RESUMO
In this brief communication, we retrospectively describe COVID-19 severe patient's characteristics in ICU, and report 37,6% of secondary bacterial infections, mainly with nosocomial respiratory infections and rarely from community source.
Assuntos
Infecções Bacterianas , COVID-19 , Infecção Hospitalar , Infecção Hospitalar/microbiologia , Humanos , Unidades de Terapia Intensiva , Estudos RetrospectivosRESUMO
Resumen En esta comunicación breve, describimos retrospectivamente las características de los pacientes internados graves con COVID-19 en UCI. Reportamos 37,6 % de infecciones bacterianas secundarias, principalmente de origen nosocomial respiratorio y muy infrecuente comunitario.
Abstract In this brief communication, we retrospectively describe COVID-19 severe patient's characteristics in ICU, and report 37,6% of secondary bacterial infections, mainly with nosocomial respiratory infections and rarely from community source.
Assuntos
Humanos , Infecções Bacterianas , Infecção Hospitalar/microbiologia , COVID-19 , Estudos Retrospectivos , Unidades de Terapia IntensivaRESUMO
STUDY OBJECTIVES: Given the detailed respiratory waveform signal provided by the nasal cannula in polysomnographic (PSG) studies, to quantify sleep breathing disturbances by extracting a continuous variable based on the coefficient of variation of the envelope of that signal. DESIGN: Application of an algorithm for envelope analysis to standard nasal cannula signal from actual polysomnographic studies. SETTING: PSG recordings from a sleep disorders center were analyzed by an algorithm developed on the Igor scientific data analysis software. PATIENTS OR PARTICIPANTS: Recordings representative of different degrees of sleep disordered breathing (SDB) severity or illustrative of the covariation between breathing and particularly relevant factors and variables. INTERVENTIONS: The method calculated the coefficient of variation of the envelope for each 30-second epoch. The normalized version of that coefficient was defined as the respiratory disturbance variable (RDV). The method outcome was the all-night set of RDV values represented as a time series. MEASUREMENTS AND RESULTS: RDV quantitatively reflected departure from normal sinusoidal breathing at each epoch, providing an intensity scale for disordered breathing. RDV dynamics configured itself in recognizable patterns for the airflow limitation (e.g., in UARS) and the apnea/hypopnea regimes. RDV reliably highlighted clinically meaningful associations with staging, body position, oximetry, or CPAP titration. CONCLUSIONS: Respiratory disturbance variable can assess sleep breathing disturbances as a gradual phenomenon while providing a comprehensible and detailed representation of its dynamics. It may thus improve clinical diagnosis and provide a revealing descriptive tool for mechanistic sleep disordered breathing modeling. Respiratory disturbance variable may contribute to attaining simplified screening methodologies, novel diagnostic criteria, and insightful research tools.
