Table 3.

Estimates of coefficients (β) and standard errors (SE) after applying three approaches for selecting the relevant variables to discriminate patients with mild vs severe Covid-19 course. The initial set of variables consisted of the variables included in the multivariable logistic regression model and all the analyses were performed on the training dataset (N=2696). For Model 1 and Model 2, stepwise and lasso regressions with 10-fold cross-validation were respectively used as selection approaches, followed by 500 bootstrap replications and, additionally, lasso penalized coefficients have been shown; Model 3 consisted of Bayesian model averaging (BMA) and variables were selected based on the posterior inclusion probability (PIP≥0.7).

	Model 1, Stepwise		Model 2, Lasso			Model 3, BMA
General characteristics	β	SE	Penalized β	β	SE	β	SE	PIP
Age	0.04	0.01	0.04	0.04	0.01	0.04	0.01	1.00
Male	0.42	0.11	0.41	0.42	0.11	0.41	0.13	0.97
Country
Turkey	1.01	0.12	0.99	1.00	0.12	1.02	0.13	1.00
South America	1.33	0.36	1.32	1.33	0.36	1.34	0.34	0.99
Healthcare Job	x	x	x	x	x	x	x	0.06
Current of former Smoker	x	x	x	x	x	x	x	0.05
BMI	x	x	0.01	0.01	0.01	x	x	0.09
Presence of comorbidities	0.78	0.14	0.76	0.76	0.15	0.78	0.15	1.00
MS related characteristics
EDSS	0.12	0.03	0.11	0.11	0.03	0.13	0.04	0.98
Methylprednisolone 1 month before Covid	0.84	0.32	0.83	0.83	0.32	x	x	0.62
Treatment
Interferon	x	x	-0.31	-0.34	0.20	x	x	0.21
Anti-CD20	0.46	0.16	0.42	0.42	0.16	0.36	0.22	0.81