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generalized linear model with stepwise feature selection.

Risk factors associated with ICU mortality were analyzed using Cox proportional hazards regression model with stepwise selection procedure.

Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination.

Fig. 2 Prognostic model with stepwise inclusion of consecutive hemodynamic variables: mean arterial pressure (MAP) in mmHg, heart rate (HR) in bpm, (arterial) lactate in mmol/L and Microvascular Flow Index (MFI) in AU.

However, a model with stepwise inclusion of mean arterial pressure < 65 mmHg, HR ≥ 90 bpm, lactate > 2 mmol/L and MFI < 2.6 did not detect significant differences in ICU mortality.

For multivariate analyses a logistic regression model with stepwise forward variable selection was employed.

One variant is designed to perform raytracing in models with stepwise distributions of interval velocity while the other is better suited for continuous velocity models.

In order to determine the best fitting model for each country, we fitted regression models with stepwise in-/exclusion of the variables according to specific model parameters (probability of F for entry = 0.05 and of 0.1 for removal).

For Romberg tests, three models with stepwise entry of covariates were tested.

Logistic regression models with stepwise forward selection were used to explore possible predictors of maintenance of treatment and relapse.

We found that ESR1 was only a borderline significant predictor of MD in models with stepwise exclusion of covariates.