Construction of a nomogram prediction model for opportunistic sarcopenia in patients with malignant tumors.

BACKGROUND AND

OBJECTIVES

The aim of this study was to establish a predictive model for opportunistic sarcopenia applicable to Chinese cancer patients, so as to quickly detect the occurrence of this condition and provide a basis for early clinical intervention.

METHODS AND STUDY

DESIGN

A total of 522 malignant tumor patients admitted to the First Hospital of Hebei Medical University from October 2017 to March 2022 were retrospectively analyzed. Opportunistic sarcopenia was diagnosed by L3 SMI. Twelve variables were collected; risk factors were screened and modeled via univariate and multivariate regression in R Studio, and con-tinuous variable cutoff points were determined by SPSS. A nomogram was constructed and validated with clinical decision and calibration curves.

RESULTS

The prevalence of opportunistic sarcopenia in the study population was 66.1% (345/522). The final predictive model included six key variables: gender, body mass index (BMI), C-reactive protein (CRP) level, systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and SII-PNI. The area under the curve (AUC) of the model was 0.890 (95% CI: 0.854-0.926), indicating high discriminative ability. The calibration curve showed good consistency between the model's predictions and the actual diagnostic results. Clinical net benefit analysis showed that when the threshold range was greater than 0.6, the clinical benefit rate of the predictive model was higher than those of relative appendicular skeletal muscle mass (RASM) and appendicular skeletal muscle mass index (ASMI).

CONCLUSIONS

The constructed nomogram model can accurately estimate the probability of opportunistic sarcopenia in cancer patients, facilitating its early screening and targeted prevention.