This article is more than 5 years old. Information may no longer be current.
Assessment model identified trauma patients at highest risk for PE
Results from this study demonstrated use of a risk assessment model that used seven predictors of pulmonary embolism allowed investigators to identify patients at highest risk for this complication.
Researchers used the National Trauma Registry of the American College of Surgeons to identify 38,597 trauma patients, of which 239 had developed a pulmonary embolism (PE). Investigators collected data including demographics, injury data, prehospital information, treatment data and events during the hospitalization. They also assessed independent variables including age, sex and race/ethnicity.
A multivariate binary logistic regression model was used to determine the likelihood of a patient developing PE during hospitalization. Investigators also used a simulation analysis which used relative risk (RR) ratios and examined receiver operative characteristic curve plots with their corresponding sensitivity, specificity and RR measures.
Findings from multivariable logistic regression analysis showed the most statistically significant predictors of PE were age, obesity, injury from a motorcycle accident, hospital arrival by helicopter, emergency department admission pulse rate, injuries located in the thorax, abdomen and lower extremities, and admission to the ICU.
According to researchers, a model using these variables was able to determine the difference between predicted PE and actual PE with a receiver operating characteristic area under the curve of 0.87. Investigators noted they were able to predict 80.34% to 83.61% of pulmonary emboli by identifying the top 25% of high-risk patients. ‒ by Monica Jaramillo
Disclosures: Black reports no relevant financial disclosures. Please see the full study for a list of all other authors’ relevant financial disclosures.