RESUMEN
From the moment the SARS-CoV-2 virus was identified in December 2019, the COVID-19 disease spread around the world, causing an increase in hospitalisations and deaths. From the beginning of the pandemic, scientists tried to determine the major cause that led to patient deaths. In this paper, the background to creating a research model was diagnostic problems related to early assessment of the degree of damage to the lungs in patients with COVID-19. The study group comprised patients hospitalised in one of the temporary COVID hospitals. Patients admitted to the hospital had confirmed infection with SARS-CoV-2. At the moment of admittance, arterial blood was taken and the relevant parameters noted. The results of physical examinations, the use of oxygen therapy and later test results were compared with the condition of the patients in later computed tomography images and descriptions. The point of reference for determining the severity of the patient's condition in the computer imagery was set for a mild condition as consisting of a percentage of total lung parenchyma surface area affected no greater than 30%, an average condition of between 30% and 70%, and a severe condition as greater than 70% of the lung parenchyma surface area affected. Patients in a mild clinical condition most frequently had mild lung damage on the CT image, similarly to patients in an average clinical condition. Patients in a serious clinical condition most often had average levels of damage on the CT image. On the basis of the collected data, it can be said that at the moment of admittance, BNP, PE and HCO3- levels, selected due to the form of lung damage, on computed tomography differed from one another in a statistically significant manner (p < 0.05). Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of a physical examination and applied oxygen therapy. Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of BNP, HCO3 and BE parameters obtained from arterial blood.
RESUMEN
Objective: An accurate identification of patients at the need for prioritized diagnostics and care are crucial in the emergency department (ED). Blood gas (BG) analysis is a widely available laboratory test, which allows to measure vital parameters, including markers of ventilation and perfusion. The aim of our analysis was to assess whether blood gas parameters in patients with dyspnea at an increased risk of respiratory failure admitted to the ED can predict short-term outcomes. Methods: The study group eventually consisted of 108 patients, with available BG analysis. The clinical and laboratory parameters were retrospectively evaluated, and three groups were distinguished-arterial blood gas (ABG), venous blood gas (VBG), and mixed blood gas. The primary endpoint was short-term, all-cause mortality during the follow-up of median (quartile 1-quartile 3) 2 (1-4) months. The independent risk factors for mortality that could be obtained from blood gas sampling were evaluated. Results: The short-term mortality was 35.2% (38/108). Patients who died were more frequently initially assigned to the red triage risk group, more burdened with comorbidities, and the median SpO2 on admission was significantly lower than in patients who survived the follow-up period. In the multivariable analysis, lactate was the strongest independent predictor of death, with 1 mmol/L increasing all-cause mortality by 58% in ABG (95% CI: 1.01-2.47), by 80% in VBG (95% CI: 1.13-2.88), and by 68% in the mixed blood gas analysis (95% CI: 1.22-2.31), what remained significant in VBG and mixed group after correction for base excess. In each group, pH, pO2, and pCO2 did not predict short-term mortality. Conclusions: In patients admitted to the ED due to dyspnea, at risk of respiratory failure, lactate levels in arterial, venous, and mixed blood samples are independent predictors of short-term mortality.
RESUMEN
Hypercholesterolemia is the main cardiovascular (CV) risk factor with a large body of evidence. Our aim was to assess the achievement of the main therapeutic goal of Low-Density Lipoprotein Cholesterol (LDL-C) in patients with a very high CV risk and a high-dose statin therapy. The study group consisted of 1413 consecutive patients hospitalised at the Upper-Silesian Medical Centre in Katowice due to acute myocardial infarction (AMI) treated with atorvastatin ≥ 40 mg or rosuvastatin ≥ 20 mg. The lipid profile was performed on admission and within 12 months after AMI. The main therapeutic goal was defined as LDL-C < 55 mg%. The study group (n = 1413) included 979 males (69.3%) with arterial hypertension (83.3%), diabetes (33.5%), peripheral artery disease (13.6%) and nicotinism (46.2%). In the study group, only 61 patients (4.3%) were additionally taking ezetimibe. During hospitalisation, the primary LDL-C goal was found in only 186 patients (13.2%). Subsequently, a follow-up lipidogram within 12 months was performed in 652 patients (46%), and the therapeutic goal was achieved in 255 patients (39%). There were 258 (18.26%) patients who died within 12 months after myocardial infarction. The lowest mortality rate was found in the subgroup of patients with LDL-C < 55 mg% during follow-up (11.02%). The primary lipid goal attainment among patients with a high-dose statin and a very high CV risk is low and far from the expected rate. Patients hospitalised for AMI should be given a combination of statin and ezetimibe more frequently. Low LDL-C levels measured at follow-up predict a lower risk of death at 12-month follow-up in a large group of patients.
RESUMEN
Mallampati score has been identified and accepted worldwide as an independent predictor of difficult intubation and obstructive sleep apnea. We aimed to determine whether Mallampati score assessed on the first patient medical assessment allowed us to stratify the risk of worsening of conditions in patients hospitalized due to COVID-19. A total of 493 consecutive patients admitted between 13 November 2021 and 2 January 2022 to the temporary hospital in Pyrzowice were included in the analysis. The clinical data, chest CT scan, and major, clinically relevant laboratory parameters were assessed by patient-treating physicians, whereas the Mallampati score was assessed on admission by investigators blinded to further treatment. The primary endpoints were necessity of active oxygen therapy (AOT) during hospitalization and 60-day all-cause mortality. Of 493 patients included in the analysis, 69 (14.0%) were in Mallampati I, 57 (11.6%) were in Mallampati II, 78 (15.8%) were in Mallampati III, and 288 (58.9%) were in Mallampati IV. There were no differences in the baseline characteristics between the groups, except the prevalence of chronic kidney disease (p = 0.046). Patients with Mallampati IV were at the highest risk of AOT during the hospitalization (33.0%) and the highest risk of death due to any cause at 60 days (35.0%), which significantly differed from other scores (p = 0.005 and p = 0.03, respectively). Mallampati IV was identified as an independent predictor of need for AOT (OR 3.089, 95% confidence interval 1.65−5.77, p < 0.001) but not of all-cause mortality at 60 days. In conclusion, Mallampati IV was identified as an independent predictor of AOT during hospitalization. Mallampati score can serve as a prehospital tool allowing to identify patients at higher need for AOT.