Inflammatory and Nutritional Scoring System for Predicting Prognosis in Patients with Newly Diagnosed Multiple...

We aimed to assess the prognostic value of pretreatment inflammatory and nutritional parameters for predicting overall survival (OS) in patients with newly diagnosed multiple myeloma (NDMM), and to build a new scoring system using the most important variables.

We retrospectively analyzed baseline clinical and laboratory data for patients with NDMM, who were randomly grouped into training and validation cohorts at a ratio of 8:2. The Inflammatory Nutritional Score (INS) was developed based on the least absolute shrinkage and selection operator (LASSO) Cox regression. The INS and other independent prognostic factors were entered into a multivariate Cox model and merged to generate a nomogram model for predictive optimization. Performance and predictive accuracy were assessed using the concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves.

In total, 442 eligible patients were enrolled. Six inflammatory/nutritional variables, including the Nutritional Risk Index (NRI), body mass index (BMI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), and albumin-alkaline phosphatase ratio (AAPR), were integrated to construct the INS using the LASSO Cox model. The predictive nomogram constructed following the multivariate Cox analysis included INS, performance status, lactate dehydrogenase, age, and C-reactive protein. The model exhibited good predictive performance, with a C-index of 0.708 in the training cohort and 0.749 in the validation cohort. Moreover, the calibration curves also demonstrated excellent consistency between predicted and observed survival in both cohorts. In the time-dependent ROC analysis, our nomogram model exhibited better performance than other staging systems for multiple myeloma.

The INS represents an independent prognostic signature in patients with NDMM. Our novel nomogram based on INS may aid in predicting survival probability and stratifying risk.