Usefulness of a multiplying factor in predicting the final number of victims during a mass casualty incident.

OBJECTIVE: Whenever a mass casualty incident (MCI) occurs, it is essential to anticipate the final number of victims to dispatch the adequate number of ambulances. In France, the custom is to multiply the initial number of prehospital victims by 2-4 to predict the final number. However, no one has yet validated this multiplying factor (MF) as a predictive tool. We aimed to build a statistical model to predict the final number of victims from their initial count. METHODS: We observed retrospectively over 30 years of MCIs triggered in a large urban area. We considered three types of events: explosions, fires, and road traffic accidents. We collected the initial and final numbers of victims, with distinction between deaths, critical victims (T1), and delayed or minimal victims (T2-T3). The MF was calculated for each category of victims according to each type of event. Using a Poisson multivariate regression, we calculated the incidence risk ratio (IRR) of the final number of T1 as a function of the initial deaths and the initial T2-T3 counts, while controlling for potential confounding variables. RESULTS: Sixty-eight MCIs were included. The final number of T1 increased with the initial incidence of deaths [IRR: 1.8 (1.4-2.2)], the initial number of T2-T3 being greater than 12 [IRR: 1.6 (1.3-2.1)], and the presence of one or more explosion [IRR: 1.4 (1.1-1.8)]. CONCLUSION: The MF seems to be an appealing decision-making tool to anticipate the need for ambulance resources. In explosive MCIs, we recommend multiplying T1 by 1.4 to estimate final count and the need for supplementary advanced life support teams.

An empirical assessment of long-branch attraction artefacts in deep eukaryotic phylogenomics.

In the context of exponential growing molecular databases, it becomes increasingly easy to assemble large multigene data sets for phylogenomic studies. The expected increase of resolution due to the reduction of the sampling (stochastic) error is becoming a reality. However, the impact of systematic biases will also become more apparent or even dominant. We have chosen to study the case of the long-branch attraction artefact (LBA) using real instead of simulated sequences. Two fast-evolving eukaryotic lineages, whose evolutionary positions are well established, microsporidia and the nucleomorph of cryptophytes, were chosen as model species. A large data set was assembled (44 species, 133 genes, and 24,294 amino acid positions) and the resulting rooted eukaryotic phylogeny (using a distant archaeal outgroup) is positively misled by an LBA artefact despite the use of a maximum likelihood-based tree reconstruction method with a complex model of sequence evolution. When the fastest evolving proteins from the fast lineages are progressively removed (up to 90%), the bootstrap support for the apparently artefactual basal placement decreases to virtually 0%, and conversely only the expected placement, among all the possible locations of the fast-evolving species, receives increasing support that eventually converges to 100%. The percentage of removal of the fastest evolving proteins constitutes a reliable estimate of the sensitivity of phylogenetic inference to LBA. This protocol confirms that both a rich species sampling (especially the presence of a species that is closely related to the fast-evolving lineage) and a probabilistic method with a complex model are important to overcome the LBA artefact. Finally, we observed that phylogenetic inference methods perform strikingly better with simulated as opposed to real data, and suggest that testing the reliability of phylogenetic inference methods with simulated data leads to overconfidence in their performance. Although phylogenomic studies can be affected by systematic biases, the possibility of discarding a large amount of data containing most of the nonphylogenetic signal allows recovering a phylogeny that is less affected by systematic biases, while maintaining a high statistical support.