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NCS Twitter Journal Club Round-Up #NCSTJC: April 2022

By Currents Editor posted 06-10-2022 11:00

  

Nicolas Chiriboga, MD (@NChiriboga); Eric C. Lawson, MD (@EricLawson90)



Moderator: @EricLawson90
Authors: @RebeccaJLevyMD, @ewmayne
Link to Twitter Thread: https://twitter.com/neurocritical/status/1513880138328457228?s=21&t=gpKicZn6wmBOKzVi2UCg4Q 

April’s NCS Twitter Journal Club featured a lively discussion of the article Seizure Risk in Infants After Bypass Without Hypothermic Arrest by Rebecca Levy et al. This was a single-center, observational cohort study that aimed to characterize the risk of seizures in infants with congenital heart disease undergoing cardiopulmonary bypass for surgery and to develop a predictive model for seizure risk in this population. This study differed from others previously published on the matter due to the absence of deep hypothermic cardiac arrest (DHCA) in the patient population and the evolution of surgical and anesthetic technique. The study institution had a post-operative protocol for infants under 3 months of age undergoing cardiopulmonary bypass that included 48 hours of EEG. All patients in this age group that underwent CPB and post-operative EEG monitoring were included. The study results showed 12/112 (10.7%) infants undergoing cardiopulmonary bypass went on to develop seizures in the immediate post-operative period. Median time to first seizure was 28.1 hours. The random forest model utilized to predict seizure risk had an accuracy of 90.2% and all infants who had seizures were correctly classified (sensitivity of 100%, specificity of 80.9%, AUC 0.96). Authors concluded infants undergoing cardiopulmonary bypass remain at a significant risk for seizures despite the absence of DHCA. They also concluded that utilizing the novel predictive model developed for this study can be helpful in stratifying the risk for seizure development. 

The conversation that ensued in the Twitterverse was highly informative. We will review some of the key discussions ahead. 


The poll found a close match between practices of the audience members with about 30% each for EEG for 24-48 hours for all patients, just for those at high risk, and for a case-by-case basis. Some respondents such as @tchaaban1 felt like an incidence of 10.7% justified the use of a finite resource, like EEG monitoring, in all patients. Other respondents pointed to institutional protocols like that at @luriechildrens that mandate EEG monitoring for all infants undergoing CPB. 

Question 2 led to the most lively discussion of the day. Moderator @EricLawson90 opened the discussion by asking “With seizure incidence of 10.7% in this population and similar ranges found in the literature it seems prudent to identify ways to stratify EEG use in this pop. [sic] Understanding limitations of staffing, equipment etc.” 

The respondents all agreed that there was a need to utilize tools to stratify risk in resource limited settings in which EEG might not be available for a large number of patients. The author for the article @RebeccaLevyMD chimed in with a suggestion of utilizing neuroimaging as a stratifying tool. The @JUHNeuroCrit made an important point about most of the seizures in the study being electrographic, making EEG monitoring and risk stratification ever more important. Additionally, 12/14 characteristics in the predictive tool that was developed as a result of this study would be obtainable before EEG, making this an invaluable tool for clinicians when deciding which patients to connect to EEG. 

Question 3 asked participants about risk factors associated with seizures. In the study, neuromuscular blockade was the lead risk factor associated with seizure risk. 

@EricLawson90 postulated to the author that post-operative and not pre-operative imaging findings were predictive of seizures. @EricaLevyMD agreed that this opens up for prospective studies looking to unbundle what she very accurately called a chicken and egg situation; did these patients have brain injury leading to seizures? Other respondents also ascertained that other patient characteristics like pre op mechanical ventilation and hemodynamic instability (some of these are included in the authors’ predictive model) have, in their experience, been associated with seizure risk given their association with brain injury. 

Question 4 followed up on the question that @EricLawson90 had postulated to the author about abnormal post op neuroimaging.  

@Tchaaban1 opened the discussion by questioning whether the same perioperative events leading to abnormal imaging could have led to seizures. The author @EricaLevyMD stated that the study found abnormal post op imaging as an independent risk factor, likely related to brain injury and that further prospective studies would be warranted to further understand imaging as a predictor or whether its role is to serve as a marker of brain injury which would predict seizure risk. @JUHNeuroCrit again made an important point about acute brain injury playing an important role in this population and the importance of EEG monitoring regardless of imaging findings. 

The April edition of #NCSTJ led to an important discussion on the high risk of seizures related to CPB in infants with CHD. The main limitation that many institutions face is the limited availability of EEG and the painstaking job of monitoring EEGs in a large population of patients. With a significant incidence of seizures in this population, is a one size fits all (all patients get monitored) approach the right answer? Or would a risk stratification tool like the one presented in this article provide guidance to the clinical team to better allocate limited resources? These are all important questions that can only be answered by further research on this matter. 

Access the full article here: https://link.springer.com/article/10.1007/s12028-021-01313-1

#NCSRoundup 

 

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