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PURPOSE: To determine union and displacement metrics following percutaneous screw fixation (antegrade or retrograde) of superior pubic rami fractures. METHODS: This is a retrospective cohort study from a single level 1 trauma center. Skeletally mature patients with at least one superior pubic ramus fracture present as part of a lateral compression-type pelvic ring injury were included. RESULTS: Eighty-five (85) patients with 95 superior pubic rami fractures met the study's inclusion criteria. LC1, LC2, and LC3 injuries occurred in 76.5%, 15.3%, and 8.2% of patients, respectively. The majority of patients underwent concurrent posterior pelvic ring fixation (94.1%). Superior ramus screw placement occurred predominantly via retrograde technique (81.1%) with cannulated screws of size 6.5 mm or larger (93.7%). Of the 95 eligible fractures, 90 (94.7%) achieved union at a mean of 14.0 weeks (7-40 weeks). Of these united fractures, 69 (76.7%) healed with no measurable displacement, while the remaining 23.3% healed with residual mean displacement of 3.9 mm (range: 0.5-9.0 mm). Multivariable analysis demonstrated a positive association between age (p = 0.04) and initial displacement (p = 0.04) on the final degree of residual displacement at union. A Kaplan-Meier survival analysis identified increased age to be significantly related to increased time to union (X2 (2) = 21.034, p < 0.001). CONCLUSIONS: Union rates following percutaneous screw fixation of superior pubic rami fractures associated with lateral compression-type pelvic ring injuries approach 95%. Though minimal in an absolute sense, increasing age and a greater degree of initial displacement may influence the final degree of residual displacement at union. LEVEL OF EVIDENCE: IV.
RESUMO
OBJECTIVE: Vital signs and laboratory values are used to guide decisions to use damage control techniques in lieu of early definitive fracture fixation. Previous models attempted to predict mortality risk but have limited utility. There is a need for a dynamic model that captures evolving physiologic changes during a trauma patient's hospital course. METHODS: The Parkland Trauma Index of Mortality (PTIM) is a machine learning algorithm that uses electronic medical record data to predict mortality within 48 hours during the first 3 days of hospitalization. It updates every hour, recalculating as physiology changes. The model was developed using 1935 trauma patient encounters from 2009 to 2014 and validated on 516 patient encounters from 2015 to 2016. Model performance was evaluated statistically. Data were collected retrospectively on its performance after 1 year of clinical use. RESULTS: In the validation data set, PTIM accurately predicted 52 of the sixty-three 12-hour time intervals within 48 hours of mortality, for sensitivity of 82.5% [95% confidence interval (CI), 73.1%-91.9%]. The specificity was 93.6% (95% CI, 92.5%-94.8%), and the positive predictive value (PPV) was 32.5% (95% CI, 25.2%-39.7%). PTIM predicted survival for 1608 time intervals and was incorrect only 11 times, yielding a negative predictive value of 99.3% (95% CI, 98.9%-99.7%). The area under the curve of the receiver operating characteristic curve was 0.94.During the first year of clinical use, when used in 776 patients, the last PTIM score accurately predicted 20 of the twenty-three 12-hour time intervals within 48 hours of mortality, for sensitivity of 86.9% (95% CI, 73%-100%). The specificity was 94.7% (95% CI, 93%-96%), and the positive predictive value was 33.3% (95% CI, 21.4%-45%). The model predicted survival for 716 time intervals and was incorrect 3 times, yielding a negative predictive value of 99.6% (95% CI, 99.1%-100%). The area under the curve of the receiver operating characteristic curve was 0.97. CONCLUSIONS: By adapting with the patient's physiologic response to trauma and relying on electronic medical record data alone, the PTIM overcomes many of the limitations of previous models. It may help inform decision-making for trauma patients early in their hospitalization. LEVEL OF EVIDENCE: Prognostic Level I. See Instructions for Authors for a complete description of levels of evidence.
