COVID-19 diagnosis and disease severity prediction assessment through an innovative AI-based model

Introduction: CT imaging has been widely used during the COVID-19 pandemic to diagnose and assess disease severity. Its use for diagnosis is not indicated apart from specific settings such as triage of patients for referral to RTPCR testing or severity assessment. Nowadays, the added value of AI-based models is still unknown and has to be addressed. Method(s): We evaluated the added value of an automated lung involvement assessment tool, named icolung. Since software version 7.0, icolung automatically extracts the Severity Score proposed by Pan F. et al., (2020, Radiology), to help radiologists assess the severity of lung involvement in COVID-19 infected patients. We evaluate retrospectively a group of 785 COVID-19 positive patients compared to a group of 1049 COVID-19 negative patients. We used the severity score (SS) in order to predict the positivity of COVID-19 PCR testing and evaluated the potential impact in the prediction of patients' outcome. Result(s): The icolung SS allows to identify infected (PCR-proven) COVID-19 patients with a sensitivity of 83% and a specificity of 77% (AUC of 0.86, 95% CI 0.85-0.88) for patients with a SS of more than 1.5 on a scale of 0 to 25. An SS of > 7.5 identifies patients at risk of ICU admission with a sensitivity of 70% and specificity of 65% (AUC of 0.74, p<0.0001). Conclusion(s): The severity score as estimated via icolung allows to identify positive PCR-tested COVID-19 patients and helps to predict ICU admission. This automated evaluation tool can support clinicians with the in-hospital management of patients (suspected to be) infected with COVID-19.

Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting? COVID-i9

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status and pushed healthcare systems beyond the limits. We aim to develop a fully automatic framework to detect COVID-19 by applying artificial intelligence (Al). A fully automated Al framework was developed to extract radiomics features from chest CT scans to detect COVID-19 patients. We curated and analysed the data from a total of 1381 patients. A cohort of 181 RT-PCR confirmed COVID-19 patients and 1200 control patients was included for model development. An independent dataset of 697 patients was used to validate the model. The datasets were collected from CHU Liège, Belgium. Model performance was assessed by the area under the receiver operating characteristic curve (AUC). Assuming 15% disease prevalence, a comprehensive analysis of classification performance in terms of accuracy, sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) was performed for all possible decision thresholds. The final curated dataset used for model development and testing consisted of chest CT scans of 1224 patients and 641 patients, respectively. The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset. Assuming the cost of false negatives is twice as high as the cost of false positives, the optimal decision threshold resulted in an accuracy of 85.18%, a sensitivity of 69.52, a specificity of 91.63%, an NPV of 94.46% and a PPV of 59.44%. Our Al framework can accurately detect COVID-19. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the implementation of isolation procedures and early intervention.

Could KL-6 levels in COVID-19 help to predict lung disease?

BACKGROUND: Coronavirus disease COVID-19 has become a public health emergency of international concern. Together with the quest for an effective treatment, the question of the post-infectious evolution of affected patients in healing process remains uncertain. Krebs von den Lungen 6 (KL-6) is a high molecular weight mucin-like glycoprotein produced by type II pneumocytes and bronchial epithelial cells. Its production is raised during epithelial lesions and cellular regeneration. In COVID-19 infection, KL-6 serum levels could therefore be of interest for diagnosis, prognosis and therapeutic response evaluation. MATERIALS AND METHODS: Our study retrospectively compared KL-6 levels between a cohort of 83 COVID-19 infected patients and two other groups: healthy subjects (n = 70) on one hand, and a heterogenous group of patients suffering from interstitial lung diseases (n = 31; composed of 16 IPF, 4 sarcoidosis, 11 others) on the other hand. Demographical, clinical and laboratory indexes were collected. Our study aims to compare KL-6 levels between a COVID-19 population and healthy subjects or patients suffering from interstitial lung diseases (ILDs). Ultimately, we ought to determine whether KL-6 could be a marker of disease severity and bad prognosis. RESULTS: Our results showed that serum KL-6 levels in COVID-19 patients were increased compared to healthy subjects, but to a lesser extent than in patients suffering from ILD. Increased levels of KL-6 in COVID-19 patients were associated with a more severe lung disease. DISCUSSION AND CONCLUSION: Our results suggest that KL-6 could be a good biomarker to assess ILD severity in COVID-19 infection. Concerning the therapeutic response prediction, more studies are necessary.

Single-center experience of patients with interstitial lung diseases during the early days of the COVID-19 pandemic.

INTRODUCTION: Patients with interstitial lung diseases (ILD) can be suspected to be at risk of experiencing a rapid flare-up due to COVID-19. However, no specific data are currently available for these patients. METHODS: We retrospectively analyzed a cohort of 401 patients with ILD and determined the proportion of patients hospitalized for proven severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and specific symptoms of COVID-19. RESULTS: We found that 1% of patients (n = 4) were hospitalized (1 in ICU) for COVID-19. In total, 310 of the 401 patients answered the phone call. Only 33 patients (0.08%) experienced specific symptoms of SARS-CoV-2 infection. CONCLUSION: Our study did not demonstrate any increased occurrence of severe COVID-19 in ILD patients compared to the global population. Based on our findings, we could not make any conclusion on the incidence rate of SARS-CoV-2 infection in patients with ILDs, or on the overall outcome of immunocompromised patients affected by COVID-19.