An extended phase type survival tree for patient pathway prognostication

Abstract

Survival tree based analysis is a powerful method of prognostication and determining clinically meaningful patient groups from a given dataset of patients' length of stay. In our previous work [1, 2] we proposed a phase type survival tree method for clustering patients into homogeneous groups with respect to their length of stay where partitioning is based on covariates representing patient characteristics such as gender, age at the time of admission, and primary diagnosis code. This paper extends this approach to examine the relationship between LOS in hospital and destination on discharge among these patient groups. An application of this approach is illustrated using 5 year retrospective data of patients admitted to Belfast City Hospital with a diagnosis of stroke (hemorrhagic stroke, cerebral infarction, transient ischaemic attack TIA, and stroke unspecified).