Causes of suboptimal preoxygenation before tracheal intubation in elective and emergency abdominal surgery

Optimal preoxygenation (PO) prior to tracheal intubation reduces the risk of arterial desaturationand prolongs the period of safe apnoea. The common methods of PO are mask ventilation with100% O2for 3–5 minutes or, alternatively, asking the patient to take eight deep breaths in aminute. Our study group conducted a prospective study to assess the impact of the most com-mon risk factors on PO and to compare the efficiency of PO in patients undergoing elective andemergency abdominal surgery without premedication. PO was performed using mask ventilationwith 6 l/min of 100% oxygen for 5 minutes. End-tidal oxygen (EtO2) was documented in 30-second increments. We found that optimal PO (EtO2> 90%) was not achieved by almost half ofthe patients (46%) and that this was more common in the elective surgery group. Effective POwas not impacted by any of the evaluated risk factors for suboptimal oxygenation. Despite thesefindings, we believe that the identification of potential risk factors is crucial in the pre-anaesthesiastage, given the benefits of optimal PO

Comparison of mortality risk evaluation tools efficacy in critically ill COVID-19 patients

Background: As the COVID-19 pandemic continues, the number of patients admitted to the intensive care unit (ICU) is still increasing. The aim of our article is to estimate which of the conventional ICU mortality risk scores is the most accurate at predicting mortality in COVID-19 patients and to determine how these scores can be used in combination with the 4C Mortality Score. Methods: This was a retrospective study of critically ill COVID-19 patients treated in tertiary reference COVID-19 hospitals during the year 2020. The 4C Mortality Score was calculated upon admission to the hospital. The Simplified Acute Physiology Score (SAPS) II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sequential Organ Failure Assessment (SOFA) scores were calculated upon admission to the ICU. Patients were divided into two groups: ICU survivors and ICU non-survivors. Results: A total of 249 patients were included in the study, of which 63.1% were male. The average age of all patients was 61.32 ± 13.3 years. The all-cause ICU mortality ratio was 41.4% (n = 103). To determine the accuracy of the ICU mortality risk scores a ROC-AUC analysis was performed. The most accurate scale was the APACHE II, with an AUC value of 0.772 (95% CI 0.714–0.830; p < 0.001). All of the ICU risk scores and 4C Mortality Score were significant mortality predictors in the univariate regression analysis. The multivariate regression analysis was completed to elucidate which of the scores can be used in combination with the independent predictive value. In the final model, the APACHE II and 4C Mortality Score prevailed. For each point increase in the APACHE II, mortality risk increased by 1.155 (OR 1.155, 95% CI 1.085–1.229; p < 0.001), and for each point increase in the 4C Mortality Score, mortality risk increased by 1.191 (OR 1.191, 95% CI 1.086–1.306; p < 0.001), demonstrating the best overall calibration of the model. Conclusions: The study demonstrated that the APACHE II had the best discrimination of mortality in ICU patients. Both the APACHE II and 4C Mortality Score independently predict mortality risk and can be used concomitantly

Role of fat-free mass index on amino acid loss during CRRT in critically Ill patients

Background and objectives: Amino acid (AA) loss is a prevalent unwanted effect of continuous renal replacement therapy (CRRT) in critical care patients, determined both by the machine set-up and individual characteristics. The aim of this study was to evaluate the bioelectrical impedance analysis-derived fat-free mass index (FFMI) effect on amino acid loss. Materials and methods: This was a prospective, observational, single sample study of critical care patients upon initiation of CRRT. AA loss during a 24 h period was estimated. Conventional determinants of AA loss (type and dose of CRRT, concentration of AA) and FFMI were entered into the multivariate regression analysis to determine the individual predictive value. Results: Fifty-two patients were included in the study. The average age was 66.06 ± 13.60 years; most patients had a high mortality risk with APAHCE II values of 22.92 ± 8.15 and SOFA values of 12.11 ± 3.60. Mean AA loss in 24 h was 14.73 ± 9.83 g. There was a significant correlation between the lost AA and FFMI (R = 0.445, B = 0.445 CI95%: 0.541–1.793 p = 0.02). Multivariate regression analysis revealed the independent predictors of lost AA to be the systemic concentration of AA (B = 6.99 95% CI:4.96–9.04 p = 0.001), dose of CRRT (B = 0.48 95% CI:0.27–0.70 p < 0.001) and FFMI (B = 0.91 95% CI:0.42–1.41 p < 0.001). The type of CRRT was eliminated in the final model due to co-linearity with the dose of CRRT. Conclusions: A substantial amount of AA is lost during CRRT. The amount lost is increased by the conventional factors as well as by higher FFMI. Insights from our study highlight the FFMI as a novel research object during CRRT, both when prescribing the dosage and evaluating the nutritional support needed