Can Platelet Distribution Width Be Used to Predict the Possibility of Chronic Myeloproliferative Neoplasms?

Can Platelet Distribution Width Be Used to Predict the Possibility of Chronic Myeloproliferative... Abstract Platelet distribution width (PDW) and mean platelet volume are markers of platelet activation and have prognostic value in coronary heart diseases, as well as in cancers of solid organs. In this study, we evaluated the possibility of using PDW to predict chronic myeloproliferative neoplasms by comparing platelet indices obtained by automated analyzers in chronic myeloproliferative neoplasms with those in control specimens. We found that PDW greater than 66.4% has specificity of 99% and likelihood ratio of 19.5 for predicting chronic myeloproliferative neoplasms. Also, the area under curve (AUC) for platelet distribution width is 0.68. chronic myeloproliferative neoplasms, platelet distribution width, area under the curve, mean platelet volume, reactive thrombocytosis, platelet count Platelet volume indices include mean platelet volume (MPV), platelet distribution width (PDW), and platelet cell ratio. MPV has been reported to be a marker for platelet activation.1,2 PDW is an indicator of variation in platelet size and is also considered a marker of platelet activation.3 Increase in MPV has been associated with myocardial infarction and thromboembolism.4,5 Increased PDW has been reported in coronary disorders, cerebral venous thrombosis, and chronic myeloproliferative neoplasms.6–8 Increase in PDW has been related to poor prognosis in laryngeal cancer.9 Also, increase in MPV has been related to esophageal cancer, breast cancer, and hepatocellular carcinoma.10–12 However, PDW has been proposed as a stronger marker than MPV for prognosis in different types of cancer.13 The aim of this study was to evaluate PDW as a discriminator for chronic myeloproliferative neoplasms. Materials and Methods Selection of Patients For this retrospective study, the study group consisted of patients diagnosed with chronic myeloproliferative neoplasms (according to the World Health Organization 2016 classification). History was obtained from bone-marrow request forms. Patients with history of thrombosis and previous chemotherapy were excluded from the study. Patients whose blood specimens and blood smears showed clots and platelet clumps were not included in the study. The control group included patients who had no evidence of chronic myeloproliferative neoplasm (based on their medical history and complete blood counts). Patients with known reactive causes for thrombocytosis were included in the control group. Processing of Specimens We collected blood specimens in K2 ethylenediaminetetraacetic acid (EDTA) and processed them within 2 hours of specimen collection. Complete blood counts, including platelet indices, were obtained by loading specimens into the ADVIA 2120i automated analyzer (Siemens Healthcare GmbH). Platelet counts were calculated using the optical method. The MPV was determined from the platelet histogram, and the width of the platelet histogram was listed as the PDW. Statistical Analysis All values were entered into an MS Excel spreadsheet (Microsoft Corporation). Continuous variables were tested for normality using quantile-quantile (Q-Q) plots (XLSTAT software, version 2015.3 [Addinsoft]). If the variables were normally distributed, they were expressed as mean (SD). For continuous variables not having a Gaussian distribution, the values were expressed as median and interquartile range (IQR). Categorical variables were expressed as percentages. Continuous variables in the same group were tested for their association using Pearson rank correlation (if normally distributed) and Spearman rank correlation (if distribution was not normal). Continuous variables across 2 groups were correlated with t test results (if normally distributed) or Mann Whitney U test results (if distribution was not normal). Differences in frequencies of categorical variables across groups were compared by using the χ2 test of independence. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were calculated to identify whether PDW was a good discriminator for chronic myeloproliferative neoplasms. P values were considered significant if 2-tailed P values were less than .05. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios were calculated for different cutoff values of PDW. The cutoff level was considered significant if the positive likelihood ratio was greater than 1. Reference intervals were established for the control group after excluding patients with reactive thrombocytosis. The 2.5 to 97.5 percentile range in the control group was considered to be the reference interval for platelet count and platelet indices. The cutoff levels were further tested in a new cohort of patients that included patients with thrombocytosis, thrombocytopenia, and normal platelets. Observations There were 113 patients in the study group (Table 1), which included 83 men and 30 women. Among them, 61 were diagnosed with chronic myeloid leukemia, 29 with polycythemia vera, 16 with essential thrombocythemia, and 7 with primary myelofibrosis. Thrombocytosis (460–1950 × 109/ L) was present in 56 patients (49.5%), thrombocytopenia (89 and 103 × 109/ L, respectively) in 2 patients (1.8%), and normal platelet count (150–436 × 109/ L) in 55 patients (48.7%). In the control group, there were 132 patients (Table 1), which included 66 men and 66 women. Thrombocytosis (542–795 × 109/ L) was observed in 12 patients (9.1%), and 120 patients (90.9%) had normal platelet count (151–439 × 109/ L). Table 1. Comparison of Demographics and Platelet Indices in the 2 Studied Groups Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) CMPN, chronic myeloproliferative neoplasms; MPV, mean platelet volume; PCT, plateletcrit; PDW, platelet distribution width. an = 132. bn = 113. View Large Table 1. Comparison of Demographics and Platelet Indices in the 2 Studied Groups Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) CMPN, chronic myeloproliferative neoplasms; MPV, mean platelet volume; PCT, plateletcrit; PDW, platelet distribution width. an = 132. bn = 113. View Large Patients with Chronic Myeloproliferative Neoplasms Patients in this group had a median age of 49 years (IQR, 22; range, 17–80 years), median platelet count of 449 (IQR, 394.5; range, 89–1950) × 109/ L, MPV of 10 (IQR,= 2.4; range, 5.9–16.1), plateletcrit (PCT) of 0.46 (IQR, 0.33; range, 0.09–1.63), and PDW of 58.5% (IQR, 15.7; range, 15.5%–75.1%). We performed Spearman rank correlation to determine the relationship between PDW and other platelet indices (Figure 3). Platelet count (rs, −0.29, P = .001) and PCT (rs, −0.13; P = .15) was inversely related to PDW, whereas MPV had a significant positive correlation (rs, 0.59; P <.001). Control Group Patients in this group had a median age of 44 years (IQR, 19; range, 17–79 years), median platelet counts of 276 (IQR, 115.7; range, 151–795) × 109/ L, MPV of 8.85 (IQR, 1.65; range, 7.0–11.7), PCT of 0.25 (IQR, 0.09; range, 0.14–0.72), and PDW of 51.1% (IQR, 15.3; range, 30.7%–73.5%). Spearman rank correlation was performed to determine the relationship between PDW and other platelet indices (Figure 3). Platelet count (rs, −0.31; P <.001) and PCT (rs, −0.198; P = .02) was inversely related to PDW, whereas MPV had a significant positive correlation (rs, 0.30; P <.001). The median age of the control group was significantly lower than that of patients with chronic myeloproliferative neoplasms: U = 6003 and P = .009. Median platelet count (U = 3679; P <.001), MPV (U = 5221.5; P <.001), PCT (U = 2942.5; P <.001), and PDW (U = 4739.5; P <.001; Figure 2) were significantly lower in the control group than in patients with chronic myeloproliferative neoplasms. Reference Intervals The reference interval (2.5–97.5 percentile of 120 patients, after excluding 12 patients with reactive thrombocytosis) for platelet count was 162.9 to 417.0 × 109 per L. Reference intervals for MPV, PCT, and PDW were found to be 7.6 to 13.3 fl, 0.15% to 0.38%, and 34.3% to 66.4%, respectively. We discovered that PDW is a useful discriminator (Figure 1, Table 2) of chronic myeloproliferative neoplasms (AUC = 0.68; P <.001). PDW higher than 47% has sensitivity of 81.4% and specificity of 35.6% for discriminating chronic myeloproliferative neoplasms. PDW higher than 69% has specificity of 98.4% for detecting chronic myeloproliferative neoplasms and a positive likelihood ratio of 4.2. PDW higher than the normal reference interval (34.3%–66.4%) has sensitivity of 23%, specificity of 99%, and a positive likelihood ratio of 19.5. Among the 113 patients with chronic myeloproliferative neoplasms, 26 (23%) had PDW higher than 66.4%, whereas in the control group, only 1 individual (0.7%) had PDW higher than 66.4%. Figure 1 View largeDownload slide Receiver operating characteristic (ROC) curve demonstrating utility of platelet distribution width (PDW) in discriminating chronic myeloproliferative neoplasms (area under the curve [AUC], 0.68). Figure 1 View largeDownload slide Receiver operating characteristic (ROC) curve demonstrating utility of platelet distribution width (PDW) in discriminating chronic myeloproliferative neoplasms (area under the curve [AUC], 0.68). Figure 2 View largeDownload slide Platelet distribution width (PDW) for chronic myeloproliferative neoplasms vs control individuals. Figure 2 View largeDownload slide Platelet distribution width (PDW) for chronic myeloproliferative neoplasms vs control individuals. Figure 3 View largeDownload slide Spearman rank correlation showing significant positive correlation of mean platelet volume (MPV) with platelet distribution width (PDW) in study group and control group. Figure 3 View largeDownload slide Spearman rank correlation showing significant positive correlation of mean platelet volume (MPV) with platelet distribution width (PDW) in study group and control group. Table 2. Sensitivity and Specificity for Predicting Chronic Myeloproliferative Neoplasms and Their Likelihood Ratios PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW, platelet distribution width; PPV, positive predictive value; NPV, negative predictive value; PLR, positive likelihood ratio; NLR, negative likelihood ratio; … , nonapplicable. View Large Table 2. Sensitivity and Specificity for Predicting Chronic Myeloproliferative Neoplasms and Their Likelihood Ratios PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW, platelet distribution width; PPV, positive predictive value; NPV, negative predictive value; PLR, positive likelihood ratio; NLR, negative likelihood ratio; … , nonapplicable. View Large We tested this PDW value in a new cohort of 138 patients, which included 59 patients with chronic myeloproliferative neoplasms (33 with chronic myeloid leukemia, 11 with polycythemia vera, 9 with primary myelofibrosis, and 6 with essential thrombocythaemia). The group included 92 men and 46 women varying in age from 17 years to 77 years. There were 53 patients with normal platelet count (28 of whom had chronic myeloproliferative neoplasms), 56 with thrombocytosis (27 of whom had chronic myeloproliferative neoplasms), and 29 with thrombocytopenia (4 of whom had chronic myeloproliferative neoplasms). MPV showed a slight significant positive correlation with PDW in chronic myeloproliferative neoplasms (rs, 0.37; P = .003), as well as reactive conditions (rs, 0.34; P = .001). Reactive conditions included patients with anemias of chronic disease, iron-deficiency anemia, sepsis, chronic kidney disease, immune thrombocytopenia, and Hodgkins and Non-Hodgkins lymphomas. We discovered that the PDW in chronic myeloproliferative neoplasms (median, 58.9 %) was significantly higher than that of reactive conditions (median, 50.3%), by Mann Whitney U testing (U = 1621.5; P = .002). PDW in normal platelet counts and in thrombocytosis significantly differed in the 2 groups (P = .005 and P = .007, respectively). A χ2 test of independence was calculated to compare frequency of PDW being more than 66.4% in chronic myeloproliferative neoplasms. A significant interaction was found: ×2 (137) = 71.2; P <.001. The frequency of patients with PDW higher than 66.4% was 68.4% in patients with chronic myeloproliferative neoplasms, whereas it was 31.5 % in reactive conditions. The frequency of patients with PDW less than 66.4% was 38.6% in patients with chronic myeloproliferative neoplasms, whereas it was 61.3% in reactive conditions. Discussion In the present study, we discovered that in controls and in patients with chronic myeloproliferative neoplasms, the PDW showed a significant positive correlation with MPV, whereas it was slightly inversely correlated with platelet count and PCT. Age, platelet count, MPV, PCT, and PDW of patients with chronic myeloproliferative neoplasms were significantly higher than in controls. Based on these findings, PDW with AUC of 0.68 can be used as a discriminator for chronic myeloproliferative neoplasms. PDW higher than 66.4% had specificity of 99% for predicting chronic myeloproliferative neoplasms and a likelihood ratio of 19.5. However, the sensitivity was only 23%. Still, the frequency of patients with chronic myeloproliferative neoplasms and PDW higher than 66.4% is 68.4%, whereas it is only 31.5% in reactive conditions. MPV is affected by preanalytical variables, and PDW is more indicative of platelet activation.3 Hence, we chose PDW as a discriminant in this study. Yang et al14 have found that PDW was superior to MPV for identifying platelet activation. In their study, they report that PDW was able to predict severe eclampsia with an AUC of 0.74. In a recent article from India,15 reference intervals for PCT, MPV, and PDW were reported as mean (SD) 0.2% (0.07%), 14.7 (2.0) fl, and 8.9 (1.2) fl, respectively. Our reference intervals for MPV (7.6–13.3 fl) and PCT (0.15%–0.38%) are higher than those reported by Ransing et al.15 The difference could be ascribed to population-based differences in MPV. In another study from North India,16 the PDW (mean [SD], 29.4% [20.3%]) in chronic myeloproliferative neoplasms was significantly higher than in controls (mean [SD], 17.2 [2.4%]; P = .002). Likewise, in our study, patients with chronic myeloproliferative neoplasms had higher PDW than controls. Olteanu and colleagues,17 however, reported contradictory findings and did not observe any significant difference in PDW (P = .70). There are certain limitations to this study. First, there is still significant overlap in PDW in the control group and the group of patients with chronic myeloproliferative neoplasms, and PDW may be an additional feature to distinguish chronic myeloproliferative neoplasms. Further, serial measurements of PDW and its changes would be stronger indicators than a single measurement. More studies need to be conducted to verify the utility of PDW, especially after matching cases and controls for drug intake. We hope our findings will advance the ongoing conversation in the field of laboratory medicine regarding the optimal ways to predict the possibility of chronic myeloproliferative neoplasms. Personal and Professional Conflicts of Interest None reported. Abbreviations MPV mean platelet volume PDW platelet distribution width EDTA ethylenediaminetetraacetic acid Q-Q quantile-quantile IQR interquartile range ROC receiver operating characteristic AUC area under the curve PPV positive predictive value NPV negative predictive value PCT plateletcrit CMPN chronic myeloproliferative neoplasms PLR positive likelihood ratio NLR negative likelihood ratio … nonapplicable References 1. Bath PM , Butterworth RJ . Platelet size: measurement, physiology and vascular disease . Blood Coagul Fibrinolysis . 1996 ; 7 ( 2 ): 157 – 161 . Google Scholar Crossref Search ADS PubMed 2. Tsiara S , Elisaf M , Jagroop IA , Mikhailidis DP . Platelets as predictors of vascular risk: is there a practical index of platelet activity ? Clin Appl Thromb Hemost . 2003 ; 9 ( 3 ): 177 – 190 . Google Scholar Crossref Search ADS PubMed 3. Vagdatli E , Gounari E , Lazaridou E , Katsibourlia E , Tsikopoulou F , Labrianou I . Platelet distribution width: a simple, practical and specific marker of activation of coagulation . Hippokratia . 2010 ; 14 ( 1 ): 28 – 32 . Google Scholar PubMed 4. Chu SG , Becker RC , Berger PB , et al. Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis . J Thromb Haemost . 2010 ; 8 ( 1 ): 148 – 156 . Google Scholar Crossref Search ADS PubMed 5. Braekkan SK , Mathiesen EB , Njølstad I , Wilsgaard T , Størmer J , Hansen JB . Mean platelet volume is a risk factor for venous thromboembolism: the Tromsø Study, Tromsø, Norway . J Thromb Haemost . 2010 ; 8 ( 1 ): 157 – 162 . Google Scholar Crossref Search ADS PubMed 6. Khandekar MM , Khurana AS , Deshmukh SD , Kakrani AL , Katdare AD , Inamdar AK . Platelet volume indices in patients with coronary artery disease and acute myocardial infarction: an Indian scenario . J Clin Pathol . 2006 ; 59 ( 2 ): 146 – 149 . Google Scholar Crossref Search ADS PubMed 7. Kamisli O , Kamisli S , Kablan Y , Gonullu S , Ozcan C . The prognostic value of an increased mean platelet volume and platelet distribution width in the early phase of cerebral venous sinus thrombosis . Clin Appl Thromb Hemost . 2013 ; 19 ( 1 ): 29 – 32 . Google Scholar Crossref Search ADS PubMed 8. Osselaer JC , Jamart J , Scheiff JM . Platelet distribution width for differential diagnosis of thrombocytosis . Clin Chem . 1997 ; 43 ( 6 Pt 1 ): 1072 – 1076 . Google Scholar PubMed 9. Zhang H , Liu L , Fu S , et al. Higher platelet distribution width predicts poor prognosis in laryngeal cancer . Oncotarget . 2017 ; 8 ( 29 ): 48138 – 48144 . Google Scholar PubMed 10. Zhang F , Chen Z , Wang P , Hu X , Gao Y , He J . Combination of platelet count and mean platelet volume (COP-MPV) predicts postoperative prognosis in both resectable early and advanced stage esophageal squamous cell cancer patients . Tumour Biol . 2016 ; 37 ( 7 ): 9323 – 9331 . Google Scholar Crossref Search ADS PubMed 11. Gu M , Zhai Z , Huang L , et al. Pre-treatment mean platelet volume associates with worse clinicopathologic features and prognosis with invasive breast cancer . Breast Cancer . 2016 ; 23 ( 5 ): 752 – 760 . Google Scholar Crossref Search ADS PubMed 12. Cho SY , Yang JJ , You E , et al. Mean platelet volume/ platelet count ratio in hepatocellular carcinoma . Platelets . 2013 ; 24 ( 5 ): 375 – 377 . Google Scholar Crossref Search ADS PubMed 13. Takeuchi H , Abe M , Takumi Y , et al. The prognostic impact of the platelet distribution width-to-platelet count ratio in patients with breast cancer . PLoS One . 2017 ; 12 ( 12 ): e0189166 . Google Scholar Crossref Search ADS PubMed 14. Yang SW , Cho SH , Kwon HS , Sohn IS , Hwang HS . Significance of the platelet distribution width as a severity marker for the development of preeclampsia . Eur J Obstet Gynecol Reprod Biol . 2014 ; 175 : 107 – 111 . Google Scholar Crossref Search ADS PubMed 15. Ransing RS , Patil B , Grigo O . Mean platelet volume and platelet distribution width level in patients with panic disorder . J Neurosci Rural Pract . 2017 ; 8 ( 2 ): 174 – 178 . Google Scholar Crossref Search ADS PubMed 16. Parashar Y , Kushwaha R , Kumar A , et al. Haemostatic profile in patients of myeloproliferative neoplasms-a tertiary care centre experience . J Clin Diagn Res . 2016 ; 10 ( 11 ): EC01 – EC04 . Google Scholar PubMed 17. Olteanu AL , Mihaila RG , Catana AC , Flucus O , Bus C , Mihalache M . Platelet indices in Philadelphia-negative chronic myeloproliferative neoplasms . Rev Romana Med Lab . 2015 ; 23 ( 2 ): 169 – 177 . © American Society for Clinical Pathology 2018. All rights reserved. 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Can Platelet Distribution Width Be Used to Predict the Possibility of Chronic Myeloproliferative Neoplasms?

Laboratory Medicine, Volume Advance Article – Oct 17, 2018

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Abstract

Abstract Platelet distribution width (PDW) and mean platelet volume are markers of platelet activation and have prognostic value in coronary heart diseases, as well as in cancers of solid organs. In this study, we evaluated the possibility of using PDW to predict chronic myeloproliferative neoplasms by comparing platelet indices obtained by automated analyzers in chronic myeloproliferative neoplasms with those in control specimens. We found that PDW greater than 66.4% has specificity of 99% and likelihood ratio of 19.5 for predicting chronic myeloproliferative neoplasms. Also, the area under curve (AUC) for platelet distribution width is 0.68. chronic myeloproliferative neoplasms, platelet distribution width, area under the curve, mean platelet volume, reactive thrombocytosis, platelet count Platelet volume indices include mean platelet volume (MPV), platelet distribution width (PDW), and platelet cell ratio. MPV has been reported to be a marker for platelet activation.1,2 PDW is an indicator of variation in platelet size and is also considered a marker of platelet activation.3 Increase in MPV has been associated with myocardial infarction and thromboembolism.4,5 Increased PDW has been reported in coronary disorders, cerebral venous thrombosis, and chronic myeloproliferative neoplasms.6–8 Increase in PDW has been related to poor prognosis in laryngeal cancer.9 Also, increase in MPV has been related to esophageal cancer, breast cancer, and hepatocellular carcinoma.10–12 However, PDW has been proposed as a stronger marker than MPV for prognosis in different types of cancer.13 The aim of this study was to evaluate PDW as a discriminator for chronic myeloproliferative neoplasms. Materials and Methods Selection of Patients For this retrospective study, the study group consisted of patients diagnosed with chronic myeloproliferative neoplasms (according to the World Health Organization 2016 classification). History was obtained from bone-marrow request forms. Patients with history of thrombosis and previous chemotherapy were excluded from the study. Patients whose blood specimens and blood smears showed clots and platelet clumps were not included in the study. The control group included patients who had no evidence of chronic myeloproliferative neoplasm (based on their medical history and complete blood counts). Patients with known reactive causes for thrombocytosis were included in the control group. Processing of Specimens We collected blood specimens in K2 ethylenediaminetetraacetic acid (EDTA) and processed them within 2 hours of specimen collection. Complete blood counts, including platelet indices, were obtained by loading specimens into the ADVIA 2120i automated analyzer (Siemens Healthcare GmbH). Platelet counts were calculated using the optical method. The MPV was determined from the platelet histogram, and the width of the platelet histogram was listed as the PDW. Statistical Analysis All values were entered into an MS Excel spreadsheet (Microsoft Corporation). Continuous variables were tested for normality using quantile-quantile (Q-Q) plots (XLSTAT software, version 2015.3 [Addinsoft]). If the variables were normally distributed, they were expressed as mean (SD). For continuous variables not having a Gaussian distribution, the values were expressed as median and interquartile range (IQR). Categorical variables were expressed as percentages. Continuous variables in the same group were tested for their association using Pearson rank correlation (if normally distributed) and Spearman rank correlation (if distribution was not normal). Continuous variables across 2 groups were correlated with t test results (if normally distributed) or Mann Whitney U test results (if distribution was not normal). Differences in frequencies of categorical variables across groups were compared by using the χ2 test of independence. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were calculated to identify whether PDW was a good discriminator for chronic myeloproliferative neoplasms. P values were considered significant if 2-tailed P values were less than .05. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios were calculated for different cutoff values of PDW. The cutoff level was considered significant if the positive likelihood ratio was greater than 1. Reference intervals were established for the control group after excluding patients with reactive thrombocytosis. The 2.5 to 97.5 percentile range in the control group was considered to be the reference interval for platelet count and platelet indices. The cutoff levels were further tested in a new cohort of patients that included patients with thrombocytosis, thrombocytopenia, and normal platelets. Observations There were 113 patients in the study group (Table 1), which included 83 men and 30 women. Among them, 61 were diagnosed with chronic myeloid leukemia, 29 with polycythemia vera, 16 with essential thrombocythemia, and 7 with primary myelofibrosis. Thrombocytosis (460–1950 × 109/ L) was present in 56 patients (49.5%), thrombocytopenia (89 and 103 × 109/ L, respectively) in 2 patients (1.8%), and normal platelet count (150–436 × 109/ L) in 55 patients (48.7%). In the control group, there were 132 patients (Table 1), which included 66 men and 66 women. Thrombocytosis (542–795 × 109/ L) was observed in 12 patients (9.1%), and 120 patients (90.9%) had normal platelet count (151–439 × 109/ L). Table 1. Comparison of Demographics and Platelet Indices in the 2 Studied Groups Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) CMPN, chronic myeloproliferative neoplasms; MPV, mean platelet volume; PCT, plateletcrit; PDW, platelet distribution width. an = 132. bn = 113. View Large Table 1. Comparison of Demographics and Platelet Indices in the 2 Studied Groups Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) Parameter Mean (SD) Control Groupa CMPNb Age, y 43.8 (13.2) 48.6 (14.4) Platelet count × 109/ L 303.3 (119.5) 536.4 (337.4) MPV fl 9.2 (1.2) 10.1 (1.8) PCT % 0.27 (0.10) 0.52 (0.29) PDW % 50.5 (9.6) 56.4 (12.1) CMPN, chronic myeloproliferative neoplasms; MPV, mean platelet volume; PCT, plateletcrit; PDW, platelet distribution width. an = 132. bn = 113. View Large Patients with Chronic Myeloproliferative Neoplasms Patients in this group had a median age of 49 years (IQR, 22; range, 17–80 years), median platelet count of 449 (IQR, 394.5; range, 89–1950) × 109/ L, MPV of 10 (IQR,= 2.4; range, 5.9–16.1), plateletcrit (PCT) of 0.46 (IQR, 0.33; range, 0.09–1.63), and PDW of 58.5% (IQR, 15.7; range, 15.5%–75.1%). We performed Spearman rank correlation to determine the relationship between PDW and other platelet indices (Figure 3). Platelet count (rs, −0.29, P = .001) and PCT (rs, −0.13; P = .15) was inversely related to PDW, whereas MPV had a significant positive correlation (rs, 0.59; P <.001). Control Group Patients in this group had a median age of 44 years (IQR, 19; range, 17–79 years), median platelet counts of 276 (IQR, 115.7; range, 151–795) × 109/ L, MPV of 8.85 (IQR, 1.65; range, 7.0–11.7), PCT of 0.25 (IQR, 0.09; range, 0.14–0.72), and PDW of 51.1% (IQR, 15.3; range, 30.7%–73.5%). Spearman rank correlation was performed to determine the relationship between PDW and other platelet indices (Figure 3). Platelet count (rs, −0.31; P <.001) and PCT (rs, −0.198; P = .02) was inversely related to PDW, whereas MPV had a significant positive correlation (rs, 0.30; P <.001). The median age of the control group was significantly lower than that of patients with chronic myeloproliferative neoplasms: U = 6003 and P = .009. Median platelet count (U = 3679; P <.001), MPV (U = 5221.5; P <.001), PCT (U = 2942.5; P <.001), and PDW (U = 4739.5; P <.001; Figure 2) were significantly lower in the control group than in patients with chronic myeloproliferative neoplasms. Reference Intervals The reference interval (2.5–97.5 percentile of 120 patients, after excluding 12 patients with reactive thrombocytosis) for platelet count was 162.9 to 417.0 × 109 per L. Reference intervals for MPV, PCT, and PDW were found to be 7.6 to 13.3 fl, 0.15% to 0.38%, and 34.3% to 66.4%, respectively. We discovered that PDW is a useful discriminator (Figure 1, Table 2) of chronic myeloproliferative neoplasms (AUC = 0.68; P <.001). PDW higher than 47% has sensitivity of 81.4% and specificity of 35.6% for discriminating chronic myeloproliferative neoplasms. PDW higher than 69% has specificity of 98.4% for detecting chronic myeloproliferative neoplasms and a positive likelihood ratio of 4.2. PDW higher than the normal reference interval (34.3%–66.4%) has sensitivity of 23%, specificity of 99%, and a positive likelihood ratio of 19.5. Among the 113 patients with chronic myeloproliferative neoplasms, 26 (23%) had PDW higher than 66.4%, whereas in the control group, only 1 individual (0.7%) had PDW higher than 66.4%. Figure 1 View largeDownload slide Receiver operating characteristic (ROC) curve demonstrating utility of platelet distribution width (PDW) in discriminating chronic myeloproliferative neoplasms (area under the curve [AUC], 0.68). Figure 1 View largeDownload slide Receiver operating characteristic (ROC) curve demonstrating utility of platelet distribution width (PDW) in discriminating chronic myeloproliferative neoplasms (area under the curve [AUC], 0.68). Figure 2 View largeDownload slide Platelet distribution width (PDW) for chronic myeloproliferative neoplasms vs control individuals. Figure 2 View largeDownload slide Platelet distribution width (PDW) for chronic myeloproliferative neoplasms vs control individuals. Figure 3 View largeDownload slide Spearman rank correlation showing significant positive correlation of mean platelet volume (MPV) with platelet distribution width (PDW) in study group and control group. Figure 3 View largeDownload slide Spearman rank correlation showing significant positive correlation of mean platelet volume (MPV) with platelet distribution width (PDW) in study group and control group. Table 2. Sensitivity and Specificity for Predicting Chronic Myeloproliferative Neoplasms and Their Likelihood Ratios PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW, platelet distribution width; PPV, positive predictive value; NPV, negative predictive value; PLR, positive likelihood ratio; NLR, negative likelihood ratio; … , nonapplicable. View Large Table 2. Sensitivity and Specificity for Predicting Chronic Myeloproliferative Neoplasms and Their Likelihood Ratios PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW Cutoff Values Sensitivity Specificity PPV NPV PLR NLR 15%–25% 96% 0 45 0 0.96 … 26%–36% 95.5% 6% 46.5 61.5 0.86 0.70 37%–47% 81.4% 35.6% 51.9 69 1.06 0.46 48%–58% 53.9% 75% 64.8 65.5 1.8 0.53 59%–69% 10.6% 98.4% 85.7 56.2 4.2 0.78 34.3%–66.4% (reference interval) 23% 99% 96.2 60 19.5 0.66 PDW, platelet distribution width; PPV, positive predictive value; NPV, negative predictive value; PLR, positive likelihood ratio; NLR, negative likelihood ratio; … , nonapplicable. View Large We tested this PDW value in a new cohort of 138 patients, which included 59 patients with chronic myeloproliferative neoplasms (33 with chronic myeloid leukemia, 11 with polycythemia vera, 9 with primary myelofibrosis, and 6 with essential thrombocythaemia). The group included 92 men and 46 women varying in age from 17 years to 77 years. There were 53 patients with normal platelet count (28 of whom had chronic myeloproliferative neoplasms), 56 with thrombocytosis (27 of whom had chronic myeloproliferative neoplasms), and 29 with thrombocytopenia (4 of whom had chronic myeloproliferative neoplasms). MPV showed a slight significant positive correlation with PDW in chronic myeloproliferative neoplasms (rs, 0.37; P = .003), as well as reactive conditions (rs, 0.34; P = .001). Reactive conditions included patients with anemias of chronic disease, iron-deficiency anemia, sepsis, chronic kidney disease, immune thrombocytopenia, and Hodgkins and Non-Hodgkins lymphomas. We discovered that the PDW in chronic myeloproliferative neoplasms (median, 58.9 %) was significantly higher than that of reactive conditions (median, 50.3%), by Mann Whitney U testing (U = 1621.5; P = .002). PDW in normal platelet counts and in thrombocytosis significantly differed in the 2 groups (P = .005 and P = .007, respectively). A χ2 test of independence was calculated to compare frequency of PDW being more than 66.4% in chronic myeloproliferative neoplasms. A significant interaction was found: ×2 (137) = 71.2; P <.001. The frequency of patients with PDW higher than 66.4% was 68.4% in patients with chronic myeloproliferative neoplasms, whereas it was 31.5 % in reactive conditions. The frequency of patients with PDW less than 66.4% was 38.6% in patients with chronic myeloproliferative neoplasms, whereas it was 61.3% in reactive conditions. Discussion In the present study, we discovered that in controls and in patients with chronic myeloproliferative neoplasms, the PDW showed a significant positive correlation with MPV, whereas it was slightly inversely correlated with platelet count and PCT. Age, platelet count, MPV, PCT, and PDW of patients with chronic myeloproliferative neoplasms were significantly higher than in controls. Based on these findings, PDW with AUC of 0.68 can be used as a discriminator for chronic myeloproliferative neoplasms. PDW higher than 66.4% had specificity of 99% for predicting chronic myeloproliferative neoplasms and a likelihood ratio of 19.5. However, the sensitivity was only 23%. Still, the frequency of patients with chronic myeloproliferative neoplasms and PDW higher than 66.4% is 68.4%, whereas it is only 31.5% in reactive conditions. MPV is affected by preanalytical variables, and PDW is more indicative of platelet activation.3 Hence, we chose PDW as a discriminant in this study. Yang et al14 have found that PDW was superior to MPV for identifying platelet activation. In their study, they report that PDW was able to predict severe eclampsia with an AUC of 0.74. In a recent article from India,15 reference intervals for PCT, MPV, and PDW were reported as mean (SD) 0.2% (0.07%), 14.7 (2.0) fl, and 8.9 (1.2) fl, respectively. Our reference intervals for MPV (7.6–13.3 fl) and PCT (0.15%–0.38%) are higher than those reported by Ransing et al.15 The difference could be ascribed to population-based differences in MPV. In another study from North India,16 the PDW (mean [SD], 29.4% [20.3%]) in chronic myeloproliferative neoplasms was significantly higher than in controls (mean [SD], 17.2 [2.4%]; P = .002). Likewise, in our study, patients with chronic myeloproliferative neoplasms had higher PDW than controls. Olteanu and colleagues,17 however, reported contradictory findings and did not observe any significant difference in PDW (P = .70). There are certain limitations to this study. First, there is still significant overlap in PDW in the control group and the group of patients with chronic myeloproliferative neoplasms, and PDW may be an additional feature to distinguish chronic myeloproliferative neoplasms. Further, serial measurements of PDW and its changes would be stronger indicators than a single measurement. More studies need to be conducted to verify the utility of PDW, especially after matching cases and controls for drug intake. We hope our findings will advance the ongoing conversation in the field of laboratory medicine regarding the optimal ways to predict the possibility of chronic myeloproliferative neoplasms. Personal and Professional Conflicts of Interest None reported. Abbreviations MPV mean platelet volume PDW platelet distribution width EDTA ethylenediaminetetraacetic acid Q-Q quantile-quantile IQR interquartile range ROC receiver operating characteristic AUC area under the curve PPV positive predictive value NPV negative predictive value PCT plateletcrit CMPN chronic myeloproliferative neoplasms PLR positive likelihood ratio NLR negative likelihood ratio … nonapplicable References 1. Bath PM , Butterworth RJ . Platelet size: measurement, physiology and vascular disease . Blood Coagul Fibrinolysis . 1996 ; 7 ( 2 ): 157 – 161 . Google Scholar Crossref Search ADS PubMed 2. Tsiara S , Elisaf M , Jagroop IA , Mikhailidis DP . Platelets as predictors of vascular risk: is there a practical index of platelet activity ? Clin Appl Thromb Hemost . 2003 ; 9 ( 3 ): 177 – 190 . Google Scholar Crossref Search ADS PubMed 3. Vagdatli E , Gounari E , Lazaridou E , Katsibourlia E , Tsikopoulou F , Labrianou I . 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Gu M , Zhai Z , Huang L , et al. Pre-treatment mean platelet volume associates with worse clinicopathologic features and prognosis with invasive breast cancer . Breast Cancer . 2016 ; 23 ( 5 ): 752 – 760 . Google Scholar Crossref Search ADS PubMed 12. Cho SY , Yang JJ , You E , et al. Mean platelet volume/ platelet count ratio in hepatocellular carcinoma . Platelets . 2013 ; 24 ( 5 ): 375 – 377 . Google Scholar Crossref Search ADS PubMed 13. Takeuchi H , Abe M , Takumi Y , et al. The prognostic impact of the platelet distribution width-to-platelet count ratio in patients with breast cancer . PLoS One . 2017 ; 12 ( 12 ): e0189166 . Google Scholar Crossref Search ADS PubMed 14. Yang SW , Cho SH , Kwon HS , Sohn IS , Hwang HS . Significance of the platelet distribution width as a severity marker for the development of preeclampsia . Eur J Obstet Gynecol Reprod Biol . 2014 ; 175 : 107 – 111 . Google Scholar Crossref Search ADS PubMed 15. Ransing RS , Patil B , Grigo O . Mean platelet volume and platelet distribution width level in patients with panic disorder . J Neurosci Rural Pract . 2017 ; 8 ( 2 ): 174 – 178 . Google Scholar Crossref Search ADS PubMed 16. Parashar Y , Kushwaha R , Kumar A , et al. Haemostatic profile in patients of myeloproliferative neoplasms-a tertiary care centre experience . J Clin Diagn Res . 2016 ; 10 ( 11 ): EC01 – EC04 . Google Scholar PubMed 17. Olteanu AL , Mihaila RG , Catana AC , Flucus O , Bus C , Mihalache M . Platelet indices in Philadelphia-negative chronic myeloproliferative neoplasms . Rev Romana Med Lab . 2015 ; 23 ( 2 ): 169 – 177 . © American Society for Clinical Pathology 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Laboratory MedicineOxford University Press

Published: Oct 17, 2018

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