Determinants of Time-to-Death of Chronic Lymphocytic Leukemia Patients at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231183849

Gedam Derbew Addisia, Awoke Seyoum Tegegne, Denekew Bitew Belay, Mahider Abere Kassaw, Mitiku Wale Muluneh, Koyachew Bitew Abebe, Bezanesh Melese Masresha, Solomon Sisay Mulugeta, Setegn Muche Fentaw, Dejen Gedamu Damtie

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Abstract

Background: Leukemia is a group of cancers that usually begin in the bone marrow and results in a large number of abnormal white blood cells. Chronic Lymphocytic Leukemia is the most prevalent leukemia in Western countries, with an estimated incidence rate of less than 1 to 5.5 per 100 000 people, and average age at diagnosis of 64 to 72 years. It is more common in men among Chronic Lymphocytic Leukemia patients in Ethiopia’s hospitals at Felege Hiwot Referal Hospital. Methods: A retrospective cohort research design was employed to acquire critical information from patients’ medical records in order to achieve the study’s purpose. The study comprised the medical records of 312 Chronic Lymphocytic Leukemia who were followed from January 1, 2018 to December 31, 2020. A Cox proportional hazard model was used to determine the risk factors for time to death in Chronic Lymphocytic Leukemia patients. Results: Accordingly the Cox proportional hazard model, age (Hazard Ratio = 11.36; P < .001), sex of male (Hazard Ratio = 1.04; P = .004), married status (Hazard Ratio = 0.03; P = .003), medium stages of Chronic Lymphocytic Leukemia (Hazard Ratio = 1.29; P = .024), high stages of Chronic Lymphocytic Leukemia (Hazard Ratio = 1.99; P < .001), presence of anemia (Hazard Ratio =0.09; P = .005), platelets (Hazard Ratio = 2.11; P = .007), hemoglobin (Hazard Ratio = 0.02; P < .001), lymphocytes (Hazard Ratio = 0.29; P = .006), red blood cell (Hazard Ratio = 0.02; P < .001), which patients with Chronic Lymphocytic Leukemia had a significant relationship with time to death. Conclusions: Age, sex, Chronic Lymphocytic Leukemia stage, anemia, platelets, hemoglobin, lymphocytes, and red blood cells were all statistically significant determinants in the time to death of Chronic Lymphocytic Leukemia patients, according to the data. As a result, healthcare providers should pay particular attention to and emphasize the identified characteristics, as well as provide frequent counseling on how to enhance 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死亡时间的决定因素

背景:白血病是一组通常起源于骨髓并导致大量异常白细胞的癌症。慢性淋巴细胞白血病是西方国家最常见的白血病，估计发病率低于每10万人1至5.5人，诊断时的平均年龄为64至72岁。在埃塞俄比亚费利格·希沃特转诊医院的慢性淋巴细胞白血病患者中，男性更常见。方法:采用回顾性队列研究设计，从患者病历中获取关键信息，以达到研究目的。该研究包括从2018年1月1日至2020年12月31日随访的312例慢性淋巴细胞白血病患者的医疗记录。采用Cox比例风险模型确定慢性淋巴细胞白血病患者死亡时间的危险因素。结果:相应的Cox比例风险模型，年龄(风险比= 11.36;P = .004)、婚姻状况(风险比= 0.03;P = 0.003)，慢性淋巴细胞白血病中期(风险比= 1.29;P = 0.024)，高分期慢性淋巴细胞白血病(风险比= 1.99;P = 0.005)，血小板(风险比= 2.11;P = .007)，血红蛋白(风险比= 0.02;P = 0.006)、红细胞(风险比= 0.02;结论:年龄、性别、慢性淋巴细胞白血病分期、贫血、血小板、血红蛋白、淋巴细胞、红细胞是影响慢性淋巴细胞白血病患者死亡时间的有统计学意义的因素。因此，医疗保健提供者应特别关注和强调已识别的特征，并就如何增强慢性淋巴细胞白血病患者的健康提供经常的咨询。

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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.