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


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

In This Article

Abstract and Introduction


Objectives: Severity of illness measures have long been used in pediatric critical care. The Pediatric Risk of Mortality is a physiologically based score used to quantify physiologic status, and when combined with other independent variables, it can compute expected mortality risk and expected morbidity risk. Although the physiologic ranges for the Pediatric Risk of Mortality variables have not changed, recent Pediatric Risk of Mortality data collection improvements have been made to adapt to new practice patterns, minimize bias, and reduce potential sources of error. These include changing the outcome to hospital survival/death for the first PICU admission only, shortening the data collection period and altering the Pediatric Risk of Mortality data collection period for patients admitted for "optimizing" care before cardiac surgery or interventional catheterization. This analysis incorporates those changes, assesses the potential for Pediatric Risk of Mortality physiologic variable subcategories to improve score performance, and recalibrates the Pediatric Risk of Mortality score, placing the algorithms (Pediatric Risk of Mortality IV) in the public domain.

Design: Prospective cohort study from December 4, 2011, to April 7, 2013.

Measurements and Main Results: Among 10,078 admissions, the unadjusted mortality rate was 2.7% (site range, 1.3–5.0%). Data were divided into derivation (75%) and validation (25%) sets. The new Pediatric Risk of Mortality prediction algorithm (Pediatric Risk of Mortality IV) includes the same Pediatric Risk of Mortality physiologic variable ranges with the subcategories of neurologic and nonneurologic Pediatric Risk of Mortality scores, age, admission source, cardiopulmonary arrest within 24 hours before admission, cancer, and low-risk systems of primary dysfunction. The area under the receiver operating characteristic curve 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.

Conclusions: The new Pediatric Risk of Mortality data collection methods include significant improvements that minimize the potential for bias and errors, and the new Pediatric Risk of Mortality IV algorithm for survival and death has excellent prediction performance.


Severity of illness measures have been used in pediatric critical care for decades.[1–4] The Pediatric Risk of Mortality (PRISM) score is a frequently used, physiologically based severity of illness measure using 17 commonly measured physiologic variables and their ranges.[5] The PRISM score is a quantification of physiologic status using predetermined physiologic variables and their ranges that use categorical variables to facilitate accurate estimation of mortality risk.[5] PRISM is commonly used to control for severity of illness in studies and to assess quality of care through standardized mortality ratios (SMRs). Recently, we demonstrated that physiologic status as measured with PRISM variables and their ranges is significantly associated with morbidity and mortality and could be used to simultaneously estimate morbidity and mortality risk.[6]

Recently, there have been multiple changes to the data collection process for the PRISM score. First, the time period for measuring PRISM has changed. Physiologic variables are measured only in the first 4 hours of PICU care, and laboratory variables are measured in the time period from 2 hours before PICU admission through the first 4 hours.[7] This time period was chosen to best separate the predictor variables from therapy while ensuring that there would be no institutional bias because of practice pattern differences in the timing and frequency of variable measurement. Second, only the first PICU admission in any hospitalization is included and outcome at hospital discharge (instead of at PICU discharge) is predicted.[6] This change was made because the appropriateness of the PICU discharge decision should be included in quality assessments. Third, the institutionally based practice of admitting patients before surgery, especially cardiovascular surgery, required adjustment of the PRISM observation period because the presurgical admission period does not reflect the critical care portion of the admission if it is for observation or "optimizing" preoperative status. We developed a bias-free logic for classifying these patients.[6] In addition, the relative values of physiologic instability in different systems may have drifted over time and could be assessed by adjusting for the weighting in the PRISM physiologic variable subcategories of cardiovascular, neurologic, metabolic, chemistry, and hematologic groupings. Therefore, although the PRISM score for physiologic variables and their ranges did not change,[5] the prediction performance might be enhanced by assessing for this change.

Recently, the Collaborative Pediatric Critical Care Research Network (CPCCRN) conducted the Trichotomous Outcome Prediction in Critical Care (TOPICC) study. TOPICC demonstrated that physiologic status measured by PRISM physiologic variables and their ranges was associated with the risk for significant new morbidity and mortality and developed prediction algorithms for the simultaneous prediction of both significant new morbidity and mortality.[6] Although we have recommended the evolution of pediatric outcome predictors to include significant morbidity and mortality, this change will take time. Therefore, using the TOPICC dataset, we revised the PRISM prediction algorithms for the dichotomous outcomes of survival versus death using the most recent changes to the collection of PRISM data. We hypothesized that these changes would not alter the predictive value of the model. This study reports the results of that analysis and opens the prediction algorithms (PRISM IV) for the public domain.