• OBJECTIVES
    • The purpose was to define which clinical conditions warrant delay of definitive fixation for pelvis, femur, acetabulum, and spine fractures. A model was developed to predict the complications.
  • DESIGN
    • Statistical modeling based on retrospective database.
  • SETTING
    • Level 1 trauma center.
  • PATIENTS
    • A total of 1443 adults with pelvis (n = 291), acetabulum (n = 399), spine (n = 102), and/or proximal or diaphyseal femur (n = 851) fractures.
  • INTERVENTION
    • All fractures were treated surgically.
  • MAIN OUTCOME MEASUREMENTS
    • Univariate and multivariate analysis of variance assessed associations of parameters with complications. Logistic predictive models were developed with the incorporation of multiple fixed and random effect covariates. Odds ratios, F tests, and receiver operating characteristic curves were calculated.
  • RESULTS
    • Twelve percent had pulmonary complications, with 8.2% overall developing pneumonia. The pH and base excess values were lower (P < 0.0001) and the rate of improvement was also slower (all Ps < 0.007), with pneumonia or any pulmonary complication. Similarly, lactate values were greater with pulmonary complications (all Ps < 0.02), and lactate was the most specific predictor of complications. Chest injury was the strongest independent predictor of pulmonary complication. Initial lactate was a stronger predictor of pneumonia (P = 0.0006) than initial pH (P = 0.047) or the rate of improvement of pH over the first 8 hours (P = 0.0007). An uncomplicated course was associated with the absence of chest injury (P < 0.0001) and definitive fixation within 24 (P = 0.007) or 48 hours (P = 0.005). Models were developed to predict probability of complications with various injury combinations using specific laboratory parameters measuring residual acidosis.
  • CONCLUSIONS
    • Acidosis on presentation is associated with complications. Correction of pH within 8 hours to >7.25 was associated with fewer pulmonary complications. Presence and severity of chest injury, number of fractures, and timing of fixation are other significant variables to include in a predictive model and algorithm development for Early Appropriate Care. The goal is to minimize complications by definitive management of major skeletal injury once the patient has been adequately resuscitated.