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. 2025 Jan 28:18:1303-1316.
doi: 10.2147/JIR.S496007. eCollection 2025.

Development and Validation of the Systemic Inflammatory Response Index-Based Nomogram for Predicting Short-Term Adverse Events in Patients With Acute Uncomplicated Type B Aortic Intramural Hematoma

Yasong Wang  1   2 , Xuan Wu  2 , Yue Wang  2 , Zhiqiang Zhang  2 , Xuanze Liu  2 , Dongyuan Sun  2 , Xue Liu  2 , Tienan Zhou  2 , Xiaozeng Wang  2
Affiliations

Development and Validation of the Systemic Inflammatory Response Index-Based Nomogram for Predicting Short-Term Adverse Events in Patients With Acute Uncomplicated Type B Aortic Intramural Hematoma

Yasong Wang et al. J Inflamm Res. .

Abstract

Purpose: This study aims to develop and validate a nomogram based on the Systemic Inflammatory Response Index (SIRI) to predict short-term aortic-related adverse events (ARAEs) in patients with acute uncomplicated Type B intramural hematoma (IMH).

Patients and methods: We retrospectively analyzed 332 patients diagnosed with acute uncomplicated Type B IMH between April 2018 and April 2024. Patients were categorized into the stable group (N=225) and the exacerbation group (N=107) based on the occurrence of ARAEs within 30-day observation period. SIRI was calculated using neutrophil, monocyte, and lymphocyte counts. ARAEs were defined as death related to aortic disease, and the progression of IMH to aortic dissection or penetrating aortic ulcer. The nomogram was developed incorporating SIRI and other significant clinical variables. The model's performance was evaluated using the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and net reclassification index (NRI).

Results: Among the 332 patients, 217 were male (65.4%), with a mean age of 64.3±9.4 years. Multivariate logistic regression and LASSO regression analyses identified SIRI, anemia, diabetes, maximum diameter of aortic diameter (MDAD), and ulcer like projection (ULP) as independent predictors of ARAEs. Two nomogram models were developed: the Clinical model, including anemia, diabetes, MDAD, and ULP; and the Clinical-SIRI model, incorporating SIRI to the Clinical model. The Clinical-SIRI model demonstrated higher predictive accuracy, with an AUC of 0.788 (95% CI: 0.740-0.831), compared to the Clinical model's AUC of 0.742 (95% CI: 0.691-0.788, P = 0.012). SIRI improved predictive accuracy, as shown by a continuous NRI of 0.521 (95% CI: 0.301-0.743). Calibration curves and DCA further supported the clinical utility of the Clinical-SIRI model.

Conclusion: The SIRI-based nomogram is a valuable prognostic tool for predicting short-term ARAEs in patients with acute uncomplicated Type B IMH.

Keywords: Systemic inflammatory response Index; Type B aortic intramural hematoma; biomarkers; nomogram; prognosis.

Plain language summary

This study introduces a novel nomogram that integrates the Systemic Inflammatory Response Index with clinical variables to enhance the prediction of adverse events in patients with Type B aortic intramural hematoma, a condition marked by highly variable clinical outcomes. The inclusion of this biomarker not only optimizes patient management but also aligns with the growing emphasis on precision in medical interventions.

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Conflict of interest statement

All authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
Flow chart of research.
Figure 2
Figure 2
Forest plot of adverse related aortic events hazard ratios by multivariable logistic regression analyses for the association between systemic inflammatory response index and adverse related aortic events. Model 1: unadjusted. Model 2: adjusted for age, gender, and body mass index. Model 3: adjusted for age, gender, body mass index, and previous comorbidities. Model 4: adjusted for age, gender, body mass index, previous comorbidities, and imaging characteristics. Model 5: adjusted for age, gender, body mass index, previous comorbidities, imaging characteristics, and laboratory results.
Figure 3
Figure 3
Short-term exacerbation predictors in patients with acute uncomplicated Type B IMH identified using LASSO regression. (A) LASSO coefficient distribution for each clinical feature. (B) The optimal penalty coefficient λ for the LASSO model determined by 10-fold cross-validation and the 1-SE criterion.
Figure 4
Figure 4
Constructed nomograms and their calibration curves. (A) and (B) show the nomograms for the Clinical-SIRI model and the Clinical model, respectively, predicting the incidence of ARAEs in patients with acute uncomplicated Type B IMH within a 30-day follow-up period. Calibration curves for the Clinical-SIRI model (C) and the Clinical model (D) illustrate the agreement between the nomogram-predicted probabilities of 30-day ARAEs and the actual observed probabilities. The “Ideal” line represents a perfect prediction model. The “Apparent” line shows the prediction probability from the original development dataset. The “bias-corrected” line shows the prediction probability obtained through Bootstrap resampling, which involved 1000 iterations. The x-axis represents the nomogram-estimated probabilities of 30-day ARAEs, and the y-axis represents the actual observed incidence of 30-day ARAEs.
Figure 5
Figure 5
Receiver operating characteristic curves of Clinical-SIRI model, Clinical model, and SIRI model for the incidence of AREAs. SIRI, systemic inflammation response index.
Figure 6
Figure 6
Decision curve analysis for the Clinical-SIRI model, Clinical model, and SIRI model. The x-axis represents the threshold probability. The y-axis represents the net benefits in comprehensive consideration of insufficient treatment (false negative) and excessive treatment (false positive) across the threshold probability of 0–1.0. Decision curve analysis reveals that Clinical-SIRI model had a higher net benefit than Clinical model or SIRI model in clinical practice.
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