Correlation between arterial blood gas and CT volumetry in patients with SARS-CoV-2 in the emergency department

Quantitative assessment of lung opacities from CT of pulmonary artery imaging data in COVID-19 patients: artificial intelligence versus radiologist

Objectives

Artificial intelligence (AI) deep learning algorithms trained on non-contrast CT scans effectively detect and quantify acute COVID-19 lung involvement. Our study explored whether radiological contrast affects the accuracy of AI-measured lung opacities, potentially impacting clinical decisions. We compared lung opacity measurements from AI software with visual assessments by radiologists using CT pulmonary angiography (CTPA) images of early-stage COVID-19 patients.

Methods

This prospective single-centre study included 18 COVID-19 patients who underwent CTPA due to suspected pulmonary embolism. Patient demographics, clinical data, and 30-day and 90-day mortality were recorded. AI tool (Pulmonary Density Plug-in, AI-Rad Companion Chest CT, SyngoVia; Siemens Healthineers, Forchheim, Germany) was used to estimate the quantity of opacities. Visual quantitative assessments were performed independently by 2 radiologists.

Results

There was a positive correlation between radiologist estimations (r 2 = 0.57) and between the AI data and the mean of the radiologists' estimations (r 2 = 0.70). Bland-Altman plot analysis showed a mean bias of +3.06% between radiologists and -1.32% between the mean radiologist vs AI, with no outliers outside 2×SD for respective comparison.

The AI protocol facilitated a quantitative assessment of lung opacities and showed a strong correlation with data obtained from 2 independent radiologists, demonstrating its potential as a complementary tool in clinical practice.

Conclusion

In assessing COVID-19 lung opacities in CTPA images, AI tools trained on non-contrast images, provide comparable results to visual assessments by radiologists.

Advances in knowledge

The Pulmonary Density Plug-in enables quantitative analysis of lung opacities in COVID-19 patients using contrast-enhanced CT images, potentially streamlining clinical workflows and supporting timely decision-making.

Assessment of the Severity of COVID-19 on the Basis of Examination and Laboratory Diagnostics in Relation to Computed Tomography Imagery of Patients Hospitalised Due to COVID-19-Single-Centre Study

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.

COVID-19 therapy optimization by AI-driven biomechanical simulations

The COVID-19 disease causes pneumonia in many patients that in the most serious cases evolves into the Acute Distress Respiratory Syndrome (ARDS), requiring assisted ventilation and intensive care. In this context, identification of patients at high risk of developing ARDS is a key point for early clinical management, better clinical outcome and optimization in using the limited resources available in the intensive care units. We propose an AI-based prognostic system that makes predictions of oxygen exchange with arterial blood by using as input lung Computed Tomography (CT), the air flux in lungs obtained from biomechanical simulations and Arterial Blood Gas (ABG) analysis. We developed and investigated the feasibility of this system on a small clinical database of proven COVID-19 cases where the initial CT and various ABG reports were available for each patient. We studied the time evolution of the ABG parameters and found correlation with the morphological information extracted from CT scans and disease outcome. Promising results of a preliminary version of the prognostic algorithm are presented. The ability to predict the evolution of patients' respiratory efficiency would be of crucial importance for disease management.

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study

INTRODUCTION

Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score.

METHODS

We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score.

RESULTS

Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F < 164.5 showed more frequently a severe CT score than those with LDH< 367.5 U/L and P/F> 164.5, 83.4%, vs 20%, respectively.

CONCLUSIONS

A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities.

Factors which impact the length of hospitalisation and death rate of COVID-19 patients based on initial triage using capillary blood gas tests: a single centre study

An important element in the effective treatment of patients infected with the SARS-CoV-2 virus during the pandemic is an effective early triage to determine patient allocation and in-patient therapy. This paper assesses the prognostic value of capillary blood gas tests in predicting extended hospitalisation and death due to COVID-19. This retrospective statistical research is based on a group of 200 patients, hospitalised from 15 October 2020 to 08 March 2021. The study utilised the treatment documentation of these patients hospitalised due to COVID-19 at the Pulmonology and Thoracic Surgery Centre in Bystra (Southern Poland) during this period. The hospital has 50 beds with access to oxygen for COVID-19 patients and a five-bed intensive care unit. On the basis of the obtained results, conclusions were drawn that the need for early oxygen therapy with an oxygen mask and low pH values in capillary blood are significant risk factors for prolonging hospitalisation due to COVID-19. Age, the need for early oxygen mask therapy and low oxygen saturation are important risk factors for death from COVID-19. Capillary blood gas analysis is a simple and effective method of early in-patient segregation of COVID-19 patients.

Inappropriate Ventilatory Homeostatic Responses in Hospitalized COVID-19 Patients

Background

The clinical presentation of COVID-19 suggests altered breathing control - tachypnoea, relative lack of dyspnoea, and often a discrepancy between severity of clinical and radiological findings. Few studies characterize and analyse the contribution of breathing drivers and their ventilatory and perceptual responses.

Aim

To establish the prevalence of inappropriate ventilatory and perceptual response in COVID-19, by characterizing the relationships between respiratory rate (RR), dyspnoea and arterial blood gas (ABG) in a cohort of COVID-19 patients at presentation to hospital, and their post-Covid respiratory sequelae at follow-up.

Methods

We conducted a retrospective cohort study including consecutive adult patients admitted to hospital with confirmed COVID-19 between 1st March 2020 and 30th April 2020. In those with concurrent ABG, RR and documented dyspnoea status on presentation, we documented patient characteristics, disease severity, and outcomes at hospital and 6-week post-discharge.

Results

Of 492 admissions, 194 patients met the inclusion criteria. Tachypnoea was present in 75% pronounced (RR>30) in 36%, and persisted during sleep. RR correlated with heart rate (HR) (r = 0.2674), temperature (r = 0.2824), CRP (r = 0.2561), Alveolar-arterial (A-a) gradient (r = 0.4189), and lower PaO₂/FiO₂ (PF) ratio (r = −0.3636). RR was not correlated with any neurological symptoms. Dyspnoea was correlated with RR (r = 0.2932), A-a gradient (r = 0.1723), and lower PF ratio (r = −0.1914), but not correlated with PaO₂ (r = −0.1095), PaCO₂ (r = −0.0598) or any recorded neurological symptom except for altered consciousness. Impaired ventilatory homeostatic control of pH/PaCO₂ [tachypnoea (RR>20), hypocapnia (PaCO₂ <4.6 kPa), and alkalosis (pH>7.45)] was observed in 29%. This group, of which 37% reported no dyspnoea, had more severe respiratory disease (A-a gradient 38.9 vs. 12.4 mmHg; PF ratio 120 vs. 238), and higher prevalence of anosmia (21 vs. 15%), dysgeusia (25 vs. 12%), headache (33 vs. 23%) and nausea (33 vs. 14%) with similar rates of new anxiety/depression (26 vs. 23%), but lower incidence of past neurological or psychiatric diagnoses (5 vs. 21%) compared to appropriate responders. Only 5% had hypoxia sufficiently severe to drive breathing (i.e. PaO₂ <6.6 kPa). At 6 weeks post-discharge, 24% (8/34) showed a new breathing pattern disorder with no other neurological findings, nor previous respiratory, neurological, or psychiatric disorder diagnoses.

Conclusions

Impaired homeostatic control of ventilation i.e., tachypnoea, despite hypocapnia to the point of alkalosis appears prevalent in patients admitted to hospital with COVID-19, a finding typically accompanying more severe disease. Tachypnoea prevalence was between 12 and 29%. Data suggest that excessive tachypnoea is driven by both peripheral and central mechanisms, but not hypoxia. Over a third of patients with impaired homeostatic ventilatory control did not experience dyspnoea despite tachypnoea. A subset of followed-up patients developed post-covid breathing pattern disorder.

Chronic diseases, chest computed tomography, and laboratory tests as predictors of severe respiratory failure and death in elderly Brazilian patients hospitalized with COVID-19: a prospective cohort study

BACKGROUND

The primary risk factors for severe respiratory failure and death in the elderly hospitalized with COVID-19 remain unclear.

OBJECTIVE

To determine the association of chronic diseases, chest computed tomography (CT), and laboratory tests with severe respiratory failure and mortality in older adults hospitalized with COVID-19.

METHOD

This was a prospective cohort with 201 hospitalized older adults with COVID-19. Chronic diseases, chest CT, laboratory tests, and other data were collected within the first 48 h of hospitalization. Outcomes were progression to severe respiratory failure with the need of mechanical ventilation (SRF/MV) and death.

RESULTS

The mean age was 72.7 ± 9.2 years, and 63.2% were men. SRF/MV occurred in 16.9% (p < 0.001), and death occurred in 8%. In the adjusted regression analyses, lung involvement over 50% [odds ratio (OR): 3.09 (1.03-9.28; 0.043)], C-reactive protein (CRP) > 80 ng/mL [OR: 2.97 (0.99-8.93; 0.052)], Vitamin D < 40 ng/mL [OR: 6.41 (1.21-33.88; 0.029)], and hemoglobin < 12 g/mL [OR: 3.32 (1.20-9.20; 0.020)] were independent predictors for SFR/MV, while chronic atrial fibrillation [OR: 26.72 (3.87-184.11; 0.001)], cancer history [OR:8.32 (1.28-53.91; 0.026)] and IL-6 > 40 pg/mL [OR:10.01 (1.66-60.13; 0.012)] were independent predictors of death.

CONCLUSION

In hospitalized older adults with COVID-19, tomographic pulmonary involvement > 50%, anemia, vitamin D below 40 ng/mL, and CRP above 80 mg/L were independent risk factors for progression to SRF/MV. The presence of chronic atrial fibrillation, previous cancer, IL-6 > 40 pg/mL, and anemia were independent predictors of death.

Coronavirus Disease 2019 Phenotypes, Lung Ultrasound, Chest Computed Tomography and Clinical Features in Critically Ill Mechanically Ventilated Patients

Chest computed tomography (CT) may provide insights into the pathophysiology of coronavirus disease 2019 (COVID-19), although it is not suitable for a timely bedside dynamic assessment of patients admitted to intensive care unit (ICU); therefore, lung ultrasound (LUS) has been proposed as a complementary diagnostic tool. The aims of this study were to investigate different lungs phenotypes in patients with COVID-19 and to assess the differences in CT and LUS scores between ICU survivors and non-survivors. We also explored the association between CT and LUS, and oxygenation (arterial partial pressure of oxygen [PaO₂]/fraction of inspired oxygen [FiO_2]) and clinical parameters. The study included 39 patients with COVID-19. CT scans revealed types 1, 2 and 3 phenotypes in 62%, 28% and 10% of patients, respectively. Among survivors, pattern 1 was prevalent (p < 0.005). Chest CT and LUS scores differed between survivors and non-survivors both at ICU admission and 10 days after and were associated with ICU mortality. Chest CT score was positively correlated with LUS findings at ICU admission (r = 0.953, p < 0.0001) and was inversely correlated with PaO₂/FiO₂ (r = -0.375, p = 0.019) and C-reactive protein (r = 0.329, p = 0.041). LUS score was inversely correlated with PaO₂/FiO₂ (r = -0.345, p = 0.031). COVID-19 presents distinct phenotypes with differences between survivors and non-survivors. LUS is a valuable monitoring tool in an ICU setting because it may correlate with CT findings and mortality, although it cannot predict oxygenation changes.

Correlation between venous blood gas indices and radiological involvements of COVID-19 patients at first admission to emergency department

BACKGROUND

The purpose of this study was to investigate the relation between venous blood gas and chest computerized tomography findings and the clinical conditions of COVID-19 pneumonia.

METHODS

A total of 309 patients admitted to the emergency department and subsequently confirmed COVID-19 cases was examined. Patients with pneumonia symptoms, chest computerized tomography scan, venous blood gas findings, and confirmed COVID-19 on reverse transcription-polymerase chain reaction (PCR) were consecutively enrolled. Multiple linear regression was used to predict computerized tomography and blood gas findings by clinical/laboratory data.

RESULTS

The median age of patients was 51 (interquartile range 39-66), and 51.5% were male. The mortality rate at the end of follow-up was 18.8%. With respect to survival status of patients pCO2 and HCO3 levels and total computerized tomography score values were found to be higher in the surviving patients (p<0.001 and p=0.003, respectively), whereas pH and lactate levels were higher in patients who died (p=0.022 and p=0.001, respectively). With logistic regression analysis, total tomography score was found to be significantly effective on mortality (p<0.001). The diffuse and random involvement of the lungs had a significant effect on mortality (p<0.001, 95%CI 3.853-38.769, OR 12.222 and p=0.027; 95%CI 1.155-11.640, OR 3.667, respectively). With linear regression analysis, the effect of pH and lactate results were found to have a positive effect on total tomography score (p=0.003 and p<0.001, respectively), whereas pCO2 was found to have a negative effect (p=0.029).

CONCLUSION

There was correlation between venous blood gas indices and radiologic scores in COVID-19 patients. Venous blood gas taken in emergency department can be a fast, applicable, minor-invasive, and complementary test in terms of diagnosing COVID-19 pneumonia and predicting the prognosis of disease.

Mortality Predictors in Patients Diagnosed with COVID-19 in the Emergency Department: ECG, Laboratory and CT

Background and Objectives: The aim of this study was to investigate parameters that can be used to predict mortality in patients diagnosed with COVID-19 in the emergency department (ED). Materials and Methods: Patients diagnosed with COVID-19 in the ED were included in this prospective study. The patients were divided into two groups. The surviving patients were included in Group 1 (survivors), and the patients who died were included in Group 2 (non-survivors). The electrocardiogram (ECG), laboratory results and chest computerized tomography (CCT) findings of the two groups were compared. The CCT images were classified according to the findings as normal, mild, moderate and severe. Results: Of the 419 patients included in the study, 347 (83%) survived (survivor) and 72 (17%) died (non-survivor). The heart rate and respiratory rate were found to be higher, and the peripheral oxygen saturation (SpO2) and diastolic blood pressure (DBP) were found to be lower in the non-survivor patients. QRS and corrected QT interval (QTc) were measured as longer in the non-survivor patients. In the CCT images, 79.2% of the non-survivor patients had severe findings, while 11.5% of the survivor patients had severe findings. WBC, neutrophil, NLR, lactate, D-dimer, fibrinogen, C- Reactive Protein (CRP), urea, creatinine, creatine kinase-MB (CK-MB) and hs-Troponin I levels were found to be higher and partial pressure of carbon dioxide (PCO2), base excess (BE), bicarbonate (HCO3), lymphocyte eosinophil levels were found to be lower in non-survivor patients. The highest AUC was calculated at the SpO2 level and the eosinophil level. Conclusions: COVID-19 is a fatal disease whose mortality risk can be estimated when the clinical, laboratory and imaging studies of the patients are evaluated together in the ED. SpO2 that is measured before starting oxygen therapy, the eosinophil levels and the CT findings are all important predictors of mortality risk.

The ROX index can be a useful tool for the triage evaluation of COVID-19 patients with dyspnoea

Aim

To assess whether the application of a non‐invasive tool, such as ratio of oxygen saturation (ROX) index, during triage can identify patients with COVID‐19 at high risk of developing acute respiratory distress syndrome (ARDS).

Design

A multi‐centre, observational, retrospective study.

Methods

Only COVID‐19 positive patients who required an emergency department evaluation for dyspnoea were considered. The primary objective of the study was to compare the ROX value obtained during triage with the medical diagnosis of ARDS and intubation in 72 h of the triage evaluation. The ROX index value was also compared with objective outcomes, such as the pressure of arterial O₂ (PaO₂)/fraction of inspired oxygen (FiO₂) ratio and the lung parenchyma volume involved in COVID‐19‐related inflammatory processes, based on 3D reconstructions of chest computed tomography (CT).

Results

During the study period, from 20 March 2020 until 31 May 2020, a total of 273 patients with confirmed SARS‐CoV‐2 infection were enrolled. The predictive ability of ROX for the risk of developing ARDS in 72 h after triage evaluation was associated with an area under the receiver operating characteristic (AUROC) of 0.845 (0.797–0.892, p < 0.001), whereas the AUROC value was 0.727 (0.634–0.821, p < 0.001) for the risk of intubation. ROX values were strongly correlated with PaO₂/FiO₂ values (r = 0.650, p < 0.001), decreased ROX values were associated with increased percentages of lung involvement based on 3D CT reconstruction (r = −0.371, p < 0.001).

Conclusion

The ROX index showed a good ability to identify triage patients at high evolutionary risk. Correlations with objective but more invasive indicators (PaO₂/FiO₂ and CT) confirmed the important role of ROX in identifying COVID‐19 patients with extensive pathological processes.

Impact

During the difficult triage evaluation of COVID‐19 patients, the ROX index can help the nurse to identify the real severity of the patient. The triage systems could integrate the ROX in the rapid patient assessment to stratify patients more accurately.

Current methods and prospects of coronavirus detection

SARS-COV-2 is a novel coronavirus discovered in Wuhan in December 30, 2019, and is a family of SARS-COV (severe acute respiratory syndrome coronavirus), that is, coronavirus family. After infection with SARS-COV-2, patients often experience fever, cough, gas prostration, dyspnea and other symptoms, which can lead to severe acute respiratory syndrome (SARS), kidney failure and even death. The SARS-COV-2 virus is particularly infectious and has led to a global infection crisis, with an explosion in the number of infections. Therefore, rapid and accurate detection of the virus plays a vital role. At present, many detection methods are limited in their wide application due to their defects such as high preparation cost, poor stability and complex operation process. Moreover, some methods need to be operated by professional medical staff, which can easily lead to infection. In order to overcome these problems, a Surface molecular imprinting technology (SM-MIT) is proposed for the first time to detect SARS-COV-2 virus. For this SM-MIT method, this review provides detailed detection principles and steps. In addition, this method not only has the advantages of low cost, high stability and good specificity, but also can detect whether it is infected at designated points. Therefore, we think SM-MIT may have great potential in the detection of SARS-COV-2 virus.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

Chest CT imaging features of COVID-19 and its correlation with the PaO2/FiO2 ratio: a multicenter study in Upper Egypt

Background

The main challenge in managing COVID-19 pandemic is containment of the infection by early detection of the disease and wide dissemination of diagnostic tests with high sensitivity and specificity. Various imaging features were identified by chest CT with different patterns from early disease to diffuse disease with complications. However, CT cannot be performed for all patients. The arterial oxygen partial pressure/fraction of inspired oxygen (PaO₂/FiO₂) ratio is evaluated as a rapid and widely available test for the preliminary assessment of disease severity. This study aimed to evaluate the clinical and chest CT imaging features of COVID-19 in Egyptian patients as well as assess the correlation between the chest CT total severity score and the PaO₂/FiO₂ ratio to determine its value for preliminary assessment of disease severity.

Results

The most common symptoms were fever (83.2%), dry cough (77%), malaise (68.8%), prolonged headaches (48.5%), and dyspnea (37.6%). CT was positive in 79.2% of the patients. The CT features at presentation were ground-glass opacities only (40%), ground-glass opacities with consolidation (34.4%), and consolidation only (25.6%). Associated findings included crazy paving (17.5%), interlobular septal thickening (47.5%), air bronchogram (15%), bronchiectasis (12.8%), fibrous bands (8.1%), vascular enlargement within the lesion (45.6%), nodules (6.8%), pericardial thickening (5%), and pleural thickening (24.7%). The lesions were typically multilobar (50.5%), posterior (58.1%) with peripheral and central distribution (41.9%). Moderate negative correlation was observed between the CT total lung severity score and PaO₂/FiO₂ ratio with r = − 0.42 and P < 0.001.

Conclusion

The most common pattern of COVID-19 pneumonia in multiple quarantine hospitals was peripheral and central ground-glass opacities with bilateral multilobe posterior involvement and fever was the most common symptom. PaO₂/FiO₂ ratio has a moderate negative correlation with the CT total severity score and thus can be used in the preliminary assessment of disease severity.

Correlating arterial blood gas, acid-base and blood pressure abnormalities with outcomes in COVID-19 intensive care patients

BACKGROUND

During the outbreak of coronavirus disease 2019 (COVID-19), many studies have investigated laboratory biomarkers in management and prognostication of COVID-19 patients, however to date, few have investigated arterial blood gas, acid-base and blood pressure patterns. The aim of the study is to assess the arterial blood gas and acid-base patterns, blood pressure findings and their association with the outcomes of COVID-19 patients admitted to an intensive care unit.

METHODS

A single-centre retrospective, observational study in a dedicated COVID-19 intensive care unit in Cape Town, South Africa. Admission arterial blood gas, serum electrolytes, renal function and blood pressure readings performed on COVID-19 patients admitted between 26 March and 2 June 2020 were analysed and compared between survivors and non-survivors.

RESULTS

A total of 56 intensive care unit patients had admission arterial blood gas performed at the time of intensive care unit admission. An alkalaemia (pH > 7.45) was observed in 36 (64.3%) patients. A higher arterial pH (median 7.48 [interquartile range: 7.45-7.51] versus 7.46 [interquartile range: 7.40-7.48], P = 0.049) and partial pressure of oxygen in arterial blood (median 7.9 kPa [interquartile range: 7.3-9.6] versus 6.5 kPa [interquartile range: 5.2-7.3], P = <0.001) were significantly associated with survival. Survivors also tended to have a higher systolic blood pressure (median: 144 mmHg [interquartile range: 134-152] versus 139 mmHg [interquartile range: 125-142], P = 0.078) and higher arterial HCO₃ (median: 28.0 mmol/L [interquartile range: 25.7-28.8] versus 26.3 mmol/L [interquartile range: 24.3-27.9], P = 0.059).

CONCLUSIONS

The majority of the study population admitted to intensive care unit had an alkalaemia on arterial blood gas. A higher pH and lower partial pressure of oxygen in arterial blood on arterial blood gas analysis were significantly associated with survival.