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JOURNAL ARTICLE
MULTICENTER STUDY

PRISM III: an updated Pediatric Risk of Mortality score

M M Pollack, K M Patel, U E Ruttimann
Critical Care Medicine 1996, 24 (5): 743-52
8706448

OBJECTIVES: The relationship between physiologic status and mortality risk should be reevaluated as new treatment protocols, therapeutic interventions, and monitoring strategies are introduced and as patient populations change. We developed and validated a third-generation pediatric physiology-based score for mortality risk, Pediatric Risk of Mortality III (PRISM III).

DESIGN: Prospective cohort.

SETTING: There were 32 pediatric intensive care units (ICUs): 16 pediatric ICUs were randomly chosen and 16 volunteered.

PATIENTS: Consecutive admissions at each site were included until at least 11 deaths per site occurred.

MEASUREMENTS AND MAIN RESULTS: Physiologic data included the most abnormal values from the first 12 and the second 12 hrs of ICU stay. Outcomes and descriptive data were also collected. Physiologic variables where normal values change with age were stratified by age (neonate, infant, child, adolescent). The database was randomly split into development (90%) and validation (10%) sets. Variables and their ranges were chosen by computing the risk of death (odds ratios) relative to the midrange of survivors for each physiologic variable. Univariate and multivariate statistical procedures, including multiple logistic regression analysis, were used to develop the PRISM III score and mortality risk predictors. Data were collected on 11,165 admissions (543 deaths). The PRISM III score has 17 physiologic variables subdivided into 26 ranges. The variables most predictive of mortality were minimum systolic blood pressure, abnormal pupillary reflexes, and stupor/coma. Other risk factors, including two acute and two chronic diagnoses, and four additional risk factors, were used in the final predictors. The PRISM III score and the additional risk factors were applied to the first 12 hrs of stay (PRISM III-12) and the first 24 hours of stay (PRISM III-24). The Hosmer-Lemeshow chi-square goodness-of-fit evaluations demonstrated absence of significant calibration errors (p values: PRISM III-12 development = .2496; PRISM III-24 development = .1374; PRISM III-12 validation = .4168; PRISM III-24 validation = .5504). The area under the receiver operating curve and Flora's z-statistic indicated excellent discrimination and accuracy (area under the receiver operating curve - PRISM III-12 development 947 +/- 0.007; PRISM III-24 development 0.958 +/- 0.006; PRISM III-12 validation 0.941 +/- 0.021; PRISM III-24 validation 0.944 +/- 0.021; Flora's z-statistic - PRISM III-12 validation = .7479; PRISM III-24 validation = .9225), although generally, the PRISM III-24 performed better than the PRISM III-12 models. Excellent goodness-of-fit was also found for patient groups stratified by age (significance levels: PRISM III-12 = .1622; PRISM III-24 = .4137), and by diagnosis (significance levels: PRISM III-12 = .5992; PRISM III-24 = .7939).

CONCLUSIONS: PRISM III resulted in several improvements over the original PRISM. Reassessment of physiologic variables and their ranges, better age adjustment for selected variables, and additional risk factors resulted in a mortality risk model that is more accurate and discriminates better. The large number of diverse ICUs in the database indicates PRISM III is more likely to be representative of United States units.

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