Modified Erasmus GBS Respiratory Insufficiency Score: a simplified clinical tool to predict the risk of mechanical ventilation in Guillain-Barré syndrome. Issue 4 (25th November 2022) - Luijten, Linda W G; Doets, Alex Y; Arends, Samuel; Dimachkie, Mazen M; Gorson, Kenneth C; Islam, Badrul; Kolb, Noah A; Kusunoki, Susumu; Papri, Nowshin; Waheed, Waqar; Walgaard, Christa; Yamagishi, Yuko; et al.
Abstract : Background: This study aimed to determine the clinical and diagnostic factors associated with mechanical ventilation (MV) in Guillain-Barré syndrome (GBS) and to simplify the existing Erasmus GBS Respiratory Insufficiency Score (EGRIS) for predicting the risk of MV. Methods: Data from the first 1500 patients included in the prospective International GBS Outcome Study (IGOS) were used. Patients were included across five continents. Patients <6 years and patients from Bangladesh were excluded. Univariable logistic and multivariable Cox regression were used to determine which prespecified clinical and diagnostic characteristics were associated with MV and to predict the risk of MV at multiple time points during disease course. Results: 1133 (76%) patients met the study criteria. Independent predictors of MV were a shorter time from onset of weakness until admission, the presence of bulbar palsy and weakness of neck flexion and hip flexion. The modified EGRIS (mEGRIS) was based on these factors and accurately predicts the risk of MV with an area under the curve (AUC) of 0.84 (0.80–0.88). We internally validated the model within the full IGOS cohort and within separate regional subgroups, which showed AUC values of 0.83 (0.81–0.88) and 0.85 (0.72–0.98), respectively. Conclusions: The mEGRIS is a simple and accurate tool for predicting the risk of MV in GBS. Compared with the original model, the mEGRIS requires less information for predictions with equal accuracy, can be used to predict MV at multiple time points and is also applicable in less severely affected patients and GBS variants. Model performance was consistent across different regions.