A nomogram to predict mechanical ventilation in Guillain‐Barré syndrome patients. (23rd June 2020)
- Record Type:
- Journal Article
- Title:
- A nomogram to predict mechanical ventilation in Guillain‐Barré syndrome patients. (23rd June 2020)
- Main Title:
- A nomogram to predict mechanical ventilation in Guillain‐Barré syndrome patients
- Authors:
- Ning, Pingping
Yang, Baiyuan
Yang, Xinglong
Zhao, Quanzhen
Huang, Hongyan
Shen, Qiuyan
Lu, Haitao
Tian, Sijia
Xu, Yanming - Abstract:
- Abstract : Introduction: Guillain‐Barré syndrome (GBS) is one of the most common causes of acute flaccid paralysis, with up to 20%‐30% of patients requiring mechanical ventilation. The aim of our study was to develop and validate a mechanical ventilation risk nomogram in a Chinese population of patients with GBS. Methods: A total of 312 GBS patients were recruited from January 1, 2015, to June 31, 2018, of whom 17% received mechanical ventilation. The least absolute shrinkage and selection operator (LASSO) regression model was used to select clinicodemographic characteristics and blood markers that were then incorporated, using multivariate logistic regression, into a risk model to predict the need for mechanical ventilation. The model was characterized and assessed using the C‐index, calibration plot, and decision curve analysis. The model was validated using bootstrap resampling in a prospective study of 114 patients recruited from July 1, 2018, to July 10, 2019. Results: The predictive model included hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and neutrophil/lymphocyte ratio (NLR). The model showed good discrimination with a C‐index value of 0.938 and good calibration. A high C‐index value of 0.856 was reached in the validation group. Decision curve analysis demonstrated the clinical utility of the mechanical ventilation nomogram. Conclusions: A nomogram incorporating hospital stay, glossopharyngeal andAbstract : Introduction: Guillain‐Barré syndrome (GBS) is one of the most common causes of acute flaccid paralysis, with up to 20%‐30% of patients requiring mechanical ventilation. The aim of our study was to develop and validate a mechanical ventilation risk nomogram in a Chinese population of patients with GBS. Methods: A total of 312 GBS patients were recruited from January 1, 2015, to June 31, 2018, of whom 17% received mechanical ventilation. The least absolute shrinkage and selection operator (LASSO) regression model was used to select clinicodemographic characteristics and blood markers that were then incorporated, using multivariate logistic regression, into a risk model to predict the need for mechanical ventilation. The model was characterized and assessed using the C‐index, calibration plot, and decision curve analysis. The model was validated using bootstrap resampling in a prospective study of 114 patients recruited from July 1, 2018, to July 10, 2019. Results: The predictive model included hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and neutrophil/lymphocyte ratio (NLR). The model showed good discrimination with a C‐index value of 0.938 and good calibration. A high C‐index value of 0.856 was reached in the validation group. Decision curve analysis demonstrated the clinical utility of the mechanical ventilation nomogram. Conclusions: A nomogram incorporating hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and NLR may reliably predict the probability of requiring mechanical ventilation in GBS patients. … (more)
- Is Part Of:
- Acta neurologica Scandinavica. Volume 142:Number 5(2020)
- Journal:
- Acta neurologica Scandinavica
- Issue:
- Volume 142:Number 5(2020)
- Issue Display:
- Volume 142, Issue 5 (2020)
- Year:
- 2020
- Volume:
- 142
- Issue:
- 5
- Issue Sort Value:
- 2020-0142-0005-0000
- Page Start:
- 466
- Page End:
- 474
- Publication Date:
- 2020-06-23
- Subjects:
- Guillain‐Barré syndrome -- mechanical ventilation -- nomogram -- predicting model
Neurology -- Periodicals
616.8 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1111/ane.13294 ↗
- Languages:
- English
- ISSNs:
- 0001-6314
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0639.910000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 20797.xml