Pulmonary artery air embolism with consequent primary respiratory alkalosis and secondary metabolic alkalosis following ventilation therapy: A case report.

BACKGROUND: An air embolism is a rare complication that occurs after air enters blood vessels, causing almost no to mild symptoms in patients. Although uncommon, air embolism can be deadly. Critical care professionals should know the warning signs of air embolism and be prepared to carry out the necessary therapeutic interventions. To reduce morbidity and death, this clinical condition must be identified early. Here we are presenting a case of pulmonary artery air embolism as a consequence of contrast agent injection in a chest computed tomography study. CASE PRESENTATION: A 70-year-old male patient were presented with pulmonary artery air embolism as a consequence of contrast agent injection in a chest computed tomography study. The patient experienced worsening respiratory symptoms that necessitated oxygen therapy, which resulted in respiratory alkalosis with secondary metabolic alkalosis. Following removal of the BiLevel positive airway pressure, the patient was switched to a 2-L nasal cannula, and his breathing rate increased to 34 breaths/min. After 8.5 hours of monitoring the patient's vital signs, the nasal cannula was removed, and the patient began breathing room air on his own. His vital signs then stabilized and arterial blood gas parameters returned to normal. The patient's condition improved, and he was discharged from the hospital after 9 days. Due to a high level of cytomegalovirus, the discharge prescriptions included valganciclovir film-coated tablets (900 mg, oral BID every 12 hours for 30 days) and apixaban (5 mg BID). The patient was then monitored at the outpatient clinic. CONCLUSION: Although rare, an air embolism can cause minor symptoms if it is small in volume or can be fatal if large. After contrast-enhanced radiological studies, physicians should be aware of any signs of respiratory distress or worsening of symptoms in their patients. Additionally, patients should be mindful of the potential complications associated with ventilation therapy.

Haematological and radiological-based prognostic markers of COVID-19.

BACKGROUND: Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged in 2019 and caused a global pandemic in 2020, manifesting in the coronavirus disease 2019 (COVID-19). The majority of patients exhibit a mild form of the disease with no major complications; however, moderate to severe and fatal cases are of public health concerns. Predicting the potential prognosis of COVID-19 could assist healthcare workers in managing cases and controlling the pandemic in an effective way. Therefore, the objectives of the study were to search for biomarkers associated with COVID-19 mortality and predictors of the overall survival (OS). METHODS: Here, clinical data of 6026 adult COVID-19 patients admitted to two large centers in Saudi Arabia (Riyadh and Hafar Al-Batin cities) between April and June 2020 were retrospectively analysed. RESULTS: More than 23% of the study subjects with available data have died, enabling the prediction of mortality in our cohort. Markers that were significantly associated with mortality in this study were older age, increased d-dimer in the blood, higher counts of WBCs, higher percentage of neutrophil, and a higher chest X-ray (CXR) score. The CXR scores were also positively associated with age, d-dimer, WBC count, and percentage of neutrophil. This supports the utility of CXR scores in the absence of blood testing. Predicting mortality based on Ct values of RT-PCR was not successful, necessitating a more quantitative RT-PCR to determine virus quantity in samples. Our work has also identified age, d-dimer concentration, leukocyte parameters and CXR score to be prognostic markers of the OS of COVID-19 patients. CONCLUSION: Overall, this retrospective study on hospitalised cohort of COVID-19 patients presents that age, haematological, and radiological data at the time of diagnosis are of value and could be used to guide better clinical management of COVID-19 patients.

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE: The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS: Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS: In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION: The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.

Implications of COVID-19 pandemic on lung cancer management: A multidisciplinary perspective.

Treatment of patients with lung cancer during the current COVID-19 pandemic is challenging. Lung cancer is a heterogenous disease with a wide variety of therapeutic options. Oncologists have to determine the risks and benefits of modifying the treatment plans of patients especially in situation where the disease biology and treatment are complex. Health care visits carry a risk of transmission of SARS-CoV-2 and the similarities of COVID-19 symptoms and lung cancer manifestations represent a dominant problem. Efforts to modify treatment of lung cancer during the current pandemic have been adapted by many healthcare institutes to reduce exposure of lung cancer patients to SARS-CoV-2. We summarized the implications of COVID-19 pandemic on the management of lung cancer from the perspective of different specialties of thoracic oncology multidisciplinary team.

Chest Magnetic Resonance Imaging Decreases Inter-observer Variability of Gross Target Volume for Lung Tumors.

Purpose: PET/CT is a standard medical imaging used in the delineation of gross tumor volume (GTV) in case of radiation therapy for lung tumors. However, PET/CT could present some limitations such as resolution and standardized uptake value threshold. Moreover, chest MRI has shown good potential in diagnosis for thoracic oncology. Therefore, we investigated the influence of chest MRI on inter-observer variability of GTV delineation. Methods and Materials: Five observers contoured the GTV on CT for 14 poorly defined lung tumors during three contouring phases based on true daily clinical routine and acquisition: CT phase, with only CT images; PET phase, with PET/CT; and MRI phase, with both PET/CT and MRI. Observers waited at least 1 week between each phases to decrease memory bias. Contours were compared using descriptive statistics of volume, coefficient of variation (COV), and Dice similarity coefficient (DSC). Results: MRI phase volumes (median 4.8 cm3) were significantly smaller than PET phase volumes (median 6.4 cm3, p = 0.015), but not different from CT phase volumes (median 5.7 cm3, p = 0.30). The mean COV was improved for the MRI phase (0.38) compared to the CT (0.58, p = 0.024) and PET (0.53, p = 0.060) phases. The mean DSC of the MRI phase (0.67) was superior to those of the CT and PET phases (0.56 and 0.60, respectively; p < 0.001 for both). Conclusions: The addition of chest MRI seems to decrease inter-observer variability of GTV delineation for poorly defined lung tumors compared to PET/CT alone and should be explored in further prospective studies.