AUTHOR=Yuan Shuai , Zhou Jie-Yi , Yang Ben-Zhao , Xie Zhong-Lei , Zhu Ting-Jun , Hu Hui-Xian , Li Rong 

TITLE=Prediction of cardiovascular adverse events in newly diagnosed multiple myeloma: Development and validation of a risk score prognostic model

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1043869

DOI=10.3389/fonc.2023.1043869

ISSN=2234-943X

ABSTRACT=Background
To investigate a risk score prognostic model to predict the probability of cardiovascular adverse events (CVAEs) in newly diagnosed multiple myeloma (NDMM).
Patients and methods
This is a retrospective study which included patients were newly diagnosed multiple myeloma (NDMM) in Shanghai Changzheng Hospital and Affiliated Jinhua Hospital, Zhejiang University School of Medicine from June 2018 to July 2020. 253 patients from 2 medical centers were divided into train cohort and validation cohort randomly. Univariable analysis of the baseline factors was performed using CVAEs end-points. Multivariable analysis identified three factors for a prognostic model that was validated in internal validation cohorts.
Results
Factors independently associated with CVAEs in NDMM were as follows: age>61 years old, high level of baseline office blood pressure and left ventricular hypertrophy (LVH). Age contributed two points and the other two factors contributed one point to a prognostic model. The model distinguished the patients into 3 groups: three to four points, high risk; two points, intermediate risk; zero to one point, low risk. These groups had significant difference in CVAEs during follow up days in both train cohort (P<0.0001) and validation cohort (P=0.0018). In addition, the model had good calibration. The C-indexes for the prediction of overall survival of CVAEs in the training and validation cohorts were 0.73 (95% CI: 0.67–0.79) and 0.66 (95% CI:0.51–0.81), respectively. The areas under ROC curve (AUROCs) of the 1-year CVAEs probability in the training and validation cohorts were 0.738 and 0.673, respectively. The AUROCs of the 2-year CVAEs probability in the training and validation cohorts were 0.722 and 0.742, respectively. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing assessment or not providing assessment for all patients.
Conclusion
A prognostic risk prediction model for predicting CVAEs risk of NDMM patients was developed and internally validated. Patients at increased risk of CVAEs can be identified at treatment initiation and be more focused on cardiovascular protection in treatment plan.