Toward a More Robust Prediction of Pulmonary Embolism in Trauma Patients. A Risk Assessment Model Based Upon 38,000 Patients

Sheena R. Black, Jeffrey T. Howard, Paul C. Chin, Adam J. Starr

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

OBJECTIVES:: Pulmonary embolism (PE) is a rare but sometimes fatal complication of trauma. Risk stratification models identify patients at increased risk of PE, however they are often complex and difficult to use. This research aims to develop a model, based upon a large sample of trauma patients, which can be easily and quickly used at the time of admission to predict PE. METHODS:: This study used trauma registry data from 38,597 trauma patients. Of these, 239 (0.619%) developed a PE. We targeted demographic and injury data, pre-hospital information, and data on treatments and events during hospitalization. A multivariate binary logistic regression model was developed to predict the odds of developing a PE during hospitalization. The model was developed using a 50%, randomly selected development sub-sample, and then tested for accuracy using the remaining 50% validation sample. RESULTS:: We found seven statistically significant predictors of PE, including (1) age (OR=1.01, 95% CI: 1.00,1.02, p=0.05), (2) obesity (OR=2.54, 95% CI: 1.29,4.99, p

Original languageEnglish (US)
JournalJournal of Orthopaedic Trauma
DOIs
StateAccepted/In press - Nov 4 2015

ASJC Scopus subject areas

  • Surgery
  • Orthopedics and Sports Medicine

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