Using large administrative data for mining patients' trajectories for risk stratification: An example from urological diseases.

Abstract

ObjectiveTo identify latent clusters among urological patients by examining hospitalisation rate trajectories and their association with risk factors and outcome quality indicators.Materials and methodsVictorian Admitted Episodes Dataset, containing information on all hospital admissions in Victoria from 2009 to 2019. The top twenty ICD-10 primary diagnosis codes in urology were used to select patients (n = 98,782) who were included in the study. Latent class trajectory modelling (LCTM) was used to cluster urological patient hospitalisation trajectories. Logistic regression was used to find baseline factors that influence cluster membership, the variables tested included comorbidities, baseline diagnosis codes, and socio-demographic factors. The analysis was further stratified into non-surgical procedures and surgical procedures.ResultsFive clusters of hospitalisation trajectories were identified based on clustering hospitalisation rates over time. Higher hospitalisation clusters were strongly associated with longer length of stay, higher readmission rates and higher complication rates. Higher-risk groups were strongly associated with comorbidities such as renal disease and diabetes. For surgical procedures, urological cancers (kidney, prostate and bladder cancer) and irradiation cystitis were associated with higher-risk groups. For non-surgical procedures, calculus of the bladder, urethral stricture and bladder neck obstruction were associated with higher-risk groups. For patients with two or more admissions, liver cardiovascular disease and being diagnosed with benign prostatic hyperplasia were also associated with higher risk groups.ConclusionA novel statistical approach to cluster hospitalisation trajectories for urological patients was used to explore potential clusters of patient risks and their associations with outcome quality indicators. This study supports the observation that baseline comorbidities and diagnosis can be predictive of higher hospitalisation rates and, therefore, poorer health outcomes. This demonstrates that it is possible to identify patients at risk of developing complications, higher length of stay and readmissions by using baseline comorbidities and diagnosis from administrative data.