Differential Diagnostic Study of Iron Deficiency Anemia and Thalassemia Based on Logistic Regression Modeling

doi:10.13523/j.cb.2310059

China Biotechnology

2023, Vol. 43

Issue (12): 111-118 DOI: 10.13523/j.cb.2310059

Differential Diagnostic Study of Iron Deficiency Anemia and Thalassemia Based on Logistic Regression Modeling

LI Yi-xun^1,^2,³,GUO Chong^1,^2,³,PAN Yu-qing^1,^2,³,CHENG Shen-ju^1,^2,³,WU Kun^1,^2,^3,^**(

)

1 Department of Clinical Laboratory, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
2 Yunnan Key Laboratory of Laboratory Medicine, Kunming 650032, China
3 Yunnan Province Clinical Research Center for Laboratory Medicine, Kunming 650032, China

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Abstract

Objective: The objective of the research is to establish a logistic regression model for the differential diagnosis of iron deficiency anemia (IDA) and thalassemia (TT) and to explore its application value. Methods: The research analyzed medical records of 121 patients diagnosed with both IDA and TT at the First Affiliated Hospital of Kunming Medical University between 2021 and 2022. A total of 100 specimens were arbitrarily chosen from each cohort to constitute the experimental groups, while the remaining 21 were designated for validation reasons. After performing a univariate comparison to establish statistical significance, the subsequent action was to identify variables with significant differences for the multi-factor analysis. Based on the analysis, four variables - gender, hemoglobin, mean corpuscular hemoglobin concentration, and red cell distribution width - were identified as independent factors that influence the outcome. These four variables established a logistic regression model, which was evaluated for its differential diagnosis effect by analyzing sensitivity, specificity, and other indicators on a subject’s working characteristics curve. Results: The results of the univariate comparison illustrate that the IDA group has lower values of red blood cells, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration in their peripheral blood compared to the TT group. Conversely, higher values of peripheral blood red cell distribution width-standard deviation are observed in the IDA group in comparison to the TT group, and the difference is statistically significant with a P-value less than 0.05. Gender, hemoglobin (Hb), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and red cell distribution width (RDW-SD) were analyzed via binary non-conditional logistic regression. The study showed that higher RDW-SD values were linked to an increased risk of IDA, while more significant reductions in Hb and MCHC were associated with a higher risk of IDA at a significance level of P<0.05. The logistic regression model is presented as:Logit(P)=-19.288-1.685X₁+0.035X₂+0.066X₃-0.076X₄. In the experimental group, the area under the receiver operating characteristic (ROC) curve of the logistic model is 0.947, the sensitivity is 90%, and the specificity is 91%; the sensitivity and specificity of the verification group are 100% and 72.4%, respectively. Conclusions: Logistic regression models have certain application value in the differential diagnosis of IDA and TT.

Key words： Iron deficiency anemia (IDA) Thalassemia (TT) Logistic regression model Differential diagnosis

Received: 14 October 2023 Published: 16 January 2024

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	Yi-xun LI
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Yi-xun LI, Chong GUO, Yu-qing PAN, Shen-ju CHENG, Kun WU. Differential Diagnostic Study of Iron Deficiency Anemia and Thalassemia Based on Logistic Regression Modeling. China Biotechnology, 2023, 43(12): 111-118.

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https://manu60.magtech.com.cn/biotech/10.13523/j.cb.2310059 OR https://manu60.magtech.com.cn/biotech/Y2023/V43/I12/111

Table 1 Gender comparison of patients with iron deficiency anemia and thalassemia

Table 2 Age comparison of patients with iron deficiency anemia and thalassemia

Fig.1 Comparison of the degree of anemia between patients in the iron deficiency anemia group and those in the thalassemia group

Table 3 Comparison of blood cell analysis results between patients with iron deficiency anemia and thalassemia

Table 4 Comparison of RDW-SD in patients with iron deficiency anemia and thalassemia

Fig.2 Comparison of red blood cell analysis parameters between the two groups ^*** P<0.01,^* P<0.05

Table 5 Logistic regression analysis of differential diagnosis in patients with iron deficiency anemia and thalassemia

Fig.3 ROC curves of the experimental group ROC: receiver operator characteristic curve

Table 6 Verification results of the verification group


[1]	中国学生体质健康调研组. 中国学生贫血状况的动态观察. 中华预防医学杂志, 2002, 36(2): 81-83. pmid: 12410957

[1]	China’s National Group on Students’Constitution and Health Survey. Dynamic analysis on the detecting rate of iron-deficient anemia of Chinese students. Chinese Journal of Preventive Medicine, 2002, 36(2):81-83. pmid: 12410957

[2]	Nakashima T, Hayashi M, Fukunaga T, et al. Evidence for osteocyte regulation of bone homeostasis through RANKL expression. Nature medicine, 2011, 17(10): 1231-1234. doi: 10.1038/nm.2452 pmid: 21909105

[3]	Souza P P, Lerner U H. The role of cytokines in inflammatory bone loss. Immunol Invest, 2013, 42(7):555-622. doi: 10.3109/08820139.2013.822766 pmid: 24004059

[4]	Xiong J H, Onal M, Jilka R L, et al. Matrix-embedded cells control osteoclast formation. Nature Medicine, 2011, 17(10):1235-1241. doi: 10.1038/nm.2448 pmid: 21909103

[5]	Cappariello A, Maurizi A, Veeriah V, et al. Reprint of: The Great Beauty of the osteoclast. Archives of Biochemistry and Biophysics, 2014, 561:13-21. doi: 10.1016/j.abb.2014.08.009 pmid: 25282390

[6]	中华医学会血液学分会红细胞疾病贫血学组. 铁缺乏症和缺铁性贫血诊治和预防的多学科专家共识(2022年版). 中华医学杂志, 2022, 102(41):3246-3256.

[6]	Erythrocyte Disease (Anemia) Group, Hematology Branch, Chinese Medical Association multidisciplinary expert consensus on the diagnosis, treatment, and prevention of iron deficiency disorders and iron deficiency anemia (2022 ed.). Natl Med J China, 2022, 102 (41): 3246-3256.

[7]	Domitrović R, Rashed K, Cvijanović O, et al. Myricitrin exhibits antioxidant, anti-inflammatory and antifibrotic activity in carbon tetrachloride-intoxicated mice. Chemico-Biological Interactions, 2015, 230:21-29. doi: 10.1016/j.cbi.2015.01.030 pmid: 25656916

[8]	潘飒, 崔娜. 血液检验在贫血诊断与鉴别诊断中的价值分析. 中国医药指南, 2013(14): 218-219.

[8]	Pan S, Cui N. Analysis of the value of blood test in the diagnosis and differential diagnosis of anemia. China Medical Guide, 2013(14):218-219.

[9]	王英, 潘昆贻, 黄际宪. 红细胞与网织红细胞参数在缺铁性贫血与地中海贫血患儿中的意义. 热带医学杂志, 2014, 14(10):1303-1305, 1332.

[9]	Wang Y, Pan K Y, Huang J X. The value of erythrocyte parameters and reticulocyte parameters in the chirldren patients with iron deficiency anemia and Mediterranean anemia. Journal of Tropical Medicine, 2014, 14(10):1303-1305,1332.

[10]	Buttarello M. Laboratory diagnosis of anemia: are the old and new red cell parameters useful in classification and treatment, how? International Journal of Laboratory Hematology, 2016, 38(S1):123-132.

[11]	Hoffmann J J M L, Urrechaga E. Verification of 20 mathematical formulas for discriminating between iron deficiency anemia and thalassemia trait in microcytic anemia. Laboratory Medicine, 2020, 51(6):628-634. doi: 10.1093/labmed/lmaa030 pmid: 32539140

[12]	申丽, 罗燕婷, 陈志丹, 等. 红细胞参数及运算公式在广东深圳地区儿童地中海贫血与缺铁性贫血鉴别诊断中的效能评价. 中国实验血液学杂志, 2020, 28(5):1689-1693. doi: 10.19746/j.cnki.issn.1009-2137.2020.05.043 pmid: 33067975

[12]	Shen L, Luo Y T, Chen Z D, et al. Evaluation of red blood cell indices and parameter formulas in differential diagnosis of thalassemia trait and iron deficiency anemia for children in Shenzhen Area of Guangdong Province in China. Journal of Experimental Hematology, 2020, 28(5):1689-1693. doi: 10.19746/j.cnki.issn.1009-2137.2020.05.043 pmid: 33067975

[13]	Saboor M. Discrimination of iron deficiency, alpha and beta thalassemia on the basis of red cell distribution width and reticulocyte indices. Clinical laboratory, 2021, 67(6): 1365-1370.

[14]	谢又平, 胡利清, 周玉球, 等. 红细胞指数数学模型在鉴别诊断小儿地中海贫血和缺铁性贫血中的价值. 实用预防医学, 2010, 17(4):659-661.

[14]	Xie Y P, Hu L Q, Zhou Y Q, et al. Value of RBC index mathematical model in differential diagnosis of thalassemia trait and iron-deficiency anaemia in children. Practical Preventive Medicine, 2010, 17(4):659-661.

[15]	杨菊红, 林列坤, 王一娜. MCHC和RDW-SD及HbA2对地中海贫血和缺铁性贫血鉴别诊断价值. 中国实用医药, 2018, 13(24):47-48.

[15]	Yang J H, lin L K, Wang Y N. Value of MCHC and RDW-SD and HbA2 for differential diagnosis of thalassemia and iron deficiency anemia. China Practical Medicine, 2018, 13(24):47-48.

[16]	Malinzi J, Basita K B, Padidar S, et al. Prospect for application of mathematical models in combination cancer treatments. Informatics in Medicine Unlocked, 2021, 23: 100534. doi: 10.1016/j.imu.2021.100534

[17]	Vieira L C, Costa R S, Valério D. An overview of mathematical modelling in cancer research: fractional calculus as modelling tool. Fractal and Fractional, 2023, 7(8): 595. doi: 10.3390/fractalfract7080595

[18]	Mat Daud A A, Toh C Q, Saidun S. Mathematical modeling and analysis of anemia during pregnancy and postpartum. Theory Biosci, 2021, 140(1):87-95. doi: 10.1007/s12064-020-00334-2

[19]	Song X, Liu X Y, Liu F, et al. Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. International Journal of Medical Informatics, 2021, 151:104484. doi: 10.1016/j.ijmedinf.2021.104484

[20]	常莹, 杨敬春, 冷振鹏. 应用Logistic回归模型评价超声弹性成像在乳腺结节鉴别诊断中的应用价值. 医学研究杂志, 2017, 46(1): 109-112.

[20]	Chang Y, Yang J C, Len Z P. Application of logistic regression modeling to evaluate the value of ultrasound elastography in the differential diagnosis of breast nodules. Journal of Medical Research, 2017, 46(1):109-112.

[21]	刘淑霞, 赵红丽, 刘伟, 等. 弹性超声、常规超声及其联合应用诊断乳腺肿瘤性质的试验性评价. 中国全科医学, 2012, 15(16): 1846-1849.

[21]	Liu S X, Zhao H L, Liu W, et al. Test evaluation of diagnosis of breast tumor nature by ultrasonic elastography, conventional ultrasonography and both. Chinese General Practice, 2012, 15(16): 1846-1849.

[22]	王鑫, 李小兰, 李文娟, 等. 易栓症标志物对妊娠晚期血栓性疾病诊断价值. 重庆医学, 2021, 50(18):3136-3139, 3145.

[22]	Wang X, Li X L, Li W J, et al. Diagnostic value of thrombosis markers for thrombotic diseases in late pregnancy. Chongqing Medicine, 2021, 50(18):3136-3139, 3145.

[23]	汪可可, 张晓雅, 高涵, 等. 基于Logistic回归模型的低增生性骨髓增生异常综合征和再生障碍性贫血的鉴别诊断研究. 中国全科医学, 2018, 21(1):52-57. doi: 10.3969/j.issn.1007-9572.2018.01.012

[23]	Wang K K, Zhang X Y, Gao H, et al. Differential diagnosis of hypoplastic myelodysplastic syndrome and aplastic anemia based on a logistic regression model. Chinese General Practice, 2018, 21(1):52-57. doi: 10.3969/j.issn.1007-9572.2018.01.012

[24]	宋琪玲, 郭杨柳, 何勇均, 等. RDW筛查地中海贫血诊断界值的建立及其与MCV、MCH、HbA2联合筛查的价值. 中国实验血液学杂志, 2021, 29(3):847-852.

[24]	Song Q L, Guo Y L, He Y J, et al. Diagnostic cut-off value of RDW for screening thalassemia and the combined determination of MCV, MCH, HbA2 and RDW. Journal of Experimental Hematology, 2021, 29(3):847-852.

[25]	袁秋蓉, 牛诗琼, 林雪萍, 等. RBC、Ret-He与HbA2联合检测对地中海贫血的临床价值. 中国实验血液学杂志, 2021, 29(1): 203-206.

[25]	Yuan Q R, Niu S Q, Lin X P, et al. The clinical value of combined detection of RBC, ret-He and HbA 2 for thalassemia. Journal of Experimental Hematology, 2021, 29(1): 203-206.

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