Dataset Information

Development and validation of a seizure prediction model in critically ill children.

ABSTRACT: Electrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children.We developed a seizure prediction model using a retrospectively acquired multi-center database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG. We performed model validation using a separate prospectively acquired single center database. Predictor variables were chosen to be readily available to clinicians prior to the onset of CEEG and included: age, etiology category, clinical seizures prior to CEEG, initial EEG background category, and inter-ictal discharge category.The model has fair to good discrimination ability and overall performance. At the optimal cut-off point in the validation dataset, the model has a sensitivity of 59% and a specificity of 81%. Varied cut-off points could be chosen to optimize sensitivity or specificity depending on available CEEG resources.Despite inherent variability between centers, a model developed using multi-center CEEG data and few readily available variables could guide the use of limited CEEG resources when applied at a single center. Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients with seizures (but with more patients monitored) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but with failure to identify some patients with seizures).

SUBMITTER: Yang A

PROVIDER: S-EPMC4315714 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Development and validation of a seizure prediction model in critically ill children.

Yang Amy A Arndt Daniel H DH Berg Robert A RA Carpenter Jessica L JL Chapman Kevin E KE Dlugos Dennis J DJ Gallentine William B WB Giza Christopher C CC Goldstein Joshua L JL Hahn Cecil D CD Lerner Jason T JT Loddenkemper Tobias T Matsumoto Joyce H JH Nash Kendall B KB Payne Eric T ET Sánchez Fernández Iván I Shults Justine J Topjian Alexis A AA Williams Korwyn K Wusthoff Courtney J CJ Abend Nicholas S NS

Seizure 20141005

<h4>Purpose</h4>Electrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children.<h4>Method</h4>We developed a seizure prediction model using a retrospectively acquired multi-center database of children with a ...[more]

PMID: 25458097

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Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Project description:Seizures are common among critically ill children, but their relationship to outcome remains unclear. We sought to quantify the relationship between electrographic seizure burden and short-term neurological outcome, while controlling for diagnosis and illness severity. Furthermore, we sought to determine whether there is a seizure burden threshold above which there is an increased probability of neurological decline. We prospectively evaluated all infants and children admitted to our paediatric and cardiac intensive care units who underwent clinically ordered continuous video-electroencephalography monitoring over a 3-year period. Seizure burden was quantified by calculating the maximum percentage of any hour that was occupied by electrographic seizures. Outcome measures included neurological decline, defined as a worsening Paediatric Cerebral Performance Category score between hospital admission and discharge, and in-hospital mortality. Two hundred and fifty-nine subjects were evaluated (51% male) with a median age of 2.2 years (interquartile range: 0.3 days-9.7 years). The median duration of continuous video-electroencephalography monitoring was 37 h (interquartile range: 21-56 h). Seizures occurred in 93 subjects (36%, 95% confidence interval = 30-42%), with 23 (9%, 95% confidence interval = 5-12%) experiencing status epilepticus. Neurological decline was observed in 174 subjects (67%), who had a mean maximum seizure burden of 15.7% per hour, compared to 1.8% per hour for those without neurological decline (P < 0.0001). Above a maximum seizure burden threshold of 20% per hour (12 min), both the probability and magnitude of neurological decline rose sharply (P < 0.0001) across all diagnostic categories. On multivariable analysis adjusting for diagnosis and illness severity, the odds of neurological decline increased by 1.13 (95% confidence interval = 1.05-1.21, P = 0.0016) for every 1% increase in maximum hourly seizure burden. Seizure burden was not associated with mortality (odds ratio: 1.003, 95% confidence interval: 0.99-1.02, P = 0.613). We conclude that in this cohort of critically ill children, increasing seizure burden was independently associated with a greater probability and magnitude of neurological decline. Our observation that a seizure burden of more than 12 min in a given hour was strongly associated with neurological decline suggests that early antiepileptic drug management is warranted in this population, and identifies this seizure burden threshold as a potential therapeutic target. These findings support the hypothesis that electrographic seizures independently contribute to brain injury and worsen outcome. Our results motivate and inform the design of future studies to determine whether more aggressive seizure treatment can improve outcome.

Project description:OBJECTIVES:To derive and validate a score that correlates with an objective measurement of a child's effort of breathing. DESIGN:Secondary analysis of a previously conducted observational study. SETTING:The pediatric and cardiothoracic ICUs of a quaternary-care children's hospital. PATIENTS:Patients more than 37 weeks gestational age to age 18 years who were undergoing extubation. INTERVENTIONS:Effort of breathing was measured in patients following extubation using esophageal manometry to calculate pressure rate product. Simultaneously, members of a multidisciplinary team (nurse, physician, and respiratory therapist) assessed respiratory function using a previously validated tool. Elements of the tool that were significantly associated with pressure rate product in univariate analysis were identified and included in a multivariate model. An Effort of Breathing score was derived from the results of the model using data from half of the subjects (derivation cohort) and then validated using data from the remaining subjects (validation cohort) by calculating the area under the receiver operator characteristic curve for pressure rate product greater than 90th percentile and for the need for reintubation. MEASUREMENTS AND MAIN RESULTS:Among 409 subjects, the median age was 5 months, and nearly half were cardiac surgery patients (49.1%). Retractions, stridor, and pulsus paradoxus were included in the Simple Score. Area under the receiver operator characteristic curve for pressure rate product greater than 90th percentile was 0.8359 (95% CI, 0.7996-0.8722) in the derivation cohort and 0.7930 (0.7524-0.8337) in the validation cohort. Area under the receiver operator characteristic curve for reintubation was 0.7280 (0.6807-0.7752) when all scores were analyzed individually and was 0.7548 (0.6644-0.8452) if scores from three clinicians from different disciplines were summated. Results were similar regardless of provider discipline or training. CONCLUSIONS:A scoring system was derived and validated, performed acceptably to predict increased effort of breathing or need for advanced respiratory support and may function best when used by a team.

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Development and validation of a seizure prediction model in critically ill children.

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Development and validation of a seizure prediction model in critically ill children.

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