Lynne Moore

Evaluating the validity of multiple imputation for missing physiological data in the national trauma data bank

Background: The National Trauma Data Bank (NTDB) is plagued by the problem of missing physiological data. The Glasgow Coma Scale score, Respiratory Rate and Systolic Blood Pressure are an essential part of risk adjustment strategies for trauma system evaluation and clinical research. Missing data on these variables may compromise the feasibility and the validity of trauma group comparisons. Aims: To evaluate the validity of Multiple Imputation (MI) for completing missing physiological data in the National Trauma Data Bank (NTDB), by assessing the impact of MI on 1) frequency distributions, 2) associations with mortality, and 3) risk adjustment. Methods: Analyses were based on 170,956 NTDB observations with complete physiological data (observed data set). Missing physiological data were artificially imposed on this data set and then imputed using MI (MI data set). To assess the impact of MI on risk adjustment, 100 pairs of hospitals were randomly selected with replacement and compared using adjusted Odds Ratios (OR) of mortality. OR generated by the observed data set were then compared to those generated by the MI data set. Results: Frequency distributions and associations with mortality were preserved following MI. The median absolute difference between adjusted OR of mortality generated by the observed data set and by the MI data set was 3.6% (inter-quartile range: 2.4%-6.1%). Conclusions: This study suggests that, provided it is implemented with care, MI of missing physiological data in the NTDB leads to valid frequency distributions, preserves associations with mortality, and does not compromise risk adjustment in inter-hospital comparisons of mortality.

A Multiple Imputation Model for Imputing Missing Physiologic Data in the National Trauma Data Bank

BACKGROUND Like most trauma registries, the National Trauma Data Bank (NTDB) is limited by the problem of missing physiologic data. Multiple imputation (MI) has been proposed to simulate missing Glasgow Coma Scale (GCS) scores, respiratory rate (RR), and systolic blood pressure (SBP). The aim of this study was to develop an MI model for missing physiologic data in the NTDB and to provide guidelines for its implementation. STUDY DESIGN The NTDB 7.0 was restricted to patients admitted in 2005 with at least one anatomic injury code. A series of auxiliary variables thought to offer information for the imputation process was selected from the NTDB by literature review and expert opinion. The relation of these variables to physiologic variables and to the fact that they were missing was examined using logistic regression. The MI model included all auxiliary variables that had a statistically significant association with physiologic variables or with the fact that they were missing (Bonferroni-corrected p value <0.05). RESULTS The NTDB sample included 373,243 observations. Glasgow Coma Scale, respiratory rate, and systolic blood pressure were missing for 20.3%, 3.9%, and 8.5% of data observations, respectively. The MI model included information on the following: gender, age, anatomic injury severity, transfer status, injury mechanism, intubation status, alcohol and drug test results, emergency department disposition, total length of stay, ICU length of stay, duration of mechanical ventilation, and discharge disposition. The MI model offered good discrimination for predicting the value of physiologic variables and the fact that they were missing (areas under the receiver operating characteristic curve between 0.832 and 0.999). CONCLUSIONS This article proposes an MI model for imputing missing physiologic data in the NTDB and provides guidelines to facilitate its use. Implementation of the model should improve the quality of research involving the NTDB. The methodology can also be adapted to other trauma registries.

Refinement of the Quebec decision rule for radiography in shoulder dislocation

OBJECTIVE We prospectively derived a clinical decision rule to guide pre- and postreduction radiography for emergency department (ED) patients with anterior glenohumeral dislocation. METHODS This prospective cohort derivation study took place at 4 university-affiliated EDs over a 3-year period and enrolled consenting patients with anterior glenohumeral dislocation who were 18 years of age or older. We compared patients with a clinically important fracture-dislocation with those who had an uncomplicated dislocation to provide the clinical decision rule components using recursive partitioning. The final rule involved age, mechanism, prior dislocation and humeral ecchymosis. RESULTS A total of 222 patients were included in the study. Forty (18.0%) had clinically important fracture-dislocation. A clinical decision rule using 4 factors reached a sensitivity of 100% (95% confidence interval [CI] 89.4%-100%), a specificity of 34.2% (95% CI 27.7%-41.2%), a negative predictive value of 99.2% (95% CI 92.8%-99.9%) and a negative likelihood ratio of 0.04 (95% CI 0.002-0.27). Patients younger than 40 years are at high risk for clinically important fracture- dislocation only if the mechanism of injury involves substantial force (i.e., a fall greater than their own height, a sport injury, an assault or a motor vehicle collision). Patients 40 years of age or older are at high risk only in the presence of humeral ecchymosis or after their first dislocation. Projected use of the rule would reduce the absolute number of prereduction radiographs by 27.9% and of postreduction by 81.9%. CONCLUSION The Quebec shoulder dislocation rule for patients with acute anterior glenohumeral dislocation holds promise to reduce unnecessary imaging, pending validation.

Comparing regression-adjusted mortality to standardized mortality ratios for trauma center profiling

Background: Trauma center profiling is commonly performed with Standardized Mortality Ratios (SMRs). However, comparison of SMRs across trauma centers with different case mix can induce confounding leading to biased trauma center ranks. We hypothesized that Regression-Adjusted Mortality (RAM) estimates would provide a more valid measure of trauma center performance than SMRs. Objective: Compare trauma center ranks generated by RAM estimates to those generated by SMRs. Materials and Methods: The study was based on data from a provincial Trauma Registry (1999-2006; n = 88,235). SMRs were derived as the ratio of observed to expected deaths using: (1) the study population as an internal standard, (2) the US National Trauma Data Bank as an external standard. The expected death count was calculated as the sum of mortality probabilities for all patients treated in a hospital conditional on the injury severity score, the revised trauma score, and age. RAM estimates were obtained directly from a hierarchical logistic regression model. Results: Crude mortality was 5.4% and varied between 1.3% and 13.5% across the 59 trauma centers. When trauma center ranks from internal SMRs and RAM were compared, 49 out of 59 centers changed rank and six centers changed by more than five ranks. When trauma center ranks from external SMRs and RAM were compared, 55 centers changed rank and 17 changed by more than five ranks. Conclusions: The results of this study suggest that the use of SMRs to rank trauma centers in terms of mortality may be misleading. RAM estimates represent a potentially more valid method of trauma center profiling.