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.
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.
Authors: Joshua B Brown; Mark L Gestring; Nicole A Stassen; Raquel M Forsythe; Timothy R Billiar; Andrew B Peitzman; Jason L Sperry Journal: Ann Surg Date: 2016-02 Impact factor: 12.969
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Authors: Joshua B Brown; Matthew R Rosengart; Raquel M Forsythe; Benjamin R Reynolds; Mark L Gestring; William M Hallinan; Andrew B Peitzman; Timothy R Billiar; Jason L Sperry Journal: J Trauma Acute Care Surg Date: 2016-07 Impact factor: 3.313
Authors: Joshua B Brown; Mark L Gestring; Francis X Guyette; Matthew R Rosengart; Nicole A Stassen; Raquel M Forsythe; Timothy R Billiar; Andrew B Peitzman; Jason L Sperry Journal: Surgery Date: 2015-10-23 Impact factor: 3.982
Authors: Joshua B Brown; Raquel M Forsythe; Nicole A Stassen; Andrew B Peitzman; Timothy R Billiar; Jason L Sperry; Mark L Gestring Journal: J Trauma Acute Care Surg Date: 2014-07 Impact factor: 3.313
Authors: Joshua B Brown; Mark L Gestring; Francis X Guyette; Matthew R Rosengart; Nicole A Stassen; Raquel M Forsythe; Timothy R Billiar; Andrew B Peitzman; Jason L Sperry Journal: Ann Surg Date: 2016-08 Impact factor: 12.969