ABSTRACT
PURPOSE: To investigate the possible association between ventilatory settings on the first day of invasive mechanical ventilation (IMV) and mortality in patients admitted to the intensive care unit (ICU) with severe acute respiratory infection (SARI). MATERIALS AND METHODS: In this pre-planned sub-study of a prospective, multicentre observational study, 441 patients with SARI who received controlled IMV during the ICU stay were included in the analysis. RESULTS: ICU and hospital mortality rates were 23.1 and 28.1%, respectively. In multivariable analysis, tidal volume and respiratory rate on the first day of IMV were not associated with an increased risk of death; however, higher driving pressure (DP: odds ratio (OR) 1.05; 95% confidence interval (CI): 1.01-1.1, p = 0.011), plateau pressure (Pplat) (OR 1.08; 95% CI: 1.04-1.13, p < 0.001) and positive end-expiratory pressure (PEEP) (OR 1.13; 95% CI: 1.03-1.24, p = 0.006) were independently associated with in-hospital mortality. In subgroup analysis, in hypoxemic patients and in patients with acute respiratory distress syndrome (ARDS), higher DP, Pplat, and PEEP were associated with increased risk of in-hospital death. CONCLUSIONS: In patients with SARI receiving IMV, higher DP, Pplat and PEEP, and not tidal volume, were associated with a higher risk of in-hospital death, especially in those with hypoxemia or ARDS.
Subject(s)
Positive-Pressure Respiration , Respiration, Artificial , Cohort Studies , Hospital Mortality , Humans , Intensive Care Units , Prospective Studies , Tidal VolumeABSTRACT
BACKGROUND AND AIM: There is a need to determine which clinical variables predict the severity of COVID-19. We analyzed a series of critically ill COVID-19 patients to see if any of our dataset's clinical variables were associated with patient outcomes. METHODS: We retrospectively analyzed the data of COVID-19 patients admitted to the ICU of the Hospital in Pordenone from March 11, 2020, to April 17, 2020. Patients' characteristics of survivors and deceased groups were compared. The variables with a different distribution between the two groups were implemented in a generalized linear regression model (LM) and in an Artificial Neural Network (NN) model to verify the "robustness" of the association with mortality. RESULTS: In the considered period, we reviewed the data of 22 consecutive patients: 8 died. The causes of death were a severe respiratory failure (3), multi-organ failure (1), septic shock (1), pulmonary thromboembolism (2), severe hemorrhage (1). Lymphocyte and the platelet count were significantly lower in the group of deceased patients (p-value 0.043 and 0.020, respectively; cut-off values: 660/mm3; 280,000/mm3, respectively). Prothrombin time showed a statistically significant trend (p-value= 0.065; cut-off point: 16.8/sec). The LM model (AIC= 19.032), compared to the NN model (Mean Absolute Error, MAE = 0.02), was substantially alike (MSE 0.159 vs. 0.136). CONCLUSIONS: In the context of critically ill COVID-19 patients admitted to ICU, lymphocytopenia, thrombocytopenia, and lengthening of prothrombin time were strictly correlated with higher mortality. Additional clinical data are needed to be able to validate this prognostic score.
Subject(s)
COVID-19 , Humans , Intensive Care Units , Neural Networks, Computer , Prognosis , Retrospective Studies , SARS-CoV-2ABSTRACT
BACKGROUND: Recently many serological assays for detection of antibodies to SARS-COV-2 virus were introduced on the market. Aim of this study was to assess the diagnostic performance of an automated CLIA for quantitative detection of anti-SARS-CoV-2 IgM and IgG antibodies. METHODS: A total of 354 sera, 89 from consecutive patients diagnosed with COVID-19 (43 mild, 32 severe and 13 critical) and 265 from asymptomatic and negative on rRT-PCR testing healthcare workers, were evaluated for IgM and IgG anti-SARS-CoV-2 antibodies with MAGLUMI immunoassay. RESULTS: The overall sensitivity and specificity were 86.5% (95%CI: 77.6-92.8) and 98.5% (95%CI:96.2-99.6), respectively. PPV, PPN, LR+, LR- and OR were 95.1 (95%CI: 87.8-98.6), 95.6 (95%CI: 92.4-97.7), 57.3 (95%CI: 21.6-152.1), 7.3 (95%CI: 4.31-12.4) and 418.6 (95%CI: 131.2-1335.2), respectively. The levels of SARS-CoV-2 IgM and IgG antibodies were 1.22 â± â1.2 AU/mL and 15.86 â± â24.83 AU/mL, 2.86 â± â2.4 AU/mL and 69.3 â± â55.5 AU/mL, 2.47 â± â1.33 AU/mL and 83.9 â± â83.9 AU/mL in mild, severe and critical COVID-19 groups, respectively. A significant difference in antibody levels between mild and severe/critical subjects has been shown. CONCLUSIONS: The CLIA assay showed good diagnostic performance and a significant association between antibody levels and severity of the disease was found.
ABSTRACT
BACKGROUND: Early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients who could develop a severe form of COVID-19 must be considered of great importance to carry out adequate care and optimise the use of limited resources. AIMS: To use several machine learning classification models to analyse a series of non-critically ill COVID-19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome. METHODS: We retrospectively analysed non-critically ill patients with COVID-19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected. RESULTS: In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64-0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction-inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors. CONCLUSIONS: In non-critically ill COVID-19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID-19, such as age or dementia, influence clinical outcomes.