Systemic Immune-Inflammation Index: A Promising, Non-Invasive Biomarker for Crohn's Disease Activity and Severity Assessment

Abstract

Purpose: Crohn's disease (CD) is a chronic inflammatory disorder with periods of exacerbation and remission. We aim to evaluate the systemic immune-inflammation index (SII) as a prognostic biomarker in CD and its utility in predicting disease activity and severity.

Patients and methods: This retrospective study analyzed CD patients using the Harvey-Bradshaw index (HBI) for disease stratification and the Simple Endoscopic Score for Crohn's Disease (SES-CD) for post-treatment evaluation. Data analysis was conducted using R software. Serological indices underwent predictive analysis through the receiver operating characteristic (ROC) curve. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression identified independent prognostic factors to construct nomograms. Model validation was performed using the Concordance index (C-index), calibration analysis and decision curve analysis (DCA).

Results: In this study, 254 patients with Crohn's disease (CD) were enrolled, including 171 males and 83 females, with ages ranging from 13 to 74. SII was significantly elevated in active CD (p<0.001), correlating with disease severity (p<0.001). Although SII decreased in patients with mucosal healing (p<0.001), its prognostic accuracy (AUC=0.719) was lower than other biomarkers. However, SII emerged as an independent predictor for CD activity and severity with higher efficacy (AUC=0.774 and 0.807). The CD activity and severity prediction nomograms showed high C-indices (0.8038 and 0.8208), indicating strong predictive performance.

Conclusion: SII is a valuable biomarker for assessing CD severity and monitoring mucosal healing post-treatment. The SII-based nomograms offer a reliable model for evaluating CD progression, aiding in personalized treatment approaches and enhancing clinical decision-making. We recommend randomized controlled trials (RCTs) or studies with larger sample sizes to improve the model.

Keywords: Crohn’s disease; biomarkers; disease progression; inflammation; nomograms.

Figure 1

Flow diagram of patient enrollment and grouping.

Figure 2

Receiver operating characteristic (ROC) curves of inflammation indices for predicting Crohn’s disease (CD) activity (A), severity (B), and mucosal healing (C).

Figure 3

The least absolute shrinkage and selection operator (LASSO) regression analysis with tenfold cross-validation was used for predictor selection. (A) LASSO coefficient profiles of the eight risk factors to predict Crohn’s disease (CD) activity. (B) Five risk factors (CRP, ESR, SII, PLR, and LMR) selected using LASSO regression analysis, based on the 1-SE criteria (right dotted line). (C) LASSO coefficient profiles of the eight risk factors to predict CD severity. (D) Three risk factors (ESR, SII, and PLR) selected using LASSO regression analysis. (E) LASSO coefficient profiles of the eight risk factors to predict mucosal healing. (F) Four risk factors (ESR, SII, PLR, and LCR) selected using LASSO regression analysis.

Figure 4

Nomograms to estimate the progression of patients with Crohn’s disease (CD). (A) Nomogram to estimate CD activity. (B) Nomogram to estimate CD severity. To use the nomogram, a line is drawn from each indicator value to the points line, and the corresponding score is given using the indicator values. The scores for all indicators are then summed up. A line is then drawn from the total points line to the lowest line of the nomogram to determine the predicted value.

Figure 5

Calibration curves of the predictive model. (A) Calibration curve of the model to predict Crohn’s disease (CD) activity. (B) Calibration curve of the model to predict CD severity. The X-axis represents the predicted probability of the nomogram, while the Y-axis represents the actual probability of progression grade in CD. Well-calibrated nomograms should have scatter points closely aligned along the diagonal. Bootstrapping with 1000 repetitions was employed for reliable results.

Figure 6

Decision curve analysis (DCA) of the predictive model. (A) DCA for predicting Crohn’s disease (CD) activity. (B) DCA for predicting CD severity. The horizontal coordinate represents the threshold probability, while the vertical coordinate represents the net benefit rate after subtracting the drawbacks from the benefits. The nomograms provide more benefit when the threshold probability is between 24% and 96% for activity prediction and > 5% for severity prediction.