ArticleFrontiers in public health2022
Application of clinical nomograms to predicting overall survival and event-free survival in multiple myeloma patients: Visualization tools for prognostic stratification.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and validation of machine learning nomograms for predicting mortality after cardiac valve surgery.Frontiers in medicine · 2026Article
- Prognostic significance of exportin-5 in hepatocellular carcinoma.World journal of gastrointestinal oncology · 2024Article
- Calculated haemorheological profile and laboratory parameters in new diagnosed multiple myeloma patients: retrospective analysis according to survival.Therapeutic advances in hematology · 2024Article
- Calculated Whole Blood Viscosity and Albumin/Fibrinogen Ratio in Patients with a New Diagnosis of Multiple Myeloma: Relationships with Some Prognostic Predictors.Biomedicines · 2023Article
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6 authors.
Funding
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Abstract
Background: This study aimed to develop reliable nomogram-based predictive models that could guide prognostic stratification and individualized treatments in patients with multiple myeloma (MM). Methods: Clinical information of 560 patients was extracted from the MM dataset of the MicroArray Quality Control (MAQC)-II project. The patients were divided into a development cohort ( Results: Lactate dehydrogenase (LDH), albumin, and cytogenetic abnormalities were incorporated into the nomogram to predict overall survival (OS), whereas LDH, β2-microglobulin, and cytogenetic abnormalities were incorporated into the nomogram to predict event-free survival (EFS). The nomograms showed good predictive performances in the development, internal validation, and external validation cohorts. Additionally, we observed a superior prognostic predictive ability in nomograms compared to that of the International Staging System. According to the prognostic nomograms, risk stratification was applied to divide the patients into two risk groups. The OS and EFS rates of low-risk patients were significantly better than those of high-risk patients, suggesting a greater function of the nomogram models for risk stratification. Conclusion: Two simple-to-use prognostic models were established and validated. The proposed nomograms have potential clinical applications in predicting OS and EFS for patients with MM.
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