A nomogram prediction model for 30-day mortality risk in severe cerebral infarction patients integrating inflammation-related indicators and APACHE II score.
- 1Department of Intensive Care Medicine, Chongqing University Qianjiang Hospital, Chongqing, China.
- 2Department of Intensive Care Medicine, the First Affiliated Hospital of Changsha Medical University, Changsha, Hunan, China.
- 3Department of Neurology, Chongqing University Qianjiang Hospital, Chongqing, China.
Abstract
Objective
To explore independent risk factors for 30-day all-cause mortality in severe cerebral infarction and construct a nomogram prediction model incorporating inflammation-related indicators and APACHE II score.Methods
A total of 312 patients with severe cerebral infarction from Chongqing University Qianjiang Hospital and 134 external validation cases from the First Affiliated Hospital of Changsha Medical University were retrospectively analyzed. Demographic, comorbidity, laboratory, and APACHE II data were collected from all patients. The endpoint was 30-day all-cause mortality. Univariate and multivariate logistic regression were used to identify independent risk factors, and a nomogram was developed.Results
In this cohort, age distribution differed notably between the two subgroups: participants who did not survive had a higher mean age than those who survived. Regarding clinical and laboratory markers, non-survivors exhibited elevated NIHSS scores, NLR, hs-CRP, IL-6 levels, and APACHE II scores when compared with survivors, and the between-group differences were statistically significant (p < 0.05). After adjusting for confounding factors, multivariate logistic regression modeling further identified that NIHSS score, NLR, hs-CRP, IL-6, and APACHE II score were independent factors linked to fatal outcomes in cases of severe cerebral infarction (all p < 0.05). The ROC curve analysis showed that the combined use of the NIHSS, hs-CRP, IL-6, NLR, and APACHE II scores had a higher AUC value for predicting mortality in patients with severe cerebral infarction than individual tests (Z = 6.879, 6.454, 6.693, 5.480, 5.407, p < 0.05). Based on Logistic regression analysis, a prediction nomogram for the risk of 30-day mortality in patients with severe cerebral infarction was established. The nomogram included five variables, NIHSS score, NLR, hs-CRP, IL-6, and APACHE II score, and this model had high calibration ability and clinical practicability.Conclusion
NIHSS, NLR, hs-CRP, IL-6, and APACHE II are independent risk factors for 30-day mortality in patients with severe cerebral infarction. The integrated model offers excellent predictive performance, aiding early risk screening and personalizing treatment.
Related
- Comparative Associations of Inflammation and Nutrition-Based Indices with No-Reflow and Mortality After Primary PCI for ST-Elevation Myocardial Infarction
- Nomogram models integrating multiregion radiomics, quantitative computed tomography, and clinical indicators for distinguishing connective tissue disease-associated interstitial lung disease from idiopathic pulmonary fibrosis and improving the prediction of gender-age-physiology stage
- Integrating Genotype With Clinical Indicators Improves Venous Thromboembolism Risk Prediction in Cancer Patients: A Retrospective Cohort Study From Xinjiang
- Mortality risk stratification in contemporary myocardial infarction-related cardiogenic shock: development of AMICS-ADMIT prediction model and benchmarking of established scores.
- Mortality prediction in patients with acute myocardial infarction and prior ischemic stroke: a prospective cohort study with explainable machine learning and SHAP analysis
BibTeX
@article{shu2026nomogram,
title = {{A nomogram prediction model for 30-day mortality risk in severe cerebral infarction patients integrating inflammation-related indicators and APACHE II score.}},
author = {Zhou Shu and Fengbo Fu and Jinyi Tian},
journal = {Frontiers in neurology},
year = {2026},
month = sep,
doi = {10.3389/fneur.2026.1918006},
url = {https://europepmc.org/article/MED/42845558},
note = {CC BY},
}
RIS
TY - JOUR
TI - A nomogram prediction model for 30-day mortality risk in severe cerebral infarction patients integrating inflammation-related indicators and APACHE II score.
AU - Zhou Shu
AU - Fengbo Fu
AU - Jinyi Tian
PY - 2026
DA - 2026/09/23
JO - Frontiers in neurology
DO - 10.3389/fneur.2026.1918006
UR - https://europepmc.org/article/MED/42845558
AB - Objective To explore independent risk factors for 30-day all-cause mortality in severe cerebral infarction and construct a nomogram prediction model incorporating inflammation-related indicators and APACHE II score.Methods A total of 312 patients with severe cerebral infarction from Chongqing University Qianjiang Hospital and 134 external validation cases from the First Affiliated Hospital of Changsha Medical University were retrospectively analyzed. Demographic, comorbidity, laboratory, and APACHE II data were collected from all patients. The endpoint was 30-day all-cause mortality. Univariate and multivariate logistic regression were used to identify independent risk factors, and a nomogram was developed.Results In this cohort, age distribution differed notably between the two subgroups: participants who did not survive had a higher mean age than those who survived. Regarding clinical and laboratory markers, non-survivors exhibited elevated NIHSS scores, NLR, hs-CRP, IL-6 levels, and APACHE II scores when compared with survivors, and the between-group differences were statistically significant (p < 0.05). After adjusting for confounding factors, multivariate logistic regression modeling further identified that NIHSS score, NLR, hs-CRP, IL-6, and APACHE II score were independent factors linked to fatal outcomes in cases of severe cerebral infarction (all p < 0.05). The ROC curve analysis showed that the combined use of the NIHSS, hs-CRP, IL-6, NLR, and APACHE II scores had a higher AUC value for predicting mortality in patients with severe cerebral infarction than individual tests (Z = 6.879, 6.454, 6.693, 5.480, 5.407, p < 0.05). Based on Logistic regression analysis, a prediction nomogram for the risk of 30-day mortality in patients with severe cerebral infarction was established. The nomogram included five variables, NIHSS score, NLR, hs-CRP, IL-6, and APACHE II score, and this model had high calibration ability and clinical practicability.Conclusion NIHSS, NLR, hs-CRP, IL-6, and APACHE II are independent risk factors for 30-day mortality in patients with severe cerebral infarction. The integrated model offers excellent predictive performance, aiding early risk screening and personalizing treatment.
SN - 1664-2295
ER -