| 李秋红,周 莉,何金孝,许东亮,张 娟,吕香萍,吴海霞,常子维.八种常见儿童感染性疾病相关指标差异性研究[J].,2020,(21):4072-4075 |
| 八种常见儿童感染性疾病相关指标差异性研究 |
| Differences in Indicators Related to Eight Common Childhood Infectious Diseases |
| 投稿时间：2020-02-27 修订日期：2020-03-24 |
| DOI：10.13241/j.cnki.pmb.2020.21.015 |
| 中文关键词: 异型淋巴细胞 白细胞 C-反应蛋白 血沉 感染性疾病 |
| 英文关键词: Atypical lymphocytes White blood cells C-reactive protein Erythrocyte sedimentation rate Infectious diseases |
| 基金项目:国家自然科学基金项目（81701487）；学科助推计划项目（XJZT18MJ23） |
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| 中文摘要: |
| 摘要 目的：探讨异型淋巴细胞、白细胞、中性粒细胞、淋巴细胞、单核细胞、C-反应蛋白及血沉等在儿童感染性相关疾病不同疾病种类、不同年龄段及性别的差异情况，并进行分析，从而为疾病的诊断、鉴别诊断及治疗提供依据。方法：选取2017年1月-2018年12月266例患感染性疾病的儿童，根据疾病类型分为八组，分别为传染性单核细胞增多症（41例）、EB病毒感染（18例）、支气管肺炎（43例）、支气管炎（42例）、急性上呼吸道感染（48例）、急性化脓性扁桃体炎（18例）、肺炎（19例）、粒细胞减少（37例）等，应用检验相关手段，对患儿异型淋巴细胞、白细胞、中性粒细胞、淋巴细胞、单核细胞、C-反应蛋白及血沉等进行检测，采用SPSS17.0统计学分析软件，计算各检验结果的平均值与标准差，进行相关t检验，进而对不同疾病的检验项目进行统计学分析。结果：在八种疾病相关检验的比较中，传染性单核细胞增多症与其余七种感染性疾病在异型淋巴细胞、中性粒细胞、淋巴细胞及血沉的数据进行比较，其之间比较差异有统计学意义（P<0.05），其余各组疾病相关指标没有统计学差异。在年龄段的分组比较中，异型淋巴细胞在各年龄段之间比较差异有统计学意义（P<0.05）。在性别分组比较中，白细胞在性别与各年龄段之间比较差异有统计学意义（P<0.05）。结论：不同种类感染性疾病及不同年龄、性别相关细胞检查存在差异，各类检测结果的综合对比分析，更有利于疾病的诊断及鉴别诊断。 |
| 英文摘要: |
| ABSTRACT Objective: To investigate and analyze the differences in heterogeneous lymphocytes, white blood cells, neutrophils, lymphocytes, monocytes, c-reactive protein and erythrocyte sedimentation rate in children with infectious diseases with different age groups and genders. Provide a basis for the diagnosis, differential diagnosis and treatment of diseases. Methods: 266 children with infectious diseases from January to February 2018 were selected and divided into eight groups according to the type of disease, including infectious mononucleosis (41 cases), EB virus infection (18 cases), and bronchus. Pneumonia (43 cases), bronchitis (42 cases), acute upper respiratory tract infection (48 cases), acute suppurative tonsillitis (18 cases), pneumonia (19 cases), neutropenia (37 cases), etc. Means, the specific lymphocytes, white blood cells, neutrophils, lymphocytes, monocytes, C-reactive protein and erythrocyte sedimentation rate were detected. SPSS17.0 statistical analysis software was used to calculate the average value of each test result. Standard deviation, carry out correlation t test, and then carry out statistical analysis on the test items of different diseases. Results: In the comparison of eight disease-related tests, the data of infectious mononucleosis and the other seven infectious diseases were compared in the data of atypical lymphocytes, neutrophils, lymphocytes and erythrocyte sedimentation rate. The significance of learning (P<0.05), the other groups of disease-related indicators were not statistically different. In the group comparison of age groups, the difference of atypical lymphocytes between different age groups was statistically significant (P<0.05). In the gender group comparison, there was a statistically significant difference in the scores of white blood cells between different age groups (P<0.05). Conclusion: Different types of infectious diseases and different age and gender related cell examinations are different. Comprehensive comparative analysis of various test results is more conducive to the diagnosis and differential diagnosis of diseases. |
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