Home Journal Watch Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery

Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery

buy indian accutane The Annals of Thoracic Surgery
Volume 111, Issue 6, June 2021, Pages 2041-2048

300 mg neurontin Maryam Y. Naim MD, MSCE, Mary Putt PhD, Nicholas S. Abend MD, MSCE, Christopher W. Mastropietro MD, Deborah U. Frank MD, Jonathan M. Chen MD, Stephanie Fuller MD, James J. Gangemi MD, William Gaynor MD, Kristin Heinan MD, Daniel J. Licht MD, Christopher E. Mascio MD, Shavonne Massey MD, MSCE, Mark E. Roeser MD, Clyde J. Smith MD, Stephen E. Kimmel MD, MSCE

Abstract

Background

where to buy antabuse in uk Electroencephalographic seizures (ESs) after neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates.

Methods

ES prediction models were developed and validated in a multicenter prospective cohort where all postoperative neonates who underwent cardiopulmonary bypass (CPB) also underwent CEEG.

Results

ESs occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single-ventricle defect, deep hypothermic circulatory arrest duration, cardiac arrest, nitric oxideextracorporeal membrane oxygenation, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic, 0.77; Hosmer-Lemeshow, P = .56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, P = .60); however, discrimination was poor (c-statistic, 0.61), and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. By using a cut point that emphasized negative predictive value in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) of neonates with ESs (negative predictive value, 99%; sensitivity, 97%).

Conclusions

In this large prospective cohort, a prediction model of ESs in neonates after CPB had good performance in the derivation cohort, with an NB in DCA. However, performance in the validation cohort was weak, with poor discrimination, poor calibration, and no NB in DCA. These findings support CEEG of all neonates after CPB.

Full Article

Commentary

Post-operative electroencephalographic seizures (ESs) are associated with worsening neurodevelopmental outcomes. The American Clinical Neurophysiology Society guidelines recommend continuous electroencephalographic monitoring (CEEG) which will have a huge strain on the resources1.  Naim and colleagueshave a reported a multicentre prospective study to develop a predictive tool for electroencephalographic seizures (ESs)wherein pre-operative and operative characteristics would predict the probability of post-operative ESs. However, the study could not develop the predictor models as the study excluded background CEEG variables, lack of routine neuroimaging to identify the peri-operative brain injury, inter-observer variability in interpreting the EEG and the need for key predictors to achieve higher levels of accuracy. This study does shed some light on how future prediction models should be designed. Early detection and appropriate management of EEG seizures may be a key factor in improving long-term neurologic outcomes in this high- risk cohort of patients.

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