Featured Publications
Impact of Timing of Preprocedural Opioids on Adverse Events in Procedural Sedation
Bhatt M, Cheng W, Roback MG, Johnson DW, Taljaard M, Canada T. Impact of Timing of Preprocedural Opioids on Adverse Events in Procedural Sedation. Academic Emergency Medicine 2020, 27: 217-227. PMID: 31894606, DOI: 10.1111/acem.13913.Peer-Reviewed Original ResearchConceptsPositive pressure ventilationOxygen desaturationOpioid administrationAdverse eventsSedation-related adverse eventsCanadian pediatric EDsPrimary risk factorImpact of timingOpioid typeSedation medicationsRespiratory depressionProspective cohortPediatric EDPressure ventilationSedative agentsOriginal cohortProcedural sedationRisk factorsPainful proceduresProcedure typeSedation outcomesMultivariable regressionOpioidsHigh incidenceSedationExternal validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh
Murphy MS, Hawken S, Cheng W, Wilson LA, Lamoureux M, Henderson M, Pervin J, Chowdhury A, Gravett C, Lackritz E, Potter BK, Walker M, Little J, Rahman A, Chakraborty P, Wilson K. External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh. ELife 2019, 8: e42627. PMID: 30887951, PMCID: PMC6424558, DOI: 10.7554/elife.42627.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBangladeshBiostatisticsBlood Chemical AnalysisGestational AgeHumansInfant, NewbornMetabolomeMetabolomicsOntarioConceptsGestational ageGestational age estimationCord bloodNewborn metabolic profilesHeel prick samplesLow-resource settingsTerm infantsPreterm birthHeel prickPopulation-level estimatesPostnatal gestational age estimationBlood spotsMetabolic profileExternal validationInfantsScreening facilityAgeBloodFeasible optionMajority of samplesNewbornsPrickCordWeeks
2023
Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers
Hawken S, Ducharme R, Murphy M, Olibris B, Bota A, Wilson L, Cheng W, Little J, Potter B, Denize K, Lamoureux M, Henderson M, Rittenhouse K, Price J, Mwape H, Vwalika B, Musonda P, Pervin J, Chowdhury A, Rahman A, Chakraborty P, Stringer J, Wilson K. Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers. PLOS ONE 2023, 18: e0281074. PMID: 36877673, PMCID: PMC9987787, DOI: 10.1371/journal.pone.0281074.Peer-Reviewed Original ResearchConceptsGestational ageCord blood dataClinical dataBlood dataMetabolomic markersEarly pregnancy ultrasoundHeel-prick blood sampleProspective birth cohortMultivariable linear regressionBlood sample dataExternal validationGestational age estimationRetrospective cohortPregnancy ultrasoundHeel prickExternal cohortIndependent cohortBlood samplesBirth cohortNewbornsPostnatal gestational age estimationCohortUltrasound estimatesInternal model validationLow-income countries