European Journal of Cardio-Thoracic Surgery · 2012 · 154 citations · 16 references
MedicineCardiologySurgeryCardiothoracic SurgeryClinical Decision SupportGeneral SurgeryCardiac Risk AssessmentThoracic SurgeryCardiac Outcome AssessmentClinical Risk PredictionClinical Risk ScoresDiscrimination Of Risk ModelsIn-hospital MortalityPredictive Model CalibrationRisk Prediction CalibrationSurgical Risk Prediction
OBJECTIVES: Progressive loss of calibration of the original EuroSCORE models has necessitated the introduction of the EuroSCORE II model. Poor model calibration has important implications for clinical decision-making and risk adjustment of governance analyses. The objective of this study was to explore the reasons for the calibration drift of the logistic EuroSCORE. METHODS: Data from the Society for Cardiothoracic Surgery in Great Britain and Ireland database were analysed for procedures performed at all National Health Service and some private hospitals in England and Wales between April 2001 and March 2011. The primary outcome was in-hospital mortality. EuroSCORE risk factors, overall model calibration and discrimination were assessed over time. RESULTS: A total of 317 292 procedures were included. Over the study period, mean age at surgery increased from 64.6 to 67.2 years. The proportion of procedures that were isolated coronary artery bypass grafts decreased from 67.5 to 51.2%. In-hospital mortality fell from 4.1 to 2.8%, but the mean logistic EuroSCORE increased from 5.6 to 7.6%. The logistic EuroSCORE remained a good discriminant throughout the study period (area under the receiver-operating characteristic curve between 0.79 and 0.85), but calibration (observed-to-expected mortality ratio) fell from 0.76 to 0.37. Inadequate adjustment for decreasing baseline risk affected calibration considerably. DISCUSSIONS: Patient risk factors and case-mix in adult cardiac surgery change dynamically over time. Models like the EuroSCORE that are developed using a 'snapshot' of data in time do not account for this and can subsequently lose calibration. It is therefore important to regularly revalidate clinical prediction models.
16
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Assessing the Performance of Prediction Models
Ewout W. Steyerberg, Andrew J. Vickers, Nancy R. Cook et al. · Epidemiology · 2009 · 4.9K citations · Full text