Constructing a comprehensive nursing risk assessment model for older adults inpatients with multiple chronic...

To build a comprehensive nursing risk model for older adults inpatients with multiple chronic conditions to identify nursing risks.

This study was conducted in Sichuan Province, China from March 2020 to June 2022. We used Python to build logistic regression, decision tree and random forest models.

The study included 4458 patients, 2529 were male (56.7 %) and had an average age of 74.31±6.79 years. The mean number of chronic diseases in elderly hospitalized patients was 2.81±0.96. Hypertension (67.0%), diabetes (43.4%), and coronary heart disease (31.4%) are the three diseases with the highest prevalence. There were 552 (12.4%) patients who had nursing risk event. The random forest model has the best comprehensive prediction ability. It has the highest accuracy(0.917), precision(0.667), recall(0.706), Macro-F1(0.933), and AUC values [0.933(95 % CI:0.924-0.943) ] in training set.

The random forest(AUC=0.933) superior compared to logistic regression and decision tree, suggesting strong potential for clinical implementation.