Quantitative Integrative Survival Prediction in Multiple Myeloma Patients Treated With Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation

Abstract

Purpose: Given the high heterogeneity in survival for patients with multiple myeloma, it would be clinically useful to quantitatively predict the individual survival instead of attributing patients to two to four risk groups as in current models, for example, revised International Staging System (R-ISS), R2-ISS, or Mayo-2022-score.

Patients and methods: Our aim was to develop a quantitative prediction tool for individual patient's 3-/5-year overall survival (OS) probability. We integrated established clinical and molecular risk factors into a comprehensive prognostic model and evaluated and validated its risk discrimination capabilities versus R-ISS, R2-ISS, and Mayo-2022-score.

Results: A nomogram for estimating OS probabilities was built on the basis of a Cox regression model. It allows one to translate the individual risk profile of a patient into 3-/5-year OS probabilities by attributing points to each prognostic factor and summing up all points. The nomogram was externally validated regarding discrimination and calibration. There was no obvious bias or overfitting of the prognostic index on the validation cohort. Resampling-based and external evaluation showed good calibration. The c-index of the model was similar on the training (0.76) and validation cohort (0.75) and significantly higher than for the R-ISS (P < .001) or R2-ISS (P < .01).

Conclusion: In summary, we developed and validated individual quantitative nomogram-based OS prediction. Continuous risk assessment integrating molecular prognostic factors is superior to R-ISS, R2-ISS, or Mayo-2022-score alone.

FIG 1.

Cox model on training cohort. HRs for the final Cox model based on age, ISS stage, LDH, and molecular prognostic factors, that is, del17p13, t(4;14), gain 1q21, the UAMS GEP70-score (UAMS70), and the gene expression-based proliferation index (GPI50). HR, hazard ratio; ISS, international staging system; LDH, lactate dehydrogenase; ULN, upper limit of normal.

FIG 2.

Nomogram. (A) Nomogram for estimating survival probabilities. (B) Exemplary patient. Here, 170 total points correspond to a 3-/5-year OS probability of 51% and 26%, respectively. Contribution of each risk factor is visualized by different colors. OS, overall survival.

FIG 3.

Continuous risk assessment (nomogram-core) versus grouped assessment regarding R2-ISS and Mayo-2022-score and transitions plots. The median nomogram score (continuous predicted survival probability, y-axis) is significantly different between groups for (A) R2-ISS and (B) Mayo-2022-score (Kruskal-Wallis-test, P < .001). At the same time, within each of the R2-ISS or Mayo-2022-score risk groups, score values vary up to four-fold, eg, R2-ISS3 from 50 to 200 points. Continuous risk assessment thus allows substratification within each of the risk groups. Dashed lines depict cut-points to match number and size of R2-ISS and Mayo-2022-groups, respectively. Alluvial plot for (C) R2-ISS and (D) Mayo-2022-score versus nomogram score. The nomogram score is grouped for both scores so that the number/size of the groups corresponds to the respective comparison score. Transitioning patients represent extreme scores within each of the risk groups.

FIG 4.

Kaplan-Meier curves. Kaplan-Meier curves for the patient groups using the upper quartile of the prognostic index from the training group as cutoff confirmed good discrimination in training (black curve) and validation data (red curve).

FIG 5.

Calibration plots for training group and validation group data. Shown are smoothed calibration plots of observed versus estimated survival probabilities after 3 years for (A) training data and (B) validation data. Bootstrapping is used for training group (A) to get bias-corrected estimates.