This week's seminars
Stats4Grads: Monitoring Renal Failure: An application of Dynamic Models
20 June 2018 11:30 in CM105
Evidence suggests that changes in the urine output and blood chemistries indicate injury to the kidney or impairment of kidney function. These changes are warnings of serious clinical consequences, but traditionally most studies emphasise the most severe reduction in kidney function. It has only been recently that minor decreases of kidney function have been recognised as potentially important in the critically ill. Identifying and intervening in patients with minor decreases in kidney function is clinically important as this can prevent patients from reaching more severe reductions in kidney failure.
The KDIGO (Kidney Disease Improving Global Outcomes) guidelines are a clinical practice guideline for the diagnosis, evaluation, prevention, and treatment of kidney disease and are currently used worldwide to identify a whole range of levels of kidney failure. In this presentation I will discuss how the KDIGO guidelines are too sensitive when classifying adverse outcomes due to kidney deterioration and show how dynamic models and Bayesian forecasting offer a powerful framework for the modelling and analysis of noisy time series which are subject to abrupt changes in pattern.
See the Stats4Grads page for more details about this series.