Beyond ISS staging: the prognostic value of the Inflammation Prognostic Score Index (IPSI) in multiple myeloma
Xiaomei Huang1,2, Jing Wu1, Shiying Li3
1Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Guangxi, People's Republic of China.
Objectives:
To establish a novel Inflammation Prognostic Score Index (IPSI) and evaluate its prognostic value and complementary role to the International Staging System (ISS) in newly diagnosed multiple myeloma (MM).
Methods:
This retrospective study analyzed 98 newly diagnosed MM patients. ROC-derived cutoffs stratified patients by RDW, PLT, NMLR, SII, and SIRI. The IPSI was constructed by assigning 1 point each to elevated RDW, low PLT, and high NMLR, defining three risk groups: 0-1, 2, and 3 risk factor groups. Survival and ISS correlations were evaluated using Kaplan-Meier, Cox, and Spearman's tests.
Results:
Multivariate analysis confirmed RDW, PLT, and NMLR as independent predictors of overall survival (OS) (all P < 0.05). Based on these, IPSI stratified patients into three risk groups: 0-1, 2, and 3 risk factor groups, with median OS of 24, 21.5, and 14 months, respectively (log-rank P < 0.001). IPSI was an independent prognostic factor (2-risk-factors group: HR = 8.74; 3-risk-factors group: HR = 18.98 vs 0-1-risk-factors group; both P < 0.001) and correlated with ISS stage (rs = 0.35, P < 0.001). Critically, IPSI refined risk stratification within all ISS subgroups (P < 0.001). SII and SIRI correlated with ISS but were not independent prognostic factors.
Conclusions:
As an independent prognostic index that integrates RDW, PLT, and NMLR, IPSI optimizes ISS staging and provides a cost-effective risk stratification tool. It may be a good measure indice of identifying high-risk MM patients in resource constraint setting where access to molecular testing is not available.
More Related Videos
07:32Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
07:41Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
