Purpose: Veno-arterial Extracoporeal Membrane Oxygenation (VA-ECMO) provides circulatory assistance for patients in cardiogenic shock. Large quantities of fluids are often required, especially during the early stages, but can result in a potentially harmful fluid-overload. The objective was to determine the association of early fluid-balance and mortality. Methods: Retrospective single-center study in 101 VA-ECMO patients between 2013 and 2016. Daily fluid-balance and weight changes over the 5 first days were observed. Analyses between survivors and non-survivors were conducted using Mann–Whitney tests and logistic regression multivariable and Cox hazard-proportional analyses to determine any association with 28-days mortality. Results: Mortality was 47.5%. The fluid-balance was higher in non-survivors at day-1 (47.3[18.1–71.9] vs. 19.3[1.5–36.2] mL/kg, P < 0.0001) and day-2 (30.6[14.8–71.0] vs. 10.1[−9.8 to 34.7] mL/kg, P = 0.025), as was the cumulative fluid-balance over the first 5 days (107.3[40.5–146.2] vs. 53.0[7.5–74.3] mL/kg, P = 0.04). The administration of unintentional fluids (used for preparation and infusion of drugs) represented an important part of the administrated fluids (15 mL/kg/d–23 mL/kg/d). A significant but moderate correlation was observed between fluid-balance and weight variations over the 5 days (r values ranging from 0.36 to 0.54). Among other parameters, day-1 fluid-balance was independently associated with mortality (OR = 14.34 [1.58–129.79], P = 0.02) and day-1 and day-2 with time to death (HR = 8.26 [1.12–60.98], P = 0.04 and 2.89 [1.26–6.65], P = 0.01). A threshold of 38.8 mL/kg predicted mortality with a sensitivity of 60% and specificity of 83% (area under the curve: 0.749). Conclusion: Early positive fluid-balance is associated with mortality in VA-ECMO patients.

Background: Social media (SoMe) have taken a major place in the medical field, and younger generations are increasingly using them as their primary source to find information. Objective: This study aimed to describe the use of SoMe for medical education among French medical students and assess the prevalence of smartphone addiction in this population. Methods: A cross-sectional web-based survey was conducted among French medical students (second to sixth year of study). The questionnaire collected information on SoMe use for medical education and professional behavior. Smartphone addiction was assessed using the Smartphone Addiction Scale Short-Version (SAS-SV) score. Results: A total of 762 medical students responded to the survey. Of these, 762 (100%) were SoMe users, spending a median of 120 (IQR 60‐150) minutes per day on SoMe; 656 (86.1%) used SoMe for medical education, with YouTube, Instagram, and Facebook being the most popular platforms. The misuse of SoMe in a professional context was also identified; 27.2% (207/762) of students posted hospital internship content, and 10.8% (82/762) searched for a patient’s name on SoMe. Smartphone addiction was prevalent among 29.1% (222/762) of respondents, with a significant correlation between increased SoMe use and SAS-SV score (r=0.39, 95% CI 0.33‐0.45; P<.001). Smartphone-addicted students reported a higher impact on study time (211/222, 95% vs 344/540, 63.6%; P<.001) and a greater tendency to share hospital internship content on social networks (78/222, 35.1% vs 129/540, 23.8%; P=.002). Conclusions: Our findings reveal the extensive use of SoMe for medical education among French medical students, alongside a notable prevalence of smartphone addiction. These results highlight the need for medical schools and educators to address the responsible use of SoMe and develop strategies to mitigate the risks associated with excessive use and addiction.

Bacterial infections are frequent triggers for diabetic ketoacidosis. In this context, delayed antibiotic treatment is associated with increased morbidity and mortality. Unnecessary administration of antimicrobial therapy might however, also negatively impact the prognosis. The usefulness of sepsis markers in diabetic ketoacidosis has not been assessed. Thus, we sought to investigate diagnostic performances of clinical and biological sepsis markers during diabetic ketoacidosis. In this monocentric retrospective cohort study, all consecutive episodes of diabetic ketoacidosis (defined as pH ≤ 7.25, glycaemia > 300 mg/dL and presence of ketones) admitted in intensive care unit were included. A proven bacterial infection was defined as bacteriological documentation on any bacterial sample. Clinical (presence of fever: temperature > 38 °C and presence of hypothermia: temperature < 36 °C) and biological markers (whole blood count, neutrophils count, neutrophils-to-lymphocytes count ratio and procalcitonin), recorded at admission, were compared according to the presence or absence of a proven bacterial infection. Between 2011 and 2018, among 134 episodes of diabetic ketoacidosis, 102 were included (91 patients). Twenty out of 102 were infected. At admission, procalcitonin (median: 3.58 ng/mL vs 0.52 ng/mL, p < 0.001) and presence of fever (25% vs 44%, p = 0.007) were different between episodes with and without proven bacterial infection in both univariate and multivariate analysis. Whole blood count, neutrophils count, neutrophils-to-lymphocytes count ratio and presence of hypothermia were not different between both groups. The diagnostic performance analysis for procalcitonin revealed an area under the curve of 0.87 with an optimal cutoff of 1.44 ng/mL leading to a sensitivity of 0.90 and a specificity of 0.76. Combining procalcitonin and presence of fever allowed to distinguish proven bacterial infection episodes from those without proven bacterial infection. Indeed, all patients with procalcitonin level of more than 1.44 ng/mL and fever had proven bacterial infection episodes. The presence of one of these 2 markers was associated with 46% of proven bacterial infection episodes. No afebrile patient with procalcitonin level less than 1.44 ng/mL had a proven bacterial infection. At admission, combining procalcitonin and presence of fever may be of value to distinguish ketoacidosis patients with and without proven bacterial infection, admitted in intensive care unit.

Background: Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective: The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods: Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results: We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions: Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse.

Background: Critical care teams are on the front line of managing the COVID-19 pandemic, which is stressful for members of these teams. Objective: Our objective was to assess whether the use of social networks is associated with increased anxiety related to the COVID-19 pandemic among members of critical care teams. Methods: We distributed a web-based survey to physicians, residents, registered and auxiliary nurses, and nurse anesthetists providing critical care (anesthesiology, intensive care, or emergency medicine) in several French hospitals. The survey evaluated the respondents’ use of social networks, their sources of information on COVID-19, and their levels of anxiety and information regarding COVID-19 on analog scales from 0 to 10. Results: We included 641 respondents in the final analysis; 553 (86.3%) used social networks, spending a median time of 60 minutes (IQR 30-90) per day on these networks. COVID-19–related anxiety was higher in social network users than in health care workers who did not use these networks (median 6, IQR 5-8 vs median 5, IQR 3-7) in univariate (P=.02) and multivariate (P<.001) analyses, with an average anxiety increase of 10% in social network users. Anxiety was higher among health care workers using social networks to obtain information on COVID-19 than among those using other sources (median 6, IQR 5-8 vs median 6, IQR 4-7; P=.04). Social network users considered that they were less informed about COVID-19 than those who did not use social networks (median 8, IQR 7-9 vs median 7, IQR 6-8; P<.01). Conclusions: Our results suggest that social networks contribute to increased anxiety in critical care teams. To protect their mental health, critical care professionals should consider limiting their use of these networks during the COVID-19 pandemic.

Background: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. Objective: The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. Methods: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). Results: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. “Track” is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). “Feature” is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables “TRACK” and “FEATURE” to store variables obtained in feature extraction and extend the OMOP CDM. Conclusions: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.