Acute admission risk stratification of New Zealand primary care patients using demographic, multimorbidity, service usage and modifiable variables

Abstract

Introduction Risk stratification tools in primary care may help practices better identify high-risk patients and plan for their treatment. Patients of all ages can be at high risk of acute hospital admissions. Aim We aim to improve existing risk stratification tools by using larger datasets, and accounting for practice-level variations in hospitalisation rates and read-code quality. Methods This work has derived an acute admission risk stratification tool in the Wellington, Kāpiti Coast and Wairarapa regions of New Zealand. An open cohort, starting 1 March 2017 and finishing 1 November 2021, contains 319 943 patients. An accelerated failure time survival regression model is used to model acute admission risk. Candidate models are tested on holdout data using six different test metrics. Results Patient risk is most affected by demographic, and the frequency of recent healthcare system usage. Morbidity categories have less predictive capability, but may still be useful from a practical perspective. The preferred model has an area under the receiver operating characteristic curve (AUROC) of 0.75 for a 6-month forecast period. Discussion The model is straightforward to apply to other datasets. Although most of the highest-risk patients will be well-known to their primary care practices already, the model helps to identify the patients who are high risk but not regularly attendees of the practice, and may benefit from proactive care planning.

Keywords

• algorithmic
• care planning
• Health Care Home
• hospitalisation
• morbidity
• primary health care