Validation of an algorithm of time-dependent electro-clinical risk stratification for electrographic seizures (TERSE) in critically ill patients

Abstract

h i g h l i g h t sElectroclinical risk stratification identifies 95% of acutely ill patients with electrographic seizures. Electroclinical risk stratification reduces continuous EEG monitoring recording time by 67%. Accounting for evolving brain injury will be required to further improve prediction accuracy. a b s t r a c tObjective: The clinical implementation of continuous electroencephalography (CEEG) monitoring in critically ill patients is hampered by the substantial burden of work that it entails for clinical neurophysiologists. Solutions that might reduce this burden, including by shortening the duration of EEG to be recorded, would help its widespread adoption. Our aim was to validate a recently described algorithm of time-dependent electro-clinical risk stratification for electrographic seizure (ESz) (TERSE) based on simple clinical and EEG features. Methods: We retrospectively reviewed the medical records and EEG recordings of consecutive patients undergoing CEEG between October 1, 2015 and September, 30 2016 and assessed the sensitivity of TERSE for seizure detection, as well as the reduction in EEG time needed to be reviewed. Results: In a cohort of 407 patients and compared to full CEEG review, the model allowed the detection of 95% of patients with ESz and 97% of those with electrographic status epilepticus. The amount of CEEG to be recorded to detect ESz was reduced by two-thirds, compared to the duration of CEEG taht was actually recorded. Conclusions: TERSE allowed accurate time-dependent ESz risk stratification with a high sensitivity for ESz detection, which could substantially reduce the amount of CEEG to be recorded and reviewed, if applied prospectively in clinical practice. Significance: Time-dependent electro-clinical risk stratification, such as TERSE, could allow more efficient practice of CEEG and its more widespread adoption. Future studies should aim to improve risk stratification in the subgroup of patients with acute brain injury and absence of clinical seizures.

Early Neurologic Recovery, Practice Pattern Variation, and the Risk of Endotracheal Intubation Following Established Status Epilepticus

“…37 These tools allow emergency practitioners to differentiate patients with depressed level of consciousness who are intubated for airway protection from those intubated for continued seizure activity by directly measuring the neurophysiologic response to treatment. 38 In future clinical trials, novel endpoints incorporating an ordinal “desirability of outcome” ranking 39 may allow testing the tradeoff between seizure cessation and intubation related to lack of clinical or neurophysiologic improvement. These and other strategies may more precisely define the efficacy of seizure cessation by identifying more biologically based surrogates of clinical outcome.…”

Recent advances in clinical electroencephalography

“…Therefore, depending on resource availability and in an effort to optimize costs, it seems reasonable to apply the recently validated 2HELPS2B score based on the ACNS nomenclature for risk stratification of subsequent seizures/SE occurrence [60 ▪ ]. Furthermore, the validated TERSE algorithm, considering consciousness level, seizure occurrence, and detection of EEG features related to seizures/status epilepticus risk (epileptiform discharges, lateralized periodic discharges or rhythmic delta, brief potentially-ictal rhythmic discharges) may orient on the optimal EEG duration and help reducing the recording time by two-third [61].…”

Non‐convulsive seizures and non‐convulsive status epilepticus in neuro‐intensive care unit

“…If a patient had both a history of seizure and was comatose with an EEG risk factor, then for at least 48 h of cEEG monitoring is reasonable 42 . This time‐dependent ESz risk stratification model can identify 95% of patients with ESz, while at the same time reducing the duration of required cEEG by two‐thirds 43 . In 2017, based on a prospective multicenter database, the 2HELPS2B score (Table 2) was created to estimate the seizure risk in acutely hospitalized patients receiving cEEG 44 .…”

“…ESz risk stratification model can identify 95% of patients with ESz, while at the same time reducing the duration of required cEEG by two-thirds. 43 In 2017, based on a prospective multicenter database, the 2HELPS2B score (Table 2) was created to estimate the seizure risk in acutely hospitalized patients receiving cEEG. 44 According to the 2HELPS2B score calculated from the first hour of cEEG results, patients can be stratified into 3 categories: low-(2HELPS2B = 0), medium-(2HELPS2B = 1), and high-risk (2HELPS2B ≥2) risk.…”