Mining Association Rules Between Pressure Injury Risk Factors in Adult Inpatients Based on the Apriori Algorithm

This study investigated clinically significant association rules within pressure injury (PI) data from adult hospitalized patients to inform evidence-based prevention and management strategies. A retrospective cohort analysis was performed on a multicenter sample comprising 2386 PI cases (January 2018 to October 2023) across two tertiary hospital districts in Zhejiang Province, China. The analytical framework incorporated five patient-level demographic/clinical variables and six PI-specific characteristics. Association rule mining was conducted using the Apriori algorithm (minimum support = 10%, confidence threshold = 80%, lift >1), yielding 579 preliminary rules. Subsequent validation via χ2 testing retained 540 statistically significant associations ( P < .05), of which 11 clinically actionable rules were established through Delphi consensus by a multidisciplinary expert panel. The results corroborate existing epidemiological evidence: advanced age (≥65 years), hypoalbuminemia (<35 g/L), and comorbid respiratory/neurological disorders constitute predominant risk factors for PI development. This study demonstrates the methodological rigor of association rule mining in identifying high-risk patient profiles, facilitating targeted early interventions to reduce PI incidence in inpatient populations.