120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
脓毒症患者新发心房颤动的影响因素及基于LASSO回归的列线图预测模型构建
Authors Tang J, Ou W, Han B, Wen W
Received 29 September 2025
Accepted for publication 16 January 2026
Published 22 January 2026 Volume 2026:19 565434
DOI https://doi.org/10.2147/IJGM.S565434
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Redoy Ranjan
JianYong Tang,1 WeiLiang Ou,1 BeiBei Han,1 Wen Wen2
1Department of Critical Care Medicine, Guangdong Medical University Affiliated Hospital, Zhanjiang, Guangdong, 524000, People’s Republic of China; 2Department of Arrhythmia Specialty, Guangdong Medical University Affiliated Hospital, Zhanjiang, Guangdong, 524000, People’s Republic of China
Correspondence: Wen Wen, Department of Arrhythmia Specialty, Guangdong Medical University Affiliated Hospital, No. 56, South Renmin Avenue, Xiashan District, Zhanjiang, Guangdong, 524000, People’s Republic of China, Tel +8613360709227, Email skdij34739845@sina.com
Objective: To investigate the influencing factors of new-onset atrial fibrillation (AF) in patients with sepsis and to construct a nomogram prediction model.
Methods: A retrospective analysis of 245 sepsis patients admitted to our hospital from March 2021 to March 2024 was used as the training set. An additional 107 sepsis patients admitted to our hospital from April 2024 to April 2025 were included as the validation set. The training and validation sets were divided into an AF group and a non-AF group based on the occurrence of new-onset AF.
Results: In the training set, there were significant differences between the two groups in terms of age, mechanical ventilation, APACHE II score, acute kidney injury, metabolic disorders, theophylline medication, TNF-α, E/e′, and NT-proBNP (P < 0.05). LASSO regression analysis was used to screen for 7 predictive factors. Logistic regression analysis identified age, mechanical ventilation, APACHE II score, theophylline medication, TNF-α, E/e′, and NT-proBNP as risk factors for new-onset AF in sepsis patients (P < 0.05). The area under the curve (AUC) of the ROC curve for the training set was 0.869, and the Hosmer-Lemeshow test yielded χ 2=7.346 (P=0.713). The decision curve analysis (DCA) showed that the model has high clinical application value when the threshold probability is between 0.10 and 0.89. For external validation, the AUC of the ROC curve was 0.875, the Hosmer-Lemeshow test yielded χ 2=6.992 (P=0.703), and the DCA curve showed that the model has high clinical application value when the threshold probability is between 0.12 and 0.83.
Conclusion: Age, mechanical ventilation, APACHE II score, theophylline medication, TNF-α, E/e′, and NT-proBNP are influencing factors for new-onset AF in patients with sepsis. The nomogram prediction model constructed on the basis of these factors has good clinical applicability.
Keywords: sepsis, atrial fibrillation, influencing factors, nomogram model
1Department of Critical Care Medicine, Guangdong Medical University Affiliated Hospital, Zhanjiang, Guangdong, 524000, People’s Republic of China; 2Department of Arrhythmia Specialty, Guangdong Medical University Affiliated Hospital, Zhanjiang, Guangdong, 524000, People’s Republic of China
Correspondence: Wen Wen, Department of Arrhythmia Specialty, Guangdong Medical University Affiliated Hospital, No. 56, South Renmin Avenue, Xiashan District, Zhanjiang, Guangdong, 524000, People’s Republic of China, Tel +8613360709227, Email skdij34739845@sina.com
Objective: To investigate the influencing factors of new-onset atrial fibrillation (AF) in patients with sepsis and to construct a nomogram prediction model.
Methods: A retrospective analysis of 245 sepsis patients admitted to our hospital from March 2021 to March 2024 was used as the training set. An additional 107 sepsis patients admitted to our hospital from April 2024 to April 2025 were included as the validation set. The training and validation sets were divided into an AF group and a non-AF group based on the occurrence of new-onset AF.
Results: In the training set, there were significant differences between the two groups in terms of age, mechanical ventilation, APACHE II score, acute kidney injury, metabolic disorders, theophylline medication, TNF-α, E/e′, and NT-proBNP (P < 0.05). LASSO regression analysis was used to screen for 7 predictive factors. Logistic regression analysis identified age, mechanical ventilation, APACHE II score, theophylline medication, TNF-α, E/e′, and NT-proBNP as risk factors for new-onset AF in sepsis patients (P < 0.05). The area under the curve (AUC) of the ROC curve for the training set was 0.869, and the Hosmer-Lemeshow test yielded χ 2=7.346 (P=0.713). The decision curve analysis (DCA) showed that the model has high clinical application value when the threshold probability is between 0.10 and 0.89. For external validation, the AUC of the ROC curve was 0.875, the Hosmer-Lemeshow test yielded χ 2=6.992 (P=0.703), and the DCA curve showed that the model has high clinical application value when the threshold probability is between 0.12 and 0.83.
Conclusion: Age, mechanical ventilation, APACHE II score, theophylline medication, TNF-α, E/e′, and NT-proBNP are influencing factors for new-onset AF in patients with sepsis. The nomogram prediction model constructed on the basis of these factors has good clinical applicability.
Keywords: sepsis, atrial fibrillation, influencing factors, nomogram model