Journal of clinical laboratory analysis · 2023
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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Xu Meihong, Lin Guojin, Dong Zhigao, Wang Qingqing, Ma Lili, Su Junnan
Аннотация
BACKGROUND: Differentiation 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.
METHODS: RBC 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.
RESULTS: The 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).
CONCLUSION: The RBC parameter-based Logistic-Nomogram model shows high performance in differentiating patients with TT and IDA from the southern region of Fujian Province.
METHODS: RBC 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.
RESULTS: The 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).
CONCLUSION: The RBC parameter-based Logistic-Nomogram model shows high performance in differentiating patients with TT and IDA from the southern region of Fujian Province.