Pediatric Trauma Assessment and Management Database: Leveraging Existing Data Systems to Predict Mortality and Functional Status after Pediatric Injury

Abstract

Background: Efforts to improve pediatric trauma outcomes need detailed data, optimally collected at lowest cost, to assess processes of care. We developed a novel database by merging 2 national data systems for 5 pediatric trauma centers to provide benchmarking metrics for mortality and non-mortality outcomes and to assess care provided throughout the care continuum.

Study design: Trauma registry and Virtual Pediatric Systems, LLC (VPS) from 5 pediatric trauma centers were merged for children younger than 18 years discharged in 2013 from a pediatric ICU after traumatic injury. For inpatient mortality, we compared risk-adjusted models for trauma registry only, VPS only, and a combination of trauma registry and VPS variables (trauma registry+VPS). To estimate risk-adjusted functional status, we created a prediction model de novo through purposeful covariate selection using dichotomized Pediatric Overall Performance Category scale.

Results: Of 688 children included, 77.3% were discharged from the ICU with good performance or mild overall disability and 17.6% with moderate or severe overall disability or coma. Inpatient mortality was 5.1%. The combined dataset provided the best-performing risk-adjusted model for predicting mortality, as measured by the C-statistic, pseudo-R², and Akaike Information Criterion, when compared with the trauma registry-only model. The final Pediatric Overall Performance Category model demonstrated adequate discrimination (C-statistic = 0.896) and calibration (Hosmer-Lemeshow goodness-of-fit p = 0.65). The probability of poor outcomes varied significantly by site (p < 0.0001).

Conclusions: Merging 2 data systems allowed for improved risk-adjusted modeling for mortality and functional status. The merged database allowed for patient evaluation throughout the care continuum on a multi-institutional level. Merging existing data is feasible, innovative, and has potential to impact care with minimal new resources.

Figure 1.

Select data elements available along multiple phases of the care continuum. ALT, alanine transaminase; AST, aspartate transaminase; ED, emergency department; GCS, Glasgow Coma Score; PICU, pediatric ICU; PT/PTT, prothrombin time/partial thromboplastin time.

Figure 2.

Receiver operating characteristic by data source. Mortality model comparisons for discrimination (area under the curve [AUC]), goodness-of-fit (McFadden’s R²), and parsimony (Akaike Information Criterion [AIC]); n = 583. Chi-square test for equality of AUC estimates between the trauma registry (TR)+ Virtual Pediatric Systems, LLC (VPS) model and the TR-only model is statistically significant with p < 0.001. *McFadden’s R², a measure of goodness-of-fit. ^†TR-only covariates: age, mechanism of injury, transfer status, emergency department systolic blood pressure, Glasgow Coma Scale score, maximum head Abbreviated Injury Scale score, and congenital comorbidity. ^‡VPS-only model: performance of the Pediatric Index of Mortality 2 (PIM2).

Figure 3.

Discrimination plot for functional status model prediction (n = 504). Area under receiver operating characteristic curve = 0.8958.

Figure 4.

Calibration plot for functional status model prediction (n = 504). POPC, Pediatric Overall Performance Category.

Figure 5.

Observed vs expected probability of poor functional outcomes at ICU discharge by site (n = 504).