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.

Pyrogenic organic matter production from wildfires: a missing sink in the global carbon cycle.

Wildfires release substantial quantities of carbon (C) into the atmosphere but they also convert part of the burnt biomass into pyrogenic organic matter (PyOM). This is richer in C and, overall, more resistant to environmental degradation than the original biomass, and, therefore, PyOM production is an efficient mechanism for C sequestration. The magnitude of this C sink, however, remains poorly quantified, and current production estimates, which suggest that ~1-5% of the C affected by fire is converted to PyOM, are based on incomplete inventories. Here, we quantify, for the first time, the complete range of PyOM components found in-situ immediately after a typical boreal forest fire. We utilized an experimental high-intensity crown fire in a jack pine forest (Pinus banksiana) and carried out a detailed pre- and postfire inventory and quantification of all fuel components, and the PyOM (i.e., all visually charred, blackened materials) produced in each of them. Our results show that, overall, 27.6% of the C affected by fire was retained in PyOM (4.8 ± 0.8 t C ha(-1)), rather than emitted to the atmosphere (12.6 ± 4.5 t C ha(-1)). The conversion rates varied substantially between fuel components. For down wood and bark, over half of the C affected was converted to PyOM, whereas for forest floor it was only one quarter, and less than a tenth for needles. If the overall conversion rate found here were applicable to boreal wildfire in general, it would translate into a PyOM production of ~100 Tg C yr(-1) by wildfire in the global boreal regions, more than five times the amount estimated previously. Our findings suggest that PyOM production from boreal wildfires, and potentially also from other fire-prone ecosystems, may have been underestimated and that its quantitative importance as a C sink warrants its inclusion in the global C budget estimates.