239 Using real-world data to predict pain recording and pain severity in the pre-hospital emergency setting – an observational analysis of 212, 401 episodes of care. (1st May 2022)

• Record Type: Journal Article
• Authors: Quinn, R; Masterson, S; Willis, D; Hennelly, D; Deasy, C; O'Donnell, C
• Is Part Of: BMJ open. Volume 12(2022)Supplement 1
• Page Start: A4
• Page End: A4
• Publication Date: 2022-05-01
• Subjects: Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2022-EMS.8
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Background: Previous studies in the prehospital setting have reported wide variation in the incidence and severity of pain, and that documentation of pain scores is poor. The aim of our study was to investigate and describe the incidence and severity of patient-reported pain that is recorded by pre-hospital emergency care patients in Ireland. Method: We used data from our electronic patient care record (ePCR) repository to perform this retrospective cohort study of all emergency care episodes recorded by National Ambulance Service practitioners during 2020. Descriptive analysis of patient and care characteristics and regression analyses for the outcomes pain recorded and severity of pain were performed. Results: Of the 212, 401 patient care episodes included, 138, 195 (65%) included a pain score (75, 445 = no pain; 18, 378 = mild pain; 21, 451 = moderate pain; 22, 921 = severe pain). The likelihood of pain being recorded was most strongly associated with the Glasgow Coma Score, working diagnosis, call location, and patient age. The variables showing strongest association with pain severity were transport outcome, working diagnosis, and patient age. Sensitivity analysis confirmed that all regression models performed better than chance, but that all models were relatively weak at predicting the outcomes. Conclusion: Using a large real-world dataset, we have demonstrated patient and care episode characteristics that are associated with recording and severity of self-reported pain. We have identified actionable improvements that will strengthen the prediction accuracy of routinely collected data and ultimately improve pain management for our patients. Conflict of interest: None to declare. Funding: No specific funding received or sought for this study.