Search

The respiratory consequences of acute COVID‐19 infection and related symptoms tend to resolve 4 weeks post‐infection. However, for some patients, new, recurrent, or persisting symptoms remain beyond the acute phase and persist for months, post‐infection. The symptoms that remain have been referred to as long‐COVID. A number of research sites employed 129Xe magnetic resonance imaging (MRI) during the pandemic and evaluated patients post‐infection, months after hospitalization or home‐based care as a way to better understand the consequences of infection on 129Xe MR gas‐exchange and ventilation imaging. A systematic review and comprehensive search were employed using MEDLINE via PubMed (April 2023) using the National Library of Medicine's Medical Subject Headings and key words: post‐COVID‐19, MRI, 129Xe, long‐COVID, COVID pneumonia, and post‐acute COVID‐19 syndrome. Fifteen peer‐reviewed manuscripts were identified including four editorials, a single letter to the editor, one review article, and nine original research manuscripts (2020–2023). MRI and MR spectroscopy results are summarized from these prospective, controlled studies, which involved small sample sizes ranging from 9 to 76 participants. Key findings included: 1) 129Xe MRI gas‐exchange and ventilation abnormalities, 3 months post‐COVID‐19 infection, and 2) a combination of MRI gas‐exchange and ventilation abnormalities alongside persistent symptoms in patients hospitalized and not hospitalized for COVID‐19, 1‐year post‐infection. The persistence of respiratory symptoms and 129Xe MRI abnormalities in the context of normal or nearly normal pulmonary function test results and chest computed tomography (CT) was consistent. Longitudinal improvements were observed in long‐term follow‐up of long‐COVID patients but mean 129Xe gas‐exchange, ventilation heterogeneity values and symptoms remained abnormal, 1‐year post‐infection. Pulmonary functional MRI using inhaled hyperpolarized 129Xe gas has played a role in detecting gas‐exchange and ventilation abnormalities providing complementary information that may help develop our understanding of the root causes of long‐COVID. Level of Evidence: 1 Technical Efficacy: Stage 5 [ABSTRACT FROM AUTHOR]

Background: Lung ultrasound (LUS) is increasingly used as an extension of physical examination, informing clinical diagnosis, and decision making. There is particular interest in the assessment of patients with pulmonary congestion and extravascular lung water, although gaps remain in the evidence base underpinning this practice as a result of the limited evaluation of its inter-rater reliability and comparison with more established radiologic tests. Methods: 30 patients undergoing haemodialysis were prospectively recruited to an observational cohort study (NCT01949402). Patients underwent standardised LUS assessment before, during and after haemodialysis; their total LUS B-line score was generated, alongside a binary label of whether appearances were consistent with an interstitial syndrome. LUS video clips were recorded and independently scored by two blinded expert clinician sonographers. Low-dose non-contrast thoracic CT, pre- and post dialysis, was used as a "gold standard" radiologic comparison. Results: LUS detected a progressive reduction in B-line scores in almost all patients undergoing haemodialysis, correlating with the volume of fluid removed once individuals with no or minimal B-lines upon pre-dialysis examination were discounted. When comparing CT scans pre- and post dialysis, radiologic evidence of the change in fluid status was only identified in a single patient. Conclusions: This is the first study to demonstrate that LUS detects changes in extravascular lung water caused by changing fluid status during haemodialysis using a blinded outcome assessment and that LUS appears to be more sensitive than CT for this purpose. Further research is needed to better understand the role of LUS in this and similar patient populations, with the aim of improving clinical care and outcomes. [ABSTRACT FROM AUTHOR]

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model—the CanPredict (lung) model—for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005–March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004–Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25–84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R2 D ]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP] v2 , LLP v3 , Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO] M2012 , PLCO M2014 , Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55–74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R2 D in both sexes in the QResearch validation cohort and 59% of the R2 D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP v2 and PLCO M2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. Innovate UK (UK Research and Innovation). For the Chinese translation of the abstract see Supplementary Materials section. [ABSTRACT FROM AUTHOR]

Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy. What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test. Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P =.03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P =.35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P <.05 for 240 s after contrast only). An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules. ClinicalTrials.gov Identifier; No.: NCT02013063 [ABSTRACT FROM AUTHOR]

The therapeutic role of intrapleural streptokinase is explored. The results reveal that intrapleural administration of streptokinase does not improve mortality, the rate of surgery, or the length of the hospital stay among patients with pleural infection.