Risk Factors of White Blood Cell Progression Among Patients With Chronic Lymphocytic Leukemia at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2022-01-14 eCollection Date: 2022-01-01 DOI:10.1177/11769351211069902

Gedam Derbew Addisia, Awoke Seyoum Tegegne, Denekew Bitew Belay, Mitiku Wale Muluneh, Mahider Abere Kassaw

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引用次数: 0

Abstract

Background: Leukemia is a type of cancers that start in the bone marrow and produce a serious number of abnormal white blood cells. Bleeding and bruising problems, fatigue, fever, and an increased risk of infection are among symptoms of the disease. The main objective of this study is to identify the determinant of the progression rate of white blood cells among patients with chronic lymphocytic leukemia at Felege Hiwot Referral Hospital (FHRH), Bahir Dar, Ethiopia.

Methods: A retrospective study design was conducted on 312 patients with chronic lymphocytic leukemia at FHRH, Bahir Dar, Ethiopia under treatment from 1 January 2017 to 31 December 2019. A linear mixed-effects model was considered for the progression of the white blood cell data.

Results: The estimated coefficient of the fixed effect intercept was 84.68, indicating that the average white blood cell (WBC) count of the patients was 84.68 at baseline time by excluding all covariates in the model (P-value <.001). Male sex (β = 2.92, 95% confidence interval [CI] 0.58, 0.5.25), age (β = .17, 95% CI 0.08, 0.28), widowed/divorced marital status (β = 3.30, 95% CI 0.03, 6.57), medium chronic lymphocytic leukemia (CLL) stage (β = -4.34, 95% CI -6.57, -2.68), high CLL stage (β = -2.76, 95% CI -4.86, -0.67), hemoglobin (β = .15, 95% CI 0.07, 0.22), platelet (β = .09, 95% CI 0.02, 0.17), lymphocytes (β = .16, 95% CI 0.03, 0.29), red blood cell (RBC) (β = .17, 95% CI 0.09, 0.25), and follow-up time (β = .27, 95% CI 0.19, 0.36) were significantly associated with the average WBC count of chronic lymphocytic leukemia patients.

Conclusions: The finding showed that age, sex, lymphocytic, stage of chronic lymphocytic leukemia, marital status, platelet, hemoglobin, RBC, and follow-up time were significantly associated with the average WBC count of chronic lymphocytic leukemia patients. Therefore, health care providers should give due attention and prioritize those identified factors and give frequent counseling about improving the health of chronic lymphocytic leukemia patients.

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埃塞俄比亚巴希尔达尔Felege Hiwot转诊医院慢性淋巴细胞白血病患者白细胞进展的危险因素

背景:白血病是一种起源于骨髓并产生大量异常白细胞的癌症。这种疾病的症状包括出血和瘀伤、疲劳、发烧和感染风险增加。本研究的主要目的是确定埃塞俄比亚巴希尔达尔Felege Hiwot转诊医院(FHRH)慢性淋巴细胞白血病患者白细胞进展率的决定因素。方法:对2017年1月1日至2019年12月31日在埃塞俄比亚Bahir Dar FHRH接受治疗的312例慢性淋巴细胞白血病患者进行回顾性研究设计。考虑了白细胞数据进展的线性混合效应模型。结果:固定效应截距的估计系数为84.68，排除模型中所有协变量(p值β = 2.92, 95%可信区间[CI] 0.58, 0.5.25)，患者基线时平均白细胞(WBC)计数为84.68。17, 95% CI 0.08, 0.28)，丧偶/离婚婚姻状况(β = 3.30, 95% CI 0.03, 6.57)，中度慢性淋巴细胞白血病(CLL)分期(β = -4.34, 95% CI -6.57， -2.68)，高慢性淋巴细胞白血病分期(β = -2.76, 95% CI -4.86， -0.67)，血红蛋白(β = 0.67)。15, 95% CI 0.07, 0.22)，血小板(β =。09, 95% CI 0.02, 0.17)，淋巴细胞(β =。16, 95% CI 0.03, 0.29)，红细胞(RBC) (β =。17, 95% CI 0.09, 0.25)，随访时间(β =。27, 95% CI 0.19, 0.36)与慢性淋巴细胞白血病患者的平均白细胞计数显著相关。结论:年龄、性别、淋巴细胞、慢性淋巴细胞白血病分期、婚姻状况、血小板、血红蛋白、红细胞、随访时间与慢性淋巴细胞白血病患者平均白细胞计数有显著相关。因此，卫生保健提供者应给予应有的重视和优先考虑这些确定的因素，并经常提供有关改善慢性淋巴细胞白血病患者健康的咨询。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.