Association and limited incremental discrimination of the systemic coagulation-inflammation index for Gensini-defined coronary anatomical burden

Background The systemic coagulation-inflammation index (SCI), calculated as platelet count × fibrinogen/white blood cell count, integrates coagulation-related and inflammatory components, but its value for coronary anatomical burden remains uncertain. We evaluated the association of SCI with Gensini-defined coronary anatomical burden and its incremental discrimination beyond conventional clinical variables and comparative performance relative to established inflammatory indices. Methods Eligible angiography records from a single center were retrospectively analyzed. SCI was evaluated in relation to continuous Gensini burden and high Gensini burden defined by the 75th and 80th percentiles, and was compared with NLR, SII, SIRI, PLR, and MLR. Multivariable regression, nonlinear spline analysis, multiplicity-adjusted comparisons, incremental-performance analysis, sensitivity analyses, and exploratory machine-learning models were applied. Results Higher SCI was associated with lower Gensini-defined anatomical burden in the primary multivariable model, although the relationship was not uniformly monotonic across analyses. For high Gensini burden at the 75th percentile, the adjusted OR per 1-SD increase in log-transformed SCI was 0.843 (95% CI 0.727–0.978). Adding SCI to the clinical base model increased AUC only from 0.764 to 0.766 ( Δ AUC 0.0019, 95% CI −0.0010 to 0.0083). Conventional inflammatory markers, particularly NLR, SII, and SIRI, showed larger estimated incremental gains. Machine-learning findings were exploratory and limited by the selected complete-feature sample and lack of external validation. Important limitations included unavailable ACS classification, uncertain timing of blood sampling relative to angiography and treatment, non-random exclusion of records with invalid or missing SCI components, and inability to verify unique-individual linkage. Conclusions SCI was associated with Gensini-defined coronary anatomical burden, but its incremental discriminatory value was limited and does not support routine clinical use. Prospective multicenter studies with standardized clinical characterization and external validation are warranted.

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Journal
Frontiers in Cardiovascular Medicine
Published
2026-11-02
DOI
https://doi.org/10.3389/fcvm.2026.1925478
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
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article
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article

Association and limited incremental discrimination of the systemic coagulation-inflammation index for Gensini-defined coronary anatomical burden

Zhi Xing, Shajidan Abudureyimu, Palida Abulaiti, Maolin Lv et al.
2 links
Frontiers in Cardiovascular Medicine
Inflammatory Biomarkers in Disease Prognosis
article

Association and limited incremental discrimination of the systemic coagulation-inflammation index for Gensini-defined coronary anatomical burden

Zhi Xing, Shajidan Abudureyimu, Palida Abulaiti, Maolin Lv, Palizhati Abudureyimu, Jing Shang, Zhuo Chen, Hui Li, Ying Gao
article en

Abstract

Background The systemic coagulation-inflammation index (SCI), calculated as platelet count × fibrinogen/white blood cell count, integrates coagulation-related and inflammatory components, but its value for coronary anatomical burden remains uncertain. We evaluated the association of SCI with Gensini-defined coronary anatomical burden and its incremental discrimination beyond conventional clinical variables and comparative performance relative to established inflammatory indices. Methods Eligible angiography records from a single center were retrospectively analyzed. SCI was evaluated in relation to continuous Gensini burden and high Gensini burden defined by the 75th and 80th percentiles, and was compared with NLR, SII, SIRI, PLR, and MLR. Multivariable regression, nonlinear spline analysis, multiplicity-adjusted comparisons, incremental-performance analysis, sensitivity analyses, and exploratory machine-learning models were applied. Results Higher SCI was associated with lower Gensini-defined anatomical burden in the primary multivariable model, although the relationship was not uniformly monotonic across analyses. For high Gensini burden at the 75th percentile, the adjusted OR per 1-SD increase in log-transformed SCI was 0.843 (95% CI 0.727–0.978). Adding SCI to the clinical base model increased AUC only from 0.764 to 0.766 ( Δ AUC 0.0019, 95% CI −0.0010 to 0.0083). Conventional inflammatory markers, particularly NLR, SII, and SIRI, showed larger estimated incremental gains. Machine-learning findings were exploratory and limited by the selected complete-feature sample and lack of external validation. Important limitations included unavailable ACS classification, uncertain timing of blood sampling relative to angiography and treatment, non-random exclusion of records with invalid or missing SCI components, and inability to verify unique-individual linkage. Conclusions SCI was associated with Gensini-defined coronary anatomical burden, but its incremental discriminatory value was limited and does not support routine clinical use. Prospective multicenter studies with standardized clinical characterization and external validation are warranted.

Frontiers in Cardiovascular MedicineVol. 13
Xinjiang Medical University (CN), First Affiliated Hospital of Xinjiang Medical University (CN)
Good health and well-being
Openalex Percentile: Top 17%
Inflammatory Biomarkers in Disease Prognosis
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