol. 2018;18:30. Jeong HG, Kim BJ, Yang MH, Han MK, Bae HJ. Neuroimaging markers for early neurologic deterioration in single small subcortical infarction. Stroke. 2015;46:687–91. Liu X, Li T, Diao S, Cai X, Kong Y, Zhang L, et al. The global burden of cerebral small vessel disease related to neurological deficit severity and clinical outcomes of acute ischemic stroke after IV rt-PA treatment. Neurol Sci. 2019;40:1157–66. Onteddu SR, Goddeau RP Jr., Minaeian A, Henninger N. Clinical impact of leukoaraiosis burden and chronological age on neurological deficit recovery and 90-day outcome after minor ischemic stroke. J Neurol Sci. 2015;359:418–23. Helenius J, Mayasi Y, Henninger N. White matter hyperintensity lesion burden is associated with the infarct volume and 90-day outcome in small subcortical infarcts. Acta Neurol Scand. 2017;135:585–92. Zhou JY, Shi YB, Xia C, Lu CQ, Tang TY, Lu T, et al. Beyond collaterals: brain frailty additionally improves prediction of clinical outcome in acute ischemic stroke. Eur Radiol. 2022;32:6943–52. Bu N, Khlif MS, Lemmens R, Wouters A, Fiebach JB, Chamorro A, et al. Imaging Markers of Brain Frailty and Outcome in Patients With Acute Ischemic Stroke. Stroke. 2021;52:1004–11. Giurgiutiu DV, Yoo AJ, Fitzpatrick K, Chaudhry Z, Leslie-Mazwi T, Schwamm LH, et al. Severity of leukoaraiosis, leptomeningeal collaterals, and clinical outcomes after intra-arterial therapy in patients with acute ischemic stroke. J Neurointerv Surg. 2015;7:326–30. Lin MP, Brott TG, Liebeskind DS, Meschia JF, Sam K, Gottesman RF. Collateral Recruitment Is Impaired by Cerebral Small Vessel Disease. Stroke. 2020;51:1404–10. Ter Telgte A, Van Leijsen EMC, Wiegertjes K, Klijn CJM, De Tuladhar AM. Leeuw F E Cerebral small vessel disease: from a focal to a global perspective. Nat Rev Neurol. 2018;14:387–98. Kellert L, Martin E, Sykora M, Bauer H, Gussmann P, Diedler J, et al. Cerebral oxygen transport failure? decreasing hemoglobin and hematocrit levels after ischemic stroke predict poor outcome and mortality: STroke: RelevAnt Impact of hemoGlobin, Hematocrit and Transfusion (STRAIGHT)--an observational study. Stroke. 2011;42:2832–7. Shen H, Shen L. Red blood cell distribution width as a predictor of mortality and poor functional outcome after acute ischemic stroke: a meta-analysis and meta-regression. BMC Neurol. 2024;24:122. Huang YL, Hu ZD. Lower mean corpuscular hemoglobin concentration is associated with poorer outcomes in intensive care unit admitted patients with acute myocardial infarction. Ann Transl Med. 2016;4:190. Cao W, Song Y, Bai X, Yang B, Li L, Wang X, et al. Systemic-inflammatory indices and clinical outcomes in patients with anterior circulation acute ischemic stroke undergoing successful endovascular thrombectomy. Heliyon. 2024;10:e31122. Pernow J, Mahdi A, Yang J, Zhou Z. Red blood cell dysfunction: a new player in cardiovascular disease. Cardiovasc Res. 2019;115:1596–605. Liu SL, Feng BY, Song QR, Zhang YM, Wu SL, Cai J. Neutrophil to high-density lipoprotein cholesterol ratio predicts adverse cardiovascular outcomes in subjects with pre-diabetes: a large cohort study from China. Lipids Health Dis. 2022;21:86. Guo J, Chen M, Hong Y, Huang Y, Zhang H, Zhou Y, et al. Comparison of the Predicting Value of Neutrophil to high-Density Lipoprotein Cholesterol Ratio and Monocyte to high-Density Lipoprotein Cholesterol Ratio for in-Hospital Prognosis and Severe Coronary Artery Stenosis in Patients with ST-Segment Elevation Acute Myocardial Infarction Following Percutaneous Coronary Intervention: A Retrospective Study. J Inflamm Res. 2023;16:4541–57. Chen G, Yang N, Ren J, He Y, Huang H, Hu X, et al. Neutrophil Counts to High-Density Lipoprotein Cholesterol Ratio: a Potential Predictor of Prognosis in Acute Ischemic Stroke Patients After Intravenous Thrombolysis. Neurotox Res. 2020;38:1001–9. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table1.docx table2.docx table3.docx table4.docx Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2025 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 31 Dec, 2024 Reviews received at journal 29 Dec, 2024 Reviewers agreed at journal 19 Dec, 2024 Reviews received at journal 14 Dec, 2024 Reviewers agreed at journal 04 Dec, 2024 Reviewers agreed at journal 03 Dec, 2024 Reviews received at journal 02 Dec, 2024 Reviewers agreed at journal 01 Dec, 2024 Reviewers invited by journal 31 Jul, 2024 Editor invited by journal 29 Jul, 2024 Editor assigned by journal 27 Jul, 2024 Submission checks completed at journal 27 Jul, 2024 First submitted to journal 26 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4806191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336065483,"identity":"dbf5c0e3-1682-4a65-af6a-2c57d4d2c51e","order_by":0,"name":"Xiao feng","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"feng","suffix":""},{"id":336065484,"identity":"8647d0cc-9af3-479b-8f2d-be955fb38016","order_by":1,"name":"Meiherinisa Taiwakuli","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Meiherinisa","middleName":"","lastName":"Taiwakuli","suffix":""},{"id":336065485,"identity":"5a7ae6a3-af19-4ebc-9349-851805d655da","order_by":2,"name":"junyong Du","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"junyong","middleName":"","lastName":"Du","suffix":""},{"id":336065486,"identity":"f5f7eb99-edce-4623-8cbd-c86edefcf241","order_by":3,"name":"wenhao Zhu","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"wenhao","middleName":"","lastName":"Zhu","suffix":""},{"id":336065487,"identity":"cffca0c7-1490-4b4e-8a06-23623c128c9b","order_by":4,"name":"Shabei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYHACNgbGBgYefmbmgw9I0yLZzpZsQJIWBoPzPGYCRKk3uJH+7MHPHTYyxocZzBgYamyiCWqRnJFjbth7Jo3H7DBD2gOGY2m5DYS08EvksEnwth0GaTluwNhwmLAWNon0Z5J/2/7zGDcztkkQpYVfIsFMmrftAI8BMzMbcVoke96YScueSeaROMzGbJBAjF8MjgMd9naHnT1///mPDz7U2BDWggoSSFM+CkbBKBgFowAXAAC3yTkUtpIz0wAAAABJRU5ErkJggg==","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Shabei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-07-26 07:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4806191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4806191/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12883-025-04067-x","type":"published","date":"2025-02-15T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63423094,"identity":"a0c15f00-5c20-4f74-812c-a24528a5b0eb","added_by":"auto","created_at":"2024-08-28 03:02:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169921,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of independent risk factors and predictive model associated with poor long-term prognosis (mRS\u0026gt;2) (a) and early neurological deterioration (b)\u003c/p\u003e\n\u003cp\u003emRS modified rankin scale, NIHSS national institutes of health stroke scale, NHR neutrophil to high-density lipoprotein cholesterol ratio\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4806191/v1/6043b33e53bb06b3faf6808a.png"},{"id":76487670,"identity":"f3baa657-ff17-429c-b266-bd602682cd21","added_by":"auto","created_at":"2025-02-17 16:10:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":777978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4806191/v1/bd1798b8-5afd-4b0e-86df-58e994c7bd57.pdf"},{"id":63421329,"identity":"d5caaa7a-073a-4cad-851f-03b471ff7647","added_by":"auto","created_at":"2024-08-28 02:46:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27601,"visible":true,"origin":"","legend":"","description":"","filename":"table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4806191/v1/9a915cb64614b91520abedbd.docx"},{"id":63421332,"identity":"5f92047d-063c-49a9-84d1-10e38986f7c7","added_by":"auto","created_at":"2024-08-28 02:46:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16729,"visible":true,"origin":"","legend":"","description":"","filename":"table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4806191/v1/b72b3612bfcb4690c412dde5.docx"},{"id":63422668,"identity":"b2ecb832-daf5-439f-8251-f807143a7c03","added_by":"auto","created_at":"2024-08-28 02:54:04","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15979,"visible":true,"origin":"","legend":"","description":"","filename":"table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4806191/v1/afcb2a3a64794f9e7bc1cc1c.docx"},{"id":63421333,"identity":"f681ae61-1713-4558-acfd-077ff0964531","added_by":"auto","created_at":"2024-08-28 02:46:04","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13012,"visible":true,"origin":"","legend":"","description":"","filename":"table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4806191/v1/162fbfb97e72e88764feeb50.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical and Imaging Risk Factors for Early Neurological Deterioration and Long-Term Neurological Disability in Patients with Single Subcortical Small Infarction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSingle small subcortical infarction (SSSI), an infarction in the territory of penetrating arteries[1], accounts for approximately 20% of all ischemic strokes[2]. Due to the small size of the lesions and generally mild symptoms, clinicians may misjudge the prognosis of these patients. However, SSSI can still result in severe long-term neurological disabilities or even death. Additionally, despite adequate antithrombotic and lipid-lowering therapies, some patients with SSSI may experience early neurological deterioration (END) during hospitalization. END is characterized by sudden worsening of symptoms such as limb weakness, speech difficulties, and swallowing issues[3]. END can significantly impact long-term outcomes[4], and previous studies have reported END incidence rates ranging from 3% to 31.1%[5,6].\u0026nbsp;Admission NIHSS score, history of hypertension, and low-density lipoprotein-cholesterol (LDL-C) levels are predictors of outcomes in SSSI patients[6]. Additionally, studies have suggested that systemic inflammation markers, such as the neutrophil-to-lymphocyte ratio (NLR), are associated with END[7]. However, studies focusing on risk factors for END and long-term neurological disability in SSSI patients is still limited. Some demographic and cardiovascular risk factors have not been thoroughly examined, and Comprehensive models for predicting adverse outcomes are currently unavailable.\u003c/p\u003e\n\u003cp\u003eCurrently, SSSI is mostly considered as an important neuroimaging feature of cerebral small vessel disease (CSVD)[8]. Other imaging markers of CSVD include white matter hyperintensities (WMHs), lacunes, enlarged perivascular spaces (ePVS), cerebral microbleeds, and brain atrophy[9]. The occurrence of SSSI is closely associated with CSVD and SSSI lesions may convert to other CSVD imaging markers, such as lacunes and WMHs[10,11]. Previous studies have also demonstrated that the overall burden of CSVD is associated with poor outcomes at discharge in SSSI patients[12,13]. However, there is limited research on the differential impact of CSVD imaging marker types and locations on the END and long-term outcomes of SSSI. It remains unclear which combinations of markers are most predictive of SSSI prognosis. Additionally, the morphology and location characteristics of SSSI lesions may be related to outcomes. Lesion size and specific locations, such as the pons and internal capsule, have been shown to be associated with END in SSSI patients[4,14,15], but overall, the sample sizes in these studies have been limited.\u003c/p\u003e\n\u003cp\u003eGiven the current insufficient exploration of factors influencing the occurrence of END during hospitalization and long-term neurological disability in SSSI patients, this study first aimed to evaluate potential risk factors for END and long-term prognosis. Clinical, neuroimaging (including CSVD features and the morphology and location of infarcts) and laboratory indicators were comprehensively considered. Furthermore, we aimed to develop predictive models for adverse outcomes (END and long-term disability) of SSSI to facilitate early identification and intervention for high-risk patients in clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively included consecutive patients with SSSI who were hospitalized in the Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and completed head MRI scans from April 2021 to June 2022. The inclusion criteria were: 1) admission within 72 hours after symptom onset, 2) maximum lesion diameter \u0026lt; 20 mm on diffusion-weighted imaging (DWI)\u0026nbsp;sequence, and 3) age \u0026gt; 18 years. The exclusion criteria were: 1) receiving endovascular thrombectomy, 2) pre-stroke modified Rankin Scale score (mRS) \u0026gt; 1, 3) concurrent malignancy, vasculitis, systemic immune diseases, or hemorrhagic stroke, 4) multiple infarct lesions, and 5) current or previous cortical infarction and/or cerebellar infarction. The study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20210731). All patient information in this retrospective study was de-identified and anonymized, and written informed consent was obtained from each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo neurologists who were blinded to the baseline data conducted follow-up assessments of SSSI patients independently. The NIHSS score on admission was used to assess the severity of symptoms of SSSI. END was defined as an increase in the NIHSS score by \u0026ge;2 points or in the limb weakness sub-score by \u0026ge;1 point after admission[7]. Long-term poor neurological outcomes were determined based on whether the mRS score \u0026gt;2 at follow-up. The mRS scoring criteria range from 0 (no symptoms) to 6 (death).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe systematically collected demographic information (e.g., gender, age), medical history (e.g., hypertension, diabetes, hyperlipidemia, coronary artery disease, stroke), laboratory test results (e.g., eGFR), time from onset to admission (OTT), diastolic blood pressure (DBP) and systolic blood pressure (SBP) on admission, and TOAST classification. Laboratory samples were collected on the day following admission. Hypertension was defined as a pre-admission or in-hospital systolic blood pressure \u0026ge;140 mmHg and/or diastolic blood pressure \u0026ge;90 mmHg, or current use of antihypertensive medications. Diabetes was defined as a pre-admission or in-hospital fasting blood glucose \u0026gt;7 mmol/L and/or postprandial blood glucose \u0026ge;11.1 mmol/L and/or HbA1c \u0026ge;6.5%, or current use of hypoglycemic medications. Hyperlipidemia was defined as total cholesterol (TC) \u0026ge;5.2 mmol/L and/or low-density lipoprotein-cholesterol (LDL-C) \u0026ge;3.4 mmol/L and/or triglycerides (TG) \u0026ge;1.7 mmol/L, or current use of lipid-lowering medications. Renal dysfunction was determined by an eGFR \u0026lt;60 ml/min/m\u0026sup2; measured during hospitalization. TOAST classification was used to categorize the stroke subtype by causes. Neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-high density lipoprotein cholesterol(HDL-C) ratio (NHR) were calculated by the formulas: NLR = neutrophil count (cells) / lymphocyte count (cells)[16]\u0026nbsp;NHR = neutrophil count (10^9 cells) / HDL-C (mmol/L)[17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 3 Tesla brain MRI was performed within 48 hours of admission, including fluid-attenuated inversion recovery imaging (FLAIR), T2-weighted imaging, T1-weighted imaging, and DWI. (FLAIR: repetition time (TR): 8000 ms, echo time (TE): 150 ms,\u0026nbsp;matrix size of 512 \u0026times; 512. slice thickness: 5 mm, interslice gap:1.5 mm; DWI: TR of 3000 ms, TE: 65.3ms, slice thickness: 5 mm, interslice gap: 1 mm, a b-value: 1000 s/m^2).\u0026nbsp;Two neuroradiologists, blinded to the clinical information and follow-up data, independently performed the imaging evaluations. The assessments included arterial stenosis, infarct lesion diameter, lesion location, degree of suspected vascular-original white matter hyperintensities (WMHs), extent of enlarged perivascular spaces (EPVS), number of chronic lacunar infarctions, and degree of brain atrophy.\u003c/p\u003e\n\u003cp\u003eThe severity of arterial stenosis was assessed using digital subtraction angiography (DSA), computed tomography angiography (CTA), or magnetic resonance angiography (MRA). Symptomatic extracranial arterial stenosis was defined as \u0026ge;50% stenosis of the cervical arteries (common carotid artery, extracranial internal carotid artery, and extracranial vertebral artery) with ipsilateral acute cerebral infarction or brain ischemic symptoms. Symptomatic intracranial arterial stenosis was defined as \u0026ge;50% stenosis of intracranial arteries with downstream acute cerebral infarction or brain ischemic symptoms.\u003c/p\u003e\n\u003cp\u003eSSSI lesion were identified as areas of hyperintensity on DWI, T2, and FLAIR, and hypointensity on apparent diffusion coefficient (ADC) maps. Suspected vascular-original WMHs were identified as hyperintensities in the white matter on FLAIR or T2-weighted images, categorized into periventricular WMHs and subcortical WMHs, and assessed using the Fazekas scale[18], scoring from 0 to 3 at each location. EPVS were identified as small (\u0026le;3 mm) round or linear hyperintensities in the basal ganglia and centrum semiovale on T2-weighted images and graded from 0 to 4 based on number: 0 = no EPVS, 1 = 1\u0026ndash;10 EPVS, 2 = 11\u0026ndash;20 EPVS, 3 = 21\u0026ndash;40 EPVS, 4 = \u0026gt;40 EPVS[19]. Lacunes were defined as lesions with cerebrospinal fluid intensity on T2, typically 3\u0026ndash;15 mm in diameter, with a hyperintense rim on FLAIR, and were graded into three categories: 0: no lacunes, 1: 1-2 lacunes, 2: \u0026gt;2 lacunes[20]. The degree of brain atrophy was assessed using a 0-3 grading scale based on comparisons of cortical sulcal depth and ventricular size with standard MRI images of age-matched elderly controls: 0 = no atrophy, 1 = mild atrophy (similar to the 95th percentile template image), 2 = moderate atrophy (greater than the 95th percentile but not exceeding twice the ventricular size and sulcal depth of the standard image), 3 = severe atrophy (ventricular size and sulcal depth more than twice the 95th percentile template image)[21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS version 26 (SPSS, Inc., Chicago, IL, USA). Continuous variables with a normal distribution were presented as mean \u0026plusmn; standard deviation, while non-normally distributed continuous variables and ordinal variables were presented as median (interquartile range). Categorical variables were expressed as counts (percentages). Group comparisons for normally distributed variables were performed using the independent samples t-test, while non-normally distributed variables were analyzed using the Mann-Whitney U test. Categorical variables were compared using the chi-square test, with or without continuity correction and Fisher\u0026apos;s exact test. Spearman correlation analysis was used to assess the degree of association between two variables. Binary logistic regression analysis was conducted to identify clinical, imaging, and laboratory factors associated with long-term poor neurological outcomes and END. Variables with P \u0026lt; 0.1 in univariate analysis and those considered potentially influential based on previous studies were included in the multivariate logistic regression model. A stepwise regression method was used to sequentially include variables with P \u0026lt; 0.05 in the multivariate logistic regression model, eliminating irrelevant factors to develop the final prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity, specificity, and predictive value (area under curve, AUC) of risk factors and the final model. The cut-off value was determined based on the maximum Youden\u0026apos;s index on the curve. Youden\u0026apos;s index was calculated by the formula: Youden\u0026rsquo;s Index = Sensitivity + Specificity - 1. A two-sided p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic Characteristics of Study Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, a total of 1,329 patients with SSSI were hospitalized. Among them, 304 patients met the inclusion and exclusion criteria. Excluding patients with missing baseline information, loss to follow-up, and incomplete MRI examinations (15/304), a final total of 289 patients with SSSI were included in the analysis. The clinical information of the subjects is summarized in Table 1. The mean age of the patients was 60 years (60.2 \u0026plusmn; 10.3), with 216 (74.7%) males. The stroke lesions were primarily located in the brainstem (93/289, 33.2%), thalamus (36/289, 12.5%), basal ganglia (56/289, 19.4%), and corona radiata/centrum semiovale (97/289, 33.6%). During hospitalization, 18 patients (6.2%) experienced early neurological deterioration (END). After a median follow-up of 21.4 (16.7\u0026ndash;25.2) months, 29 patients (10%) had a modified Rankin Scale (mRS) score \u0026gt;2, while 260 patients (90%) had an mRS score \u0026le;2. Periventricular white matter hyperintensities (WMHs) were present in 84.4% (244/289) of the patients, and subcortical WMHs were present in 78.5% (227/289). Lacunes were detected in 45.7% (132/289) of the patients, and brain atrophy was observed in 33.7% (97/289), with moderate to severe brain atrophy ( \u0026gt; 1 score) present in 14.2% (41/289) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate Analysis of Early Neurological Deterioration (END)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to patients without END during hospitalization, those with END had higher DBP and NIHSS scores on admission, larger lesion diameters, and higher values of total cholesterol (TC), neutrophil count, NLR, and NHR. After adjusting for confounding factors, multivariate logistic regression analysis showed that NIHSS score (OR = 1.438, 95% CI = 1.182\u0026ndash;1.749, P \u0026lt; 0.001), neutrophil count (OR = 1.333, 95% CI = 1.025\u0026ndash;1.733, P = 0.032), TC (OR = 1.545, 95% CI = 1.014\u0026ndash;2.355, P = 0.043), and NHR (OR = 1.371, 95% CI = 1.074\u0026ndash;1.75, P = 0.011) were independent predictors of END (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA predictive model for END in SSSI was constructed using stepwise regression, which included NIHSS score and NHR on admission (Table 2), with an area under the ROC curve of 0.836 (95% CI = 0.744\u0026ndash;0.927). ROC analysis for the independent predictive factors indicated that the optimal cut-off value for NIHSS score in predicting END was 4.5, with a sensitivity of 83.3% and specificity of 76%. The cut-off value for NHR was 5.28, with a sensitivity of 66.7% and specificity of 65.7% (Table 4, Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate Analysis of Poor Neurological Outcomes (mRS \u0026gt; 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis indicated that age, diastolic blood pressure (DBP) and NIHSS score on admission, responsible intracranial artery stenosis, periventricular and subcortical WMHs, number and grading of lacunes, degree of brain atrophy, eGFR, MCHC, NLR, and NHR were associated with long-term poor outcomes in SSSI patients. Multivariate logistic regression models adjusted for age, gender, imaging evaluations, and laboratory results showed that age (OR = 1.083, 95% CI = 1.008\u0026ndash;1.163, P = 0.029), NIHSS score on admission (OR = 1.685, 95% CI = 1.33\u0026ndash;2.134, P \u0026lt; 0.001), maximum lesion diameter (OR = 1.121, 95% CI = 1.001\u0026ndash;1.255, P = 0.048), grading of lacunes (OR = 3.644, 95% CI = 1.468\u0026ndash;9.048, P = 0.005), grading of brain atrophy (OR = 2.232, 95% CI = 1.199\u0026ndash;4.154, P = 0.011), MCHC (OR = 0.952, 95% CI = 0.912\u0026ndash;0.994, P = 0.026), and symptomatic intracranial artery stenosis (OR = 6.655, 95% CI = 1.618\u0026ndash;27.38, P = 0.009) were independently associated with poor outcomes at follow-up (Table 3).\u003c/p\u003e\n\u003cp\u003eAfter stepwise regression to exclude irrelevant variables, we developed a predictive model for poor prognosis, which consisting of age, NIHSS score at admission, symptomatic intracranial artery stenosis, number of lacunes, and brain atrophy (Table 3). Subsequent ROC curve analysis showed that the model had good predictive performance (AUC 0.926, 95% CI = 0.889\u0026ndash;0.964). ROC analysis for the independent predictive factors indicated that the optimal cut-off value for NIHSS in predicting poor outcomes was 3.5, with a sensitivity of 86.2% and specificity of 65%. The cut-off value for age was 67.5 years, with a corresponding sensitivity of 65.5% and specificity of 79.2%. The cut-off value for the number of lacunes was 1.5, with a sensitivity of 55.2% and specificity of 75%. The cut-off value for the degree of brain atrophy was 0.5, with a sensitivity of 72.4% and specificity of 70.8% (Table 4, Fig. 1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we systematically analyzed the clinical, imaging, and laboratory factors influencing END and long-term poor outcomes in SSSI patients. Our findings indicate that patients with more severe WMH at baseline are more likely to experience long-term poor outcomes. Independent predictors of long-term poor outcomes included advanced age, lower MCHC, higher admission NIHSS score, larger lesion diameter, greater number of lacunes, higher degree of brain atrophy, and the presence of symptomatic intracranial artery stenosis. Independent predictors of END during hospitalization included higher NIHSS score, TC, neutrophil count, and NHR. Finally, we developed predictive models for END and long-term outcomes, with ROC curve analysis demonstrating good predictive performance.\u003c/p\u003e\n\u003cp\u003ePrevious study focusing on minor ischemic stroke (NIHSS\u0026le;3) showed that age, NIHSS score, large artery atherosclerosis, non-culprit vessel stenosis, and lesion diameter were associated with poor outcomes at 3 months[22]. Kim et al. reported that a history of diabetes and NIHSS score were independently associated with poor outcomes at 3 months in patients with mild stroke (NIHSS \u0026le;5) undergoing thrombolytic therapy[23]. Studies have identified NIHSS score, culprit artery stenosis, and branch atheromatous disease as independent predictors of END in acute lacunar stroke patients[7,24]. Another study indicated that elevated LDL-C was an independent risk factor for END[4]. Consistent with previous findings, ROC analysis showed that even lower NIHSS scores at admission could predict END and long-term poor outcomes (cut-off values of 4.5 and 3.5, respectively). This suggests that despite the small lesion size and relatively mild symptoms, patients can still develop significant neurological disability, emphasizing the need for adequate clinical attention in these patients. The AUC and Youden index for prediction model of END did not show a substantial improvement over that of the independent variable NIHSS, suggesting that the NIHSS holds dominant predictive value in this model. The statistical significance of lesion diameter was borderline; it lost significance after adjusting for clinical and imaging risk factors but was statistically significant in the final model, possibly due to insufficient sample size. Unlike previous studies, the history of hypertension and diabetes showed insignificant in influenced outcomes. This discrepancy may be due to differences in the study populations; our study had a higher proportion of hypertensive patients and included those with large artery stenosis. Our study also demonstrated that SSSI patients with symptomatic intracranial artery stenosis were more likely to have poor long-term outcomes compared to other etiologies. The sample sizes for patients with extracranial artery stenosis and atrial fibrillation were insufficient to detect differences, further studies are needed to elucidate their relationship with SSSI outcomes.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that the total burden of CSVD is the risk factor for in-hospital worsening of mRS and lack of improvement in NIHSS scores in patients with acute lacunar infarction[13]. Liu et al. also demonstrated that the total CSVD score is an independent predictor of poor outcomes at three months in stroke patients receiving thrombolytic therapy[25]. Sanjeeva et al. found that WMHs are associated with poor symptom improvement and disability at three months post-discharge in patients with minor stroke (NIHSS \u0026lt; 6)[26]. Helenius et al. conducted a study involving 80 patients with small subcortical infarction (SSI) and found that the severity of WMHs was related to lesion volume and poor outcomes at three months[27], although two studies did not report differences in outcomes based on the location of WMHs. Another study, which included 119 patients with acute ischemic stroke, demonstrated that brain atrophy and lacunes were associated with poor outcomes at three months[28]. Our findings are consistent with these results but with a longer follow-up period. We provided a detailed classification and localization of CSVD imaging characteristics, finding differences in periventricular and subcortical WMHs between different outcome groups, though which were not statistically significant after adjusting for confounding factors. ePVS were not related to outcomes, whereas lacunes and brain atrophy were independent predictors of poor prognosis. The different relationships between CSVD subtypes and outcomes suggest distinct underlying mechanisms and risk factors, highlighting the varying importance of CSVD markers when assessing prognosis in SSSI patients. The extent of CSVD may represent \u0026quot;brain frailty[29],\u0026quot; affecting post-stroke functional recovery by reducing cerebral functional reserve or collateral compensation[28,30,31]. The ROC curve cut-off values for brain atrophy and lacunes suggest that few lacunes and mild brain atrophy can predict adverse outcomes. However, whether the relationship between brain atrophy and outcomes differs based on the vascular or neurodegenerative origin of brain atrophy remains to be further studied. Some studies suggest that neurodegenerative causes may have a more significant impact on outcomes[32]. The lack of susceptibility-weighted imaging in most patients precluded an assessment of cerebral microbleeds, limiting the comprehensiveness of our analysis regarding the impact of CSVD on stroke outcomes.\u003c/p\u003e\n\u003cp\u003ePrevious studies have demonstrated that red blood cell-related tests such as hemoglobin, hematocrit, and red cell distribution width are independently associated with ischemic stroke outcomes[33,34]. However, there are few reports on the relationship between MCHC and ischemic stroke prognosis. Huang et al. found that MCHC is independently associated with mortality during hospitalization and within one year post-discharge in acute myocardial infarction patients[35]. Inflammatory responses play a crucial role in the pathogenesis and progression of ischemic stroke. Various inflammatory markers, such as D-dimer, C-reactive protein, and the widely recognized inflammation index NLR, have been shown to be independently associated with ischemic stroke outcome[12,36], which suggested that inflammation might mediate the relationship between MCHC and SSSI by impairing iron metabolism[35]. Additionally, red blood cells may influence cerebral atherosclerosis by regulating vascular function and maintaining vascular integrity[37]. The relationship between red blood cell parameters and ischemic stroke warrants further investigation.\u003c/p\u003e\n\u003cp\u003eNam et al. reported that NLR is associated with END in a study involving 438 SSSI patients[7]. NHR, a novel inflammation index proposed in recent years, has been supported by several studies to be associated with the incidence of adverse cardiovascular events and stroke[17,38], as well as poor outcomes in acute coronary syndrome and ischemic stroke patients[39,40]. To our knowledge, no studies have yet reported on the relationship between NHR and SSSI outcomes. We observed that both NLR and NHR were elevated in patients with poor outcomes, but NLR lost its statistical significance in predicting END after adjusting for admission NIHSS or lesion diameter. This suggests that, compared to NHR, the association of NLR with END is primarily mediated by stroke severity and lesion size (Spearman correlation analysis: NLR: NIHSS: correlation coefficient 0.208, P \u0026lt; 0.0001; long-axis diameter: correlation coefficient 0.141, P = 0.016; NHR: NIHSS: correlation coefficient 0.157, P = 0.008; long-axis diameter: correlation coefficient 0.013, P = 0.826). NHR may be a better independent marker for predicting in-hospital outcomes in stroke patients, which needs to be validated in further large-scale prospective studies.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths: 1. Unlike previous studies with shorter follow-up periods, our study provides insights into the long-term prognosis of SSSI patients. 2.We included a broader range of variables related to SSSI prognosis compared to prior studies, eventually obtained highly efficient model for predicting END and long-term outcomes. However, the limitations must also be point out: 1. The relatively small sample size and outcome events led to wide confidence intervals for some predictive factors and might have masked certain risk factors. 2.The retrospective method of the study may introduce bias due to record inaccuracies and missing information. 3. The long follow-up period means that the health status of patients might have been influenced by other comorbidities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study reveals that the severity of symptoms at admission, elevated neutrophil counts, TC, and NHR are risk factors for END during hospitalization. the lesion size, severity of symptoms at admission, presence of symptomatic intracranial arterial stenosis, lacune, and brain atrophy are associated with poor long-term prognosis in SSSI patients. This study provides a predictive model for the END and long-term adverse outcomes of SSSI. The findings have potential value for stratifying high-risk patients and evaluating therapeutic efficacy, offering clues for patient and risk factor selection in future prospective studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"380\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eFull name\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eApparent Diffusion Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eArea Under Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eCSVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eCerebral Small Vessel Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eCTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eComputed Tomography Angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eDSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eDigital Subtraction Angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eDiffusion-Weighted Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eEstimated Glomerular Filtration Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eEND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eEarly Neurological Deterioration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eePVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eEnlarged Perivascular Spaces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eFLAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eFluid-Attenuated Inversion Recovery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eHigh Density Lipoprotein Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eLow-Density Lipoprotein Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eMCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eMiddle Cerebral Artery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eMean Corpuscular Hemoglobin Concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003emRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eModified Rankin Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eMRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eMagnetic Resonance Angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eNHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eNeutrophil to High Density Lipoprotein Cholesterol Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eNIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eNational Institutes of Health Stroke Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eOTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eOnset to Admission Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eSSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eSingle Subcortical Small Infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eTotal Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eEcho Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\" valign=\"top\"\u003e\n \u003cp\u003eTIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eTIA transient ischemic attack\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eTOAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eTrial of Org 10172 in Acute Stroke Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eRepetition Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.36842105263158%\"\u003e\n \u003cp\u003eWMHs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.63157894736842%\"\u003e\n \u003cp\u003eWhite Matter Hyperintensities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20210731). The patient information in this retrospective study was de-identified and anonymized, and written informed consent was obtained from each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon reasonable request, the corresponding author will provide access to the datasets generated and/or analyzed during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Hubei Province (2021CFB382).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFX: Responsible for study design, drafting the manuscript, data collection, and data analysis. MH, MT, DJY: Responsible for data collection and analysis. ZWH, XSB: Responsible for study design, data analysis, data interpretation, and manuscript revision. All authors have commented on previous versions of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to extend their sincere appreciation to all contributors and collaborators involved in this research endeavor.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDuan Z, Sun W, Liu W, Xiao L, Huang Z, Cao L, et al. Acute diffusion-weighted imaging lesion patterns predict progressive small subcortical infarct in the perforator territory of the middle cerebral artery. Int J Stroke. 2015;10:207\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamford J, Sandercock P, Jones L, Warlow C. 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Red blood cell dysfunction: a new player in cardiovascular disease. Cardiovasc Res. 2019;115:1596\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu SL, Feng BY, Song QR, Zhang YM, Wu SL, Cai J. Neutrophil to high-density lipoprotein cholesterol ratio predicts adverse cardiovascular outcomes in subjects with pre-diabetes: a large cohort study from China. Lipids Health Dis. 2022;21:86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo J, Chen M, Hong Y, Huang Y, Zhang H, Zhou Y, et al. Comparison of the Predicting Value of Neutrophil to high-Density Lipoprotein Cholesterol Ratio and Monocyte to high-Density Lipoprotein Cholesterol Ratio for in-Hospital Prognosis and Severe Coronary Artery Stenosis in Patients with ST-Segment Elevation Acute Myocardial Infarction Following Percutaneous Coronary Intervention: A Retrospective Study. J Inflamm Res. 2023;16:4541\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen G, Yang N, Ren J, He Y, Huang H, Hu X, et al. Neutrophil Counts to High-Density Lipoprotein Cholesterol Ratio: a Potential Predictor of Prognosis in Acute Ischemic Stroke Patients After Intravenous Thrombolysis. Neurotox Res. 2020;38:1001\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Single subcortical small infarction, Cerebral Small vessel disease, Magnetic resonance imaging, Early neurological deterioration, Long-term neurological disability","lastPublishedDoi":"10.21203/rs.3.rs-4806191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4806191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eThis study aims to evaluate the clinical and imaging risk factors for early neurological deterioration (END) and long-term neurological disability in patients with Single subcortical small infarction (SSSI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe retrospectively included SSSI patients hospitalized. Outcomes were defined as modified Rankin Scale (mRS) score \u0026gt;2 at follow-up and the occurrence of END during hospitalization. Multivariate logistic regression identified independent predictors of END and long-term outcomes. Stepwise regression analysis was used to develop a predictive model for poor outcomes. The predictive performance of risk factors and the model was assessed using receiver operating characteristic (ROC) curves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 289 SSSI patients were included. During hospitalization, 18 patients (6.2%) experienced END, and 29 patients (10%) had neurological disability at a median follow-up of 21.4 (16.7–25.2) months. Multivariate analysis showed the National Institutes of Health Stroke Scale (NIHSS) score (OR 1.438, 95% CI 1.182–1.749, P \u0026lt; 0.001), Total cholesterol (TC) (OR 1.545, 95% CI 1.014–2.355, P = 0.043), neutrophil to High density lipoprotein cholesterol ratio (NHR) (OR 1.371, 95% CI 1.074–1.75, P = 0.011), and neutrophil count (OR 1.333, 95% CI 1.025–1.733, P = 0.032) were independently associated with END. Age (OR 1.083, 95% CI 1.008–1.163, P = 0.029), lesion diameter (OR 1.121, 95% CI 1.001–1.255, P = 0.048), NIHSS (OR 1.685, 95% CI 1.33–2.134, P \u0026lt; 0.001), symptomatic intracranial artery stenosis (OR 6.655, 95% CI 1.618–27.38, P = 0.009), lacune grading (OR 3.644, 95% CI 1.468–9.048, P = 0.005), and The degree of brain atrophy (OR 2.232, 95% CI 1.199–4.154, P = 0.011) were independently associated with neurological disability. The predictive model for END (included NIHSS score and NHR level) and long-term neurological disability (included age, NIHSS score, symptomatic intracranial artery stenosis, number of lacunes, and brain atrophy) showed areas under the ROC curve of 0.836 and 0.926, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigh NIHSS, TC, NHR, and neutrophil count are independent risk factors for END. Age, NIHSS, lesion size, symptomatic intracranial artery stenosis, the degree of lacunes and brain atrophy are predictors of neurological disability in SSSI patients.\u003c/p\u003e","manuscriptTitle":"Clinical and Imaging Risk Factors for Early Neurological Deterioration and Long-Term Neurological Disability in Patients with Single Subcortical Small Infarction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 02:45:59","doi":"10.21203/rs.3.rs-4806191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-31T14:02:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-29T14:43:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258194407199124479567082883809843116882","date":"2024-12-20T04:46:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-14T14:16:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69863551210265075957570309228966912183","date":"2024-12-04T11:13:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209018487778389368339453419951297297902","date":"2024-12-03T23:00:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-02T07:12:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53514313422958802826934538058998486435","date":"2024-12-02T02:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-31T09:40:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-29T15:12:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-27T07:02:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-27T07:02:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2024-07-26T07:36:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af02d620-cae4-4347-953f-06ddd3c0eb8e","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:04:50+00:00","versionOfRecord":{"articleIdentity":"rs-4806191","link":"https://doi.org/10.1186/s12883-025-04067-x","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2025-02-15 15:58:07","publishedOnDateReadable":"February 15th, 2025"},"versionCreatedAt":"2024-08-28 02:45:59","video":"","vorDoi":"10.1186/s12883-025-04067-x","vorDoiUrl":"https://doi.org/10.1186/s12883-025-04067-x","workflowStages":[]},"version":"v1","identity":"rs-4806191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4806191","identity":"rs-4806191","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}