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Towards a consensus-based classification of childhood arterial ischemic stroke.

ABSTRACT: The implementation of uniform nomenclature and classification in adult arterial ischemic stroke (AIS) has been critical for defining outcomes and recurrence risks according to etiology and in developing risk-stratified treatments. In contrast, current classification and nomenclature in childhood AIS are often overlapping or contradictory. Our purpose was to develop a comprehensive consensus-based classification system for childhood AIS.Using a modified-Delphi method, members of the International Pediatric Stroke Study (IPSS) developed the Childhood AIS Standardized Classification And Diagnostic Evaluation (CASCADE) criteria. Two groups of pediatric stroke specialists from the IPSS classified 7 test cases using 2 methods each: (1) classification typical of the individual clinician's current clinical practice; and (2) classification based on the CASCADE criteria. Group 1 underwent in-person training in the utilization of the CASCADE criteria. Group 2 classified the same cases via an online survey, including definitions but without training. Inter-rater reliability (IRR) was assessed via multi-rater unweighted ?-statistic.In Group 1 (with training), IRR was improved using CASCADE criteria (?=0.78, 95% CI=[0.49, 0.94]), compared with typical clinical practice (?=0.40, 95% CI=[0.11, 0.60]). In Group 2 (without training), IRR was lower than among trained raters (?=0.61, 95% CI=[0.29, 0.77]), but higher than current practice (?=0.23, 95% CI=[0.03, 0.36]).A new, consensus-based classification system for childhood AIS, the CASCADE criteria, can be used to classify cases with good IRR. These preliminary findings suggest that the CASCADE criteria may be particularity useful in the setting of prospective multicenter studies in childhood-onset AIS, where standardized training of investigators is feasible.

SUBMITTER: Bernard TJ

PROVIDER: S-EPMC3312781 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Towards a consensus-based classification of childhood arterial ischemic stroke.

Bernard Timothy J TJ Manco-Johnson Marilyn J MJ Lo Warren W MacKay Mark T MT Ganesan Vijeya V DeVeber Gabrielle G Goldenberg Neil A NA Armstrong-Wells Jennifer J Dowling Michael M MM Roach E Steve ES Tripputi Mark M Fullerton Heather J HJ Furie Karen L KL Benseler Susanne M SM Jordan Lori C LC Kirton Adam A Ichord Rebecca R

Stroke 20111208 2

<h4>Background and purpose</h4>The implementation of uniform nomenclature and classification in adult arterial ischemic stroke (AIS) has been critical for defining outcomes and recurrence risks according to etiology and in developing risk-stratified treatments. In contrast, current classification and nomenclature in childhood AIS are often overlapping or contradictory. Our purpose was to develop a comprehensive consensus-based classification system for childhood AIS.<h4>Methods</h4>Using a modif ...[more]

PMID: 22156694

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Project description:ImportanceChildhood arterial ischemic stroke (CAIS) affects approximately 1.6 per 100 000 children per year, while stroke recurs in up to 20% of patients at 5 years. Factors determining the risk of recurrence are incompletely understood.ObjectiveTo investigate the incidence of the recurrence of CAIS in the posterior and anterior circulations to determine if the risk differs between the 2 locations.Design, setting, and participantsA retrospective analysis of CAIS was conducted among children enrolled in a single-center prospective consecutive cohort at The Children's Hospital of Philadelphia between January 1, 2006, and January 1, 2015. Children with confirmed CAIS occurring between 29 days and 17.99 years were evaluated for inclusion. Patients were excluded if infarcts were located in both the anterior and posterior distributions or if CAIS occurred as a complication of intracranial surgery or brain tumor.Main outcomes and measuresStroke recurrence.ResultsThe study population included 107 patients (75 boys [70.1%] and 32 girls [29.9%]; median age at AIS, 7.7 years [interquartile range, 3.1-13.6 years]). Sixty-one children had anterior circulation CAIS (ACAIS) and 46 had posterior circulation CAIS (PCAIS). Median follow-up was 20.9 months (interquartile range, 8.7-40.4 months). For ACAIS, recurrence-free survival was 100% at 1 month and 96% (95% CI, 85%-99%) at 1 and 3 years. For PCAIS, recurrence-free survival was 88% (95% CI, 75%-95%) at 1 month and 81% (95% CI, 66%-90%) at 1 and 3 years. The hazard ratio for recurrence after PCAIS compared with ACAIS was 6.4 (95% CI, 1.4-29.8; P = .02) in univariable analysis and 5.3 (95% CI, 1.1-26.4; P = .04) after adjusting for sex and cervical dissection.Conclusions and relevanceWe identified a subgroup of patients that comprise more than 80% of recurrences of CAIS. Three years after incident stroke, 19% of children with PCAIS had a recurrence compared with 4% of patients with ACAIS. Different mechanisms of stroke may account for this difference. Children with PCAIS may warrant increased monitoring. This study highlights the necessity for further research focused on recurrence prevention.

Project description:Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Dataset Information

Towards a consensus-based classification of childhood arterial ischemic stroke.

Publications

Towards a consensus-based classification of childhood arterial ischemic stroke.

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