Fig. 2

Forest plots showing the characteristics of the logistic regression model indicating the predictive powers of respective logistic classifiers (characterized mainly by odds ratios - OR and its 95% confidence intervals - CI), estimating the GOS from explanatory binarized variables dichotomized by optimal cut-off points. On the ‘log(odds)’ or logit axis, the depicted intervals correspond to ln (95% CI) of estimated OR listed in Suppl. Table 2 with middle bold dots matching the ln (OR). OR represents the ratio of ‘odds of becoming GOS 4–5 if the predictor parameter < than the cut-off value’ / ‘odds of becoming GOS 4–5 if predictor parameter is > than the cut-off value. The further the OR is from zero, the higher the predictive power of the corresponding binarized explanatory variable. Variables in blue are considered statistically significant (p < 0.05), representing the identified predictors regarding our dataset analyzed by logistic regression. OR: odds ratio; CI: confidence interval; GOS hosp: Glasgow Outcome Scale at hospital discharge; GCS 3 M: Glasgow Outcome Scale three months after hospital discharge; STESS: Status Epilepticus Severity Score at admission; CFS: Clinical Frailty Scale; mFI-11: Modified 11-item Frailty index; GCS: Glasgow Coma Scale; BMI: body mass index; Alc_ab: alcohol abuse; Alc_tox: alcohol intoxication; Nic_ab: nicotine abuse; S100: serum protein S-100; TNT: serum high-sensitivity Troponin T; Na: serum natrium; K: serum potassium; Cl: serum chloride; Mg: serum magnesium; P: serum phosphorus; Ca: serum calcium; Alb: serum albumin; CRP: C-reactive protein; Lac: lactate; BE: base excess; Sosm: serum osmolality; OG: osmotic gap