Effect of Mean Platelet Volume and Platelet Count on the Prognosis of Branch Atheromatous Disease

doi:10.21203/rs.3.rs-2234515/v1

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Research Article

Effect of Mean Platelet Volume and Platelet Count on the Prognosis of Branch Atheromatous Disease

https://doi.org/10.21203/rs.3.rs-2234515/v1

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published 01 May, 2024

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Objective: The purpose of this study was to investigate the predictive value of mean platelet volume (MPV) and platelet count (PC) in branch atheromatous disease (BAD).

Methods: This retrospective study included 216 patients with BAD-stroke within 48 h of symptom onset. These patients were divided into good and poor prognosis groups according to their 3-month modified Rankin Scale (mRS) scores after discharge. Multiple logistic regression analysis was used to evaluate independent predictors of poor prognosis in BAD-stroke patients. Receiver operating characteristic (ROC) analysis was used to estimate the predictive value of MPV and PC on BAD-stroke.

Results: Our research showed that a higher MPV (aOR, 2.926; 95% CI, 2.040-4.196; P<0.001) and PC (aOR, 1.013; 95% CI, 1.005-1.020; P=0.001) were independently associated with poor prognosis after adjustment for confounders. The ROC analysis of MPV for predicting poor prognosis showed that the sensitivity and specificity were 74% and 84.9%, respectively, and that the AUC was 0.843 (95% CI, 0.776–0.909, P<0.001). The optimal cut-off value was 12.35. The incidence of END was 24.5% (53 of 163), and 66% of patients in the poor prognosis group had END (33 of 50). Multiple logistic regression analyses showed that elevated MPV and PC were associated with the occurrence of END (P<0.05).

Conclusion: Our results suggested that an elevated MPV and PC may be important in predicting a worse outcome in BAD-stroke patients. Our study also demonstrated an independent association of MPV and PC with END, which is presumably the main reason for the poor prognosis.

mean platelet volume

platelet count

Branch atheromatous disease

stroke outcome

early neurological deterioration

Branch atheromatous disease (BAD) is a single deep cerebral infarction caused by stenosis or occlusion of the perforator artery orifice [1]. BAD-stroke is a common type of acute ischaemic stroke, and its prevalence was reported to be as high as 10.4–18.3% [2]. Clinically, BAD primarily manifests as early neurological deterioration (END) dominated by progressive motor deficiency, which often results in a poor prognosis [3; 4]. A study found that the incidence of END was as high as 39.4% among BAD stroke patients [5]. In daily practice, END and prediction of the prognosis of BAD stroke patients are the most important clinical problems.

Platelets play a crucial role in the development and progression of ischaemic stroke because of their involvement in thromboembolism [6]. Mean platelet volume (MPV) and platelet count (PC) are the two main parameters that reflect platelet function and activities of platelets. Platelets with a higher MPV are more functionally and metabolically active, contain more dense granules and promote further platelet aggregation as well as activation [7; 8]. Patients with acute ischaemic stroke (AIS) have been shown to have significantly higher MPV. MPV and PC were observed to be independent risk factors for ischaemic stroke [8; 9]. Previous studies have suggested that MPV is associated with the prognosis of acute ischaemic stroke [6; 10; 11; 12; 13; 14], although some studies have reported contradictory findings [9; 12]. The studies of PC have mostly been related to cardiology and showed that PC was an independent risk factor for cardiovascular mortality in acute myocardial infarction (AMI) patients [15; 16; 17]. However, the few studies on the relationship between PC and prognosis of BAD-stroke have reached different conclusions [9; 13; 14].

At present, there are no notable reports on the relationship between MPV or PC and the development and prognosis of BAD stroke. Thus, the principal aim of this study was to explore whether baseline MPV and PC have an impact on the development and prognosis of BAD patients.

This retrospective study was approved by the Medical and Health Research Ethics Committee of the Second People’s Hospital of Chengdu (Chengdu, China) and adhered to the Declaration of Helsinki. Because it was a retrospective study, informed consent was not required, and all included patient information was anonymous.

2.1 Research Subjects

We retrospectively collected BAD-stroke patients at Chengdu Second People's Hospital from January 2020 to February 2022. The inclusion criteria were as follows: (1) patients presenting within 48 h of onset; (2) diffusion-weighted imaging[DWI] within 48 hours of admission showing BAD-related stroke; and (3) patients with a modified Rankin scale (mRS) score ≤ 1 before admission. The exclusion criteria included the following: (1) patients who did not undergo blood cell analysis; (2) patients who did not meet the diagnostic criteria for BAD; (3) patients receiving thrombolytic therapy; (4) patients with various bleeding diseases or coagulation dysfunction; (3) patients with cardiogenic embolism or use of coagulation medication; (4) patients with severe cardiopulmonary, liver, and kidney insufficiency, combined with malignant tumours; (5) patients with incomplete follow-up at 3 months poststroke; and (6) patients with incomplete clinical data. All the patients included in the study received 21 consecutive days of double-antibody therapy and 14-day intensive lipid-lowering treatment(Atorvastatin 40mg/day ) after admission and were then treated with long-term single antibody therapy and Lipid-lowering therapy(Atorvastatin 20mg/day) after discharge.

2.2 Data Collection

Two clinicians reviewed the electronic medical record system from Chengdu Second People’s Hospital to collect information, and a data extraction form was designed to record patient information. Basic information included age, sex, hypertension, diabetes, smoking, drinking, history of stroke, and history of taking antiplatelet drugs, and clinical data included time from onset to arrival, blood pressure at admission, random blood sugar, National Institutes of Health Stroke Scale (NIHSS) score at admission, presence of END, infarct site, hospital days and follow-up results. These risk factors were evaluated as follows: 1) hypertension: repeated blood pressure measurements of ≥ 140/90 mmHg or a history of previous hypertension or use of antihypertensive drugs; 2) diabetes: history of previous diabetes or the use of diabetes medications, or more than two measurements of fasting plasma glucose > 7.0 mmol/L or random plasma glucose > 11.1 mmol/L at the time of admission; 3) smoking: ≥10 cigarettes per day; and 4) drinking: alcohol consumption > 2 U/d [18]. Baseline examinations included routine laboratory tests, such as blood cell, triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), creatinine, and glycated haemoglobin tests.

2.3 MPV and PC Determination

Peripheral venous blood samples (used to measure routine blood parameters, coagulation, renal function, and so on) were collected from all included patients in EDTA anticoagulant tubes on admission (before the administration of antiplatelet drugs), stored at room temperature, and submitted for examination within 2 hours. All blood samples were analysed by a Sysmex XN-9000 automatic blood cell analyser. Laboratory physicians were responsible for reviewing the results.

2.4 BAD and END Definitions

BAD was defined as follows: 1) DWI showing that the infarct in the lenticulostriate artery blood supply area involved 3 levels or more at the horizontal level or lesions extending to the ventral pontine surface in the blood-supply region of the paramedian pontine artery; and 2) no evidence of responsible vessel stenosis (> 50%) or occlusion and embolism [1; 4; 19]. END was defined as an NIHSS score increase of more than 2 points within 1 week [2; 19].

2.5 Follow-Up

All the included subjects were followed up 3 months after onset by telephone or face-to-face interviews, and the patients were grouped into good prognosis and poor prognosis groups according to the 3-month MRS score. Poor prognosis at 90 days was defined as an mRS score > 2 points.

2.6 Statistical Analyses

SPSS Version 25.0 software (IBM Corp, Armonk, NY, USA) was used for statistical analysis. Continuous variables are expressed as the mean ± standard deviation (SD) or as the median and interquartile range (IQR). Differences between groups were compared using a t test or the rank-sum test. Categorical data are presented as frequencies (percentages), and the differences between groups were compared using the chi-squared test or Fisher’s exact test. Variables in the univariate analyses (P < 0.10) were included in the multivariate analysis. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic value of MPV and PC in predicting prognosis. Statistical significance was set at P < 0.05.

3.1 Baseline Characteristics and Follow-up Results

Between January 2020 and February 2022, 282 BAD-stroke patients within 48 h of stroke onset were enrolled in this study. Fifty-three patients met the exclusion criteria, and 13 patients were lost to follow-up. Finally, 216 patients were included in the study. Figure 1 shows the flowchart of the selection of eligible study subjects. At the 3-month follow-up, 166 patients (76.8%) had a good prognosis, and 50 patients (23.2%) had a poor prognosis.

3.2 Comparison of Clinical Baseline Data in the Good and Poor Prognosis Groups

Univariate analysis results showed that MPV and PC were significantly higher in the poor prognosis group than in the good prognosis group (p < 0.05; Table 1). There were also statistically significant differences in sex, NIHSS score at admission, random blood sugar and creatinine between the good prognosis group and the poor prognosis group (P < 0.05; Table 1).

Table 1

Univariate analysis results of the general and clinical data of BAD-stroke patients in the good prognosis group and poor prognosis group.
	Good*(n = 166)	Poor* (n = 50)	P
Age, year	68.0(57.0,76.0)	66.5(58,74.5)	0.911
Female, sex, n(%)	55(33.1)	25(50.0)	0.03
Hypertension, n(%)	123(74.1)	40(80.0)	0.395
Diabetes, n(%)	55(33.1)	16(32.0)	0.881
Smoking, n(%)	51(30.7)	11(22.0)	0.232
Drinking, n(%)	26(15.7)	5(10.0)	0.317
History of ischaemic stroke, n (%)	3(1.8)	1(2.0)	0.929
History of taking antiplatelet drugs, n (%)	2(1.2)	1(2.0)	0.548
Blood pressure at admission
SBP, mmHg	152.9 ± 22.0	157.7 ± 22.5	0.191
DBP, mmHg	85.0(76.0,97.0)	89.5(78.8,97.0)	0.584
Arrival time, hours	24.0(11.0,39.0)	24.0(12.0,35.3)	0.892
NIHSS score at admission, hours	2.5(2,3)	4.5(3,6)	< 0.001
Infarct site, n (%)
LSA	103(62.0)	27(54.0)	0.308
PPA	63(38.0)	23(46.0)
Hospital days, days	12(10,14)	12(10.8,14.3)	0.079
Laboratory test
Random blood sugar, mmol/L	5.6(4.9,7.4)	6.3(5.2,10.7)	0.037
TC, mmol/l	4.78 ± 1.29	4.98 ± 1.66	0.378
TG, mmol/l	1.38(1.03,2.21)	1.43(0.99,1.91)	0.472
HDL, mmol/l	1.09(0.90,1.29)	1.13(0.92,1.42)	0.520
LDL, mmol/l	2.78(2.21,3.51)	2.82(2.25,3.93)	0.598
Creatinine, mmol/l	72.0(62.0,84.0)	63.5(52.5,72.0)	0.001
MPV (fL)	10.85(10.2,11.9)	13.55 (12.0,14.2)	< 0.001
PC (×10³/mL)	185.0(158.0,227.0)	216.5(187.75,263.75)	0.001
MPV/PC	0.059(0.048,0.0733)	0.059(0.050,0.071)	0.780
Glycated haemoglobin (%)	6.1(5.6,7.4)	6.3(5.7,8.9)	0.241
SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; LSA, lenticulostriate artery; PPA, paramedian pontine artery; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MPV, mean platelet volume; PC, platelet count

[Insert Table 1 near here]

3.3 Multivariate Logistic Regression Analysis Results of Factors Affecting Prognosis

When the factors associated with poor prognosis in univariate analyses (P < 0.10) were entered into multivariate logistic regression analysis, the results showed that the associations of NIHSS score at admission (aOR, 1.870; 95% CI, 1.391–2.514; P < 0.001), MPV (aOR, 2.926; 95% CI, 2.040–4.196; P < 0.001) and PC (aOR, 1.013; 95% CI, 1.005–1.020; P = 0.001)) with poor prognosis remained (Table 2).

Table 2

Multivariate logistic regression analysis of factors influencing poor prognosis.
Risk factors	OR	95% CI	P
Sex	0.560	0.195–1.611	0.283
NIHSS score at admission	1.870	1.391–2.514	< 0.001
Random blood sugar, mmol/L	1.108	0.973–1.263	0.123
Creatinine, mmol/l	0.973	0.943–1.003	0.077
MPV (fL)	2.926	2.040–4.196	< 0.001
PC (×10³/mL)	1.013	1.005–1.020	0.001
Hospital days	1.003	0.975–1.031	0.850
NIHSS, National Institutes of Health Stroke Scale; END, early neurological deterioration; MPV, mean platelet volume; PC, platelet count

[Insert Table 2 near here]

3.4 Univariate Analysis and Multivariate Logistic Regression Analysis Results of Clinical Characteristics in the END and no-END Group

END was observed in 24.5% (53/216) of all patients and affected 66% (33 of 50) of poor prognosis patients. The incidence of END in the poor prognosis group was significantly higher than that in the good prognosis group (Fig. 2). Univariate analysis results showed that there were no significant differences in age, hypertension, diabetes, smoking, drinking, history of ischaemic stroke, history of antiplatelet drug use, blood pressure at admission, infarct site, random blood sugar, TG, TC, LDL, HDL, and glycated haemoglobin between the END and non-END groups (P > 0.05) (Table 3). After adjustment for confounders (sex, hospital days, creatinine, MPV and PC), multivariate logistic regression analysis showed that the association of creatinine, MPV and PC with the occurrence of END remained (P < 0.05, Table 4).

Table 3

Univariate analysis results of the general and clinical data of BAD-stroke patients in the END group and no-END group.
	END*(n = 53)	No-END* (n = 163)	P
Ages, year	67.0(57.5,74.0)	68.0(57.0,76.0)	0.775
Female, n(%)	26(49.1)	54(33.1)	0.037
Hypertension, n(%)	42(79.2)	121(74.2)	0.461
Diabetes, n(%)	15(28.3)	56(34.4)	0.415
Smoking, n(%)	15(28.3)	47(28.8)	0.941
Drinking, n(%)	5(9.4)	26(16.0)	0.240
History of ischaemic stroke, n (%)	1(1.9)	3(1.8)	0.983
History of taking antiplatelet drugs, n (%)	0(0)	3(1.8)	0.320
Blood pressure at admission
SBP, mmHg	150.0(136.5,168.5)	153.0(141.0,168.0)	0.552
DBP, mmHg	84.0(77.0,96.5)	87.0(76.0,97.0)	0.564
Arriving time, hours	24.0(12.0,24.0)	24.0(11.0,48.0)	0.585
NIHSS score at admission, hours	3.0(2.0,4.0)	3.0(2.0,4.0)	0.336
Infarct site, n (%)
LSA	30(56.6)	100(61.3)	0.540
PPA	23(43.4)	63(38.7)
Hospital days, days	13.0(11.5,15.0)	12(10.0,14.0)	0.007
Laboratory test
Random blood sugar, mmol/L	6.2(5.0,8.9)	5.6(4.9,8.2)	0.699
TC, mmol/l	4.73(3.80,5.78)	4.76(3.83,5.65)	0.912
TG, mmol/l	1.43(1.04,1.91)	1.36(1.02,2.21)	0.814
HDL, mmol/l	1.15(0.91,1.42)	1.09(0.90,1.31)	0.435
LDL, mmol/l	2.88(2.23,3.82)	2.78(2.21,3.52)	0.499
Creatinine, mmol/l	63.0(51.5,74.0)	72.0(63.0,84.0)	0.002
MPV (fL)	13.0(11.2,13.8)	10.90 (10.2,12.0)	0.000
PC (×10³/mL)	208.0(163.0,257.0)	191.0(162.0,229.0)	0.084
MPV/PC	0.060 (0.050,0.078)	0.057(0.049,0.073)	0.463
Glycated haemoglobin (%)	6.2(5.6,7.6)	6.1(5.6,7.5)	0.870
SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; LSA, lenticulostriate artery; PPA, paramedian pontine artery; END, early neurological deterioration; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MPV, mean platelet volume; PC, platelet count

Table 4

Multivariate logistic regression analysis of factors influencing END.
	Multivariate logistic regression analysis
	OR	95% CI	P
Female (%)	0.716	0.328–1.561	0.401
Hospital days	0.997	0.985–1.008	0.550
Creatinine, mmol/l	0.975	0.954–0.998	0.030
MPV (fL)	1.896	1.494–2.407	< 0.001
PC (×10³/mL)	1.006	1.000-1.012	0.043
MPV, mean platelet volume; PC, platelet count.

[Insert Table 3 and Table 4 near here]

3.5 ROC Analysis of MPV and PC in Predicting Poor Prognosis

The value of MPV and PC in predicting the prognosis of BAD was assessed by ROC curves. The ROC analysis results showed that the sensitivity and specificity of MPV to predict poor prognosis were 74% and 84.9%, respectively, and the AUC was 0.842 (95% CI, 0.776–0.909, P < 0.0001). The optimal cut-off value was 12.35. However, the AUC of PC for predicting poor prognosis was 0.655 (95% CI: 0.570–0.739, P = 0.0009) (Fig. 3). Comparison of the mRS scores between the good prognosis group and poor prognosis group at 3 months was shown in Fig. 4.

In our study, we assessed the association between MPV and PC with the development and prognosis of BAD-stroke, finding that MPV and PC were closely related to the prognosis of BAD-stroke. Patients with a higher MPV and PC were more prone to developing END, which occurred in 66% of patients in the poor prognosis group, indicating that there is a strong association between END and poor prognosis in BAD-stroke.

To our knowledge, there are few analyses of the effect of MPV on stroke subtypes in previous studies, and our study, for the first time, investigated the predictive value of MPV and PC for prognosis in BAD-stroke patients. MPV was found to be associated with poor prognosis of BAD-stroke, and a higher MPV had a negative relationship with 3-month outcomes of BAD-stroke patients. The ROC analysis results showed that an MPV ≥ 12.35 had a sensitivity of 74% in predicting poor prognosis, and the specificity was 84.9%. Previous studies have demonstrated that MPV was associated with poor prognosis in patients with acute cerebral infarction [6; 9; 10; 11], which was consistent with our findings. There are currently few studies on the relationship between PC and stroke prognosis. In 2019, a subgroup analysis of the China National Stroke Registry (CNSR) II that included 16,842 stroke participants found that PC within the normal range may be a qualified predictor for long-term recurrent stroke, mortality, and poor functional outcome in ischaemic stroke or transient ischaemic attack (TIA) patients [14]. Ye et al. [13] showed that patients with cerebral infarction with a PC of 126–225×10⁹/L had the lowest mRS scores between three months and one year after onset. Several studies have previously shown an independent association between PC and cardiovascular mortality or adverse short-term outcomes in AMI patients [15; 16; 17; 20]. However, a study on the prognostic role of MPV and PC in ischaemic and haemorrhagic stroke suggested that PC may be a predictor of the prognosis of haemorrhagic stroke but not ischaemic stroke [9]. In addition, Du et al. [12] revealed that neither MPV nor PC has a significant relationship with the prognosis of haemorrhagic and ischaemic stroke. However, Du et al’s. [12] study defined poor outcomes as mRS scores ≥ 4 at 30 days after discharge, while our study and other studies defined poor outcomes as mRS scores > 2 at 90 days after discharge. In addition, MPV and PC were transformed into categorical variables in Du et al.’s [12] study. However, in our study, MPV and PC were assessed as continuous variables, which could be useful and more reliable in determining the relationship between the prognosis of stroke and platelet parameters. In addition, higher initial NIHSS scores on admission served as risk factors for prognosis in our study, which was similar to Wan and Ma’s [10] study. Higher initial NIHSS scores are suggested to indicate more severe paralysis, so they have a negative correlation with the prognosis of BAD stroke.

Clinically, BAD primarily manifests as END dominated by progressive motor deficiency, and the incidence of END in our study was 24.5%. Patients who experience END often have more severe paralysis. Our study showed that the proportion of patients with END in the poor prognosis group was 66% (33 of 50), which suggested that the presence of END has a close negative correlation with the prognosis of BAD-stroke (Table 4). Therefore, preventing the occurrence of END can greatly improve the prognosis of patients. The infarct size, infarct location and NIHSS score on admission have been shown to be significantly associated with END in previous studies [21; 22; 23; 24; 25]. Our study found that the MPV and PC values in the END group were significantly higher than those in the non-END group, and we demonstrated that elevated MPV and PC are independent risk factors for END in BAD (Table 4). In the PubMed database, only one article concentrated on the relationship between platelets and END in BAD. Oji et al. [26]retrospectively investigated 64 patients with BAD (17 in the END group and 47 in the non-END group) and found that the MPV on admission was significantly greater in the END group than in the non-END group (P < 0.05). Multivariate logistic regression analysis was not performed because of the relatively small number of patients in Oji et al.’s study[26], and our study compensates for this shortcoming. A previous study (n = 1468) reported that the combination of PC and plasma D-dimer in cerebral infarction may have more significant prognostic value for END (OR, 3.622; 95% CI, 1.732–7.573) than plasma D-dimer alone [27]. Thus, the combination of high MPV and PC values may be an independent biomarker for END in BAD. Clinicians should be alert to the occurrence of END in BAD patients, as this leads to poor prognosis. In addition, creatinine values were found to be associated with END in our study (P = 0.030).

Platelets play an important role in the process of thrombus formation and atherogenesis [28]. The pathological basis of BAD is the blockage of perforating arteries by atherosclerotic plaques [1]. Therefore, the number and function of platelets greatly affect the occurrence and development of cerebral infarction [12]. When atherosclerosis occurs, the vessel wall is damaged, which increases the area in contact with platelets, making the vessel wall more prone to thrombosis. An increased MPV leads to more thromboxane A2, the expression of more glycoprotein receptors, such as IIIa and IIb receptors, and stronger chemotaxis, adhesion, and aggregation functions. In addition, a larger MPV can promote the release more active factors, which exacerbate endothelial cell damage and inflammation, thereby promoting the progression of atherosclerosis and thrombosis [28]. We speculate that, due to the abovementioned mechanism, platelets with a larger MPV and a higher PC can cause local thrombus prolongation and repeated embolization of small blood vessels, which lead to END and poor prognosis in BAD patients.

The strengths of our research are as follows: (1) Little is known about the effects of MPV or PC on stroke subtype, and our study explored the relationship between platelet parameters (MPV and PC) and prognosis in BAD stroke for the first time. There are also several limitations to our study. First, it was a single-centre retrospective study with a modest sample size, and patients may have had a certain recall bias for functional recovery after 3 months. Second, we did not investigate the compliance of patients taking secondary prevention drugs for cerebral infarction and rehabilitation treatment after discharge, which would have an impact on the patient's 3-month functional prognosis. Third, platelet count was collected only at admission, with no record of changes in platelet values during disease. Although platelet indexes are relatively stable [29; 30], research has reported a paradoxical increase in MPV after initiation of antiplatelet therapy [31].

In conclusion, our findings suggested that elevated MPV and PC may be good predictors of poor prognosis in patients with BAD-stroke at 3 months. In addition, BAD patients with a high MPV and PC are more likely to develop END, which is the main cause of poor prognosis in BAD patients. For BAD-stroke patients with MPV values higher than 12.35, more aggressive treatment may be needed to prevent the occurrence of END and improve the outcome of BAD.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval and consent to participate

We obtained ethical approval for this study from the Medical and Health Research Ethics Committee of the Second People’s Hospital of Chengdu. Because it was a retrospective study, the Medical and Health Research Ethics Committee of the Second People’s Hospital of Chengdu waived the requirement for informed consent and information obtained from all included patients was anonymous.

Consent to publish

Not applicable.

Author Contributions

Y-LL was involved in the entire process of the study, including designing the study, collecting data, analysing the data, and drafting the original manuscript. R-HX and J-HX participated in data analysis, and K-W participated in data management and statistical analysis. L-L participated in data management. L-YH and M-Z revised the manuscript. J-W and F-X designed the study and revised the manuscript. All authors have read and approved the final manuscript.

Funding

This work was funded by the Chengdu Science and Technology Bureau (2022-YF05-01776-SN) and (20PJ199).

Acknowledgments

We sincerely thank all patients and medical staff who participated in this study for their contributions.

Data Availability Statement

All original data can be obtained via email correspondence to [email protected] charts in this study are presented in the article/Supplementary Material.

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No competing interests reported.

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published 01 May, 2024

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Effect of Mean Platelet Volume and Platelet Count on the Prognosis of Branch Atheromatous Disease

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Journal Publication

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Abstract

Figures

1 Introduction

2 Materials And Methods

2.1 Research Subjects

2.2 Data Collection

2.3 MPV and PC Determination

2.4 BAD and END Definitions

2.5 Follow-Up

2.6 Statistical Analyses

3 Results

3.1 Baseline Characteristics and Follow-up Results

3.2 Comparison of Clinical Baseline Data in the Good and Poor Prognosis Groups

3.3 Multivariate Logistic Regression Analysis Results of Factors Affecting Prognosis

3.5 ROC Analysis of MPV and PC in Predicting Poor Prognosis

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

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Journal Publication

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