Department of Medical Informatics, School of Medicine and Faculty Member of Health Information Technology and Medical Records Department, School of Paramedical Sciences, Mashhad Univer-sity of Medical Sciences, Mashhad, Iran.
Department of Medical informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran and Deputy for Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Iran J Basic Med Sci. 2013 Mar;16(3):247-51.
OBJECTIVE(S): Infection caused by Human T-Lymphotropic Virus Type 1 (HTLV-I) can be observed in some areas of Iran in form of endemic. Most of the cases are asymptomatic, and few cases progress to malignancies and neural diseases. Designing and implementing a model to screen people especially in endemic regions can help timely detection of infected people and improve the prognosis of the disease.
In this study, results of the complete blood count (CBC-diff) for 599 healthy people and the patients with different types of Leukemia and HTLV-I have been examined. Modeling was made using CHAID method. The final model was carried out based on the number of white blood cells (WBC), platelets, and percentages of eosinophils.
The accuracy of the final model was 91%. By applying this model to the CBC-diff results of people without symptoms or miscellaneous patients in endemic regions of our country, disease carriers can be identified and referred for supplementary tests.
With regard to the prevalence of different complications in infected people, these individuals can be identified earlier, leading to the improvement of the prognosis of this disease and the increase of the health status especially in endemic regions.
在伊朗的一些地区,可观察到人类嗜 T 淋巴细胞病毒 1 型(HTLV-I)引起的感染呈地方性流行。大多数病例无症状,少数病例进展为恶性肿瘤和神经疾病。设计和实施一种模型来筛选人群,特别是在流行地区,可以帮助及时发现感染者,并改善疾病的预后。
在这项研究中,对 599 名健康人和不同类型白血病和 HTLV-I 患者的全血细胞计数(CBC-diff)结果进行了检查。使用 CHAID 方法进行建模。最终模型是基于白细胞(WBC)、血小板和嗜酸性粒细胞百分比的数量建立的。
最终模型的准确率为 91%。通过将该模型应用于我国流行地区无症状人群或其他患者的 CBC-diff 结果,可以识别出疾病携带者,并进行补充检测。
鉴于感染者存在不同并发症的流行,这些个体可以更早地被识别出来,从而改善该疾病的预后,并提高特别是在流行地区的健康状况。