Shuaishuai Wu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shuaishuai Wu has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
- Hao Wu
Department of Neurosurgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan 030032, China
2 shared publications - Hongming Ji
The Neurosurgery Department of Shanxi Provincial People’s Hospital, Shanxi Medical University, China
2 shared publications - Linrui Qi
Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
2 shared publications - Ning Li
Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
2 shared publications - Rui Cheng
The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China
2 shared publications - Tengyue Fu
Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
2 shared publications
Latest Publications
- 26 September 2024
- Published by Elsevier in Diabetes Research and Clinical Practice
- Vol. 217, 111869
Association of glycemic variability and prognosis in patients with traumatic brain injury: A retrospective study from the MIMIC-IV database
- 26 September 2024
- Published by Elsevier in Diabetes Research and Clinical Practice
- Vol. 217, 111869
Association of glycemic variability and prognosis in patients with traumatic brain injury: A retrospective study from the MIMIC-IV database
- 21 November 2023
- Published by Springer Nature in Neurosurgical Review
- Vol. 46 (1) , 1-10
Construction and validation of a predictive model of pneumonia for ICU patients with traumatic brain injury (TBI)
The incidence of pneumonia in ICU patients with TBI is very high, seriously affecting the prognosis. This study aims to construct a predictive model for pneumonia in ICU patients with TBI and provide help for the prevention of TBI-related pneumonia. Clinical data of ICU patients with TBI were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and hospital data. Variables were screened by lasso and multivariate logistic regression to construct a predictive nomogram model, verified in internal validation cohort and external validation cohort by receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). A total of 1850 ICU patients with TBI were enrolled in the study from the MIMIC-IV database, including 1298 in the training cohort and 552 in internal validation cohort. The external validation cohort included 240 ICU patients with TBI from hospital data. Nine variables were selected from the training cohort by lasso regression and multivariate logistic regression, and a pneumonia prediction nomogram was constructed. This nomogram has a high discrimination in training, internal validation and external validation cohorts (AUC = 0.857, 0.877, 0.836). The calibration curve and DCA showed that this nomogram had a high calibration and better clinical decision-making efficiency. The nomogram showed excellent discrimination and clinical utility to predict pneumonia, and could identify pneumonia high-risk patients early, thus providing personalised treatment strategies for ICU patients with TBI.
- 21 November 2023
- Published by Springer Nature in Neurosurgical Review
- Vol. 46 (1) , 1-10
Construction and validation of a predictive model of pneumonia for ICU patients with traumatic brain injury (TBI)
The incidence of pneumonia in ICU patients with TBI is very high, seriously affecting the prognosis. This study aims to construct a predictive model for pneumonia in ICU patients with TBI and provide help for the prevention of TBI-related pneumonia. Clinical data of ICU patients with TBI were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and hospital data. Variables were screened by lasso and multivariate logistic regression to construct a predictive nomogram model, verified in internal validation cohort and external validation cohort by receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). A total of 1850 ICU patients with TBI were enrolled in the study from the MIMIC-IV database, including 1298 in the training cohort and 552 in internal validation cohort. The external validation cohort included 240 ICU patients with TBI from hospital data. Nine variables were selected from the training cohort by lasso regression and multivariate logistic regression, and a pneumonia prediction nomogram was constructed. This nomogram has a high discrimination in training, internal validation and external validation cohorts (AUC = 0.857, 0.877, 0.836). The calibration curve and DCA showed that this nomogram had a high calibration and better clinical decision-making efficiency. The nomogram showed excellent discrimination and clinical utility to predict pneumonia, and could identify pneumonia high-risk patients early, thus providing personalised treatment strategies for ICU patients with TBI.