A Two-stage Dynamic Model to Enable Updating of Clinical Risk Prediction from Longitudinal Health Record Data: Illustrated with Kidney Function.
We demonstrate the use of electronic records and repeated measures of risk factors therein, to enable deeper understanding of the relationship between the full longitudinal trajectory of risk factors and outcomes. To illustrate, dynamic mixed effect modelling is used to summarise the level, trend and monitoring intensity of kidney function. The output from this model then forms covariates for a recurrent event Cox proportional hazards model for predicting adverse events (AE). Using data from Salford, UK, our multivariate model finds that steeper declines in kidney function raise the hazard of AE (HR: 1.13, 95% CI (1.05, 1.22)). There is a non-proportional relationship between the hazard of AE and the monitoring intensity of kidney function. Neither of these variables would be present in a classical risk prediction model.. This work illustrates the potential of using the full longitudinal profile of risk factors, rather than just their level. There is an opportunity for deep statistical learning leading to rich clinical insight using longitudinal signals in electronic data.
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Artur
A
Akbarov
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Richard
R
Williams
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Benjamin
B
Brown
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Mamas
M
Mamas
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Niels
N
Peek
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Iain
I
Buchan
Health eResearch Centre, Farr Institute, University of Manchester, UK.
Matthew
M
Sperrin
D003198:Computer Simulation; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D007674:Kidney Diseases / Q000175:diagnosis* / Q000401:mortality*; D007677:Kidney Function Tests / Q000706:statistics & numerical data*; D008137:Longitudinal Studies; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D006113:United Kingdom / Q000453:epidemiology