  Objective Hospitals register information in the electronic health records
(EHR) continuously until discharge or death. As such, there is no censoring for
in-hospital outcomes. We aimed to compare different dynamic regression modeling
approaches to predict central line-associated bloodstream infections (CLABSI)
in EHR while accounting for competing events precluding CLABSI. Materials and
Methods We analyzed data from 30,862 catheter episodes at University Hospitals
Leuven from 2012 and 2013 to predict 7-day risk of CLABSI. Competing events are
discharge and death. Static models at catheter onset included logistic,
multinomial logistic, Cox, cause-specific hazard, and Fine-Gray regression.
Dynamic models updated predictions daily up to 30 days after catheter onset
(i.e. landmarks 0 to 30 days), and included landmark supermodel extensions of
the static models, separate Fine-Gray models per landmark time, and regularized
multi-task learning (RMTL). Model performance was assessed using 100 random 2:1
train-test splits. Results The Cox model performed worst of all static models
in terms of area under the receiver operating characteristic curve (AUC) and
calibration. Dynamic landmark supermodels reached peak AUCs between 0.741-0.747
at landmark 5. The Cox landmark supermodel had the worst AUCs (<=0.731) and
calibration up to landmark 7. Separate Fine-Gray models per landmark performed
worst for later landmarks, when the number of patients at risk was low.
Discussion and Conclusion Categorical and time-to-event approaches had similar
performance in the static and dynamic settings, except Cox models. Ignoring
competing risks caused problems for risk prediction in the time-to-event
framework (Cox), but not in the categorical framework (logistic regression).
