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Updated: Jan 24, 2026

A Porcine Model of Acute Autologous Pulmonary Embolism
Published on: September 6, 2024
Innovative Inflammatory Burden Index for identification of high mortality risk in patients with pulmonary embolism: a
Ning Zhu1,2, Lei Zhang3, Shanhong Lin4
1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Background:
The prognostic significance of systemic inflammation in pulmonary embolism (PE) remains unclear. This study aimed to evaluate the predictive value of a novel Inflammatory Burden Index (IBI) for long-term mortality in patients with acute PE.
Methods:
A total of 1642 patients with acute PE were retrospectively analysed. The optimal cut-off for IBI was determined using maximally selected rank statistics. The association between high IBI and all-cause mortality was assessed using multivariable Cox proportional hazards models. The predictive performance of IBI and its individual components was compared using time-dependent receiver operating characteristic (ROC) curves. Variable importance for mortality prediction was further evaluated using random survival forest (RSF) analysis.
Results:
During a median follow-up of 41.2 months, 262 patients (16.0%) died. High IBI was independently associated with increased risk of all-cause mortality (adjusted HR 2.33, 95% CI 1.78 to 3.04; p<0.001). Time-dependent ROC analysis demonstrated that IBI provided superior prognostic accuracy for mortality, with area under the curve (AUC) values of 0.73, 0.74 and 0.75 at 1, 3 and 5 years, respectively, which were higher than those of C-reactive protein, neutrophil count and lymphocyte count. Addition of IBI to the basic clinical model significantly improved the AUC at all time points. RSF analysis confirmed that IBI was the most important inflammatory predictor of long-term mortality.
Conclusions:
The IBI is a robust and independent predictor of long-term mortality in patients with acute PE and offers incremental prognostic value beyond conventional risk factors. Incorporating IBI into clinical risk stratification may improve patient management and outcomes.
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