ArticleNeurosciences (Riyadh, Saudi Arabia)2021
Derivation and validation of the CANP scoring model for predicting the neurological outcome in post-cardiac arrest patients.
Article in Neurosciences (Riyadh, Saudi Arabia), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo establish and validate a prognostic scoring model in a Chinese population to predict the neurological outcome among comatose survivors of cardiac arrest (CA).
methods159 CA patients between January 2016 and November 2020 were recruited in this retrospective study. In the derivation cohort, prognostic factors available from arrest circumstances and early in-hospital indicators were measured. The Cardiac Arrest Neurological Prognosis (CANP) score was then constructed to predict unfavorable outcomes at 30 days after CA. The assessment of predictive effectiveness of this scoring model was conducted in both derivation and validation cohorts.
resultsWitnessed status, bystander cardiopulmonary resuscitation, initial rhythm, duration of resuscitation, Glasgow Coma Scale motor score, pupillary/corneal reflex, gray-white matter ratio and neuron-specific enolase exhibit strong correlations with the neurological outcomes in the derivation cohort (all
conclusionThe CANP score was established and validated for predicting the possible neurological prognosis in comatose post-CA survivors.
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