Background: After the 2002/2003 severe acute respiratory syndrome outbreak, 30% of survivors exhibited persisting structural pulmonary abnormalities. The long-term pulmonary sequelae of coronavirus disease 2019 (COVID-19) are yet unknown, and comprehensive clinical follow-up data are lacking.Methods: In this prospective, multicentre, observational study, we systematically evaluated the cardiopulmonary damage in subjects recovering from COVID-19 at 60 and 100 days after confirmed diagnosis. We conducted a detailed questionnaire, clinical examination, laboratory testing, lung function analysis, echocardiography and thoracic low-dose computed tomography (CT).Results: Data from 145 COVID-19 patients were evaluated, and 41% of all subjects exhibited persistent symptoms 100 days after COVID-19 onset, with dyspnoea being most frequent (36%). Accordingly, patients still displayed an impaired lung function, with a reduced diffusing capacity in 21% of the cohort being the most prominent finding. Cardiac impairment, including a reduced left ventricular function or signs of pulmonary hypertension, was only present in a minority of subjects. CT scans unveiled persisting lung pathologies in 63% of patients, mainly consisting of bilateral ground-glass opacities and/or reticulation in the lower lung lobes, without radiological signs of pulmonary fibrosis. Sequential follow-up evaluations at 60 and 100 days after COVID-19 onset demonstrated a vast improvement of symptoms and CT abnormalities over time.Conclusion: A relevant percentage of post-COVID-19 patients presented with persisting symptoms and lung function impairment along with radiological pulmonary abnormalities >100 days after the diagnosis of COVID-19. However, our results indicate a significant improvement in symptoms and cardiopulmonary status over time.

Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

To assess the prognostic value of coronary CT angiography (CTA) for prediction of major adverse cardiac events (MACE) over a long-term follow-up period. A total of 1469 low-to-intermediate-risk patients (65.9 years; 44.2% females) were included in our prospective cohort study. CTA was evaluated for (i) stenosis severity (minimal <10%; mild 70%), (ii) plaque types (calcified, mixed dominantly calcified, mixed dominantly non-calcified, non-calcified), and (iii) high-risk plaque criteria [low-attenuation plaque (LAP) quantified by HU, napkin-ring (NR) sign, spotty calcification <3 mm, and remodelling index (RI)]. Over a follow-up of mean 7.8 years, MACE rate was 41 (2.8%) and 0% in patients with negative CTA. MACE rate increased along with stenosis severity by CTA (from 1.3 to 7.8%) (P < 0.001) and was higher in T3/T4 plaques than in T2/T1 (7.8 vs. 1.9%; P < 0.0001). LAP density was lower (35.2 HU ± 32 vs. 108.8 HU ± 53) (P < 0.001) and both NR-sign prevalence with n = 26 (63.4%) vs. n = 40 (28%) and LAP <30, <60, and <90 HU prevalence with 46.3–78% vs. 2.4–7% were higher in the MACE group (P < 0.001). On univariate and unadjusted multivariable proportional Hazards model, LAP <60 HU and NR were the strongest MACE predictors (HR 4.96; 95% CI: 2.0–12.2 and HR 3.85; 95% CI: 1.7–8.6) (P < 0.0001), while spotty calcification (HR 2.2; 95% CI: 1.1–4.3, P < 0.001), stenosis severity, and plaque type (HR 1.5; 95% CI: 1.1–2.3 and HR 1.7; 95% CI: 1.1–2.6) (P < 0.001) were less powerful. After adjusting for risk factors, CTA stenosis severity, and plaque type, LAP <60 HU and the NR sign remained significant (P < 0.001), while the effect of NR sign was even enhancing. HRP criteria were independent predictors from other risk factors. Prognosis is excellent over a long-term period if CTA is negative and worsening with an increasing non-calcifying plaque component. LAP <60 HU and NR sign are the most powerful MACE predictors.

Background—Coronary computed tomography angiography (CTA) enables accurate anatomic evaluation of coronary artery stenosis but lacks information about hemodynamic significance. The aim of this study was to evaluate 128-slice myocardial CT perfusion (CTP) imaging with adenosine stress using a high-pitch mode, in comparison with cardiac MRI (CMR). Methods and Results—Thirty-nine patients with intermediate to high coronary risk profile underwent adenosine stress 128-slice dual source CTP (128×0.6 mm, 0.28 seconds). Among those, 30 patients (64±10 years, 6% women) also underwent adenosine stress CMR (1.5T). The 2-step CTP protocol consisted of (1) adenosine stress-CTP using a high-pitch factor (3.4) ECG-synchronized spiral mode and (2) rest-CTP/coronary-CTA using either high-pitch (heart rate 63 bpm). Results were compared with CMR and with invasive angiography in 25 patients. The performance of stress-CTP for detection of myocardial perfusion defects compared with CMR was sensitivity, 96%; specificity, 88%; positive predictive value (PPV), 93%; negative predictive value (NPV), 94% (per vessel); and sensitivity, 78%; specificity, 87%; PPV, 83%; NPV, 84% (per segment). The accuracy of stress-CTP for imaging of reversible ischemia compared with CMR was sensitivity, 95%; specificity, 96%; PPV, 95%; and NPV, 96% (per vessel). In 25 patients who underwent invasive angiography, the accuracy of CTA for detection of stenosis >70% was (per segment): sensitivity, 96%; specificity, 88%; PPV, 67%; and NPV, 98.9%. The accuracy improved from 84% to 95% after adding stress CTP to CTA. Radiation exposure of the entire stress/rest CT protocol was only 2.5 mSv. Conclusions—Adenosine-induced stress 128-slice dual-source high-pitch myocardial CTP allows for simultaneously assessment of reversible myocardial ischemia and coronary stenosis, with good diagnostic accuracy as compared with CMR and invasive angiography, at a very low radiation exposure.