The Pediatric Risk of Mortality Score: Update 2015

Murray M. Pollack, MD; Richard Holubkov, PhD; Tomohiko Funai, MS; J. Michael Dean, MD; John T. Berger, MD; David L. Wessel, MD; Kathleen Meert, MD; Robert A. Berg, MD; Christopher J. L. Newth, MD, FRCPC; Rick E. Harrison, MD; Joseph Carcillo, MD; Heidi Dalton, MD; Thomas Shanley, MD; Tammara L. Jenkins, MSN, RN; Robert Tamburro, MD, MSc

Disclosures

Pediatr Crit Care Med. 2016;17(1):2-9. 

In This Article

Results

There were 10,078 patients included from the seven sites. The site ranges and summary data are presented in Table 1. Individual site data have been presented elsewhere.[6] The distribution of all patient characteristics except cardiac arrest varied significantly between sites (p < 0.001). The unadjusted mortality rate was 2.7% (site range, 1.3–5.0%).

Initially, we assessed the univariate mortality odds ratios in the development dataset to select variables for inclusion into the final model building process (Supplemental Table 1, Supplemental Digital Content 1, http://links.lww.com/PCC/A203). The total PRISM physiologic variable score and each of its subcategories were statistically significant. Of the categorical variables, age, admission source, admission status, cardiac arrest with 24 hours of PICU admission, interventional classification, cancer, and primary system of dysfunction were statistically significantly associated with mortality.

The final dichotomous (survival and death) model is found in Table 3. Two of the age categories, 14 days to less than 1 month and 1 month to less than 12 months, were significant only at the level of p value less than 0.10 but were included separately to maintain a parallel structure to the trichotomous predictor and because this stratification better reflects the age categories that were significant in previous models. The area under the ROC for the development and validation sets was 0.88 ± 0.013 and 0.90 ± 0.018, respectively. The Hosmer-Lemeshow goodness of fit statistics indicated adequate model fit for both the development (p = 0.39) and validation (p = 0.50) sets (Table 4).

Table 5 illustrates the SMRs for common diagnostic and descriptive categories not used to develop the model. The SMRs assessing model fit within the levels of PICU type, interventional category, elective/emergency status, diagnoses of septic shock, respiratory disease, congenital cardiac conditions, and neurologic trauma were not significantly different from unity. For the levels of insurance status, the SMRs of commercial and government status were statistically different from unity.

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