Validity of the ROX index in predicting invasive mechanical ventilation requirement in pneumonia.

BACKGROUND: The ROX index (Respiratory rate-OXygenation) has been described as a prediction tool to identify the need for invasive mechanical ventilation (IMV) in community-acquired pneumonia (CAP) with acute hypoxaemic respiratory failure treated with high-flow nasal cannula in order to avoid delay of a necessary intubation. However, its use in predicting the need for ventilatory support in hospitalised patients with CAP has not been validated. METHODS: This is a retrospective cohort study including subjects with CAP treated in the general ward, emergency service or intensive care unit of a third-level centre in Cundinamarca, Colombia, between January 2001 and February 2020. The ROX index was estimated as the ratio of oxygen saturation/fraction of inspired oxygen to respiratory rate. RESULTS: A total of 895 patients were included, of whom 93 (10%) required IMV. The ROX index proved to be a good predictor, presenting an area under the curve of receiver operating characteristics (AUROC) of 0.733 (95% CI 0.671 to 0.795, p<0.001) when determined by pulse oximetry and an AUROC of 0.779 (95% CI 0.699 to 0.859, p<0.001) when estimated by arterial blood gas (ABG) parameters, with an intraclass correlation of 0.894. The estimated cut-off point was 14.8; a score less than 14.8 indicates high risk of requiring IMV. CONCLUSION: The ROX index is a good predictor of IMV in hospitalised patients with CAP. It presents good performance when calculated through pulse oximetry and can replace the one calculated by ABG.

A hybrid approach for composing groups in collaborative learning contexts.

Considering that there are many alternatives in the literature for composing groups in collaborative learning contexts, we present a proposal that exhibits several features. First, and from the operational point of view, our proposal is highly flexible because i) it allows for several group sizes and an arbitrary array of grouping attributes, and ii) it may be easily adapted to consider several homogeneity/heterogeneity criteria. Second, and from the algorithmic point of view, it combines the best of two apparently opposite worlds: it uses a local brute-force search within an iterative process guided by a randomized heuristic criterion. Thus, this approach is still Non-Polynomic (NP) but in terms of the size of the groups, whereas is Polynomic (P) in terms of the number of students. Third, the experiments with several datasets, with student numbers varying from 20 to 3500, demonstrate reasonable performance and running times for this approach. We contrasted these times with those reported in 19 related works and, first taking into account certain considerations, we found that ours were lower in most cases. Nevertheless, and as the fourth feature, we make available both the datasets and the source code to allow for more objective comparisons of approaches, including our own.