Acute ischemic stroke is an important cause of death and long-term disability worldwide. Prognosis should be assessed early, as this will help the clinician to identify patients who may need more intensive monitoring, aggressive management and structured rehabilitation. While clinical scores and neuroimaging are the mainstays of stroke assessment, simple blood-based markers may provide valuable prognostic information, particularly in resource-limited settings [1,2].

Red cell distribution width is a complete blood count parameter that is routinely available and reflects variation in red blood cell size. Increased RDW is primarily employed in the assessment of anemia, but has also been associated with inflammation, oxidative stress, endothelial dysfunction and adverse vascular outcomes. These mechanisms can affect the progression of the infarct and recovery in acute ischemic stroke. Previous studies have demonstrated that elevated RDW is linked to more severe stroke, poor functional outcome and higher mortality following ischemic stroke [3–5].

Another simple hematological marker that reflects platelet size and activity is mean platelet volume. The bigger the platelet, the more reactive and the more thrombotic it is. Since platelet activation plays a major role in arterial thrombosis and ischemic stroke, MPV has been studied as a possible marker of stroke severity and outcome [6]. Several studies have found that high MPV is linked to poor functional outcome, mortality or readmission following acute ischemic stroke, but some studies have reported inconsistent results [7–9].

RDW and MPV are cheap, readily available, and are routinely measured at admission. They could be two key mechanisms in acute ischemic stroke: inflammation-induced vascular damage and platelet-induced thrombosis. But information on their prognostic value in Indian acute ischemic stroke patients is scarce. Thus, the current study was conducted to assess the relationship between admission RDW and MPV with stroke severity and short-term clinical outcomes in patients with AIS.

Study design and setting This prospective cohort study was conducted in the Department of General Medicine, Sri Chamundeshwari Medical College Hospital and Research Institute, Channapatna. The study was carried out over a period of 6 months. Study population The study included patients admitted with acute ischemic stroke during the study period. A total of 150 eligible patients were enrolled after applying the inclusion and exclusion criteria. Inclusion criteria Patients aged 18 years and above with a clinical diagnosis of acute stroke confirmed as ischemic stroke on CT or MRI brain, presenting within 72 hours of symptom onset, and willing to provide written informed consent were included in the study. In patients unable to provide consent because of neurological deficits, consent was obtained from a legally acceptable representative. Exclusion criteria Patients with hemorrhagic stroke, transient ischemic attack, cerebral venous thrombosis, traumatic brain injury, known hematological disorders, active malignancy, chronic inflammatory or autoimmune disease, chronic liver disease, chronic kidney disease, recent major surgery, recent blood transfusion, active infection or sepsis, and those receiving drugs known to significantly affect platelet indices or red cell parameters were excluded. Patients with incomplete clinical or laboratory data were also excluded. Data collection After obtaining approval from the Institutional Ethics Committee, eligible patients were enrolled after informed consent. Data were collected using a predesigned proforma. Demographic details, vascular risk factors, comorbidities, time from symptom onset to admission, clinical presentation, treatment details, and relevant medical history were recorded. Clinical assessment All patients underwent detailed neurological examination at admission. Stroke severity was assessed using the National Institutes of Health Stroke Scale. Functional status was assessed using the modified Rankin Scale. Blood pressure, pulse rate, temperature, oxygen saturation, and other relevant systemic findings were documented. Neuroimaging All patients underwent CT or MRI brain to confirm the diagnosis of acute ischemic stroke and to exclude intracranial hemorrhage or other stroke mimics. Imaging findings, including site and extent of infarct, were recorded where available. Laboratory assessment Venous blood samples were collected at admission before initiation of major inpatient interventions wherever possible. Complete blood count parameters, including red cell distribution width and mean platelet volume, were measured using an automated hematology analyzer. Other relevant investigations, including blood glucose, renal function tests, liver function tests, serum electrolytes, lipid profile, and coagulation profile, were performed as clinically indicated. Outcome assessment Patients were followed during hospitalization and outcomes were assessed at discharge. The primary outcome was poor clinical outcome, defined as death during hospitalization or functional dependency at discharge based on modified Rankin Scale score. Patients were categorized into favourable and unfavourable outcome groups according to functional status at discharge. Secondary outcomes included stroke severity at admission, duration of hospital stay, neurological deterioration, and in-hospital mortality. Statistical analysis Data were analyzed using IBM SPSS Statistics version 24.0. Continuous variables were expressed as mean ± standard deviation or median with interquartile range, and categorical variables as frequency and percentage. Welch’s t-test, Mann–Whitney U test, Chi-square test, and Fisher’s exact test were used as appropriate. Correlations were assessed using Spearman’s rank correlation coefficient. Multivariable logistic regression was performed to identify independent predictors of unfavourable discharge outcome. A p-value <0.05 was considered statistically significant. Ethical considerations The study was conducted after approval from the Institutional Ethics Committee of Sri Chamundeshwari Medical College Hospital and Research Institute, Channapatna. Written informed consent was obtained from all participants or their legally acceptable representatives. Confidentiality of patient information was maintained throughout the study. Participation was voluntary and did not interfere with standard patient care.

A total of 150 patients with acute ischemic stroke were included in the analysis. At discharge, 90 (60.0%) patients had a favourable outcome and 60 (40.0%) had an unfavourable outcome, defined as death during hospitalization or functional dependency at discharge. Neurological deterioration occurred in 50 (33.3%) patients, and in-hospital mortality was recorded in 12 (8.0%) patients. Baseline demographic and vascular risk profile Baseline characteristics are summarized according to discharge outcome in Table 1. Patients with unfavourable outcomes were older than those with favourable outcomes (66.8 ± 11.6 vs 58.9 ± 10.3 years; t=-4.23, p<0.001). The distribution of age groups differed significantly between outcome groups, with a higher proportion of patients aged ≥75 years in the unfavourable outcome group. Sex distribution and most vascular risk factors were comparable between groups. Table 1. Baseline demographic and vascular risk profile by discharge outcome Variable Overall (N=150) Favourable outcome (n=90) Unfavourable outcome (n=60) Test statistic p value Age (years) 62.0 ± 11.5 58.9 ± 10.3 66.8 ± 11.6 t=-4.23 <0.001 Age group: <45 10 (6.7) 8 (8.9) 2 (3.3) χ²=19.44 <0.001 Age group: 45-59 56 (37.3) 42 (46.7) 14 (23.3) Age group: 60-74 60 (40.0) 34 (37.8) 26 (43.3) Age group: ≥75 24 (16.0) 6 (6.7) 18 (30.0) Sex: Male 86 (57.3) 52 (57.8) 34 (56.7) χ²=0.02 0.893 Sex: Female 64 (42.7) 38 (42.2) 26 (43.3) BMI (kg/m²) 24.8 ± 3.4 24.7 ± 3.5 25.0 ± 3.3 t=-0.46 0.650 Hypertension: Yes 84 (56.0) 49 (54.4) 35 (58.3) χ²=0.22 0.638 Hypertension: No 66 (44.0) 41 (45.6) 25 (41.7) Diabetes mellitus: Yes 65 (43.3) 39 (43.3) 26 (43.3) χ²=0.00 1.000 Diabetes mellitus: No 85 (56.7) 51 (56.7) 34 (56.7) Dyslipidemia: Yes 60 (40.0) 34 (37.8) 26 (43.3) χ²=0.46 0.496 Dyslipidemia: No 90 (60.0) 56 (62.2) 34 (56.7) Current smoking: Yes 39 (26.0) 23 (25.6) 16 (26.7) χ²=0.02 0.879 Current smoking: No 111 (74.0) 67 (74.4) 44 (73.3) Alcohol use: Yes 28 (18.7) 15 (16.7) 13 (21.7) χ²=0.59 0.441 Alcohol use: No 122 (81.3) 75 (83.3) 47 (78.3) Prior stroke/TIA: Yes 9 (6.0) 5 (5.6) 4 (6.7) Fisher exact 1.000 Prior stroke/TIA: No 141 (94.0) 85 (94.4) 56 (93.3) Atrial fibrillation: Yes 11 (7.3) 8 (8.9) 3 (5.0) Fisher exact 0.527 Atrial fibrillation: No 139 (92.7) 82 (91.1) 57 (95.0) Ischemic heart disease: Yes 21 (14.0) 14 (15.6) 7 (11.7) χ²=0.45 0.501 Ischemic heart disease: No 129 (86.0) 76 (84.4) 53 (88.3) Values are presented as mean ± SD or n (%). Test statistics represent Welch’s t-test for continuous variables and chi-square test or Fisher’s exact test for categorical variables, as appropriate. Clinical severity, imaging profile, and in-hospital course Admission stroke severity was strongly associated with discharge outcome. The median admission NIHSS score was higher in the unfavourable outcome group than in the favourable outcome group [16.0 (11.0–18.2) vs 8.0 (4.0–11.0); U=1080.5, p<0.001]. Moderate-severe and severe stroke categories were more frequent among patients with unfavourable outcome. Neurological deterioration and in-hospital mortality were also more common in this group, and hospital stay was longer. Table 2. Clinical stroke profile, imaging features, and in-hospital outcomes by discharge outcome Variable Overall (N=150) Favourable outcome (n=90) Unfavourable outcome (n=60) Test statistic p value Onset-to-admission time (hours) 21.5 ± 12.6 21.4 ± 12.8 21.6 ± 12.3 t=-0.09 0.928 Systolic blood pressure (mmHg) 156.1 ± 20.8 156.2 ± 20.6 155.9 ± 21.2 t=0.06 0.951 Diastolic blood pressure (mmHg) 91.0 ± 12.1 92.2 ± 12.2 89.2 ± 11.7 t=1.51 0.133 Admission NIHSS score 10.0 (6.0-16.0) 8.0 (4.0-11.0) 16.0 (11.0-18.2) U=1080.5 <0.001 Stroke severity: Minor (0-4) 28 (18.7) 26 (28.9) 2 (3.3) χ²=37.59 <0.001 Stroke severity: Moderate (5-15) 80 (53.3) 54 (60.0) 26 (43.3) Stroke severity: Moderate-severe (16-20) 28 (18.7) 6 (6.7) 22 (36.7) Stroke severity: Severe (>20) 14 (9.3) 4 (4.4) 10 (16.7) Infarct territory: MCA territory 78 (52.0) 46 (51.1) 32 (53.3) χ²=7.34 0.197 Infarct territory: ACA territory 8 (5.3) 4 (4.4) 4 (6.7) Infarct territory: PCA territory 16 (10.7) 10 (11.1) 6 (10.0) Infarct territory: Posterior circulation 20 (13.3) 11 (12.2) 9 (15.0) Infarct territory: Lacunar/internal capsule 21 (14.0) 17 (18.9) 4 (6.7) Infarct territory: Multiple territory 7 (4.7) 2 (2.2) 5 (8.3) Infarct size: Small 50 (33.3) 39 (43.3) 11 (18.3) χ²=16.28 <0.001 Infarct size: Moderate 63 (42.0) 38 (42.2) 25 (41.7) Infarct size: Large 37 (24.7) 13 (14.4) 24 (40.0) Thrombolysis received: Yes 1 (0.7) 1 (1.1) 0 (0.0) Fisher exact 1.000 Thrombolysis received: No 149 (99.3) 89 (98.9) 60 (100.0) Neurological deterioration: Yes 50 (33.3) 26 (28.9) 24 (40.0) χ²=2.00 0.157 Neurological deterioration: No 100 (66.7) 64 (71.1) 36 (60.0) Hospital stay (days) 11.0 (8.0-14.0) 10.0 (7.0-12.0) 14.0 (10.0-17.0) U=1319.5 <0.001 In-hospital mortality: Yes 12 (8.0) 0 (0.0) 12 (20.0) Fisher exact <0.001 In-hospital mortality: No 138 (92.0) 90 (100.0) 48 (80.0) Values are presented as mean ± SD, median (IQR), or n (%). NIHSS and hospital stay were compared using Mann–Whitney U test; categorical variables were compared using chi-square test or Fisher’s exact test. Admission laboratory profile and hematological markers Admission laboratory findings are shown in Table 3. Mean RDW was significantly higher in the unfavourable outcome group than in the favourable outcome group (14.0 ± 0.9% vs 13.4 ± 0.8%; t=-4.58, p<0.001). High RDW was observed in 19 (31.7%) patients with unfavourable outcome compared with 9 (10.0%) patients with favourable outcome. Mean MPV was not significantly different between the two groups; however, high MPV was more frequent in the unfavourable outcome group. Table 3. Admission laboratory profile by discharge outcome Variable Overall (N=150) Favourable outcome (n=90) Unfavourable outcome (n=60) Test statistic p value Hemoglobin (g/dL) 12.7 ± 1.4 12.7 ± 1.5 12.8 ± 1.4 t=-0.37 0.712 Total leukocyte count (×10³/µL) 9.2 ± 2.0 8.9 ± 2.1 9.6 ± 1.8 t=-2.25 0.026 Platelet count (×10³/µL) 245 ± 53 243 ± 53 248 ± 53 t=-0.57 0.566 RDW (%) 13.7 ± 0.9 13.4 ± 0.8 14.0 ± 0.9 t=-4.58 <0.001 MPV (fL) 10.0 ± 0.9 9.9 ± 0.9 10.1 ± 0.9 t=-1.09 0.279 Fasting blood glucose (mg/dL) 152 ± 43 149 ± 43 157 ± 42 t=-1.19 0.237 HbA1c (%) 6.9 ± 1.2 6.8 ± 1.2 7.1 ± 1.1 t=-1.36 0.177 Serum creatinine (mg/dL) 1.00 ± 0.21 0.99 ± 0.22 1.01 ± 0.19 t=-0.69 0.494 Total cholesterol (mg/dL) 196 ± 33 194 ± 32 200 ± 35 t=-0.97 0.334 LDL cholesterol (mg/dL) 124 ± 28 125 ± 27 123 ± 30 t=0.44 0.661 HDL cholesterol (mg/dL) 44 ± 10 43 ± 9 45 ± 11 t=-1.06 0.291 Triglycerides (mg/dL) 181 ± 53 183 ± 56 178 ± 49 t=0.67 0.502 RDW category: High RDW (≥14.5%) 28 (18.7) 9 (10.0) 19 (31.7) χ²=11.13 <0.001 RDW category: Normal RDW (<14.5%) 122 (81.3) 81 (90.0) 41 (68.3) MPV category: High MPV (≥10.5 fL) 40 (26.7) 18 (20.0) 22 (36.7) χ²=5.11 0.024 MPV category: Normal MPV (<10.5 fL) 110 (73.3) 72 (80.0) 38 (63.3) Values are presented as mean ± SD or n (%). RDW = red cell distribution width; MPV = mean platelet volume. Figure 1. Admission RDW and MPV by discharge outcome. The figure shows higher mean RDW in patients with unfavourable discharge outcome. Error bars represent 95% confidence intervals. Association of RDW and MPV categories with clinical outcomes The association of RDW and MPV categories with clinically relevant outcomes is shown in Table 4. High RDW was associated with moderate-severe/severe stroke, neurological deterioration, and unfavourable discharge outcome. High MPV was associated with moderate-severe/severe stroke and unfavourable discharge outcome, but its association with neurological deterioration and in-hospital mortality was not statistically significant. Table 4. Association of RDW and MPV categories with clinical outcomes Outcome Normal category, n (%) High category, n (%) Test statistic p value RDW category Moderate-severe/severe stroke 28 (23.0) 14 (50.0) χ²=8.27 0.004 Neurological deterioration 35 (28.7) 15 (53.6) χ²=6.35 0.012 In-hospital mortality 10 (8.2) 2 (7.1) Fisher exact 1.000 Unfavourable discharge outcome 41 (33.6) 19 (67.9) χ²=11.13 <0.001 MPV category Moderate-severe/severe stroke 24 (21.8) 18 (45.0) χ²=7.82 0.005 Neurological deterioration 34 (30.9) 16 (40.0) χ²=1.09 0.296 In-hospital mortality 7 (6.4) 5 (12.5) Fisher exact 0.305 Unfavourable discharge outcome 38 (34.5) 22 (55.0) χ²=5.11 0.024 RDW categories: normal <14.5%, high ≥14.5%. MPV categories: normal <10.5 fL, high ≥10.5 fL. Categorical comparisons were performed using chi-square test or Fisher’s exact test. Correlation of RDW and MPV with stroke severity and discharge status Correlation analysis demonstrated significant positive correlations between RDW and admission NIHSS, hospital stay, and discharge mRS. MPV showed significant positive correlations with admission NIHSS and hospital stay, while its correlation with discharge mRS did not reach statistical significance. Table 5. Correlation of admission RDW and MPV with stroke severity and discharge outcomes Marker Clinical variable Spearman rho p value RDW (%) Admission NIHSS 0.328 <0.001 RDW (%) Hospital stay 0.353 <0.001 RDW (%) Discharge mRS 0.324 <0.001 MPV (fL) Admission NIHSS 0.222 0.006 MPV (fL) Hospital stay 0.263 0.001 MPV (fL) Discharge mRS 0.137 0.096 Correlation was assessed using Spearman rank correlation. Figure 2. ROC curves for RDW and MPV in predicting unfavourable discharge outcome. RDW showed better discrimination for unfavourable discharge outcome than MPV (AUROC 0.707, 95% CI 0.621–0.788 vs AUROC 0.572, 95% CI 0.475–0.668). Multivariable analysis for unfavourable discharge outcome A multivariable logistic regression model was used to assess whether admission RDW and MPV were associated with unfavourable discharge outcome after adjustment for age, admission NIHSS, hypertension, and diabetes mellitus. Higher RDW, older age, and higher admission NIHSS remained independently associated with unfavourable discharge outcome, whereas MPV was not an independent predictor after adjustment. Table 6. Multivariable logistic regression for unfavourable discharge outcome Variable Adjusted odds ratio 95% CI Wald z p value RDW, per 1% increase 1.97 1.15-3.37 2.49 0.013 MPV, per 1 fL increase 0.98 0.59-1.61 -0.09 0.927 Age, per year 1.05 1.01-1.09 2.42 0.016 Admission NIHSS, per point 1.17 1.09-1.25 4.19 <0.001 Hypertension 0.79 0.35-1.80 -0.56 0.579 Diabetes mellitus 1.21 0.53-2.73 0.45 0.650 Adjusted model includes RDW, MPV, age, admission NIHSS, hypertension, and diabetes mellitus. OR = odds ratio; CI = confidence interval; NIHSS = National Institutes of Health Stroke Scale.

In this current prospective cohort study of 150 patients with acute ischemic stroke, 40.0% had an unfavourable discharge outcome and 8.0% died in hospital. Admission RDW was much higher in the unfavourable outcome group than in the favourable outcome group (14.0 ± 0.9% vs 13.4 ± 0.8%, p<0.001). Moderate-severe/severe stroke, neurological deterioration and unfavourable discharge outcome were associated with high RDW. RDW was also strongly positively associated with admission NIHSS, hospital stay and discharge mRS on multivariable analysis, with each 1% increase in RDW associated with a nearly twofold increase in the odds of poor outcome (adjusted OR 1.97; 95% CI 1.15–3.37; p=0.013). MPV, on the other hand, was significantly associated with moderate-severe/severe stroke and unfavourable outcome in categorical analyses, but the mean difference in MPV between outcome groups was small and MPV was not an independent predictor after adjustment. In our study, the correlation of RDW with stroke severity is similar to that of Kara et al., who assessed 88 patients with acute ischemic stroke within 24 hours of onset and found that RDW was correlated with the severity of the neurological score. They found that RDW of 14% was the best cut-off value for discriminating between stroke patients and controls and suggested that RDW could be used to predict stroke severity and functional outcome [10]. The RDW cut-off of ≥14.5% was slightly higher, but the pattern was similar, with 50.0% of moderate-severe/severe strokes having high RDW, compared with 23.0% of less severe strokes. But the predictive power of RDW has not been consistent in all populations. In the Acute Stroke Registry and Analysis of Lausanne, Ntaios et al. studied 1504 patients and found that RDW was linked to NIHSS and poor functional outcome in univariate analysis, but not in the multivariate analysis [11]. Dias et al. also found that RDW was not an independent predictor of 90-day functional outcome or mortality in acute ischemic stroke after adjusting for potential confounders [12]. These divergent findings underscore the age, baseline disability, renal function, hemoglobin level, and comorbid illness as factors affecting RDW. In our cohort, despite adjustment for age and admission NIHSS, RDW remained independently associated with poor discharge outcome, suggesting that it added prognostic information beyond initial clinical severity. Balaji et al. assessed both RDW and MPV for predicting 30-day mortality in acute ischemic stroke and found that RDW, MPV, NIHSS score, and infarct volume were significantly associated with 30-day outcome [13]. Their findings are especially relevant to our study as both studies assessed the routinely available hematological markers in an Indian clinical setting. RDW was more robust than MPV in our study, with better correlation with admission NIHSS, hospital stay, and discharge mRS, and independent association in regression analysis. Xu and Peng analyzed 1358 patients with acute ischemic stroke in the MIMIC-III database and found that the red cell distribution width-to-platelet ratio was a good indicator of the inflammatory and thrombotic background of RDW. They reported that elevated RPR was associated with increased 30-day mortality (HR 1.45; 95% CI 1.10–1.92; p=0.009) and 1-year mortality (HR 1.54; 95% CI 1.23–1.93; p<0.001) [14]. While they did not use RDW alone, but rather a ratio, the results do support the idea that red cell size variability, along with platelet-related parameters, is a marker of systemic inflammation and vascular risk following ischemic stroke. Our MPV results were more modest. On categorical analysis, high MPV was significantly associated with moderate-severe/severe stroke and unfavourable discharge outcome, but MPV was not significant after adjustment. This partly agrees with Mohamed et al., who studied 157 ischemic stroke patients and found MPV to be higher in the unfavourable outcome group than in the favourable outcome group (10.4 ± 2.3 fL vs 8.7 ± 1.3 fL; p<0.001), with MPV acting as an independent predictor of poor short-term outcome [15]. The weaker independent effect in our study may be due to the smaller MPV difference between outcome groups, earlier outcome assessment at discharge, and adjustment for admission NIHSS, which was a dominant predictor of outcome. Other MPV studies have also demonstrated that platelet activation markers might be more valuable in certain stroke populations. Elsayed and Mohamed found that MPV and MPV/platelet count ratio were helpful in stratifying the severity of acute ischemic stroke [16]. Staszewski et al. investigated 237 patients with acute ischemic stroke who received intravenous thrombolysis and observed that there was a strong correlation between admission MPV and discharge outcome [17]. Likewise, Peng et al. found that high MPV levels were linked to poor outcome following mechanical thrombectomy [18]. These studies indicate that MPV might be more relevant in patients treated with reperfusion therapy or with large vessel occlusion, but in our general medicine population, MPV was not independently predictive after adjustment. More recently, composite platelet-inflammatory markers have been investigated. Chen et al. studied ischemic stroke patients treated with intravenous thrombolysis and found that MPV-to-lymphocyte ratio at admission and 18–24 hours after thrombolysis was independently associated with poor functional outcome [19]. This is in line with the idea that MPV might be more useful when used in conjunction with inflammatory markers than as a stand-alone parameter. In our cohort, the unfavourable outcome group had a higher total leukocyte count and RDW had better prognostic value than MPV, indicating that inflammation-related hematological changes may have been more significant than platelet size alone. These findings are biologically plausible. RDW is a measure of anisocytosis and can increase with inflammation, oxidative stress, nutritional deficiency, decreased red cell deformability, and impaired erythropoiesis. These factors can exacerbate microvascular perfusion and oxygen delivery to ischemic brain tissue. MPV, however, is a measure of platelet size and reactivity. MPV can be affected by pre-analytical factors, timing of blood sampling, exposure to anticoagulants, and analyzer variability, and larger platelets are metabolically more active and may promote thrombosis. This could be one of the reasons why RDW was more stable in association with outcome than MPV in the present study. Strengths and limitations The strengths of this study were its prospective design, diagnosis confirmed by neuroimaging, and the evaluation of routinely available hematological markers at admission. It was, however, constrained by its single-centre design, small sample size and assessment of outcomes only at discharge. No serial RDW and MPV measurements, no quantification of infarct volume, no inflammatory markers, no nutritional parameters, and no longer-term functional outcomes were assessed.

Admission RDW was an independent predictor of unfavourable discharge outcome in patients with acute ischemic stroke, whereas MPV showed a weaker association and was not independently predictive after adjustment. RDW could be used as a simple and inexpensive adjunct marker for early risk stratification in acute ischemic stroke, particularly in resource-limited settings.

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