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Peer-reviewedCC BY 4.0Frontiers in Neurology

Clinical severity predominates over routine inflammatory and metabolic biomarkers in predicting hemorrhagic transformation after intravenous thrombolysis: internal temporal validation of incremental biomarker value

  • Huan Liang1
  • Baiyu Zeng2
  • Jingquan Wang3
  • Lizhen Yi1
  • Linxing Huang4
  • Ziyan Lin1
  • Cai Zhou3
  • Zenghui Yue1
4 affiliations
  1. 1College of Acupuncture-Moxibustion, Tuina and Rehabilitation, Hunan University of Chinese Medicine
  2. 2Teaching and Research Office of TCM Internal Medicine, Hunan Traditional Chinese Medical College
  3. 3Changsha Central Hospital
  4. 4School of Physical Education, Hunan University of Arts and Science
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Abstract

Background and Objective: Hemorrhagic transformation (HT) remains an important safety concern after intravenous thrombolysis for acute ischemic stroke. We evaluated whether routine inflammatory and metabolic biomarkers improve HT prediction beyond clinical features.

Methods: We analyzed 847 thrombolysis-treated admissions from 2020 to 2026. In-hospital imaging-confirmed HT was the primary outcome, and strict 7-day ECASS II-compatible symptomatic intracranial hemorrhage (sICH) was exploratory. Admission date defined derivation ( n = 593; 67 HT events) and internal temporal-validation ( n = 254; 27 HT events) cohorts. The six-variable clinical model included age, baseline NIHSS, atrial fibrillation, large-vessel occlusion, the rapid-glucose field, and onset-to-treatment time. Models added ln(NLR), AIP plus residual cholesterol, or all three biomarkers. Twenty imputed datasets were generated using derivation-fitted imputation models. We assessed discrimination, calibration, Brier score, decision curves, and sensitivity analyses.

Results: HT occurred during 94 of 847 admissions (11.1%). The clinical model yielded AUCs of 0.812 (95% CI 0.761–0.860) in derivation and 0.801 (0.707–0.881) in temporal validation. Adding ln(NLR) increased the validation AUC to 0.818 (ΔAUC +0.017; DeLong p = 0.220). Adding AIP and residual cholesterol yielded an AUC of 0.803 (ΔAUC +0.002; p = 0.818). The full model yielded AUCs of 0.817 in derivation and 0.818 in validation (ΔAUC +0.018; p = 0.346). Baseline NIHSS was independently associated with HT (OR 1.084 per point, 95% CI 1.044–1.126; p < 0.001). Atrial fibrillation was independently associated with HT (OR 2.66, 95% CI 1.34–5.27; p = 0.005). The biomarkers ln(NLR), AIP, and residual cholesterol were not independently associated with HT. Validation calibration was similar, with a Brier score of 0.081 for both models. Biomarker gains were inconsistent across sensitivity analyses. In the exploratory sICH analysis (37 events), ln(NLR) was associated with sICH (OR 1.89, 1.04–3.42; p = 0.037). Validation included only 12 events, and the AUC increment was not statistically significant ( p = 0.097).

Conclusion: Clinical severity and established clinical features provided most of the predictive information for post-thrombolysis HT. Routine ln(NLR), AIP, and residual cholesterol did not provide sufficiently robust incremental predictive value beyond the clinical model. The NLR–sICH signal remains exploratory and requires external validation.

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Cite

BibTeXRIS
BibTeX
@article{liang2026clinical,
  title = {{Clinical severity predominates over routine inflammatory and metabolic biomarkers in predicting hemorrhagic transformation after intravenous thrombolysis: internal temporal validation of incremental biomarker value}},
  author = {Huan Liang and Baiyu Zeng and Jingquan Wang and Lizhen Yi and Linxing Huang and Ziyan Lin and Cai Zhou and Zenghui Yue},
  journal = {Frontiers in Neurology},
  year = {2026},
  month = oct,
  doi = {10.3389/fneur.2026.1940110},
  url = {https://www.frontiersin.org/articles/10.3389/fneur.2026.1940110/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Clinical severity predominates over routine inflammatory and metabolic biomarkers in predicting hemorrhagic transformation after intravenous thrombolysis: internal temporal validation of incremental biomarker value
AU  - Huan Liang
AU  - Baiyu Zeng
AU  - Jingquan Wang
AU  - Lizhen Yi
AU  - Linxing Huang
AU  - Ziyan Lin
AU  - Cai Zhou
AU  - Zenghui Yue
PY  - 2026
DA  - 2026/10/21
JO  - Frontiers in Neurology
DO  - 10.3389/fneur.2026.1940110
UR  - https://www.frontiersin.org/articles/10.3389/fneur.2026.1940110/full
AB  - Background and Objective: Hemorrhagic transformation (HT) remains an important safety concern after intravenous thrombolysis for acute ischemic stroke. We evaluated whether routine inflammatory and metabolic biomarkers improve HT prediction beyond clinical features.  Methods: We analyzed 847 thrombolysis-treated admissions from 2020 to 2026. In-hospital imaging-confirmed HT was the primary outcome, and strict 7-day ECASS II-compatible symptomatic intracranial hemorrhage (sICH) was exploratory. Admission date defined derivation ( n = 593; 67 HT events) and internal temporal-validation ( n = 254; 27 HT events) cohorts. The six-variable clinical model included age, baseline NIHSS, atrial fibrillation, large-vessel occlusion, the rapid-glucose field, and onset-to-treatment time. Models added ln(NLR), AIP plus residual cholesterol, or all three biomarkers. Twenty imputed datasets were generated using derivation-fitted imputation models. We assessed discrimination, calibration, Brier score, decision curves, and sensitivity analyses.  Results: HT occurred during 94 of 847 admissions (11.1%). The clinical model yielded AUCs of 0.812 (95% CI 0.761–0.860) in derivation and 0.801 (0.707–0.881) in temporal validation. Adding ln(NLR) increased the validation AUC to 0.818 (ΔAUC +0.017; DeLong p = 0.220). Adding AIP and residual cholesterol yielded an AUC of 0.803 (ΔAUC +0.002; p = 0.818). The full model yielded AUCs of 0.817 in derivation and 0.818 in validation (ΔAUC +0.018; p = 0.346). Baseline NIHSS was independently associated with HT (OR 1.084 per point, 95% CI 1.044–1.126; p < 0.001). Atrial fibrillation was independently associated with HT (OR 2.66, 95% CI 1.34–5.27; p = 0.005). The biomarkers ln(NLR), AIP, and residual cholesterol were not independently associated with HT. Validation calibration was similar, with a Brier score of 0.081 for both models. Biomarker gains were inconsistent across sensitivity analyses. In the exploratory sICH analysis (37 events), ln(NLR) was associated with sICH (OR 1.89, 1.04–3.42; p = 0.037). Validation included only 12 events, and the AUC increment was not statistically significant ( p = 0.097).  Conclusion: Clinical severity and established clinical features provided most of the predictive information for post-thrombolysis HT. Routine ln(NLR), AIP, and residual cholesterol did not provide sufficiently robust incremental predictive value beyond the clinical model. The NLR–sICH signal remains exploratory and requires external validation.
SN  - 1664-2295
ER  -