Fig. 2.

Modified versions of Fig. 1 with exploratory alterations of the ERC/ESICM algorithm. Step 0 has been removed for clarity and is identical to Fig. 1. The figures a + b demonstrate how alterations of GCS-M as a screening criterion in Step 1 impact prognostic accuracy of the algorithm. In a, patients with day 4 GCS-M ≤ 3 are prognosticated further, and in b, patients are prognosticated irrespectable of GCS-M. In c, any ≥ 2 pathological findings in Steps 2 and 3 combined are considered indicative of poor outcome (as in the TTM2 and TAME Trials [39, 40], but we here used the ERC/ESICM definitions of pathological EEG [41] as stated in the methods section). d Represents the simplest model of multimodal prognostication, with Steps 2 and 3 combined (as in c), but without considering GCS-M in Step 1. Pathological findings were defined according to ERC/ESICM criteria [2] as described in the legend of Fig. 1 and in the methods section. True positive, TP; predicted and reported outcome poor (CPC3–5), True negative, TN; predicted and reported outcome good (CPC1–2), False negative, FN; predicted good and reported poor outcome. There were no false positive, FP, predictions of poor outcome in patients with reported good outcome. 95% confidence intervals (CI) were calculated with Wilson’s method