Northern Greece was struck by an intense second COVID-19 (coronavirus disease 2019) epidemic wave during the fall of 2020. Because of the coinciding silent epidemic of multidrug-resistant organisms, the handling of COVID-19 patients became even more challenging. In the present study, the microbiological characteristics of bacteremias in confirmed cases of hospitalized COVID-19 patients were determined. Data from 1165 patients hospitalized between September and December 2020 were reviewed regarding the frequency of bloodstream infections, the epidemiology and the antibiotic susceptibility profiles of the causative bacteria. The hospital's antibiotic susceptibility data for all major nosocomial pathogens isolated from bacteremias of COVID-19 patients between September and December 2020 versus those between September and December 2019 were also compared. Overall, 122 patients developed bacteremia (10.47%). The average of time interval between hospitalization date and development of bacteremia was 13.98 days. Admission to ICU occurred in 98 out of 122 patients with an average stay time of 15.85 days and 90.81% in-hospital mortality. In total, 166 pathogens were recovered including 114 Gram-negative bacteria and 52 Gram-positive cocci. Acinetobacter baumannii was the most frequent (n = 51) followed by Klebsiella pneumoniae (n = 45) and Enterococcus faecium (n = 31). Bacteremias in hospitalized COVID-19 patients were related with prolonged time of hospitalization and higher in-hospital mortality, and the isolated microorganisms represented the bacterial species that were present in our hospital before the COVID-19 pandemic. Worryingly, the antibiotic resistance rates were increased compared with the pre-pandemic era for all major opportunistic bacterial pathogens. The pandemic highlighted the need for continuous surveillance of patients with prolonged hospitalization.
- Award ID(s):
- 1632402
- NSF-PAR ID:
- 10275867
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Critical Care
- Volume:
- 24
- Issue:
- 1
- ISSN:
- 1364-8535
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background Acute respiratory failure is generally treated with invasive mechanical ventilation or noninvasive respiratory support strategies. The efficacies of the various strategies are not fully understood. There is a need for accurate therapy-based phenotyping for secondary analyses of electronic health record data to answer research questions regarding respiratory management and outcomes with each strategy. Objective The objective of this study was to address knowledge gaps related to ventilation therapy strategies across diverse patient populations by developing an algorithm for accurate identification of patients with acute respiratory failure. To accomplish this objective, our goal was to develop rule-based computable phenotypes for patients with acute respiratory failure using remotely monitored intensive care unit (tele-ICU) data. This approach permits analyses by ventilation strategy across broad patient populations of interest with the ability to sub-phenotype as research questions require. Methods Tele-ICU data from ≥200 hospitals were used to create a rule-based algorithm for phenotyping patients with acute respiratory failure, defined as an adult patient requiring invasive mechanical ventilation or a noninvasive strategy. The dataset spans a wide range of hospitals and ICU types across all US regions. Structured clinical data, including ventilation therapy start and stop times, medication records, and nurse and respiratory therapy charts, were used to define clinical phenotypes. All adult patients of any diagnoses with record of ventilation therapy were included. Patients were categorized by ventilation type, and analysis of event sequences using record timestamps defined each phenotype. Manual validation was performed on 5% of patients in each phenotype. Results We developed 7 phenotypes: (0) invasive mechanical ventilation, (1) noninvasive positive-pressure ventilation, (2) high-flow nasal insufflation, (3) noninvasive positive-pressure ventilation subsequently requiring intubation, (4) high-flow nasal insufflation subsequently requiring intubation, (5) invasive mechanical ventilation with extubation to noninvasive positive-pressure ventilation, and (6) invasive mechanical ventilation with extubation to high-flow nasal insufflation. A total of 27,734 patients met our phenotype criteria and were categorized into these ventilation subgroups. Manual validation of a random selection of 5% of records from each phenotype resulted in a total accuracy of 88% and a precision and recall of 0.8789 and 0.8785, respectively, across all phenotypes. Individual phenotype validation showed that the algorithm categorizes patients particularly well but has challenges with patients that require ≥2 management strategies. Conclusions Our proposed computable phenotyping algorithm for patients with acute respiratory failure effectively identifies patients for therapy-focused research regardless of admission diagnosis or comorbidities and allows for management strategy comparisons across populations of interest.more » « less
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CONCLUSIONS: For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s13054-020-03352-0
