Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia
Background/Aims The diagnosis of immune thrombocytopenia (ITP) is based on clinical manifestations and there is no gold standard. Thus, even hematologic malignancy is sometimes misdiagnosed as ITP and adequate treatment is delayed. Therefore, novel diagnostic parameters are needed to distinguish ITP...
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| Format: | Article |
| Language: | English |
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The Korean Association of Internal Medicine
2020-07-01
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| Series: | The Korean Journal of Internal Medicine |
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| Online Access: | http://www.kjim.org/upload/pdf/kjim-2019-093.pdf |
| _version_ | 1818643706825146368 |
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| author | Min Ji Jeon Eun Sang Yu Ka-Won Kang Byung-Hyun Lee Yong Park Se Ryeon Lee Hwa Jung Sung Soo Yong Yoon Chul Won Choi Byung Soo Kim Dae Sik Kim |
| author_facet | Min Ji Jeon Eun Sang Yu Ka-Won Kang Byung-Hyun Lee Yong Park Se Ryeon Lee Hwa Jung Sung Soo Yong Yoon Chul Won Choi Byung Soo Kim Dae Sik Kim |
| author_sort | Min Ji Jeon |
| collection | DOAJ |
| description | Background/Aims The diagnosis of immune thrombocytopenia (ITP) is based on clinical manifestations and there is no gold standard. Thus, even hematologic malignancy is sometimes misdiagnosed as ITP and adequate treatment is delayed. Therefore, novel diagnostic parameters are needed to distinguish ITP from other causes of thrombocytopenia. Immature platelet fraction (IPF) has been proposed as one of new parameters. In this study, we assessed the usefulness of IPF and developed a diagnostic predictive scoring model for ITP. Methods We retrospectively studied 568 patients with thrombocytopenia. Blood samples were collected and IPF quantified using a fully-automated hematology analyzer. We also estimated other variables that could affect thrombocytopenia by logistic regression analysis. Results The median IPF was significantly higher in the ITP group than in the non-ITP group (8.7% vs. 5.1%). The optimal cut-off value of IPF for differentiating ITP was 7.0%. We evaluated other laboratory variables via logistic regression analysis. IPF, hemoglobin, lactate dehydrogenase (LDH), and ferritin were statistically significant and comprised a diagnostic predictive scoring model. Our model gave points to each of variables: 1 to high hemoglobin (> 12 g/dL), low ferritin (≤ 177 ng/mL), normal LDH (≤ upper limit of normal) and IPF ≥ 7 and < 10, 2 to IPF ≥ 10. The final score was obtained by summing the points. We defined that ITP could be predicted in patients with more than 3 points. Conclusions IPF could be a useful parameter to distinguish ITP from other causes of thrombocytopenia. We developed the predictive scoring model. This model could predict ITP with high probability. |
| first_indexed | 2024-12-17T00:03:13Z |
| format | Article |
| id | doaj.art-ad375885e41f4af38b6a160bbea511e4 |
| institution | Directory Open Access Journal |
| issn | 1226-3303 2005-6648 |
| language | English |
| last_indexed | 2024-12-17T00:03:13Z |
| publishDate | 2020-07-01 |
| publisher | The Korean Association of Internal Medicine |
| record_format | Article |
| series | The Korean Journal of Internal Medicine |
| spelling | doaj.art-ad375885e41f4af38b6a160bbea511e42022-12-21T22:11:01ZengThe Korean Association of Internal MedicineThe Korean Journal of Internal Medicine1226-33032005-66482020-07-0135497097810.3904/kjim.2019.093170347Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopeniaMin Ji Jeon0Eun Sang Yu1Ka-Won Kang2Byung-Hyun Lee3Yong Park4Se Ryeon Lee5Hwa Jung Sung6Soo Yong Yoon7Chul Won Choi8Byung Soo Kim9Dae Sik Kim10 Division of Hematology-Oncology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Ansan, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Ansan, Korea Department of Laboratory Medicine, Korea University Guro Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea Division of Hematology-Oncology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, KoreaBackground/Aims The diagnosis of immune thrombocytopenia (ITP) is based on clinical manifestations and there is no gold standard. Thus, even hematologic malignancy is sometimes misdiagnosed as ITP and adequate treatment is delayed. Therefore, novel diagnostic parameters are needed to distinguish ITP from other causes of thrombocytopenia. Immature platelet fraction (IPF) has been proposed as one of new parameters. In this study, we assessed the usefulness of IPF and developed a diagnostic predictive scoring model for ITP. Methods We retrospectively studied 568 patients with thrombocytopenia. Blood samples were collected and IPF quantified using a fully-automated hematology analyzer. We also estimated other variables that could affect thrombocytopenia by logistic regression analysis. Results The median IPF was significantly higher in the ITP group than in the non-ITP group (8.7% vs. 5.1%). The optimal cut-off value of IPF for differentiating ITP was 7.0%. We evaluated other laboratory variables via logistic regression analysis. IPF, hemoglobin, lactate dehydrogenase (LDH), and ferritin were statistically significant and comprised a diagnostic predictive scoring model. Our model gave points to each of variables: 1 to high hemoglobin (> 12 g/dL), low ferritin (≤ 177 ng/mL), normal LDH (≤ upper limit of normal) and IPF ≥ 7 and < 10, 2 to IPF ≥ 10. The final score was obtained by summing the points. We defined that ITP could be predicted in patients with more than 3 points. Conclusions IPF could be a useful parameter to distinguish ITP from other causes of thrombocytopenia. We developed the predictive scoring model. This model could predict ITP with high probability.http://www.kjim.org/upload/pdf/kjim-2019-093.pdfthrombocytopeniaimmature platelet fractionimmune thrombocytopenia |
| spellingShingle | Min Ji Jeon Eun Sang Yu Ka-Won Kang Byung-Hyun Lee Yong Park Se Ryeon Lee Hwa Jung Sung Soo Yong Yoon Chul Won Choi Byung Soo Kim Dae Sik Kim Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia The Korean Journal of Internal Medicine thrombocytopenia immature platelet fraction immune thrombocytopenia |
| title | Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia |
| title_full | Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia |
| title_fullStr | Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia |
| title_full_unstemmed | Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia |
| title_short | Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia |
| title_sort | immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia |
| topic | thrombocytopenia immature platelet fraction immune thrombocytopenia |
| url | http://www.kjim.org/upload/pdf/kjim-2019-093.pdf |
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