A nomogram to predict mechanical ventilation in Guillain-Barré syndrome patients.

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.