108139
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
老年脓毒症重症监护病房患者脓毒症相关性脑病动态列线图的开发与验证：一项回顾性队列研究
Received 11 March 2025
Accepted for publication 7 September 2025
Published 30 September 2025 Volume 2025:18 Pages 5977—5987
DOI https://doi.org/10.2147/IJGM.S527691
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Gauri Agarwal
Simeng Zhu, Lianmin Ye, Jie Chen
Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
Correspondence: Simeng Zhu, Email sm201907@163.com
Introduction: The study aimed to develop and validate a nomogram for predicting sepsis-associated encephalopathy (SAE) in elderly patients with sepsis admitted to the intensive care unit (ICU).
Methods: We conducted a retrospective study at the First Affiliated Hospital of Wenzhou Medical University. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify characteristic predictors for SAE, and a nomogram was subsequently developed. The nomogram’s performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, the Brier score, and decision curve analysis (DCA) to assess discrimination, calibration, and clinical utility. Internal validation was performed using the bootstrap resampling method.
Results: A total of 231 elderly sepsis patients were included in the study, among whom 66 were diagnosed with SAE. The study identified invasive mechanical ventilation (IMV), platelet count, white blood cell (WBC) count, glucose levels, lactate levels, and calcium levels as significant risk factors for SAE. The nomogram demonstrated an area under the curve (AUC) of 0.861, outperforming other predictive factors. The corrected C-index, determined through 500 bootstrap validations, was 0.842. Additionally, the calibration curve indicated strong agreement between predicted outcomes and actual observations. The Brier score of the prediction model was 0.139. Finally, DCA revealed that the nomogram had high clinical applicability.
Conclusion: The prediction nomogram and online website demonstrated strong predictive performance for the occurrence of SAE in elderly patients with sepsis, which made the evaluation process of SAE more convenient and efficient.
Keywords: sepsis-associated encephalopathy, nomogram, sepsis, elderly, intensive care unit, least absolute shrinkage and selection operator
Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
Correspondence: Simeng Zhu, Email sm201907@163.com
Introduction: The study aimed to develop and validate a nomogram for predicting sepsis-associated encephalopathy (SAE) in elderly patients with sepsis admitted to the intensive care unit (ICU).
Methods: We conducted a retrospective study at the First Affiliated Hospital of Wenzhou Medical University. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify characteristic predictors for SAE, and a nomogram was subsequently developed. The nomogram’s performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, the Brier score, and decision curve analysis (DCA) to assess discrimination, calibration, and clinical utility. Internal validation was performed using the bootstrap resampling method.
Results: A total of 231 elderly sepsis patients were included in the study, among whom 66 were diagnosed with SAE. The study identified invasive mechanical ventilation (IMV), platelet count, white blood cell (WBC) count, glucose levels, lactate levels, and calcium levels as significant risk factors for SAE. The nomogram demonstrated an area under the curve (AUC) of 0.861, outperforming other predictive factors. The corrected C-index, determined through 500 bootstrap validations, was 0.842. Additionally, the calibration curve indicated strong agreement between predicted outcomes and actual observations. The Brier score of the prediction model was 0.139. Finally, DCA revealed that the nomogram had high clinical applicability.
Conclusion: The prediction nomogram and online website demonstrated strong predictive performance for the occurrence of SAE in elderly patients with sepsis, which made the evaluation process of SAE more convenient and efficient.
Keywords: sepsis-associated encephalopathy, nomogram, sepsis, elderly, intensive care unit, least absolute shrinkage and selection operator