Respiratory Infections in the Aging Lung: Implications for Diagnosis, Therapy, and Prevention.

Respiratory infections pose a significant health problem among elderly individuals, particularly during the COVID-19 pandemic. The increased mortality and morbidity rates among individuals over 65 highlight the criticality of these infections. The normal aging process in the lungs increases vulnerability to respiratory infections due to the accumulation of cellular damage and senescence. Consequently, the lung environment undergoes major changes in mechanical function and other systemic factors. This review aims to examine the influence of aging on respiratory infections from a clinical perspective by analyzing clinical studies. Additionally, the review will emphasize potential prevention and diagnostic developments to enhance therapy options available for elderly patients over 65 years of age.

Artificial intelligence for assistance of radiology residents in chest CT evaluation for COVID-19 pneumonia: a comparative diagnostic accuracy study.

BACKGROUND: In hospitals, it is crucial to rule out coronavirus disease 2019 (COVID-19) timely and reliably. Artificial intelligence (AI) provides sufficient accuracy to identify chest computed tomography (CT) scans with signs of COVID-19. PURPOSE: To compare the diagnostic accuracy of radiologists with different levels of experience with and without assistance of AI in CT evaluation for COVID-19 pneumonia and to develop an optimized diagnostic pathway. MATERIAL AND METHODS: The retrospective, single-center, comparative case-control study included 160 consecutive participants who had undergone chest CT scan between March 2020 and May 2021 without or with confirmed diagnosis of COVID-19 pneumonia in a ratio of 1:3. Index tests were chest CT evaluation by five radiological senior residents, five junior residents, and an AI software. Based on the diagnostic accuracy in every group and on comparison of groups, a sequential CT assessment pathway was developed. RESULTS: Areas under receiver operating curves were 0.95 (95% confidence interval [CI]=0.88-0.99), 0.96 (95% CI=0.92-1.0), 0.77 (95% CI=0.68-0.86), and 0.95 (95% CI=0.9-1.0) for junior residents, senior residents, AI, and sequential CT assessment, respectively. Proportions of false negatives were 9%, 3%, 17%, and 2%, respectively. With the developed diagnostic pathway, junior residents evaluated all CT scans with the support of AI. Senior residents were only required as second readers in 26% (41/160) of the CT scans. CONCLUSION: AI can support junior residents with chest CT evaluation for COVID-19 and reduce the workload of senior residents. A review of selected CT scans by senior residents is mandatory.

Observer variability in the assessment of CT coronary angiography and coronary artery calcium score: substudy of the Scottish COmputed Tomography of the HEART (SCOT-HEART) trial.

INTRODUCTION: Observer variability can influence the assessment of CT coronary angiography (CTCA) and the subsequent diagnosis of angina pectoris due to coronary heart disease. METHODS: We assessed 210 CTCAs from the Scottish COmputed Tomography of the HEART (SCOT-HEART) trial for intraobserver and interobserver variability. Calcium score, coronary angiography and image quality were evaluated. Coronary artery disease was defined as none (<10%), mild (10-49%), moderate (50-70%) and severe (>70%) luminal stenosis and classified as no (<10%), non-obstructive (10-70%) or obstructive (>70%) coronary artery disease. Post-CTCA diagnosis of angina pectoris due to coronary heart disease was classified as yes, probable, unlikely or no. RESULTS: Patients had a mean body mass index of 29 (28, 30) kg/m(2), heart rate of 58 (57, 60)/min and 62% were men. Intraobserver and interobserver agreements for the presence or absence of coronary artery disease were excellent (95% agreement, κ 0.884 (0.817 to 0.951) and good (91%, 0.791 (0.703 to 0.879)). Intraobserver and interobserver agreement for the presence or absence of angina pectoris due to coronary heart disease were excellent (93%, 0.842 (0.918 to 0.755) and good (86%, 0.701 (0.799 to 0.603)), respectively. Observer variability of calcium score was excellent for calcium scores below 1000. More segments were categorised as uninterpretable with 64-multidetector compared to 320-multidetector CTCA (10.1% vs 2.6%, p<0.001) but there was no difference in observer variability. CONCLUSIONS: Multicentre multidetector CTCA has excellent agreement in patients under investigation for suspected angina due to coronary heart disease. TRIAL REGISTRATION NUMBER: NCT01149590.