<scp>Logistic‐Nomogram</scp> model based on red blood cell parameters to differentiate thalassemia trait and iron deficiency anemia in southern region of Fujian Province, China

Xu, Mingzhen; Ge, Lin; Dong, Zhang; Wang, Qingqing; Ma, Lili; Su, Junnan

doi:10.1002/jcla.24940

Cited by 2 publications

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References 31 publications

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“…However, the cost and time associated with genetic testing often hinder timely diagnosis for many thalassemia patients. Conversely, blood routine indicators play a crucial role in the early identification of thalassemia due to the widespread availability of blood routine tests and the ability to distinguish different types of anemia based on red blood cell morphology (8,9).…”

Section: Introductionmentioning

confidence: 99%

Constructing a novel clinical indicator model to predict the occurrence of thalassemia in pregnancy through machine learning algorithm

Long,

Bai

2024

Front. Hematol.

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Thalassemia is one of the inherited hemoglobin disorders worldwide, resulting in ineffective erythropoiesis, chronic hemolytic anemia, compensatory hemopoietic expansion, hypercoagulability, etc., and when a mother carries the thalassemia gene, the child is more likely to have severe thalassemia. Furthermore, the economic and time costs of genetic testing for thalassemia prevent many thalassemia patients from being diagnosed in time. To solve this problem, we performed least absolute shrinkage and selection operator (LASSO) regression to analyze the correlation between thalassemia and blood routine indicators containing mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and red blood cell (RBC). We then built a nomogram to predict the occurrence of thalassemia, and receiver operating characteristic (ROC) curve was used to verify the prediction efficiency of this model. In total, we obtained 7,621 cases, including 847 thalassemia patients and 6,774 non-thalassemia. Among the 847 thalassemia patients, with a positivity rate of 67.2%, 569 cases were positive for α-thalassemia, and with a rate of 31.5%, 267 cases were positive for β-thalassemia. The remaining 11 cases were positive for both α- and β-thalassemia. Based on machine learning algorithm, we screened four optimal indicators, namely, MCV, MCH, RBC, and MCHC. The AUC value of MCV, MCH, RBC, and MCHC were 0.907, 0.906, 0.796, and 0.795, respectively. Moreover, the AUC value of the prediction model was 0.911. In summary, a novel and effective machine learning model was built to predict thalassemia, which functioned accurately, and may provide new insights for the early screening of thalassemia in the future.

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Section: Introductionmentioning

confidence: 99%

Constructing a novel clinical indicator model to predict the occurrence of thalassemia in pregnancy through machine learning algorithm

Long,

Bai

2024

Front. Hematol.

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Logistic‐Nomogram model based on red blood cell parameters to differentiate thalassemia trait and iron deficiency anemia in southern region of Fujian Province, China

Xu¹,

Ge²,

Dong³

et al. 2023

Clinical Laboratory Analysis

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BackgroundDifferentiation between thalassemia trait (TT) and iron deficiency anemia (IDA) is challenging and costly. This study aimed to construct and evaluate a model based on red blood cell (RBC) parameters to differentiate TT and IDA in the southern region of Fujian Province, China.MethodsRBC parameters of 364 TT patients and 316 IDA patients were reviewed. RBC parameter‐based Logistic‐Nomogram model to differentiate between TT and IDA was constructed by multivariate logistic regression analysis plus nomogram, and then compared with 22 previously reported differential indices.ResultsThe patients were randomly selected to a training cohort (nTT = 248, nIDA = 223) and a validation cohort (nTT = 116, nIDA = 93). In the training cohort, multivariate logistic regression analysis identified RBC count, mean corpuscular hemoglobin (MCH), and MCH concentration (MCHC) as independent parameters associated with TT susceptibility. A nomogram was plotted based on these parameters, and then the RBC parameter‐based Logistic‐Nomogram model g (μy) = 1.92 × RBC count‐0.51 × MCH + 0.14 × MCHC‐39.2 was devised. The area under the curve (AUC) (95% CI) was 0.95 (0.93–0.97); sensitivity and specificity at the best cutoff score (120.24) were 0.93 and 0.89, respectively; the accuracy was 0.91. In the validation cohort, the RBC parameter‐based Logistic‐Nomogram model had AUC (95% CI) of 0.95 (0.91–0.98); sensitivity and specificity were 0.92 and 0.87, respectively; accuracy was 0.90. Moreover, compared with 22 reported differential indices, the RBC parameter‐based Logistic‐Nomogram model showed numerically higher AUC, net reclassification index, and integrated discrimination index (all p < 0.001).ConclusionThe RBC parameter‐based Logistic‐Nomogram model shows high performance in differentiating patients with TT and IDA from the southern region of Fujian Province.

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Logistic‐Nomogram model based on red blood cell parameters to differentiate thalassemia trait and iron deficiency anemia in southern region of Fujian Province, China

Cited by 2 publications

References 31 publications

Constructing a novel clinical indicator model to predict the occurrence of thalassemia in pregnancy through machine learning algorithm

Constructing a novel clinical indicator model to predict the occurrence of thalassemia in pregnancy through machine learning algorithm

Logistic‐Nomogram model based on red blood cell parameters to differentiate thalassemia trait and iron deficiency anemia in southern region of Fujian Province, China

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