# Platelet Estimation from Peripheral Blood Smear: Does it Really Work?

Estimation des plaquettes à partir de frottis sanguins périphériques : ça marche vraiment ?

Ikram Bachiri¹, Zineb Yahiaoui¹, Nacera Fenni¹, Imad Ararem², Mohamed Arezki Aouni²

¹ Haematology laboratory of the central military hospital, Algiers, Algeria

² Preventive medicine service of the central military hospital, Algiers, Algeria

**Corresponding author:**
Ikram BACHIRI
drbachiriikram@gmail.com

DOI: https://doi.org/10.48087/BJMSle.2017.4214

This is an open access article distributed under the terms of the Creative Commons Attribution International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original author and journal are appropriately credited.

**Citation:**
Bachiri I, Yahiaoui Z, Fenni N. Platelet estimation from peripheral blood smear: does it really work?. Batna J Med Sci 2017;4(2):180-181.
https://doi.org/10.48087/BJMSle.2017.4214

Dear Editor,

Assessment of platelet count is an important diagnostic parameter in haematology. However, despite of the performing techniques used by automated analysers, platelet count is sometimes challenging due to their small size. Therefore, manual methods are still used to verify any questionable platelet count and to avoid any interferences that can make the automated counting erroneous (macro thrombocytes, aggregates...) [1].

In this study we used three manual methods proposed by many researchers [2-6] and compared our results to the automated method, which we considered as a reference one.

220 blood samples were collected, 131 from blood donors with a healthy physical condition, and 89 from patients admitted in our hospital. The tubes used to collect blood samples contained ethylenediaminetetraacetic acid (K2 or K3EDTA) to avoid any in vitro clotting, blood samples containing clots were systematically excluded from the study.

Complete blood count was performed on the Sysmex XT4000i counter, which uses both impedance and optical numeration techniques for the platelet count.

Two blood smears were performed for every blood sample, then stained with an automatic May Grunwald Giemsa stain (Hemateck), and examined under light microscopy first with a 40X lens to search any aggregates or macrothrombocytes, if any are found that exclude the blood sample from the study. Platelet estimation was then done on a 100X oil immersion fields as follow:

¹st method: this method was proposed by Brahimi and other researchers [2] and consists of calculating the red cell: platelet ratio: The number of erythrocytes observed in a quarter of the oil-immersion field was multiplied by four. Then all the platelets in the same field were counted. Other fields were examined until we reached a number of 1000 erythrocytes. The number of platelets per 1000 erythrocytes was multiplied by the automated Red Blood Count to give an approximate count.

2nd method: we counted the number of platelets per 10 oil immersion fields and divided by 10 to find the average number of platelets per field then multiply by the field factor that must be determined for each brand of as follow:

First, we perform 30 consecutive automated patient samples and ensure the PLT QC is within two Standard Deviations, and then we prepare and stain a smear on each specimen. For each smear, we count the number of PLTs in 10 consecutive OIFs and divide by 10 to get the average number per field, after we divide the automated PLT count by the average number of PLTs per field for each specimen, we add the numbers obtained in step four and divide by number of specimens (30) to get the average ratio of automated PLTs to PLTs per OIF. Round ratio value to the nearest whole number. For example, if the ratio value is 15.0, one platelet per oil field 15.0 x 10⁹/L. Finally, we calculated the average number of platelets per OIF and multiplied by the platelet factor. Therefore, for an average of 15 PLTs per OIF, the calculation would be: $15 \times 15 = 225 \times 10^9/L$ [3-5]. In our study the field factor was equal to 14.

³rd method: proposed by Sheila Torres et al. [6], we multiplied the average number of platelets per 10 fields by the patient's haemoglobin and then by 1,000 mm³ to obtain the number of platelets/mm³.

Data were analyzed by statistical software (Excel, EpiData and MedCalc) and expressed in mean ± SD. Comparison between the methods was done by calculating the intraclass correlation ICC. An ICC ≥ 0.75 refers to an excellent correlation (Cicchetti 1994) [7]. A paired t-test was performed in order to assess the match between platelet count results by both methods. In this evaluation, a statistically significant difference in platelet level was set at a level of p=0.05 [2]. We also analysed the ROC curves (receiver operating characteristic curves), which allowed us to compare both sensitivity and specificity of the four methods, and to identify the most performing method and thus the reference one.

Three populations were obtained based on the automated platelet count: low, normal and high platelet count (table 1):

<table><caption>**Table 1.** Populations based on platelet count.</caption><thead><tr><th>Populations</th><th>Total<br>N = 220</th><th>Male<br>N = 123</th><th>Female<br>N = 97</th></tr></thead><tbody><tr><td>Low plat acc<br>&lt; 120 G/l</td><td>57</td><td>34</td><td>23</td></tr><tr><td>High plat acc<br>&gt; 450 G/l</td><td>32</td><td>18</td><td>14</td></tr><tr><td>Normal<br>(120-450 G/l)</td><td>131</td><td>71</td><td>60</td></tr></tbody></table>
The results showed positive correlation between the four methods (automated and manual). In our study, the ICC was equal to 0.946, 0.937, 0.888 for method 1, 2 and 3 respectively. The paired t-test showed no significant difference between the four methods (p>0.05).

<table><caption>**Table 2.** The mean platelet count by manual versus automated methods.</caption><thead><tr><th>Platelet count (G/L)</th><th>N</th><th>Automated count</th><th>Method 1</th><th>p</th><th>Method 2</th><th>p</th><th>Method 3</th><th>p</th></tr></thead><tbody><tr><td>&lt; 120</td><td>57</td><td>81 ± 30</td><td>70 ± 36</td><td>0,158</td><td>94±45</td><td>0,266</td><td>65 ± 35</td><td>0,228</td></tr><tr><td>120-450</td><td>131</td><td>250 ± 55</td><td>274 ± 68</td><td>0,126</td><td>271 ± 58</td><td>0,236</td><td>273 ± 60</td><td>0,387</td></tr><tr><td>&gt; 450</td><td>32</td><td>570 ± 119</td><td>562 ± 151</td><td>0,244</td><td>600±183</td><td>0,170</td><td>450 ± 162</td><td>0,244</td></tr></tbody></table>

The mean platelets count did not show significant difference between the four methods (p≥0.05).

<table><caption>**Table 3.** Correlation of samples with low/normal and high platelet count by manual vs. automated methods.</caption><thead><tr><th rowspan="2">Sex</th><th colspan="7">Platelet count (G/L)</th></tr><tr><th>automate</th><th>M1</th><th>Correlation</th><th>M2</th><th>C</th><th>M3</th><th>C</th></tr></thead><tbody><tr><td>Male</td><td>244±175</td><td>257±180</td><td>r=0,952<br>p=0,260</td><td>266±187</td><td>r=0,942<br>p=0,280</td><td>247±<br>152</td><td>r=0,898<br>p=0,170</td></tr><tr><td>Female</td><td>259±144</td><td>268±163</td><td>r=0,934<br>p=0,262</td><td>282±163</td><td>r=0,930<br>p=0,300</td><td>241±<br>137</td><td>r=0,874<br>p=0,188</td></tr></tbody></table>

After analyzing ROC curves which allowed us to compare both specificity and sensitivity of the three manual methods, we have concluded to the following results:

We defined two reference methods for manual platelet count based on the highest sensitivity and specificity of each method. Method 1 for patients with low platelet count (sensitivity: 89 %, specificity: 100 %) and method 2 for patients with high platelet count (sensitivity: 84 %, specificity: 99%) with a cut-off of 130 and 327 G/L respectively.

The platelet count is possible without any automated help thanks to the manual methods using peripheral blood smears. These methods allow an easy and simple count using only a slide and an optical microscope.

This is particularly interesting when automated platelet count may be affected by some sample characteristic such as blood clots or by a particular physical condition especially in disseminated intravascular coagulation (DIC) where schizocytes found in the blood can be confused with platelet due to their small size [1].

Up to date, many researchers attempted to verify the effectiveness of these manual methods under the name of indirect platelet count [2,6]. Our results join the majority of the studies done in this context [2,6,8,9]. However, even though the easiness these methods provide, several limits have been noticed while performing the platelet count, for example for method 1 is still an automated dependent method as well as method 3.

**Acknowledgement:** The authors thank Professor Mohamed Brahimi for re-reading and correcting the manuscript.

## RÉFÉRENCES

1. Mikhail Roshal. Measurement of Platelet Count. Mean Platelet Volume and Reticulated Platelets, Chapter 133.

2. Brahimi M, Osmani S, Arabi A, Enta-Soltan B, Taghezout Z, Elkahili BS, et al. The estimation of platelet count from a blood smear on the basis of the red cell: platelet ratio. Turk J Hematol 2009; 26:21-4.

3. Bennes D, Karon BS. Review of platelet estimates. Medical Laboratory Observer website, May 2011

4. Stiene-Martin EA, Lotspeich-Steininger CA, Koepke JA. Clinical Hematology, 2nd ed. Philadelphia, PA: J.B. Lippincott-Raven; 1992:337.

5. Mogadam L. Application of the Miller's Disc for the Estimation and Quality Control of the Platelet Count. Lab Med. 1980;11:131

6. Sheila López Torres, MS, MT(ASCP), Eliud López Velez. Platelet Verification Under Microscope Calculated by the Patient's Hemoglobin Factor. Laboratory medicine, july 2004, number 7, volume 35

7. Cicchetti, Domenic V. (1994). "Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology."

8. Bakhubaira S (2013) Automated Versus Manual Platelet Count in Aden. J Clin Exp Pathol 3: 149.

9. Anitha K et al. Comparison of Platelet Count by Peripheral Smear Method and Automated Method. National Journal of Physiology, Pharmacy & Pharmacology | 2014 | Vol 4 | Issue 1 | 39-42

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