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Background: We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO 2 ) signals., Methods: We recorded the BF and SpO 2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S 24 severity score that represented the patient's severity over the previous 24 h; the validity of MS 24 , the maximum S 24 score, was checked against rates of intubation risk and prolonged ICU stay., Results: Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S 24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS 24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS 24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87)., Conclusions: Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Study Design: Observational study., Purpose: The actual sanitary crisis led to a massive mobilization of the sanitary system toward intensive care units and management of coronavirus disease 2019 (COVID-19) patients. However, some patients still require spinal interventions. The present study aimed to assess the impact of the COVID-19 pandemic on spine surgical in a moderate COVID-19 cluster region., Overview of Literature: Previous studies have reported screening and management of patients with spinal conditions during the COVID-19 pandemic. However, to date, knowledge, no observational study on spine surgeries during the pandemic has not been reported., Methods: Between March 17, 2020 and April 17, 2020, information on spine surgical activity was prospectively collected at our institution. This surgical activity related to the first month of confinement in France was compared to the activity during the same period in 2019 to evaluate the impact of the COVID-19 pandemic on surgical activities., Results: In order to reduce the contamination rate of patients and medical staff during hospitalization, the spine department was completely reorganized. Non-urgent elective spine surgeries were cancelled. When considering the global amount of surgeries procedures during the first month of confinement, a decrease of almost 50% was observed in the number of surgical procedures. During the study period, 62 patients were eligible for spine surgery. The numbers of patients managed for tumor and infectious cases were stable, while a considerable reduction was observed in the number of trauma and degenerative cases. During the follow-up period, two patients were tested as COVID+ during the postoperative course, and no cases of medical or paramedical staff contamination were reported using polymerase chain reaction-testing., Conclusions: During the COVID-19 pandemic, it is possible to maintain spine surgical activity. Each surgical procedure must be discussed and organized with all the caregivers involved. Indications for surgery must be in line with the scientific guidelines and adapted to each healthcare facility.

Background: The link between bacterial resistance and prognosis remains controversial. Predominant pathogen causing ventilator-associated pneumonia (VAP) is Pseudomonas aeruginosa (Pa), which has increasingly become multidrug resistant (MDR). The aim of this study was to evaluate the relationship between MDR VAP Pa episodes and 30-day mortality., Methods: From a longitudinal prospective French multicenter database (2010-2016), Pa VAP onset and physiological data were recorded. MDR was defined as non-susceptibility to at least 1 agent in 3 or more antimicrobial categories. To analyze if MDR episodes were associated with greater in-hospital 30-day mortality, we performed a multivariate survival analysis using the multivariate nonlinear frailty model., Results: A total of 230 patients presented 286 Pa VAP. A maximum of 3 episodes per patient was observed; 73 episodes were MDR and 213 were susceptible. In the multivariate model, factors independently associated with 30-day mortality included hospitalization in the 6 months preceding the first episode (hazard ratio [HR], 2.31; 95% confidence interval [CI], 1.50-3.60; P = .0002), chronic renal failure (HR, 2.34; 95% CI, 1.15-4.77; P = .0196), and Pa VAP recurrence (HR, 2.29; 95% CI, 1.79-4.87; P = .032). Finally, MDR Pa VAP was not associated with death (HR, 0.87; 95% CI; 0.52-1.45; P = .59)., Conclusions: This study did not identify a relationship between the resistance profile of Pseudomonas aeruginosa and mortality., (Copyright © 2019 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

This study assessed age-related changes in body composition (specifically in trunk fat and appendicular lean masses), with consideration of body mass index (BMI) at age 20 years (BMI reference age, "BMIref"), ethnicity and lifetime weight change history. A cross-sectional dual-energy X-ray absorptiometry-based dataset was extracted from the U.S. National Health and Nutrition Examination Survey (NHANES) 1999-2004. Only European-American and African-American subjects were used (2705 men, 2527 women). For each gender and ethnicity, 6 analytic cases were considered, based on three BMIref categories (normal, overweight and obese, being 22, 27 and 30 kg/m², respectively) and two weight contexts (stable weight or weight gain across the lifespan). A nonparametric model was developed to investigate age-related changes in body composition. Then, parametric modelling was developed for assessing BMIref- and ethnicity-specific effects during aging. In the stable weight, both genders' and ethnicities' trunk fat (TF) increased gradually; body fat (BF) remained stable until 40 years and increased thereafter; trunk lean (TL) remained stable, but appendicular lean (APL) and body lean (BL) declined from 20 years. In the weight gain context, TF and BF increased at a constant rate, while APL, TL and BL increased until 40-50 years, and then declined slightly. Compared with European-American subjects of both genders, African-American subjects had lower TF and BF masses. Ethnic differences in body composition were quantified and found to remain constant across the lifespan.

The detection and characterization of emerging infectious agents has been a continuing public health concern. High Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies have proven to be promising approaches for efficient and unbiased detection of pathogens in complex biological samples, providing access to comprehensive analyses. As NGS approaches typically yield millions of putatively representative reads per sample, efficient data management and visualization resources have become mandatory. Most usually, those resources are implemented through a dedicated Laboratory Information Management System (LIMS), solely to provide perspective regarding the available information. We developed an easily deployable web-interface, facilitating management and bioinformatics analysis of metagenomics data-samples. It was engineered to run associated and dedicated Galaxy workflows for the detection and eventually classification of pathogens. The web application allows easy interaction with existing Galaxy metagenomic workflows, facilitates the organization, exploration and aggregation of the most relevant sample-specific sequences among millions of genomic sequences, allowing them to determine their relative abundance, and associate them to the most closely related organism or pathogen. The user-friendly Django-Based interface, associates the users' input data and its metadata through a bio-IT provided set of resources (a Galaxy instance, and both sufficient storage and grid computing power). Galaxy is used to handle and analyze the user's input data from loading, indexing, mapping, assembly and DB-searches. Interaction between our application and Galaxy is ensured by the BioBlend library, which gives API-based access to Galaxy's main features. Metadata about samples, runs, as well as the workflow results are stored in the LIMS. For metagenomic classification and exploration purposes, we show, as a proof of concept, that integration of intuitive exploratory tools, like Krona for representation of taxonomic classification, can be achieved very easily. In the trend of Galaxy, the interface enables the sharing of scientific results to fellow team members., Competing Interests: Competing interests: No competing interests were disclosed.

The detection and characterization of emerging infectious agents has been a continuing public health concern. High Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies have proven to be promising approaches for efficient and unbiased detection of pathogens in complex biological samples, providing access to comprehensive analyses. As NGS approaches typically yield millions of putatively representative reads per sample, efficient data management and visualization resources have become mandatory. Most usually, those resources are implemented through a dedicated Laboratory Information Management System (LIMS), solely to provide perspective regarding the available information. We developed an easily deployable web-interface, facilitating management and bioinformatics analysis of metagenomics data-samples. It was engineered to run associated and dedicated Galaxy workflows for the detection and eventually classification of pathogens. The web application allows easy interaction with existing Galaxy metagenomic workflows, facilitates the organization, exploration and aggregation of the most relevant sample-specific sequences among millions of genomic sequences, allowing them to determine their relative abundance, and associate them to the most closely related organism or pathogen. The user-friendly Django-Based interface, associates the users' input data and its metadata through a bio-IT provided set of resources (a Galaxy instance, and both sufficient storage and grid computing power). Galaxy is used to handle and analyze the user's input data from loading, indexing, mapping, assembly and DB-searches. Interaction between our application and Galaxy is ensured by the BioBlend library, which gives API-based access to Galaxy's main features. Metadata about samples, runs, as well as the workflow results are stored in the LIMS. For metagenomic classification and exploration purposes, we show, as a proof of concept, that integration of intuitive exploratory tools, like Krona for representation of taxonomic classification, can be achieved very easily. In the trend of Galaxy, the interface enables the sharing of scientific results to fellow team members., Competing Interests: Competing interests: No competing interests were disclosed.