2019
Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding
Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, Laine L. Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2019, 158: 160-167. PMID: 31562847, PMCID: PMC7004228, DOI: 10.1053/j.gastro.2019.09.009.Peer-Reviewed Original ResearchConceptsUpper gastrointestinal bleedingHospital-based interventionsComposite endpointScoring systemRockall scoreGastrointestinal bleedingClinical riskConsecutive unselected patientsLow-risk patientsClinical scoring systemRisk-scoring systemExternal validation cohortCharacteristic curve analysisInternal validation setOutpatient managementUnselected patientsValidation cohortEmergency departmentMedical CenterGreater AUCPatientsAbstractTextCurve analysisEndpointAUC
2017
Comparison of risk scoring systems for patients presenting with upper gastrointestinal bleeding: international multicentre prospective study
Stanley AJ, Laine L, Dalton HR, Ngu JH, Schultz M, Abazi R, Zakko L, Thornton S, Wilkinson K, Khor CJ, Murray IA, Laursen SB. Comparison of risk scoring systems for patients presenting with upper gastrointestinal bleeding: international multicentre prospective study. The BMJ 2017, 356: i6432. PMID: 28053181, PMCID: PMC5217768, DOI: 10.1136/bmj.i6432.Peer-Reviewed Original ResearchConceptsUpper gastrointestinal bleedingRockall scoreGastrointestinal bleedingInternational multicentre prospective studyAdmission Rockall scoreFull Rockall scoreGlasgow-Blatchford scoreHigh-risk patientsMulticentre prospective studyAssessment of patientsPNED scoreHospital stayBlatchford scoreDay mortalityConsecutive patientsRisk patientsComposite endpointEndoscopic treatmentProspective studyClinical endpointsClinical utilityLower riskPatientsLarge hospitalsBleeding