A Predictive Model for Progression to Overt Primary Myelofibrosis in Early/Prefibrotic Primary Myelofibrosis Patients
Authors: Hu Shiwei , Yang Xiudi , Zhu Xiaoqiong , Qian Honglan , Lu Ying , Han Yanxia , Wang Huafeng , Huang Jian 
Published: December 2024

Introduction:The 2016 WHO reclassified myelofibrosis into two distinct entities: early/prefibrotic primary myelofibrosis (pre-PMF) and overt primary myelofibrosis (overt-PMF). Approximately 15% of pre-PMF patients progress to overt-PMF during the disease course. Currently, there is limited research on the risk factors for fibrotic progression, and no models to predict the progression of pre-PMF to overt-PMF. This study aimed to establish a nomogram model to predict the overt-PMF free survival probability in pre-PMF patients, assisting clinical practitioners in early disease monitoring and timely implementation of appropriate therapy.

Methods: Data from 2,275 patients diagnosed with ET, pre-PMF and overt-PMF were collected from 19 hematology centers from January 2010 to May 2024. After re-evaluation of bone marrow biopsy specimens, 338 pre-PMF patients were included. We randomly assigned 218 patients to the training group and 120 patients to the validation group. Least absolute shrinkage and selection operator (LASSO) regression was used to screen out potential prognostic factors. These factors were further analyzed by multivariate Cox regression analysis. And a nomogram model was constructed based on the weight of these independent risk factors. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were employed to assess the performance of the nomogram.

Results:A total of 338 pre-PMF patients were included, with 167 (49.4%) males. During a median follow-up of 52 months (range: 1-335 months), 44 (13.0%) patients progressed to overt-PMF. There were no statistically differences in all variables included between the two groups. The Lasso regression selected a model with excellent performance but minimum variables when the λ value was 0.063. The variables included gender, MF grade, constitutional symptoms, splenomegaly, platelet count (PLT), lactate dehydrogenase (LDH), and peripheral blood blasts. These factors were subsequently included in the multivariate Cox regression analysis. Male (P=0.008, HR=3.185, 95% CI 1.355-7.488), MF grade 1 (P=0.008, HR=16.700, 95% CI 2.083-133.863), PLT (P=0.037, HR=0.999, 95% CI 0.998-1.000), LDH (P=0.016, HR=1.002, 95% CI 1.000-1.003) and peripheral blood blasts (P&lt;0.0001, HR=5.235, 95% CI 2.071-13.237) were found to be independent risk factors for the progression of overt-PMF in pre-PMF patients. Nomogram was constructed according to these risk factors. The C-indices of the training and validation cohorts were 0.888 and 0.736, respectively. ROC analysis showed the AUC values at 3, 5, and 10 years were 0.899 (95% CI 0.823-0.975), 0.938 (95% CI 0.888-0.9788), and 0.899 (95% CI 0.802-0.997) for the training cohort and 0.761 (95% CI 0.570-0.951), 0.754 (95% CI 0.606-0.902), and 0.902 (95% CI 0.794-1.010) for the validation cohort. The calibration plots and DCA analysis showed that the model had good early fibrotic progression prediction and clinical application value in both training and validation cohorts. Furthermore, we divided the patients into low-risk (≤245) and high-risk (&gt;245) groups. In the training and validation cohorts, the Kaplan-Meier curve showed a significant difference between the high-risk and low-risk groups (P&lt;0.0001, P&lt;0.0001). The 3-, 5-, and 10-year overt-PMF free survival probability for the low-risk group were 98.1% (95% CI 96.5%-99.8%), 96.4% (95% CI 93.9%-99.0%), and 89.1% (95% CI 82.9%-95.6%), respectively, while for the high-risk group, 3-, 5-, and 10-year overt-PMF free survival probability were 68.7% (95% CI 56.2%-84.0%), 49.4% (95% CI 36.0%-67.9%), and 23.9% (95% CI 12.0%-47.7%), respectively.

Conclusions: We developed a nomogram capable of predicting the overt-PMF free survival probability at 3 year, 5 years and 10 years in pre-PMF patients. This tool helps doctors identify high-risk patients for overt-PMF transformation, enabling close monitoring to improve patient prognosis.

Acknowledgement: This research was funded by Key R&D Program of Zhejiang (No. 2022C03137) and Zhejiang Medical Association Clinical Medical Research Special Fund Project (No. 2022ZYC-D09).

*Correspondence to: Jian Huang, M.D., Ph.D., Department of Hematology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China. E-mail: househuang@zju.edu.cn