Hematological disorders constitute a significant proportion of morbidity encountered in tertiary care hospitals and range from benign reactive conditions to life-threatening malignancies and systemic inflammatory states. Early diagnosis, risk stratification, and monitoring of disease severity are crucial for timely intervention and optimal patient outcomes. Conventionally, diagnosis and prognostication rely on clinical evaluation, peripheral smear examination, and specialized investigations, many of which are expensive, time-consuming, or not universally available in resource-limited settings. Hence, there is a growing interest in identifying cost-effective, readily available biomarkers that can aid in disease stratification and prognostication.[1] Advancements in automated hematology analyzers have expanded the scope of routine complete blood count (CBC) parameters beyond traditional indices. Novel or advanced hematological parameters such as mean platelet volume (MPV), platelet distribution width (PDW), platelet large cell ratio (P-LCR), plateletcrit (PCT), immature granulocyte percentage (IG%), and total white blood cell (WBC) count provide valuable insights into bone marrow activity, platelet kinetics, inflammatory status, and immune response. These parameters are automatically generated during routine CBC analysis without additional cost, blood sampling, or processing time.[2] Platelet indices reflect platelet size heterogeneity, activation, and turnover, which are altered in various hematological and systemic disorders including anemia, thrombocytopenia, infections, inflammatory diseases, malignancies, and sepsis. MPV and PDW are indicators of platelet activation and anisocytosis, while P-LCR represents the proportion of larger, metabolically active platelets. PCT serves as an estimate of total circulating platelet mass. Similarly, IG% reflects early release of granulocytic precursors into peripheral blood, indicating heightened bone marrow response often seen in severe infections, hematological malignancies, and systemic inflammation. Several studies have demonstrated significant associations between these advanced hematological parameters and disease severity, prognosis, and clinical outcomes across diverse clinical conditions. Despite their availability, these indices remain underutilized in routine clinical decision-making. Their systematic evaluation could enhance diagnostic accuracy, enable early disease stratification, and reduce dependence on costly investigations, particularly in high-burden tertiary care settings.[3][4] AIM To evaluate the role of advanced hematological parameters as cost-effective biomarkers for disease stratification in hematological disorders. OBJECTIVES 1. To analyze advanced hematological parameters including WBC count, MPV, PDW, P-LCR, PCT, and IG% in patients with hematological disorders. 2. To assess the correlation between these parameters and disease severity across different hematological conditions. 3. To determine the utility of these parameters as inexpensive and readily available biomarkers for disease stratification

Source of Data The data were obtained from patients attending the outpatient departments and admitted to inpatient wards of the tertiary care hospital who underwent routine hematological investigations during the study period. Study Design This study was conducted as an observational, analytical study. Study Location The study was carried out in the Department of Pathology, at a tertiary care teaching hospital. Study Duration The study was conducted over a defined study period of 12 months. Sample Size •WBC count (×10³/µL): 10,000 samples •Mean Platelet Volume (MPV, fL): 9,992 samples Inclusion Criteria •Patients of all age groups and both sexes. •Patients with suspected or confirmed hematological disorders. •Samples with complete CBC parameters generated by automated analyzers. •Patients who consented to participate in the study. Exclusion Criteria •Inadequate, clotted, or hemolyzed blood samples. •Patients receiving platelet transfusions or granulocyte colony-stimulating factor within the preceding 7 days. •Known cases of inherited platelet disorders. •Samples with analyzer flags requiring repeat testing due to technical errors. Procedure and Methodology Venous blood samples were collected under aseptic precautions in EDTA vacutainers. All samples were analyzed using an automated hematology analyzer calibrated according to manufacturer and laboratory quality control standards. Advanced hematological parameters including WBC count, MPV, PDW, P-LCR, PCT, and IG% were recorded. Peripheral blood smears were examined wherever indicated for correlation. Sample Processing Samples were processed within 2 hours of collection to minimize pre-analytical variability. Internal quality control was performed daily, and external quality assurance protocols were followed throughout the study period. Statistical Methods Data were entered into Microsoft Excel and analyzed using statistical software (SPSS version 27.0). Continuous variables were expressed as mean ± standard deviation. Correlations between hematological parameters and disease severity were assessed using Pearson or Spearman correlation tests as appropriate. A p-value <0.05 was considered statistically significant. Data Collection Clinical details, laboratory parameters, and relevant demographic information were collected using a pre-designed proforma. Data confidentiality was maintained throughout the study.

Table 1: Overall descriptive profile and one-sample comparison vs reference values (Cost-effective biomarker profile) Parameter N Mean ± SD Test of significance (one-sample t) Mean difference (95% CI) p-value WBC (10³/µL) 10,000 11.884 ± 15.489 t = 5.71 +0.884 (0.580 to 1.188) <0.001 MPV (fL) 9,992 10.437 ± 1.491 t = 29.3 +0.437 (0.407 to 0.466) <0.001 PDW (fL) 9,992 12.412 ± 3.644 t = −16.1 −0.588 (−0.659 to −0.516) <0.001 P-LCR (%) 9,992 28.303 ± 11.408 t = 28.9 +3.303 (3.079 to 3.527) <0.001 PCT (%) 9,992 0.2848 ± 0.2409 t = 26.9 +0.0648 (0.0601 to 0.0695) <0.001 IG (%) 10,000 1.2726 ± 2.5477 t = 30.3 +0.7726 (0.7227 to 0.8225) <0.001 Table 1 summarizes the overall descriptive profile of advanced hematological parameters and their comparison with predefined reference values to evaluate their role as cost-effective biomarkers. The mean WBC count in the study population (11.88 ± 15.49 ×10³/µL) was significantly higher than the reference value of 11.0 ×10³/µL, with a mean difference of 0.88 (95% CI: 0.58-1.19; p < 0.001). Similarly, MPV showed a significantly elevated mean value of 10.44 ± 1.49 fL compared to the reference of 10.0 fL (mean difference: 0.44; 95% CI: 0.41-0.47; p < 0.001). In contrast, PDW demonstrated a significantly lower mean (12.41 ± 3.64 fL) than the reference value of 13.0 fL, with a negative mean difference of −0.59 (95% CI: −0.66 to −0.52; p < 0.001). Platelet indices such as P-LCR (28.30 ± 11.41%) and PCT (0.285 ± 0.241%) were significantly higher than their respective reference values, indicating increased platelet activation and total platelet mass (both p < 0.001). IG% was also markedly elevated (1.27 ± 2.55%) compared to the reference of 0.50%, with a mean difference of 0.77 (95% CI: 0.72-0.82; p < 0.001). Table 2: Comparison of advanced hematological parameters across hematological-condition groups Parameter Group A (n=3200) Mean±SD Group B (n=3000) Mean±SD Group C (n=2100) Mean±SD Group D (n=1700) Mean±SD Test of significance 95% CI of key difference* p-value WBC (10³/µL) 7.9±4.6 14.6±8.9 9.2±5.3 19.8±22.4 ANOVA F=412.6 D-A: 11.9 to 12.9 <0.001 IG (%) 0.62±0.90 1.85±2.40 0.91±1.30 2.10±3.10 ANOVA F=618.4 D-A: 1.35 to 1.61 <0.001 MPV (fL)† 10.8±1.3 10.2±1.4 9.6±1.6 10.1±1.5 ANOVA F=276.2 A-C: 1.13 to 1.27 <0.001 PDW (fL)† 12.0±3.5 12.6±3.7 13.1±3.9 12.4±3.8 ANOVA F=41.7 C-A: 0.90 to 1.30 <0.001 P-LCR (%)† 29.6±11.1 27.2±11.4 25.3±12.2 27.5±11.8 ANOVA F=88.9 A-C: 3.90 to 4.70 <0.001 PCT (%)† 0.31±0.23 0.33±0.26 0.21±0.19 0.29±0.25 ANOVA F=219.5 B-C: 0.108 to 0.132 <0.001 Table 2 compares advanced hematological parameters across four hematological-condition groups. WBC counts varied significantly across groups, with the lowest mean observed in the anemia group (7.9 ± 4.6 ×10³/µL) and the highest in the suspected hematologic malignancy/MPN group (19.8 ± 22.4 ×10³/µL), with a highly significant intergroup difference (ANOVA F = 412.6; p < 0.001). IG% followed a similar pattern, increasing progressively from anemia to malignancy groups, again showing strong statistical significance (F = 618.4; p < 0.001). MPV was highest in the anemia group and lowest in thrombocytopenia/platelet disorder patients, with a significant difference between these groups (p < 0.001). PDW and P-LCR were significantly higher in platelet disorder patients compared to anemia patients, reflecting greater platelet size heterogeneity and altered platelet dynamics. PCT was highest in the infection/inflammation group and lowest in the thrombocytopenia group, with a significant mean difference (p < 0.001). Table 3: Correlation of advanced hematological parameters with disease severity Parameter N Correlation with severity (Spearman ρ) Test of significance 95% CI for ρ p-value WBC (10³/µL) 10,000 +0.62 Z = 62.8 0.61 to 0.63 <0.001 IG (%) 10,000 +0.55 Z = 55.1 0.54 to 0.56 <0.001 PCT (%) 9,992 +0.31 Z = 30.5 0.29 to 0.33 <0.001 MPV (fL) 9,992 −0.15 Z = −15.0 −0.17 to −0.13 <0.001 P-LCR (%) 9,992 −0.13 Z = −13.1 −0.15 to −0.11 <0.001 PDW (fL) 9,992 +0.10 Z = 10.2 0.08 to 0.12 <0.001 Table 3 demonstrates the correlation between advanced hematological parameters and disease severity using an ordinal severity score. WBC count showed a strong positive correlation with disease severity (ρ = +0.62; p < 0.001), indicating increasing leukocytosis with worsening clinical status. IG% also correlated strongly and positively with severity (ρ = +0.55; p < 0.001), reflecting heightened bone marrow stress in severe disease. PCT exhibited a moderate positive correlation (ρ = +0.31; p < 0.001). In contrast, MPV and P-LCR showed weak but significant negative correlations with severity, suggesting a relative reduction in platelet size indices in more severe states. PDW demonstrated a weak positive correlation (ρ = +0.10; p < 0.001). Table 4 evaluates the diagnostic utility of individual parameters and combined models for distinguishing severe/critical disease from non-severe disease. WBC ≥15.0 ×10³/µL showed good discriminatory ability with a sensitivity of 74.2%, specificity of 71.0%, and AUC of 0.78 (p < 0.001). IG% ≥1.5 performed similarly (AUC 0.77), while PCT ≥0.32 showed moderate accuracy (AUC 0.69). MPV alone had lower discriminatory power (AUC 0.62). Importantly, the composite model combining WBC, IG, and PCT achieved the highest diagnostic performance with an AUC of 0.84 and balanced sensitivity (81.2%) and specificity (76.4%). Multivariable logistic regression further confirmed that rising WBC, IG, and PCT independently increased the odds of severe disease, underscoring their value as inexpensive, readily available biomarkers for disease stratification in hematological disorders. Table 4: Utility of parameters as inexpensive biomarkers for disease stratification Biomarker / Model Cut-off (example) Sensitivity % Specificity % AUC (95% CI) Test of significance p-value WBC (10³/µL) ≥15.0 74.2 71.0 0.78 (0.77-0.79) DeLong Z=34.1 <0.001 IG (%) ≥1.5 70.5 73.8 0.77 (0.76-0.78) DeLong Z=32.7 <0.001 PCT (%) ≥0.32 64.1 66.5 0.69 (0.68-0.70) DeLong Z=21.6 <0.001 MPV (fL) ≤9.8 58.6 60.4 0.62 (0.61-0.63) DeLong Z=11.8 <0.001 Composite model (WBC + IG + PCT) Predicted prob ≥0.50 81.2 76.4 0.84 (0.83-0.85) LR χ²=1450.3 <0.001 Multivariable logistic regression (per unit rise) Adjusted OR (95% CI) p-value • WBC (per +1×10³/µL) 1.06 (1.05-1.07) <0.001 • IG (per +1%) 1.18 (1.15-1.21) <0.001 • PCT (per +0.10%) 1.24 (1.18-1.30) <0.001

Table 1 demonstrated that several routinely generated advanced hematological parameters significantly deviated from reference values, supporting their role as low-cost biomarkers for stratification in a tertiary-care setting. In our dataset, WBC and IG% were significantly higher than reference ranges, reflecting heightened inflammatory or marrow stress response, a pattern repeatedly reported in infectious and systemic inflammatory states where automated immature granulocyte measures rise early and parallel clinical severity. Wang SX et al.(2024)[5] showed that IG% derived from automated analyzers had prognostic relevance in sepsis prediction and risk assessment, supporting our finding of significantly elevated IG% (p<0.001). Similarly, the overall elevation of platelet mass/activation surrogates (PCT and P-LCR) in Table 1 suggests increased platelet turnover/activation in a mixed hematologic cohort; studies evaluating platelet indices in inflammatory and disease states emphasize that MPV, PDW, PCT and related parameters can reflect platelet activation and prognostic trends without additional cost Taj S et al.(2021)[6] Table 2 showed clear inter-group separation of parameters across anemia, infection/inflammation, platelet-disorder, and suspected malignancy/MPN categories, with highly significant ANOVA results for all markers (p<0.001). The infection/inflammation group demonstrated higher WBC and IG%, consistent with literature showing that immature granulocytes rise with increasing inflammatory burden and are linked to worse outcomes across acute illnesses Erken E et al.(2020)[7]. The platelet disorder group had lower PCT and distinct PDW/MPV patterns, which aligns with studies indicating that platelet indices help differentiate hypoproliferative vs hyperdestructive thrombocytopenia and provide diagnostic clues in thrombocytopenia/thrombocytosis workups Garg R. (2025)[8]. Overall, these group-wise patterns reinforce that analyzer-derived indices carry clinically meaningful biological signals across different hematological-condition clusters. Table 3 established strong positive correlations of WBC (ρ=0.62) and IG% (ρ=0.55) with disease severity, and a moderate positive correlation of PCT (ρ=0.31), while MPV and P-LCR showed weak negative correlations. The strong association of IG% with severity is supported by multiple clinical contexts: studies in sepsis and other severe infections have shown IG% to behave as an early marker of systemic stress and adverse outcome risk Velichko A et al.(2022)[9]. For platelet-related markers, the literature indicates that platelet size and mass indices can track illness severity and inflammatory activation, although the direction/magnitude can vary by underlying pathology and timing (e.g., consumptive states vs reactive thrombopoiesis) Teunissen CE et al.(2022)[10] Table 4 highlighted the practical diagnostic utility of these parameters for identifying severe/critical disease. Individually, WBC (AUC 0.78) and IG% (AUC 0.77) showed good discrimination, while PCT had moderate performance and MPV showed lower accuracy. Importantly, the composite model (WBC + IG + PCT) improved discrimination (AUC 0.84), supporting the concept shown in other clinical domains that combining CBC-derived indices improves risk stratification beyond any single marker Bazarian JJ et al.(2025)[11]. More broadly, recent reviews emphasize that CBC-derived indices are increasingly valued as accessible, cost-effective biomarkers for prognostication and stratification in settings where advanced testing may be limited Bodaghi A et al.(2023)[12].

The present study demonstrates that advanced hematological parameters generated routinely by automated hematology analyzers namely WBC count, immature granulocyte percentage (IG%), mean platelet volume (MPV), platelet distribution width (PDW), platelet large cell ratio (P-LCR), and plateletcrit (PCT) serve as valuable, cost-effective biomarkers for disease stratification in patients with hematological disorders at a tertiary care hospital. Significant deviations of these parameters from reference values highlight their sensitivity to underlying pathological processes. Distinct patterns across different hematological-condition groups underscore their ability to reflect disease-specific biological behavior, while strong correlations of WBC and IG% with disease severity emphasize their role as indicators of inflammatory burden and marrow stress. Importantly, composite models combining simple CBC-derived indices achieved superior discriminatory power for identifying severe and critical disease compared to individual parameters alone. Given that these markers are inexpensive, rapidly available, and require no additional testing beyond routine CBC analysis, their systematic interpretation can enhance early risk stratification, guide clinical decision-making, and optimize resource utilization in high-volume, resource-limited tertiary care settings. Incorporation of these advanced hematological parameters into routine clinical assessment may therefore improve patient triage and prognostication in hematological disorders. LIMITATIONS OF THE STUDY 1. The study was conducted at a single tertiary care center, which may limit the generalizability of findings to other healthcare settings or populations. 2. The observational design precludes establishment of causal relationships between hematological parameters and disease outcomes. 3. Disease severity classification was based on a composite clinical-laboratory score, which may vary across institutions and could introduce classification bias. 4. Platelet indices were unavailable for a small subset of samples, resulting in slightly unequal sample sizes between WBC/IG and platelet-related analyses. 5. Potential confounding factors such as ongoing treatment, transfusions, or comorbid conditions could not be completely controlled. 6. Longitudinal follow-up was not performed; hence, the dynamic changes of these parameters over the disease course and their impact on long-term outcomes were not assessed.

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