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Machine learning analyses constructed a novel model to predict recurrent thrombosis in adults with essential thrombocythemia

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Abstract

The current study involving 318 essential thrombocythemia (ET) patients with prior thrombosis was designed to identify risk factors that were predictive of recurrent thrombosis. The whole cohort was randomly split into derivation and validation cohorts. The random forest method, support vector machine with built-in recursive feature elimination model, and logistic multivariable analysis were performed in the derivation cohort, and cardiovascular risk factor (CVF) and RBC distribution width with standard deviation (RDW-SD) were finally selected as independent predictors. Subsequently we devise a 3-tiered model (low risk: 0 points; intermediate risk: 1-1.5 points; and high risk: 2.5 points) and it showed good discrimination in all cohorts. Moreover, the model was significantly correlated with rethrombosis-free survival (rTFS) (p = 0.0007 in the derivation cohort; p = 0.0019 in the validation cohort). In the whole cohort, cytoreductive therapy was more effective than antiplatelet agents alone for 10-year rTFS (p = 0.0336). No significant difference in 10-year rTFS was observed among interferon (IFN), hydroxyurea (HU), and IFN + HU therapy (p = 0.444). The present study helps identify individuals who need close monitoring and provides valuable risk signals for recurrence in ET patients with prior thrombosis.

Highlights

Reliable biomarkers to accurately predict recurrence in essential thrombocythemia patients who have a previous thrombosis have been lacking thus far, and the prognostic significance remains to be carefully defined.

Based on machine learning algorithm, we confirmed that CVF and RDW-SD ≥ 47 fL were independent predictors of recurrence, and both were associated with reduced rTFS.

Cytoreductive agents still play a fundamental role on treating thrombosis in high-risk ET patients.

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Data Availability

The data sets generated and analyzed during the current study are available from the corresponding authors upon reasonable request.

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Funding

This work was supported by grants from the National Natural Science Foundation of China (81970121, 82270152), CAMS Innovation Fund for Medical Sciences (CIFMS) (2021-I2M-1-073, 2021-I2M-1-003, 2022-I2M-2-003), National Key Research and Development Program of China (2019YFA0110802), Haihe laboratory of Cell Ecosystem Innovation Fund (22HHXBSS00022).

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Author notes

  1. Jia Chen, and Huan Dong contributed equally to this study and should be regarded as co-first authors.

Authors and Affiliations

  1. State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China

    Jia Chen, Huan Dong, Rongfeng Fu, Xiaofan Liu, Feng Xue, Wei Liu, Yunfei Chen, Ting Sun, Mankai Ju, Xinyue Dai, Huiyuan Li, Wentian Wang, Ying Chi, Renchi Yang & Lei Zhang

  2. Tianjin Institutes of Health Science, Tianjin, 301600, China

    Jia Chen, Huan Dong, Rongfeng Fu, Xiaofan Liu, Feng Xue, Wei Liu, Yunfei Chen, Ting Sun, Mankai Ju, Xinyue Dai, Huiyuan Li, Wentian Wang, Ying Chi, Renchi Yang & Lei Zhang

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  1. Jia Chen
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Contributions

L.Z., and R.C.Y. designed the research, was the principal investigator, and took primary responsibility for the paper; J.C, and H.D. acquired the data, analysed and interpreted the data, performed statistical analysis and drafted the article; R.F.F., F.X., Y.F.C., W.L., X.F.L., T.S., M.K.J., X.Y.D., H.Y.L., W.T.W., and Y.C. recruited the patients. We express our sincere gratitude to Mr. Ma Yueshen (a statistical expert of the Institute of Hematology & Blood Diseases Hospital of the Chinese Academy of Medical Sciences), for providing invaluable statistical review and guidance.

Corresponding author

Correspondence to Lei Zhang.

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This study was approved by the Institutional Ethics Committee of the Institute of Hematology & Blood Diseases Hospital of the Chinese Academy of Medical Sciences.

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Chen, J., Dong, H., Fu, R. et al. Machine learning analyses constructed a novel model to predict recurrent thrombosis in adults with essential thrombocythemia. J Thromb Thrombolysis 56, 291–300 (2023). https://doi.org/10.1007/s11239-023-02833-7

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