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A predictive model based on the systemic immune-inflammation index combined with other hematologic indices: A dynamic
Seyedeh-Tarlan Mirzohreh1, Samad Ghaffari2, Mohammad Asghari-Jafarabadi3
1Women's Reproductive Health Research Center, Al-Zahra Hospital, Tabriz University of Medical Sciences, Tabriz, Iran; Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Background:
Massive acute pulmonary embolism (MACPE) is a life-threatening condition where early risk stratification is essential. The systemic immune-inflammation index (SII) is a promising biomarker, but its role in predicting MACPE has not been fully defined.
Objectives:
To develop and validate an SII-based predictive model, augmented by other hematologic indices, for early MACPE detection, and to present it as a dynamic web-based nomogram.
Methods:
We retrospectively analyzed 444 patients with confirmed acute pulmonary embolism from the Persian Pulmonary Embolism Registry. Hematologic indices, including SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), mean platelet volume-to-platelet count ratio (MPV/PLT), hemoglobin-to-red cell distribution width ratio (Hb/RDW), and others, were evaluated using correlation analysis, logistic regression, and receiver operating characteristic (ROC) curves. SII served as the base predictor, with additional variables added sequentially if they significantly improved the area under the curve (AUC). Continuous and binary multivariable models were developed and calibrated.
Results:
SII, RDW, and MPV/PLT were the strongest independent predictors. The best continuous model (SII, RDW, MPV/PLT, and diabetes mellitus) achieved an AUC of 0.829 with good calibration. The corresponding binary model, using optimal cut-offs (SII ≥ 1.152, RDW ≥ 14.55 %, MPV/PLT ≥ 0.545), achieved an AUC of 0.806 with acceptable calibration.
Conclusions:
We developed an SII-based predictive model enhanced by RDW, MPV/PLT, and diabetes mellitus, presented as a web-based nomogram for real-time MACPE risk estimation. Prospective multicenter validation is warranted.
Insights
A new predictive model using the systemic immune-inflammation index (SII), red cell distribution width (RDW), and mean platelet volume-to-platelet count ratio (MPV/PLT) aids early detection of massive acute pulmonary embolism (MACPE). This model, including diabetes mellitus, offers improved risk stratification for this life-threatening condition.
Area of Science:
- Cardiology
- Pulmonology
- Hematology
Background:
- Massive acute pulmonary embolism (MACPE) is a critical condition requiring prompt risk assessment.
- The systemic immune-inflammation index (SII) shows potential as a biomarker for MACPE, but its predictive value needs further definition.
Purpose of the Study:
- To create and validate a predictive model for early MACPE detection.
- The model integrates the SII with other hematologic indices.
- A dynamic, web-based nomogram will be developed for accessible risk estimation.
Main Methods:
- Retrospective analysis of 444 patients with confirmed acute pulmonary embolism from the Persian Pulmonary Embolism Registry.
- Evaluation of hematologic indices including SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), MPV/PLT, and Hb/RDW.
- Logistic regression and ROC curve analysis were used to identify independent predictors and assess model performance (AUC).
Main Results:
- The SII, RDW, and MPV/PLT were identified as significant independent predictors of MACPE.
- A continuous model incorporating SII, RDW, MPV/PLT, and diabetes mellitus achieved an AUC of 0.829.
- A binary model using optimal cut-offs for these predictors demonstrated an AUC of 0.806.
Conclusions:
- An SII-based predictive model, enhanced by RDW, MPV/PLT, and diabetes mellitus, was successfully developed.
- The model is presented as a web-based nomogram for real-time MACPE risk assessment.
- Further prospective, multicenter validation is recommended.
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