Journal
EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY
Volume 43, Issue 6, Pages 1146-1152Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/ejcts/ezs584
Keywords
Patient trends; Risk model; Cardiac surgery; Model expiry; EuroSCORE
Funding
- Heart Research UK [RG2583]
- National Institute of Health Research
- MRC [MR/K006665/1] Funding Source: UKRI
- Medical Research Council [MR/K006665/1, MC_PC_13042] Funding Source: researchfish
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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. 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. 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. 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.
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