Development and validation of a model for the early prediction of progression from essential thrombocythemia to post-essential thrombocythemia myelofibrosis: a multicentre retrospective studyResearch in context - Danhong Xiang; Xiudi Yang; Honglan Qian; Li Zhang; Yanxia Han; Yongcheng Sun; Ying Lu; Yu Chen; Dan Cao; Meiwei Hu; Lifeng Wang; Qinli Tang; et al. (EClinicalMedicine, 2024)
Summary: Background: Essential thrombocythemia (ET), a myeloproliferative neoplasm (MPN), has a substantial risk of evolving into post-essential thrombocythemia myelofibrosis (post-ET MF). This study aims to establish a prediction nomogram for early prediction of post-ET MF in ET patients. Methods: The training cohort comprised 558 patients from 8 haematology centres between January 1, 2010, and May 1, 2023, while the external validation cohort consisted of 165 patients from 6 additional haematology centres between January 1, 2010, and May 1, 2023. Univariable and multivariable Cox regression analysis was performed to identified independent risk factors and establish a nomogram to predict the post-ET MF free survival. Both bias-corrected area under the curve (AUC), calibration curves and concordance index (C-index) were employed to assess the predictive accuracy of the nomogram. Findings: Multivariate Cox regression demonstrated that elevated red blood cell distribution width (RDW), elevated levels of lactate dehydrogenase (LDH) and the level of haemoglobin (Hb), a history of smoking and the presence of splenomegaly were independent risk factors for post-ET MF. The C-index displayed of the training and validation cohorts were 0.877 and 0.853. The 5 years, 10 years AUC values in training and external validation cohorts were 0.948, 0.769 and 0.978, 0.804 respectively. Bias-corrected curve is close to the ideal curve and revealed a strong consistency between actual observation and prediction. Interpretation: We developed a nomogram capable of predicting the post-ET MF free survival probability at 5 years and 10 years in ET patients. This tool helps doctors identify patients who need close monitoring and appropriate counselling. Funding: This research was funded by the Key R&D Program of Zhejiang (No. 2022C03137); the Public Technology Application Research Program of Zhejiang, China (No. LGF21H080003); and the Zhejiang Medical Association Clinical Medical Research special fund project (No. 2022ZYC-D09).