[Validation of a Therapeutic Intervention Scoring System (TISS-28) in critically ill children]

Critical care: A concept analysis

Objective

The terms critical care and the Intensive Care Unit (ICU) are often used interchangeably to describe a place of care. Defining critical care becomes challenging because of the colloquial use of the term. Using concept analysis allows for the development of definition and meaning. The aim of this concept analysis is to distinguish the use of the term critical care to develop an operational definition which describes what constitutes critical care.

Method

Walker and Avant's eight-step approach to concept analysis guided this study. Five databases (CINAHL, Scopus, PubMed, ProQuest Dissertation Abstracts and Medline in EBSCO) were searched for studies related to critical care. The search included both qualitative and quantitative studies written in English and published between 1990 and 2022.

Results

Of the 439 papers retrieved, 47 met the inclusion criteria. The defining attributes of critical care included 1) a maladaptive response to illness/injury, 2) admission modelling criteria, 3) advanced medical technologies, and 4) specialised health professionals. Antecedents were associated with illness/injury that progressed to a level of criticality with a significant decline in both physical and psychological functioning. Consequences were identified as either death or survival with/without experiencing post-ICU syndrome.

Conclusion

Describing critical care is often challenging because of the highly technical nature of the environment. This conceptual understanding and operational definition will inform future research as to the scope of critical care and allow for the design of robust evaluative instruments to better understand the nature of care in the intensive care environment.

Predicting the Risk of Mortality in Children using a Fuzzy-Probabilistic Hybrid Model

Introduction

The mortality risk in children admitted to Pediatric Intensive Care Units (PICU) is usually estimated by means of validated scales, which only include objective data among their items. Human perceptions may also add relevant information to prognosticate the risk of death, and the tool to use this subjective data is fuzzy logic. The objective of our study was to develop a mathematical model to predict mortality risk based on the subjective perception of PICU staff and to evaluate its accuracy compared to validated scales.

Methods

A prospective observational study in two PICUs (one in Spain and another in Latvia) was performed. Children were consecutively included regardless of the cause of admission along a two-year period. A fuzzy set program was developed for the PICU staff to record the subjective assessment of the patients' mortality risk expressed through a short range and a long range, both between 0% and 100%. Pediatric Index of Mortality 2 (PIM2) and Therapeutic Intervention Scoring System 28 (TISS28) were also prospectively calculated for each patient. Subjective and objective predictions were compared using the logistic regression analysis. To assess the prognostication ability of the models a stratified B-random K-fold cross-validation was performed.

Results

Five hundred ninety-nine patients were included, 308 in Spain (293 survivors, 15 nonsurvivors) and 291 in Latvia (282 survivors, 9 nonsurvivors). The best logistic classification model for subjective information was the one based on MID (midpoint of the short range), whereas objective information was the one based on PIM2. Mortality estimation performance was 86.3% for PIM2, 92.6% for MID, and the combination of MID and PIM2 reached 96.4%.

Conclusions

Subjective assessment was as useful as validated scales to estimate the risk of mortality. A hybrid model including fuzzy information and probabilistic scales (PIM2) seems to increase the accuracy of prognosticating mortality in PICU.