How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale.

OBJECTIVES: The aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans. METHODS: Abdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC). RESULTS: Twenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396-0.814) to 0.785 (95%CI 0.676-0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034-0.500) to 0.562 (95%CI 0.337-0.729). Intraobserver agreement increased for four out of six observers. CONCLUSION: The PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement. CLINICAL RELEVANCE STATEMENT: This study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms. KEY POINTS: • Subjective assessment of diagnostic image quality via Likert scale has limited reliability. • A pairwise comparison method improves the inter- and intraobserver agreement. • The pairwise comparison method is more reliable for CT optimization studies.

Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial.

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15-20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40-50%.

Implementation of Bronchoscopic Lung Volume Reduction Using One-Way Endobronchial Valves: A Retrospective Single-Centre Cohort Study.

BACKGROUND: Bronchoscopic lung volume reduction (BLVR) using 1-way endobronchial valves (EBV) has become a guideline treatment in patients with advanced emphysema. Evidence from this minimally invasive treatment derives mainly from well-designed controlled trials conducted in high-volume specialized intervention centres. Little is known about real-life outcome data in hospitals setting up this novel treatment and which favourable conditions are required for a continuous successful program. OBJECTIVES: In this study, we aim to evaluate the eligibility rate for BLVR and whether the implementation of BLVR in our academic hospital is feasible and yields clinically significant outcomes. METHOD: A retrospective evaluation of patients treated with EBV between January 2016 and August 2019 was conducted. COPD assessment test (CAT), forced expiratory volume in 1 s (FEV1), residual volume (RV), and 6-min walking test (6MWT) were measured at baseline and 3 months after intervention. Paired sample t tests were performed to compare means before and after intervention. RESULTS: Of 350 subjects screened, 283 (81%) were not suitable for intervention mostly due to lack of a target lobe. The remaining 67 subjects (19%) underwent bronchoscopic assessment, and if suitable, valves were placed in the same session. In total, 55 subjects (16%) were treated with EBV of which 10 did not have complete follow-up: 6 subjects had their valves removed because of severe pneumothorax (n = 2) or lack of benefit (n = 4) and the remaining 4 had missing follow-up data. Finally, 45 patients had complete follow-up at 3 months and showed an average change ± SD in CAT -4 ± 6 points, FEV1 +190 ± 140 mL, RV -770 ± 790 mL, and +37 ± 65 m on the 6MWT (all p < 0.001). After 1-year follow-up, 34 (76%) subjects had their EBV in situ. CONCLUSION: Implementing BLVR with EBV is feasible and effective. Only 16% of screened patients were eligible, indicating that this intervention is only applicable in a small subset of highly selected subjects with advanced emphysema, and therefore a high volume of COPD patients is essential for a sustainable BLVR program.

Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience.

Background Clinicians need to rapidly and reliably diagnose coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions. Purpose To assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 Reporting and Data System (CO-RADS). Materials and Methods This retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT scans were obtained for primary work-up and scored using the five-point CO-RADS scheme for suspicion of COVID-19. CT was compared with severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction (RT-PCR) assay and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing, and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against both reference standards. Subgroup analysis was performed on the basis of symptom duration grouped presentations of less than 48 hours, 48 hours through 7 days, and more than 7 days. Results A total of 1070 patients (median age, 66 years; interquartile range, 54-75 years; 626 men) were included, of whom 536 (50%) had a positive RT-PCR result and 137 (13%) of whom were considered to have a possible or probable COVID-19 diagnosis based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95% CI: 0.84, 0.89) compared with RT-PCR and 0.87 (95% CI: 0.85, 0.89) compared with the clinical reference standard. A CO-RADS score of 4 or greater yielded an odds ratio of 25.9 (95% CI: 18.7, 35.9) for a COVID-19 diagnosis with RT-PCR and an odds ratio of 30.6 (95% CI: 21.1, 44.4) with the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95% CI: 0.62, 0.80; P < .001). Conclusion Chest CT analysis using the coronavirus disease 2019 (COVID-19) Reporting and Data System enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Functional Outcomes and Their Association With Physical Performance in Mechanically Ventilated Coronavirus Disease 2019 Survivors at 3 Months Following Hospital Discharge: A Cohort Study.

OBJECTIVES: We performed a comprehensive health assessment in mechanically ventilated coronavirus disease 2019 survivors to assess the impact of respiratory and skeletal muscle injury sustained during ICU stay on physical performance at 3 months following hospital discharge. DESIGN: Preregistered prospective observational cohort study. SETTING: University hospital ICU. PATIENTS: All mechanically ventilated coronavirus disease 2019 patients admitted to our ICU during the first European pandemic wave. MEASUREMENTS AND MAIN RESULTS: At 3 months after hospital discharge, 46 survivors underwent a comprehensive physical assessment (6-min walking distance, Medical Research Council sum score and handgrip strength), a full pulmonary function test, and a chest CT scan which was used to analyze skeletal muscle architecture. In addition, patient-reported outcomes measures were collected. Physical performance assessed by 6-minute walking distance was below 80% of predicted in 48% of patients. Patients with impaired physical performance had more muscle weakness (Medical Research Council sum score 53 [51-56] vs 59 [56-60]; p < 0.001), lower lung diffusing capacity (54% [44-66%] vs 68% of predicted [61-72% of predicted]; p = 0.002), and higher intermuscular adipose tissue area (p = 0.037). Reduced lung diffusing capacity and increased intermuscular adipose tissue were independently associated with physical performance. CONCLUSIONS: Physical disability is common at 3 months in severe coronavirus disease 2019 survivors. Lung diffusing capacity and intermuscular adipose tissue assessed on CT were independently associated with walking distance, suggesting a key role for pulmonary function and muscle quality in functional disability.

Serial markers of coagulation and inflammation and the occurrence of clinical pulmonary thromboembolism in mechanically ventilated patients with SARS-CoV-2 infection; the prospective Maastricht intensive care COVID cohort.

BACKGROUND: The incidence of pulmonary thromboembolism is high in SARS-CoV-2 patients admitted to the Intensive Care. Elevated biomarkers of coagulation (fibrinogen and D-dimer) and inflammation (c-reactive protein (CRP) and ferritin) are associated with poor outcome in SARS-CoV-2. Whether the time-course of fibrinogen, D-dimer, CRP and ferritin is associated with the occurrence of pulmonary thromboembolism in SARS-CoV-2 patients is unknown. We hypothesise that patients on mechanical ventilation with SARS-CoV-2 infection and clinical pulmonary thromboembolism have lower concentrations of fibrinogen and higher D-dimer, CRP, and ferritin concentrations over time compared to patients without a clinical pulmonary thromboembolism. METHODS: In a prospective study, fibrinogen, D-dimer, CRP and ferritin were measured daily. Clinical suspected pulmonary thromboembolism was either confirmed or excluded based on computed tomography pulmonary angiography (CTPA) or by transthoracic ultrasound (TTU) (i.e., right-sided cardiac thrombus). In addition, patients who received therapy with recombinant tissue plasminogen activator were included when clinical instability in suspected pulmonary thromboembolism did not allow CTPA. Serial data were analysed using a mixed-effects linear regression model, and models were adjusted for known risk factors (age, sex, APACHE-II score, body mass index), biomarkers of coagulation and inflammation, and anticoagulants. RESULTS: Thirty-one patients were considered to suffer from pulmonary thromboembolism ((positive CTPA (n = 27), TTU positive (n = 1), therapy with recombinant tissue plasminogen activator (n = 3)), and eight patients with negative CTPA were included. After adjustment for known risk factors and anticoagulants, patients with, compared to those without, clinical pulmonary thromboembolism had lower average fibrinogen concentration of - 0.9 g/L (95% CI: - 1.6 - - 0.1) and lower average ferritin concentration of - 1045 µg/L (95% CI: - 1983 - - 106) over time. D-dimer and CRP average concentration did not significantly differ, 561 µg/L (- 6212-7334) and 27 mg/L (- 32-86) respectively. Ferritin lost statistical significance, both in sensitivity analysis and after adjustment for fibrinogen and D-dimer. CONCLUSION: Lower average concentrations of fibrinogen over time were associated with the presence of clinical pulmonary thromboembolism in patients at the Intensive Care, whereas D-dimer, CRP and ferritin were not. Lower concentrations over time may indicate the consumption of fibrinogen related to thrombus formation in the pulmonary vessels.

Dual-Energy Computed Tomography Compared to Lung Perfusion Scintigraphy to Assess Pulmonary Perfusion in Patients Screened for Endoscopic Lung Volume Reduction.

BACKGROUND: Endoscopic lung volume reduction (ELVR) using one-way endobronchial valves is a technique to reduce hyperinflation in patients with severe emphysema by inducing collapse of a severely destroyed pulmonary lobe. Patient selection is mainly based on evaluation of emphysema severity on high-resolution computed tomography and evaluation of lung perfusion with perfusion scintigraphy. Dual-energy contrast-enhanced CT scans may be useful for perfusion assessment in emphysema but has not been compared against perfusion scintigraphy. AIMS: The aim of the study was to compare perfusion distribution assessed with dual-energy contrast-enhanced computed tomography and perfusion scintigraphy. MATERIAL AND METHODS: Forty consecutive patients with severe emphysema, who were screened for ELVR, were included. Perfusion was assessed with 99mTc perfusion scintigraphy and using the iodine map calculated from the dual-energy contrast-enhanced CT scans. Perfusion distribution was calculated as usually for the upper, middle, and lower thirds of both lungs with the planar technique and the iodine overlay. RESULTS: Perfusion distribution between the right and left lung showed good correlation (r = 0.8). The limits of agreement of the mean absolute difference in percentage perfusion per region of interest were 0.75-5.6%. The upper lobes showed more severe perfusion reduction than the lower lobes. Mean difference in measured pulmonary perfusion ranged from -2.8% to 2.3%. Lower limit of agreement ranged from -8.9% to 4.6% and upper limit was 3.3-10.0%. CONCLUSION: Quantification of perfusion distribution using planar 99mTc perfusion scintigraphy and iodine overlays calculated from dual-energy contrast-enhanced CTs correlates well with acceptable variability.

Impact of the COVID-19 pandemic on incidence and severity of acute appendicitis: a comparison between 2019 and 2020.

BACKGROUND: During the COVID-19 pandemic, a decrease in the number of patients presenting with acute appendicitis was observed. It is unclear whether this caused a shift towards more complicated cases of acute appendicitis. We compared a cohort of patients diagnosed with acute appendicitis during the 2020 COVID-19 pandemic with a 2019 control cohort. METHODS: We retrospectively included consecutive adult patients in 21 hospitals presenting with acute appendicitis in a COVID-19 pandemic cohort (March 15 - April 30, 2020) and a control cohort (March 15 - April 30, 2019). Primary outcome was the proportion of complicated appendicitis. Secondary outcomes included prehospital delay, appendicitis severity, and postoperative complication rates. RESULTS: The COVID-19 pandemic cohort comprised 607 patients vs. 642 patients in the control cohort. During the COVID-19 pandemic, a higher proportion of complicated appendicitis was seen (46.9% vs. 38.5%; p = 0.003). More patients had symptoms exceeding 24 h (61.1% vs. 56.2%, respectively, p = 0.048). After correction for prehospital delay, presentation during the first wave of the COVID-19 pandemic was still associated with a higher rate of complicated appendicitis. Patients presenting > 24 h after onset of symptoms during the COVID-19 pandemic were older (median 45 vs. 37 years; p = 0.001) and had more postoperative complications (15.3% vs. 6.7%; p = 0.002). CONCLUSIONS: Although the incidence of acute appendicitis was slightly lower during the first wave of the 2020 COVID-19 pandemic, more patients presented with a delay and with complicated appendicitis than in a corresponding period in 2019. Spontaneous resolution of mild appendicitis may have contributed to the increased proportion of patients with complicated appendicitis. Late presenting patients were older and experienced more postoperative complications compared to the control cohort.

The Emerging Role of Radiomics in COPD and Lung Cancer.

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.

Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?

Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A conservative treatment approach may therefore be suitable. The aim of the current study was to evaluate whether close follow-up of SSNs with computed tomography may be a safe approach. The study population consisted of participants of the Dutch-Belgian lung cancer screening trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON). All SSNs detected during the trial were included in this analysis. Retrospectively, all persistent SSNs and SSNs that were resected after first detection were segmented using dedicated software, and maximum diameter, volume and mass were measured. Mass doubling time (MDT) was calculated. In total 7135 volunteers were included in the current analysis. 264 (3.3%) SSNs in 234 participants were detected during the trial. 147 (63%) of these SSNs in 126 participants disappeared at follow-up, leaving 117 persistent or directly resected SSNs in 108 (1.5%) participants available for analysis. The median follow-up time was 95 months (range 20-110 months). 33 (28%) SSNs were resected and 28 of those were (pre-) invasive. None of the non-resected SSNs progressed into a clinically relevant malignancy. Persistent SSNs rarely developed into clinically manifest malignancies unexpectedly. Close follow-up with computed tomography may be a safe option to monitor changes.

Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation.

OBJECTIVE: To determine whether semiautomatic volumetric software can differentiate part-solid from nonsolid pulmonary nodules and aid quantification of the solid component. METHODS: As per reference standard, 115 nodules were differentiated into nonsolid and part-solid by two radiologists; disagreements were adjudicated by a third radiologist. The diameters of solid components were measured manually. Semiautomatic volumetric measurements were used to identify and quantify a possible solid component, using different Hounsfield unit (HU) thresholds. The measurements were compared with the reference standard and manual measurements. RESULTS: The reference standard detected a solid component in 86 nodules. Diagnosis of a solid component by semiautomatic software depended on the threshold chosen. A threshold of -300 HU resulted in the detection of a solid component in 75 nodules with good sensitivity (90%) and specificity (88%). At a threshold of -130 HU, semiautomatic measurements of the diameter of the solid component (mean 2.4 mm, SD 2.7 mm) were comparable to manual measurements at the mediastinal window setting (mean 2.3 mm, SD 2.5 mm [p = 0.63]). CONCLUSION: Semiautomatic segmentation of subsolid nodules could diagnose part-solid nodules and quantify the solid component similar to human observers. Performance depends on the attenuation segmentation thresholds. This method may prove useful in managing subsolid nodules. KEY POINTS: • Semiautomatic segmentation can accurately differentiate nonsolid from part-solid pulmonary nodules • Semiautomatic segmentation can quantify the solid component similar to manual measurements • Semiautomatic segmentation may aid management of subsolid nodules following Fleischner Society recommendations • Performance for the segmentation of subsolid nodules depends on the chosen attenuation thresholds.

Interscan variation of semi-automated volumetry of subsolid pulmonary nodules.

RATIONALE: We aimed to test the interscan variation of semi-automatic volumetry of subsolid nodules (SSNs), as growth evaluation is important for SSN management. METHODS: From a lung cancer screening trial all SSNs that were stable over at least 3 months were included (N = 44). SSNs were quantified on the baseline CT by two observers using semi-automatic volumetry software for effective diameter, volume, and mass. One observer also measured the SSNs on the second CT 3 months later. Interscan variation was evaluated using Bland-Altman plots. Observer agreement was calculated as intraclass correlation coefficient (ICC). Data are presented as mean (± standard deviation) or median and interquartile range (IQR). A Mann-Whitney U test was used for the analysis of the influence of adjustments on the measurements. RESULTS: Semi-automatic measurements were feasible in all 44 SSNs. The interscan limits of agreement ranged from -12.0 % to 9.7 % for diameter, -35.4 % to 28.6 % for volume and -27.6 % to 30.8 % for mass. Agreement between observers was good with intraclass correlation coefficients of 0.978, 0.957, and 0.968 for diameter, volume, and mass, respectively. CONCLUSION: Our data suggest that when using our software an increase in mass of 30 % can be regarded as significant growth. KEY POINTS: • Recently, recommendations regarding subsolid nodules have stressed the importance of growth quantification. • Volumetric measurement of subsolid nodules is feasible with good interscan agreement. • Increase of mass of 30 % can be regarded as significant growth.

Limited Additional Value of a Chest CT in Whole-Body Staging with PET-MRI: A Retrospective Cohort Study.

Hybrid PET-MRI systems are being used more frequently. One of the drawbacks of PET-MRI imaging is its inferiority in detecting lung nodules, so it is often combined with a computed tomography (CT) of the chest. However, chest CT often detects additional, indeterminate lung nodules. The objective of this study was to assess the sensitivity of detecting metastatic versus indeterminate nodules with PET-MRI compared to chest CT. A total of 328 patients were included. All patients had a PET/MRI whole-body scan for (re)staging of cancer combined with an unenhanced chest CT performed at our center between 2014 and 2020. Patients had at least a two-year follow-up. Six percent of the patients had lung metastases at initial staging. The sensitivity and specificity of PET-MRI for detecting lung metastases were 85% and 100%, respectively. The incidence of indeterminate lung nodules on chest CT was 30%. The sensitivity of PET-MRI to detect indeterminate lung nodules was poor (23.0%). The average size of the indeterminate lung nodules detected on PET-MRI was 7 ± 4 mm, and the missed indeterminate nodules on PET-MRI were 4 ± 1 mm (p < 0.001). The detection of metastatic lung nodules is fairly good with PET-MRI, whereas the sensitivity of PET-MRI for detecting indeterminate lung nodules is size-dependent. This may be an advantage, limiting unnecessary follow-up of small, indeterminate lung nodules while adequately detecting metastases.

CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19.

BACKGROUND: COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19. RESEARCH QUESTION: Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score? STUDY DESIGN AND METHODS: This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records. RESULTS: Data from 578 patients were analyzed (64.6% men; mean age, 67.7 ± 13.5 years; 18.2% 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; P = .002) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; P = .013). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; 95% CI, 0.96-1.00; P = .038). INTERPRETATION: CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.

Repurposing catheter ablation work-up to detect expiratory airflow limitation in patients with atrial fibrillation.

Background: In atrial fibrillation (AF) patients, presence of expiratory airflow limitation may negatively impact treatment outcomes. AF patients are not routinely screened for expiratory airflow limitation, but existing examinations can help identify at-risk individuals. We aimed to assess the diagnostic value of repurposing existing assessments from the pre-ablation work-up to identify and understand the characteristics of affected patients. Methods: We screened 110 consecutive AF patients scheduled for catheter ablation with handheld spirometry. Routine pre-ablation work-up included cardiac computed tomographic angiography (CCTA), transthoracic echocardiography and polygraphy. CCTA was analyzed qualitatively for emphysema and airway abnormalities. Multivariate logistic regression analysis was performed to determine predictors of expiratory airflow limitation. Results: We found that 25 % of patients had expiratory airflow limitation, which was undiagnosed in 86 % of these patients. These patients were more likely to have pulmonary abnormalities on CCTA, including emphysema (odds ratio [OR] 4.2, 95 % confidence interval [CI] 1.12-15.1, p < 0.05) and bronchial wall thickening (OR 2.6, 95 % CI 1.0-6.5, p < 0.05). The absence of pulmonary abnormalities on CCTA accurately distinguished patients with normal lung function from those with airflow limitation (negative predictive value: 85 %). Echocardiography and polygraphy did not contribute significantly to identifying airflow limitation. Conclusions: In conclusion, routine pre-ablation CCTA can detect pulmonary abnormalities in AF patients with airflow limitation, guiding further pulmonary assessment. Future studies should investigate its impact on ablation procedure success.