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

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Kaplan-Meier analysis; idiopathic inflammatory myopathies; nomogram; risk prediction; severe infection.

Figure 1

Flow-chart for patient selection process.

Figure 2

Nomogram for Severe infection prediction (HALL model). Draw a perpendicular line from each risk factor’s corresponding axis up to the line labeled ‘Points’. Sum the points for all risk factors, then draw a line down from the ‘Total Points’ axis until it intersects each risk axis to determine the 6-month, 1-year, and 3-year probabilities of severe infection.

Figure 3

Discrimination of the nomogram in the derivation and validation sets. Receiver operating characteristic curves for 6-month, 1-year, and 3-year severe infection predictions in the derivation set (A) and validation set (B). The area under the curve of the nomogram for predicting 6-month to 3-year severe infection in the derivation data (C) and validation data (D). Kaplan-Meier curves of the derivation set (E) and validation set (F) stratified into high-risk and low-risk groups based on the cutoff value of 54.

Figure 4

Calibration of the nomogram for infection predictions at 6-month (A, B), 1-year (C, D), and 3-year (E, F) intervals in the derivation and validation sets. Data are sourced from the derivation set (A, C, E) and the validation set (B, D, F). The nomogram predicted cumulative incidence rates of infection, stratified into equally sized subgroups. For each subgroup, the average predicted infection incidence rate is plotted against the observed infection incidence rate. Vertical lines represent the 95% confidence intervals for the infection incidence rates. The gray lines serve as reference lines, indicating the ideal position for the nomogram.

Figure 5

Decision Curve Analysis for 12-Month Infection Risk Prediction Models. This figure shows the net benefit of various predictive models across different risk thresholds. The lines represent different evaluated models: ‘HALL model’, compared with univariate predictors including HRCT score, LYM, Age, and LDH.

Figure 6

Discrimination of the nomogram in the DM and ASS subgroups. Receiver operating characteristic curves for 6-month, 1-year, and 3-year severe infection predictions in the DM subgroup (A) and ASS subgroup (B).

Figure 7

Calibration of the nomogram for infection predictions at 6-month (A, B), 1-year (C, D), and 3-year (E, F) intervals in the ASS and DM subgroups. Data are sourced from the ASS subgroup (A, C, E) and the DM subgroup (B, D, F). The nomogram-predicted cumulative incidence rates of infection are stratified into equally sized subgroups. For each subgroup, the average predicted infection incidence rate is plotted against the observed infection incidence rate. Vertical lines represent the 95% confidence intervals for the infection incidence rates. The gray lines serve as reference lines, indicating the ideal position for the nomogram.