European Journal of Cardiovascular Medicine

Platelets (thrombocytes) are essential blood components that function alongside coagulation factors to prevent bleeding by clumping at blood vessel injuries.^[1] Unlike other blood cells, platelets lack a nucleus (figure 1) and are cytoplasmic fragments derived from bone marrow megakaryocytes.^[2] Unactivated platelets appear as biconvex discoid structures measuring 2-3 µm in diameter.^[3] While mammals have platelets, other animals like birds and amphibians have mononuclear thrombocytes.^[4] Platelets play a crucial role in coagulation by adhering to damaged vessels and providing membrane phospholipids for coagulation factor activation.

On blood smears, platelets appear as dark purple spots approximately 20% the diameter of red blood cells. Normal platelet-to-red blood cell ratio ranges from 1:10 to 1:20 in healthy adults.

Thrombocytopenia, defined as a platelet count below 150,000/cmm,^[5] represents one of the most common reasons for hematological consultation and potentially one of the most life-threatening conditions. Normal platelet count ranges between 150,000-450,000 per microliter, significantly higher than the minimal level required to avoid hemorrhage (<50 × 10^9/L).^[6] Clinical manifestations remain mild with platelet counts above 20 × 10^9/L, typically limited to easy bruising. However, when counts fall below 10 × 10^9/L, the risk of spontaneous mucocutaneous bleeding, life-threatening intracranial hemorrhage, or gastrointestinal bleeding increases dramatically.^[7] These reference limits are determined by the 2.5th lower and upper percentiles. Emergency treatment is typically required when platelet counts fall below 50,000 per microliter.^[8]

Thrombocytopenia can be classified as inherited or acquired, with the latter further categorized based on mechanism:

1. Hyperdestructive Thrombocytopenia: Characterized by increased extramedullary platelet destruction with normal or increased bone marrow production (e.g., immune thrombocytopenic purpura, disseminated intravascular coagulation)
2. Hypoproductive Thrombocytopenia: Results from decreased bone marrow production due to primary or secondary bone marrow diseases (e.g., aplastic anemia, megaloblastic anemia, myelodysplastic syndromes, acute leukemia).
3. Abnormal Distribution: Due to splenic sequestration or other distributional abnormalities

Platelet count alone does not explain the underlying pathomechanism of thrombocytopenia.^[9] .Traditionally, bone marrow examination has been the gold standard for distinguishing between hyperdestructive and hypoproductive thrombocytopenia. However, there is no consensus regarding its necessity as a first-line diagnostic procedure for ITP (Immune Thrombocytopenia). Platelet-associated immunoglobulin (PAIgG) testing identifies anti-platelet antibodies causing platelet destruction, but its specificity is limited as elevated PAIgG levels occur in numerous conditions.^[10] Recent advances in automated blood cell analyzers enable measurement of various platelet parameters, providing insights into platelet size and volume that could help differentiate between hypoproductive and hyperdestructive thrombocytopenia.^[16] Growing evidence suggests that platelet indices such as MPV, PDW, and P-LCR play significant roles in distinguishing between these two mechanisms.^[11]

PDW measures variability in platelet size. Although a study in Ethiopia reported that most hematological parameters, including platelet indices, are underutilized in clinical management.^[12] Given that thrombocytopenia can cause severe morbidity and mortality due to major organ bleeding or intracranial hemorrhage,^[13] this hospital-based prospective study aims to demonstrate the discriminating potential of platelet indices between hyperdestructive and hypoproductive thrombocytopenia and determine optimal cut-off values for Indian patients. The study highlights the importance of non-invasive, inexpensive testing to improve clinical outcomes and expedite diagnosis.

P-LCR (Platelet Large Cell Ratio)

The percentage of platelets exceeding the normal platelet volume of 12 fl in the total platelet count.^[14] P-LCR reflects platelet activity and provides an indirect assessment of platelet stimulation. It is directly related to PDW and MPV but inversely related to platelet count.

AIMS AND OBJECTIVES

The study aimed to evaluate platelet indices, including MPV, PDW, and P-LCR, in patients with thrombocytopenia using an automated cell analyzer. Additionally, the study seeks to analyze the relationship between these indices and the underlying causes of thrombocytopenia, such as bone marrow failure, immune destruction, or peripheral consumption. By assessing variations in platelet indices across different etiologies, this research aims to enhance the diagnostic and prognostic value of automated platelet parameters in clinical practice.

This prospective cross-sectional study was conducted in the Department of Pathology, VIMSAR, Burla, Odisha, over two years from November 2015 to November 2017. It included 80 thrombocytopenic patients (platelet count <150 × 10⁹/L) who met the inclusion criteria, along with 20 age- and sex-matched healthy controls. The patients were categorized into two groups: Group I (49 cases of hypoproductive thrombocytopenia) and Group II (31 cases of diagnosed ITP). A comprehensive clinico-hematological workup, including bone marrow analysis, was performed. Participation was voluntary, with patients free to withdraw at any time without losing medical benefits.

Inclusion and Exclusion Criteria

The study included 49 cases of marrow-proven hypoproductive thrombocytopenia and 31 cases of hyperdestructive thrombocytopenia, all with a platelet count of <1.5 lakh/cmm and available platelet indices. Patients were excluded if they had received blood or a blood component transfusion in the last 10 days, were on immunosuppressants, undergoing radiotherapy, or taking drugs known to cause thrombocytopenia. Additionally, patients who denied consent or were sensitive to local anesthetics were not included in the study.

Data Collection Tools

The study utilized multiple data collection tools, including a structured proforma to record clinical and laboratory parameters, a fully automated hematology analyzer (Sysmex XN-1000) for CBC (Complete Blood Count) and platelet indices, peripheral blood smear examination, and bone marrow aspiration analysis. The proforma captured general patient information, medical history, clinical findings, and investigation results. Peripheral blood smear analysis was performed using Leishman’s stain and routine microscopy to confirm platelet count, morphology, and detect abnormalities like platelet aggregates or fragments. Bone marrow aspiration smears were examined microscopically to assess megakaryocyte adequacy and thrombocytopenia etiology. Quality control procedures were implemented for automated analysis to ensure the accuracy and reliability of results.

Data Collection Procedure

Patients meeting the inclusion criteria were enrolled after obtaining verbal consent. Clinical history and general information were recorded in the structured proforma, including chief complaints, past medical history, family history, personal habits, and systemic examination findings. Blood samples were collected in EDTA vacutainers for CBC analysis using the Sysmex XN-1000 autoanalyzer, with platelet counts manually confirmed through peripheral smear examination. If platelet clumping was observed, blood was recollected in sodium citrate tubes to rule out pseudo thrombocytopenia. Bone marrow aspiration was performed in all 49 hypoproductive and 31 hyperdestructive thrombocytopenic patients as part of their diagnostic workup. The bone marrow smears were analyzed for megakaryocyte adequacy, with findings categorized as normal, decreased, or increased. The platelet indices and bone marrow findings were correlated for each patient. Age- and sex-matched healthy individuals served as the control group for comparative analysis.

Statistical Analysis

Statistical analysis was performed using SPSS version 20. Descriptive statistics were presented as frequency tables for qualitative variables, while mean and standard deviation were used for quantitative variables. Correlation tests assessed relationships between quantitative variables, with a p-value <0.05 considered statistically significant. The student’s unpaired t-test analyzed quantitative data, while the chi-square test (χ²) was used for qualitative data. Sensitivity, specificity, positive predictive value, and negative predictive value for platelet indices (PDW, MPV, and P-LCR) were determined using a two-by-two table and ROC curve analysis. Platelet counts and indices were compared between Groups I (49 hypoproductive thrombocytopenia cases) and II (31 hyperdestructive thrombocytopenia cases with newly diagnosed ITP), as well as with the control group. MPV, PDW, and P-LCR were significantly higher in Group II than in Group I. Group I had a male-to-female ratio of 3.4:1 (ages 1–83 years), while Group II had a ratio of 0.7:1 (ages 7–81 years).

Table 1 categorizes patients into different age groups and provides the number and percentage of patients in each category. Most cases fall within the 40-49 age group (38%), followed by 50-59 years (16%).

Table 2 shows the male-to-female ratio in hypoproductive and hyperdestructive thrombocytopenia cases. Hypoproductive cases have a male predominance (3.4:1), while hyperdestructive cases show a female predominance (1:1.4).

Table 3 lists the different causes under three categories: disorders of decreased production, disorders of increased destruction, and other causes. Major causes include bone marrow failure syndromes, ITP, and hypersplenism.

Table 4 displays mean platelet count and indices (MPV, PDW, P-LCR) with p-values indicating statistical significance. Platelet count is significantly lower in the hypoproductive group compared to controls, with statistically significant differences in all indices.

Similar to Table 4, Table 5 compares hyperdestructive thrombocytopenia cases with controls. MPV, PDW, and P-LCR are significantly higher in hyperdestructive cases compared to controls.

Table 6 gives the lists of cut-off values, sensitivity, specificity, and AUC percentages for each platelet index.

Hypoproductive Thrombocytopenia

Table 7 presents the mean, standard deviation, and standard error of MPV, PDW, and P-LCR in both hypoproductive and hyperdestructive thrombocytopenia groups. It highlights the significant differences in platelet indices between the two groups, with hyperdestructive thrombocytopenia showing notably higher values for all three parameters.

Thrombocytopenia is defined by platelet counts below 1.5 lakh/mm³, but understanding the underlying pathomechanism requires bone marrow examination to determine whether there is decreased megakaryocyte production, ineffective thrombopoeisis, or increased peripheral destruction. In our study, we analyzed patient peripheral smears followed by bone marrow aspirate examinations to evaluate causes of thrombocytopenia.

The peripheral blood film itself provides significant information for evaluating thrombocytopenia causes. We categorized thrombocytopenia using peripheral smear criteria as follows:

For hyperdestruction: Increased platelet size and presence of giant platelets. In ITP (Immune Thrombocytopenic Purpura), diagnosis occurs by exclusion, with the absence of immature leukocytes, fragmented erythrocytes, and platelet clumps being as important as observed findings.

For hypoproduction: Increased variation in platelet size and presence of hypogranular or agranular platelets. Similarly, bone marrow criteria for categorizing thrombocytopenia included:

For hyperdestructive thrombocytopenia: Normal megakaryopoeisis with persistent thrombocytopenia, increased megakaryocyte numbers with decreased size, and erythroid hyperplasia.

For hypoproductive thrombocytopenia: Morphologically normal megakaryocytes but decreased in number, with some cases showing dysplastic megakaryocytes.

Since bone marrow examination is invasive and not necessary as a first-line diagnostic procedure, we investigated other parameters from automated analyzers, including platelet indices such as PDW, MPV, and P-LCR. Combined interpretation of these indices proved useful for differentiating various thrombocytopenia etiologies.^[15]

Recent technological advances have made it possible to record various platelet indices with automated hematology analyzers.^[16] Large platelets are seen in diseases like ITP, May-Hegglin anomaly, and Bernard-Soulier syndrome, while small platelets are observed in conditions like aplastic anemia, Wiskott-Aldrich syndrome, thrombocytopenia-absent radii syndrome, and storage pool disease.^[17] The Coulter counter may measure RBC fragmentation as large-sized platelets.

In splenic sequestration, large platelets are trapped and not released into systemic circulation (Bessman et al., 19850.^[17]

Although MPV is raised in consumptive thrombocytopenia, platelet size remains difficult to quantitate accurately due to wide physiological variations.^[18] Platelet RNA content, measured by flow cytometric assay, provides an indirect measure of platelet production. High total platelet IgG concentration in hyperdestruction patients indicates high young mean platelet age. MPV increases with raised platelet turnover or in Bernard Soulier's syndrome, while normal or decreased values occur in hypoproduction, sepsis, or big spleen syndromes.^[19] In ITP, PDW also increases alongside MPV and is inversely related to platelet count.^[20] MPV values are low in myeloproliferative disorders. P-LCR, representing the percentage of platelets larger than 12 fl, is significantly higher in ITP than in aplastic anemia when considered alongside MPV and PDW.^[20]

We observed that MPV and PDW variation is directly proportional to platelet count in hypoproduction and inversely proportional in hyperdestructive categories. Most patients with hyperdestructive thrombocytopenia had significantly higher MPV values than those with hypoproductive thrombocytopenia, consistent with previous studies (Bowles et al., 2005; Kaito et al., 2005).^[21]

Our study reveals a significant linear correlation between platelet count and MPV in the hypoproduction group (p significant) and a significant inverse relationship between platelet count and PDW in the hyperdestruction group (p significant).

Hyperdestructive thrombocytopenia (ITP) can be precisely differentiated from hypoproductive types (acute leukemias, aplastic anemias) based on platelet indices such as MPV, PDW, and P-LCR, as demonstrated in many studies. All parameters were significantly higher in the hyperdestructive group compared to the hypoproductive group, consistent with studies by Katti et al.^[22]

Niethammer et al.^[23] reported that the maximum of the histogram (highest peak of the platelet volume distribution curve) has better efficiency than MPV in identifying ITP-caused thrombocytopenia versus decreased platelet production from chemotherapy. MPV predicted bone marrow metastasis in solid tumor patients with 85% PPV and 90% NPV. There is growing interest in using platelet markers to discriminate between different forms of thrombocytopenia.

Since bone marrow findings provide definitive diagnosis of underlying pathomechanisms, bone marrow studies are frequently requested for thrombocytopenia cases. Findings of decreased megakaryocytes in aplastic anemia and leukemia and increased megakaryocytes in immune thrombocytopenia were consistent with other studies.^[23] Our findings of normal, increased, and decreased megakaryocytes support the hypothesis of both hypoproduction and ineffective thrombopoiesis in various thrombocytopenia cases.

The most common cause of thrombocytopenia in hypoproductive patients was bone marrow suppression, possibly due to the predominance of CML as the leading type of leukemia in this region.^[24]

We observed uneven sex distribution among study subjects, with females predominating in ITP and males in hypoproductive cases. This may reflect epidemiological differences in chronic ITP incidence and prevalence, which is more common in females (particularly women of childbearing age), while CML is more common in older ages and in males.

Our study of 80 thrombocytopenic patients showed that ITP patients had higher MPV compared to the hypoproductive thrombocytopenia group and the control group but did not show significant differences between either the ITP patients and the control group or the hypoproductive group and the control group. Similarly, Borkataky et al.^[25] found no significant difference in MPV between hyperdestructive thrombocytopenia groups and the control group. Different studies have proposed different cutoff values for platelet indices, especially MPV. These differences might be attributed to a varied selection of hypoproductive thrombocytopenia patients as comparative groups with ITP.

Another explanation for differences in cutoff values could be the types of hematology analyzers used, as older automated analyzers may have been used in some studies. Previous work by researchers such as Ntaios et al.^[25] reported higher platelet indices in hyperdestructive thrombocytopenia patients, reflecting increased production rates, and established MPV cut-off values ranging from >9 fl to >11 fl.

Although Numbenjapon et al.^[26] reported that MPV could distinguish hyperdestructive from hypoproductive thrombocytopenia, they proposed a 7.9 fl cutoff, lower than previously reported values.

Many studies have shown that platelet indices depend on variables including time of analysis after blood withdrawal, anticoagulant used, specimen storage temperature, and counter technologies.^[27] Age and sex variation was not considered in our study. However, measuring platelet indices is not always possible in severe thrombocytopenia and red cell fragmentation because a platelet histogram cannot be appropriately drawn.^[28]

Many researchers have reported increased platelet volume in hyperdestructive thrombocytopenic patients with strikingly elevated MPVs.^[29] The high MPV in ITP could be explained by newly produced platelets being larger than circulating platelets, which decrease in size with age over the 7-10 day platelet lifespan. Consequently, in patients with thrombocytopenia from peripheral destruction, MPV increases, reflecting active bone marrow compensation with release of young platelets ("left shifted").^[30]

Both MPV and PDW are reliable tests for positive ITP diagnosis and are considered tests with 100% sensitivity and specificity for diagnosing ITP cases.^[31]

In our study, P-LCR was significantly higher in ITP patients and significantly lower in hypoproductive thrombocytopenia patients compared to the control group. Therefore, it effectively distinguished ITP from hypoproductive thrombocytopenia. P-LCR was significantly higher in ITP patients compared with hypoproductive thrombocytopenia patients, and a cutoff value greater than 33.6% yielded 95% diagnostic sensitivity for ITP. Similarly, an MPV cutoff value >10.15 yielded 85% sensitivity for ITP. Although PDW can be a good aid in differential diagnosis of conditions associated with abnormal platelet counts when used alongside P-LCR and MPV.

LIMITATIONS

One limitation of this study is that when there is an abnormal distribution of platelets, the presence of RBC fragments, or blasts, the automated analyzer may not accurately display platelet indices. In such cases, interpreting results from histograms and performing a peripheral smear review become essential for accurate assessment. Additionally, the relatively small sample size and the limited representation of disease categories, particularly in the hyperdestructive thrombocytopenia group, may restrict the generalizability of these findings to a broader patient population.

This study highlights the importance of developing a structured diagnostic algorithm for thrombocytopenia to minimize errors arising from superficial identification of its etiology. Automated cell counters provide fast, accurate, and reproducible results, offering advantages such as eliminating observer variability, reducing statistical errors, and providing additional platelet indices (MPV, PDW, P-LCR) that are not available with manual counts. Our findings demonstrate that platelet indices can effectively differentiate hypoproductive from hyperdestructive thrombocytopenia, potentially reducing the need for unnecessary bone marrow aspiration and platelet transfusion. While bone marrow examination remains the gold standard, standardized platelet index measurements significantly enhance diagnostic accuracy and understanding of disease mechanisms. Incorporating platelet indices into routine CBC analysis can serve as a valuable screening tool, guiding clinical decisions and improving patient management.

1. Laki K. Our ancient heritage in blood clotting and some of its consequences. Ann N Y Acad Sci. 1972;202:297-307.
2. Machlus KR, Thon JN, Italiano JE. Interpreting the developmental dance of the megakaryocyte: a review of the cellular and molecular processes mediating platelet formation. Br J Haematol 2014;165(2):227-36.
3. Jain NC. A scanning electron microscopic study of platelets of certain animal species. Thromb Diath Haemorrh 1975;33(3):501-7.
4. Michelson AD. Platelets. 2^nd San Diego: Academic Press 2007: p. 3.
5. Greer JP, Arber DA, Glader B. Disorders of haemostasis and coagulation. The diagnostic approach to the bleeding disorders. Thrombocytopenia caused by immunological destruction. In: Wintrobe's Clinical Haematology. 10^th Philadelphia: Williams and Wilkins 1998.
6. Kaushansky K, Beutler E, Seligsohn U, et al. Williams Haematology. 8^th New York: McGraw-Hill Companies 2010.
7. Sekhon S, Roy V. Thrombocytopenia in adults: a practical approach to evaluation and management. South Med J 2006;99:491-8.
8. Strauss G, Vollert C, von Stackelerg A, et al. Immature platelet count: a simple parameter for distinguishing thrombocytopenia in pediatric acute lymphocytic leukemia. Pediatr Blood Cancer 2011;57:641-7.
9. Kelton JG, Murphy WG, Lucarelli A, et al. A prospective comparison of four techniques for measuring platelet-associated IgG. Br J Haematol 1989;71:97-105.
10. Straus G, Vollert C, Stackelberg AV, et al. Immature platelet count: a simple parameter for distinguishing thrombocytopenia in paediatric acute lymphocytic leukaemia from immune thrombocytopenia. Pediatr Blood Cancer 2011;57:641-7.
11. Ntaios G, Papadopoulous A, Chatzinikolaou A, et al. Increased values of mean platelet volume and platelet size deviation width may provide a safe positive diagnosis of idiopathic thrombocytopenic purpura. Acta Haematol 2008;119(3):173-7.
12. Birhaneselassie M, Birhanu A, Tsegaye A. How useful are complete blood count and reticulocyte reports to clinicians in Addis Ababa hospitals, Ethiopia? BMC Haematol 2013;13(1):11.
13. De La Salle BJ, McTaggart PN, Briggs C, Harrison P, Dore CJ, Longair I, et al. The accuracy of platelet counting in thrombocytopenic blood samples distributed by the UK national External Quality Assessment Scheme for General Haematology. Am J Lab Haematol 2012;137:65-74.
14. Hong H, Xiao W, Maitta RW. Steady increment of immature platelet fraction is suppressed by irradiation in single-donor platelet components during storage. PLoS One 2014;9:e85465.
15. Greer JP, Arber DA, Glader. Disorders of haemostasis and coagulation. The diagnostic approach to the bleeding disorders. Thrombocytopenia caused by immunological destruction. Wintrobe’s Clinical Haematology. 10^th edn, Part V, Chap. 60, Chap. 62. Philadelphia: Williams and Wilkins 1998.
16. Segal HC, Briggs C, Kunka S, et al. Accuracy of platelet counting haematology analysers in severe thrombocytopenia and potential impact on platelet transfusion. Br J Haematol 2005;128:520–5.
17. Bessman JD, Gilmer PR, Gardner FH. Use of mean platelet volume improves detection of platelet disorders. Blood Cells 1985;11(1):127-35.
18. Pakala R, Waksman R. Currently available methods for platelet function analysis: advantages and disadvantages. Cardiovasc Revasc Med 2011;12:312–22.
19. Costa AC, Ribeiro B, Costa E. Platelet indices in chronic alcoholic liver disease patients with thrombocytopenia. Arq Gastroenterol 2007;44(3):201-4.
20. Guidelines for the investigation and management of idiopathic thrombocytopenic purpura in adults, children and in pregnancy. Br J Haematol 2003;120(4):574-96.
21. Borkataky S, Jain R, Gupta R, et al. Role of platelet volume indices in the differential diagnosis of thrombocytopenia: a simple and inexpensive method. Hematology 2009;14(3):182–6.
22. Katti T, Mhetre S, Annigeri C. How far are the platelet indices mirror image of mechanism of thrombocytopeniamystery still remains? Int J Adv Med 2014;1(3):200.
23. Niethammer AG, Forman EN. Use of the platelet histogram maximum in evaluating thrombocytopenia. Am J Hematol 1999;60(1):19–23.
24. Andres E, Affenberger S, Zimmer J, et al. Current hematological findings in cobalamin deficiency. A study of 201 consecutive patients with documented cobalamin deficiency. Clin Lab Haem 2006;28:50-6.
25. Ntaios G, Papadopoulos A, Chatzinikolaou A, et al. Increased values of mean platelet volume and platelet size deviation width may provide a safe positive diagnosis of idiopathic thrombocytopenic purpura. Acta Haematol 2008;119:173-7.
26. Numbenjapon T, Mahapo N, Pornvipavee R, et al. A prospective evaluation of normal mean platelet volume in discriminating hyperdestructive thrombocytopenia from hypoproductive thrombocytopenia. Int J Lab Hematol 2008;30:408-14.
27. Kottke-Marchant K, Corcoran G. The laboratory diagnosis of platelet disorders: an algorithmic approach. Arch Pathol Lab Med 2002;126(2):133-46.
28. Niethammer AG, Forman EN. Use of the platelet histogram maximum in evaluating thrombocytopenia. Am J Hematol 1999;60(1):19-23.
29. Eldor A, Avitzour M, Or R, et al. Prediction of haemorrhagic diathesis in thrombocytopenia by mean platelet volume. Br Med J (Clin Res Ed) 1982;285:397-400.
30. Thon JN, Italiano JE. Does size matter in platelet production? Blood 2012;120:1552–61.
31. Kottke-Marchant K, Corcoran G. The laboratory diagnosis of platelet disorders: an algorithmic approach. Arch Pathol Lab Med 2002;126(2):133-46.