title: Dynamic prediction modeling approaches for cardiac surgery.
abstract: BACKGROUND
  The calibration of several cardiac clinical prediction models has deteriorated over time. We compare different model fitting approaches for in-hospital mortality after cardiac surgery that adjust for cross-sectional case mix in a heterogeneous patient population.
  
  
  RESULTS
  Data from >300 000 consecutive cardiac surgery procedures performed at all National Health Service and some private hospitals in England and Wales between April 2001 and March 2011 were extracted from the National Institute for Cardiovascular Outcomes Research clinical registry. The study outcome was in-hospital mortality. Model approaches included not updating, periodic refitting, rolling window, and dynamic logistic regression. Covariate adjustment was made in each model using variables included in the logistic European System for Cardiac Operative Risk Evaluation model. The association between in-hospital mortality and some variables changed with time. Notably, the intercept coefficient has been steadily decreasing during the study period, consistent with decreasing observed mortality. Some risk factors, such as operative urgency and postinfarct ventricular septal defect, have been relatively stable over time, whereas other risk factors, such as left ventricular function and surgery on the thoracic aorta, have been associated with lower risk relative to the static model.
  
  
  CONCLUSIONS
  Dynamic models or periodic model refitting is necessary to counteract calibration drift. A dynamic modeling framework that uses contemporary and available historic data can provide a continuously smooth update mechanism that also allows for inferences to be made on individual risk factors. Better models that withstand the effects of time give advantages for governance, quality improvement, and patient-level decision making.
authors.affiliation: University of Manchester, Centre for Health Informatics, Manchester, United Kingdom.
authors.forename: Graeme L
authors.initials: GL
authors.lastname: Hickey
authors.forename: Stuart W
authors.initials: SW
authors.lastname: Grant
authors.forename: Camila
authors.initials: C
authors.lastname: Caiado
authors.forename: Simon
authors.initials: S
authors.lastname: Kendall
authors.forename: Joel
authors.initials: J
authors.lastname: Dunning
authors.forename: Michael
authors.initials: M
authors.lastname: Poullis
authors.forename: Iain
authors.initials: I
authors.lastname: Buchan
authors.forename: Ben
authors.initials: B
authors.lastname: Bridgewater
mesh_terms: D000367:Age Factors; D003198:Computer Simulation; D004739:England; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D044382:Population Groups; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013903:Thoracic Surgery / Q000379:methods / Q000706:statistics & numerical data*; D016896:Treatment Outcome
keywords: Bayesian forecast; calibration; clinical governance; logistic model