# Development and validation of a nomogram for predicting the incidence of infectious events in patients with idiopathic inflammatory myopathies

## TL;DR

This study created a tool to predict the risk of severe infections in patients with idiopathic inflammatory myopathies over 6 months, 1 year, and 3 years.

## Contribution

A novel nomogram was developed and validated for predicting severe infection risk in idiopathic inflammatory myopathies.

## Key findings

- The nomogram showed strong predictive performance with AUCs of 0.84, 0.83, and 0.78 for 6 months, 1 year, and 3 years in the derivation set.
- Calibration curves and decision curve analysis confirmed the model's accuracy and clinical utility.
- The nomogram effectively predicted severe infections in subgroups of dermatomyositis and antisynthetase syndrome patients.

## Abstract

Infection is a leading cause of mortality in idiopathic inflammatory myopathies (IIMs). This study aimed to develop a nomogram for predicting severe infection risk in IIM patients.

Patients with IIMs admitted to Zhongshan Hospital, Fudan University, from January 2015 to January 2022 were enrolled. They were randomly divided into derivation (70%) and validation (30%) sets. Univariate and multivariate Cox regression identified independent risk factors for severe infection, and the Akaike information criterion (AIC) was applied for model selection. A nomogram was constructed to predict severe infection risks at 6 months, 1 year, and 3 years. Predictive accuracy and discriminative ability were evaluated using the concordance index (C-index), calibration curves, and the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) assessed clinical utility. Kaplan-Meier (K-M) curves were used to analyze survival differences between high- and low-risk groups stratified by nomogram scores.

Among 263 IIM patients, 81 experienced 106 severe infection events, with lower respiratory tract infections being the most common (47.2%). Independent risk factors included age at onset (HR 1.024, 95% CI 1.002-1.046, p=0.036), lactate dehydrogenase (HR 1.002, 95% CI 0.999-1.005, p=0.078), HRCT score (HR 1.004, 95% CI 1.001-1.006, p=0.002), and lymphocyte count (HR 0.48, 95% CI 0.23-0.99, p=0.048). The nomogram demonstrated strong predictive performance, with AUCs of 0.84, 0.83, and 0.78 for 6 months, 1 year, and 3 years in the derivation set, and 0.91, 0.77, and 0.64 in the validation set. Calibration curves showed good agreement between predicted and observed risks, while DCA demonstrated significant net benefit over individual predictors. Kaplan-Meier curves revealed significant differences in the cumulative risk of severe infection between high- and low-risk groups. Further validation in DM and ASS subgroups demonstrated that the nomogram effectively predicted severe infections, with AUCs of 0.86, 0.81, and 0.73 for DM and 0.86, 0.83, and 0.74 for ASS at 6 months, 1 year, and 3 years, respectively.

We have developed a new nomogram to predict severe infection risk in IIM patients at 6 months, 1 year, and 3 years. This model aids clinicians and patients in formulating treatment and follow-up strategies.