Publications by authors named "M de Hoog"
Objectives: To determine if a priori standardization of outcome hemostatic definitions alone was adequate to enable useful comparison between two cohorts of pediatric extracorporeal membrane oxygenation (ECMO) patients, managed according to local practice and protocol.
Design: Comparison of two separate prospective cohort studies performed at different centers with standardized outcome definitions agreed upon a priori.
Setting: General and cardiac PICUs at the Royal Children's Hospital (RCH) in Melbourne, Australia, and the Sophia Children's Hospital (SCH) in Rotterdam, The Netherlands.
This study aimed to develop an open-source algorithm for the pressure-reactivity index (PRx) to monitor cerebral autoregulation (CA) in pediatric severe traumatic brain injury (sTBI) and compared derived optimal cerebral perfusion pressure (CPPopt) with real-time CPP in relation to long-term outcome. Retrospective study in children (< 18 years) with sTBI admitted to the pediatric intensive care unit (PICU) for intracranial pressure (ICP) monitoring between 2016 and 2023. ICP was analyzed on an insult basis and correlated with outcome.
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- - This study focused on analyzing the relationship between cumulative exposures of oxygen (PaO) and carbon dioxide (PaCO) in children who had a return of circulation after cardiac arrest, looking specifically at their survival rates and neurological outcomes within 24 hours post-event.
- - Data were collected from pediatric resuscitation sites and included children aged 1 day to 17 years, with a total of 292 participants. The study excluded cases with congenital cyanotic heart disease.
- - Results showed that while 57% of the children survived to discharge and 48% had favorable neurological outcomes, the cumulative PaO and PaCO exposure was not significantly related to these outcomes; only 24% and 58% of patients adhered to AHA
Article Synopsis
- The study explored the potential of supervised machine learning applied to ECG data for real-time sleep monitoring in pediatric intensive care, which is currently not available.
- Researchers analyzed polysomnography recordings from 90 non-critically ill children, developing various machine learning models to classify sleep states based on derived features from the ECG data.
- Results showed that the models achieved moderate to good accuracy, especially in classifying two and three sleep states, with the XGBoost model performing best overall, highlighting the method's promise for bedside use.
Background: The implementation of the approved respiratory syncytial virus (RSV) preventive interventions in immunisation programmes is advancing rapidly. Insight into healthcare costs of RSV-related paediatric intensive care unit (PICU) admissions is lacking, but of great importance to evaluate the impact of implementation. Therefore, this study aimed to determine the total annual RSV-related paediatric intensive care healthcare costs in the Netherlands.
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