TY  - JOUR
PY  - 2021//
TI  - Identification of non-fatal opioid overdose cases using 9-1-1 computer assisted dispatch and prehospital patient clinical record variables
JO  - Prehospital emergency care
A1  - Ajumobi, Olufemi
A1  - Verdugo, Silvia R.
A1  - Labus, Brian
A1  - Reuther, Patrick
A1  - Lee, Bradford
A1  - Koch, Brandon
A1  - Davidson, Peter J.
A1  - Wagner, Karla D.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - BackgroundThe current epidemic of opioid overdoses in the United States necessitates a robust public health and clinical response. We described patterns of non-fatal opioid overdoses (NFOODs) in a small western region using data from the 9-1-1 Computer Assisted Dispatch (CAD) record and electronic Patient Clinical Records (ePCR) completed by EMS responders. We determined whether CAD and ePCR variables could identify NFOOD cases in 9-1-1 data for intervention and surveillance efforts.<br><br>METHODSWe conducted a retrospective analysis of one year of 9-1-1 emergency medical CAD and ePCR (including naloxone administration) data from the sole EMS provider in the response area. Cases were identified based on clinician review of the ePCR, and categorized as definitive NFOOD, probable NFOOD, or non-OOD. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the most prevalent CAD and ePCR variables were calculated. We used a machine learning technique - Random-Forests (RF) modelling - to optimize our ability to accurately predict NFOOD cases within census blocks.<br><br>RESULTSOf 37,960 9-1-1 calls, clinical review identified 158 NFOOD cases (0.4%), of which 123 (77.8%) were definitive and 35 (22.2%) were probable cases. Overall, 106 (67.1%) received naloxone from the EMS responder at the scene. As a predictor of NFOOD, naloxone administration by paramedics had 67.1% sensitivity, 99.6% specificity, 44% PPV and 99.9% NPV. Using CAD variables alone achieved a sensitivity of 36.7% and specificity of 99.7%. Combining ePCR variables with CAD variables increased the diagnostic accuracy with the best RF model yielding 75.9% sensitivity, 99.9% specificity, 71.4% PPV and 99.9% NPV.<br><br>CONCLUSIONCAD problem type variables and naloxone administration, used alone or in combination, had sub-optimal predictive accuracy. However, a Random Forests modelling approach improved accuracy of identification, which could foster improved surveillance and intervention efforts. We identified the set of NFOODs that EMS encountered in a year and may be useful for future surveillance efforts.<p />  <p>Language: en</p>
LA  - en
SN  - 1090-3127
UR  - http://dx.doi.org/10.1080/10903127.2021.1981505
ID  - ref1
ER  -