Key result
Serum thrombopoietin levels effectively distinguished immune thrombocytopenia from aplastic anemia with an AUC of 0.925, and a logistic regression model achieved 86.3% accuracy in classifying these disorders.
Why the study?
Differentiating between immune thrombocytopenia, aplastic anemia, and myelodysplastic syndromes is critical due to their distinct treatment approaches, but reliable biomarkers to aid in diagnosis are needed.
Does serum thrombopoietin level measurement combined with machine learning improve the diagnostic differentiation of ITP, AA, and MDS?
Cohort (n=891)
No
Does serum thrombopoietin level measurement combined with machine learning improve the diagnostic differentiation of ITP, AA, and MDS?
Effect estimate: AUC 0.925 (95% CI 0.902-0.944)
p-value: p=<0.0001
Serum TPO levels, especially when integrated into machine learning models, serve as a highly accurate non-invasive biomarker for differentiating between immune thrombocytopenia, aplastic anemia, and myelodysplastic syndromes.
May support differentiating immune thrombocytopenia from aplastic anemia; hypothesis-generating and requires prospective validation before clinical use.
Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) is critical due to the distinct treatment approaches required for each condition. This study investigates the role of serum thrombopoietin (TPO) levels as a potential biomarker to aid in the diagnosis of these hematological disorders. This retrospective cohort study analyzed serum TPO levels in patients diagnosed with ITP, AA, and MDS, using clinical records and stored serum samples collected from patients treated between September 2023 and May 2024. Statistical analyses were performed to determine cut-off values for TPO levels that effectively differentiate between these conditions. Additionally, machine learning models were utilized to enhance diagnostic accuracy based on clinical indicators, including TPO levels. Serum TPO levels were markedly elevated in AA (1369.19 ± 751.26 pg/ml) compared to ITP (263.57 ± 355.91 pg/ml), MDS (434.55 ± 551.56 pg/ml), and health control (71.64 ± 30.32 pg/ml) (P < 0.0001). Correlation analysis revealed a significant positive correlation between TPO levels and ITP, AA, and MDS (P < 0.0001), Linear regression analysis indicated that age was a significant predictor of TPO levels (P < 0.0001). The optimal cut-off value for TPO levels distinguishing ITP from AA was 302.43 pg/mL, yielding an AUC of 0.925 (sensitivity with 80.75%, specificity with 94.06%). Machine learning models demonstrated that Logistic Regression, XGBoost, and LightGBM performed best, with the Logistic Regression achieving an accuracy of 86.3% and an AUC of 0.910. Serum TPO levels are a promising non-invasive biomarker for distinguishing between ITP, AA, and MDS. Incorporating TPO measurements into clinical practice may enhance diagnostic accuracy and improve patient management strategies.
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Zhu et al. (2025) conducted a cohort in Immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) (n=891). Serum thrombopoietin (TPO) levels vs. Healthy controls and other hematological disorders was evaluated on Diagnostic accuracy (AUC) of serum TPO levels for distinguishing ITP from AA (AUC 0.925, 95% CI 0.902-0.944, p=<0.0001). Serum thrombopoietin levels effectively distinguished immune thrombocytopenia from aplastic anemia with an AUC of 0.925, and a logistic regression model achieved 86.3% accuracy in classifying these disorders.
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