Imputation strategies in the trauma registration.

Introduction: Trauma databases often contain relatively high proportions of missing physiological values. Multiple Imputation (MI) could be a possible adequate solution for the missing values. This study aimed to demonstrate the influence of more simplified imputation models on Standardized W statistic (number of excess survivors per hundred patients that would be achieved if the study center treated identically the same case-mix as the reference population). Methods: Data from three trauma care networks in the Netherlands were used to investigate local differences in missing data. Five different imputation models (MI 1 to 5) were created, based on literature and expert opinion. A sixth database was created using maximal single imputation (MaxI) and a seventh database with only complete cases (CCA). The Ws values were calculated for the three regions separately. Results: A total of 8,853, 24,487 and 8,599 observations were examined in region 1, region 2 and region 3 respectively. The Ws in region 1 ranged from -0.48 (95% CI: -1.71, 0.80) for CCA to 0.53 (95% CI: -0.19, 1.26) for MI 4 and a range of 0.40 (95% CI -0.91, 0.10) for CCA to -0.32 (-0.69, 0.04) for MI 1 and MI 4 was found in region 2. The Ws for region 3 ranged from -0.19 (-0.83, 0.45) in all MI datasets to -0.12 (-0.76, 0.52) in the CCA dataset. Although there were no significant differences between the Ws of the imputation datasets and the CCA analysis, large differences were found in the region with the most missing values. Conclusion: Different imputation strategies did influence Ws values. Supplementary variables showed no additional value for the imputation process and a more simplified imputation model could be used to adequately impute missing data. Study type: Prognostic Level of evidence: II (C) 2017 Lippincott Williams & Wilkins, Inc.

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