Purpose: To validate the role of Macklin effect on chest CT imaging in predicting subsequent occurrence of pneumomediastinum/pneumothorax (PMD/PNX) in COVID-19 patients.

Materials And Methods: This is an observational, case-control study. Consecutive COVID-19 patients who underwent chest CT scan at hospital admission during the study time period (October 1st, 2020-April 31st, 2021) were identified. Macklin effect accuracy for prediction of spontaneous barotrauma was measured in terms of sensitivity, specificity, positive (PPV) and negative predictive values (NPV).

Results: Overall, 981 COVID-19 patients underwent chest CT scan at hospital arrival during the study time period; 698 patients had radiological signs of interstitial pneumonia and were considered for further evaluation. Among these, Macklin effect was found in 33 (4.7%), including all 32 patients who suffered from barotrauma lately during hospital stay (true positive rate: 96.9%); only 1/33 with Macklin effect did not develop barotrauma (false positive rate: 3.1%). No barotrauma event was recorded in patients without Macklin effect on baseline chest CT scan. Macklin effect yielded a sensitivity of 100% (95% CI: 89.1-100), a specificity of 99.85% (95% CI: 99.2-100), a PPV of 96.7% (95% CI: 80.8-99.5), a NPV of 100% and an accuracy of 99.8% (95% CI: 99.2-100) in predicting PMD/PNX, with a mean advance of 3.2 ± 2.5 days. Moreover, all Macklin-positive patients developed ARDS requiring ICU admission and, in 90.1% of cases, invasive mechanical ventilation.

Conclusions: Macklin effect has high accuracy in predicting PMD/PNX in COVID-19 patients; it is also an excellent predictor of disease severity.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020841PMC
http://dx.doi.org/10.1016/j.rmed.2022.106853DOI Listing

Publication Analysis

Top Keywords

chest scan
16
covid-19 patients
16
macklin
8
macklin baseline
8
baseline chest
8
patients
8
pmd/pnx covid-19
8
patients underwent
8
underwent chest
8
scan hospital
8

Similar Publications

Accuracy of Fully Automated and Human-assisted AI-based CT Quantification of Pleural Effusion Changes after Thoracentesis.

Radiol Artif Intell

January 2025

From the Department of Radiology (E.J.H., S.K., H.K., D. K., S.H.Y.) and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak- ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine (E.J.H., H.K., S.H.Y.), Seoul, Korea; Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine (S-J.Y., Seoul, Korea).

Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023.

View Article and Find Full Text PDF

A 52-year-old female patient with a history of atrial septal defect repair presented with progressive dyspnea and echocardiographic findings suggestive of pulmonary hypertension (PH). Incidentally, a lung mass was discovered on computed tomography (CT). Initial evaluation revealed World Health Organization functional class III symptoms and significant weight loss.

View Article and Find Full Text PDF

A 76-year-old man with a past occupational history as a firefighter and construction worker presented at an urgent care center with signs and symptoms of chronic dry cough, exertional dyspnea, and fatigue. His initial chest X-ray showed interstitial thickening in the middle and lower lobes with pulmonary infiltrates bilaterally. The patient was treated with an outpatient course of antibiotics.

View Article and Find Full Text PDF

We report a rare case of urinary bladder neuroendocrine tumour (NET) in a young, non-smoking man. He had no known risk factors and no comorbidities. After being diagnosed with a bladder tumour while being investigated for flank pain and poor renal function, he was treated with transurethral resection of the bladder tumour and deroofing of ureters bilaterally.

View Article and Find Full Text PDF

Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!