Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy.
Critical care
Encephalopathy
Malignant cancer
Nomogram
Sepsis
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 10 2024
14 10 2024
Historique:
received:
22
06
2024
accepted:
30
09
2024
medline:
15
10
2024
pubmed:
15
10
2024
entrez:
14
10
2024
Statut:
epublish
Résumé
Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model's discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings.
Identifiants
pubmed: 39402135
doi: 10.1038/s41598-024-74837-z
pii: 10.1038/s41598-024-74837-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23979Subventions
Organisme : Traditional Chinese Medicine Science and Technology Project of Zhejiang Province
ID : 2024ZL750
Organisme : Construction Fund of Medical Key Disciplines of Hangzhou
ID : OO20200485
Organisme : National Health Commission Scientific Research Fund/Zhejiang Province Key Medical and Health Science and Technology Program Project
ID : WKJ-ZJ-2315
Organisme : Science and Technology Development Project of Hangzhou
ID : 202204A10
Informations de copyright
© 2024. The Author(s).
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