Introduction

    Following the rapid advances in medical therapy and critical care technology over the past 30 years, outcome analysis, including mortality risk prediction, has become a challenge for the modern day intensivists.

    Actually, Pediatric intensive care units (PICU), aiming at promoting qualified care and so, as points of major technology transfer, constitute, effectively, one of the main consumers of hospital budgets. One way to assess performance considers PICU outcomes measures, such as patient risk-adjusted mortality, often provided by clinical scoring systems. When patients with various prognoses and degrees of clinical severity are being treated, the final results of employing the resources available at such units are often uncertain¹. In this context, the incorporation of technology does not always follow strict analytical rules, with respect to support scientific evidence or, even less frequently, cost-efficiency relationships.² Routinely assessment of the quality of the services provided, thus, emerges as a central point in the way of cost-benefit analysis’ increment.

    One means of comparing the quality and efficacy of care provided at a given unit is to compare it with others in similar situations. Pediatric Intensive Care Units (PICUs) compare components that are related with disease severity and the resources available with the outcomes of specific types of patients. Mortality decrease is unquestionably a primary aim of a PICU and given the relatively high mortality among intensive care patients, death is, veritably, a sensitive, appropriate, and meaningful measure of outcome.²

    The assessment of disease severity is essential for a wide range of analysis in Intensive Care Units (ICU), including a quality assessment stratification of severity in clinical trials and studies on the management and use of resources at ICU. A physician’s estimation accuracy of mortality risk for patients admitted in PICUs may be skewed and subjective. A rational and objective way to define and quantify severity of illness is, so, through the development of probability models predicting mortality risk. In order to measure severity risk of mortality, scores are employed, making possible a reproducible comparison of the estimated mortality (in percent) with the really observed mortality. Known as prognostic scores, they integrate clinical data to estimate the probability of mortality, which can be used to evaluate the quality of medical care, including decision-making for individuals patients, and to optimize the employment of resources, aiming at final an improving of the cost-benefit relationship.³

    In fact, although rigorous experiments or large randomised controlled trials are the gold standard for evaluating existing or new interventions, these are not always possible in intensive care. The alternative is to use observational methods that study the outcome of care patients receive as part of their natural treatment. However, before inferences can be drawn about outcomes of treatment in such studies, the characteristics of the patients admitted to intensive care have to be taken into account, being this process known as adjusting for case mix. Scoring systems are aimed at quantifying case mix and designed to estimate outcome (death before discharge from hospital after intensive care), covering an intrinsic description of the health care system, PICU organization and case-mix of the population used to create it.³

    Currently, various are the scores used for the stratification of disease severity in patients hospitalized in ICU, using probabilistic prediction models of individual risk of death (e.g.: Acute Physiology, Age, Chronic Health Evaluation - APACHE II, Simplified Acute Physiology Score - SAPS II, Clinical Risk Index for Babies - CRIB, Pediatric Risk of Mortality – PRISM and Paediatric Index of Mortality - PIM). Developed by identifying variables relevant to mortality risk (namely, recovery post procedure, systolic blood pressure, mechanical ventilation, pupillary reactions, potassium, respiratory rate, heart rate, etc.) and scoring them after a multivariate statistical analysis by logistic regression, the main scores used for the pediatric population are PRISM³ and PIM⁴. Intending to respond to the necessity of regular reevaluation of the relationship between physiologic status and mortality risk as new treatment protocols, therapeutic interventions or monitoring strategies are introduced, recent versions of PRISM and PIM scoring systems, PRISM III⁵ and PIM-2⁶, have been introducing in pediatric intensive care context. Advantages and disadvantages of the most frequently used scores for pediatric intensive care population are presented in Table 1.

Table 1 – General scores description and comparison;

    Reliability and validity are important parameters that allow confident use of a scoring system in intensive care patients with different case-mixes and baseline characteristics. In fact, validation of a mortality-scoring model is really vital when assessing its ability to predict one of the many important outcome measures, namely death. In fact, the validation needs both discrimination and calibration in order to achieve a global evaluation of the score.⁷

    In spite of the development of specifics scores for pediatric populations and more, their effective validation at located realities, no validation evidences, in order to its application in Portuguese PICUs, have previously been referenced. A project, however, even though with no published evidences, Development and Assessment of Optimal Risk Scores for Outcomes in Paediatric Intensive Care (DAIP-CIP), was conducted in the institution of filiation by Pediatric Intensive Care Unit – Department of Pediatrics (H. S. João – Oporto), providing preliminary results in what concerns to assess the performance of PRISM, PRISM III and PIM in predicting patients' mortality risk in Portuguese PICUs.

    In point of fact, the development of referred prognostic systems occurred in a specific geographic context and several contradictory data on its application in other populations have already been referenced^8,9,10, emphasizing the vital necessity of its accuracy evaluation in large cohort of Portuguese context. Special caution is effectively needed in adopting a severity of illness scoring system to assess performance of care, particularly in contexts different from the ones in which the instrument was originally developed.

    Remaining the doubt about its accuracy in mortality prediction in Portuguese PICUs, the present study intend, so, to proceed in order to assess and optimize Pediatric Risk of Mortality (PRISM and PRISM III) and Pediatric Index of Mortality (PIM and PIM2) scoring systems for use in comparing the risk-adjusted mortality of children after admission for pediatric intensive care, in a large, prospective and representative sample of admissions to Portuguese PICUs.