Analysis of Risk Factors for Tracheal Stenosis Managed during COVID-19 Pandemic: A Retrospective, Case-Control Study from Two European Referral Centre.

INTRODUCTION: Benign subglottic/tracheal stenosis (SG/TS) is a life-threatening condition commonly caused by prolonged endotracheal intubation or tracheostomy. Invasive mechanical ventilation was frequently used to manage severe COVID-19, resulting in an increased number of patients with various degrees of residual stenosis following respiratory weaning. The aim of this study was to compare demographics, radiological characteristics, and surgical outcomes between COVID-19 and non-COVID patients treated for tracheal stenosis and investigate the potential differences between the groups. MATERIALS AND METHODS: We retrospectively retrieved electronical medical records of patients managed at two referral centers for airways diseases (IRCCS Humanitas Research Hospital and Avicenne Hospital) with tracheal stenosis between March 2020 and May 2022 and grouped according to SAR-CoV-2 infection status. All patients underwent a radiological and endoscopic evaluation followed by multidisciplinary team consultation. Follow-up was performed through quarterly outpatient consultation. Clinical findings and outcomes were analyzed by using SPPS software. A significance level of 5% (p < 0.05) was adopted for comparisons. RESULTS: A total of 59 patients with a mean age of 56.4 (±13.4) years were surgically managed. Tracheal stenosis was COVID related in 36 (61%) patients. Obesity was frequent in the COVID-19 group (29.7 ± 5.4 vs. 26.9 ± 3, p = 0.043) while no difference was found regarding age, sex, number, and types of comorbidities between the two groups. In the COVID-19 group, orotracheal intubation lasted longer (17.7 ± 14.5 vs. 9.7 ± 5.8 days, p = 0.001), tracheotomy (80%, p = 0.003) as well as re-tracheotomy (6% of cases, p = 0.025) were more frequent and tracheotomy maintenance was longer (21.5 ± 11.9 days, p = 0.006) when compared to the non-COVID group. COVID-19 stenosis was located more distal from vocal folds (3.0 ± 1.86 vs. 1.8 ± 2.03 cm) yet without evidence of a difference (p = 0.07). The number of tracheal rings involved was lower in the non-COVID group (1.7 ± 1 vs. 2.6 ± 0.8 p = 0.001) and stenosis were more frequently managed by rigid bronchoscopy (74% vs. 47%, p = 0.04) when compared to the COVID-19 group. Finally, no difference in recurrence rate was detected between the groups (35% vs. 15%, p = 0.18). CONCLUSIONS: Obesity, a longer time of intubation, tracheostomy, re-tracheostomy, and longer decannulation time occurred more frequently in COVID-related tracheal stenosis. These events may explain the higher number of tracheal rings involved, although we cannot exclude the direct role of SARS-CoV-2 infection in the genesis of tracheal stenosis. Further studies with in vitro/in vivo models will be helpful to better understand the role of inflammatory status caused by SARS-CoV-2 in upper airways.

Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System

Purpose: To determine the performance of a chest radiograph (CXR) severity scoring system combined with clinical and laboratory data in predicting the outcome of COVID-19 patients. Materials and Methods: We retrospectively enrolled 301 patients who had reverse transcriptase-polymerase chain reaction (RT-PCR) positive results for COVID-19. CXRs, clinical and laboratory data were collected. A CXR severity scoring system based on a qualitative evaluation by two expert thoracic radiologists was defined. Based on the clinical outcome, the patients were divided into two classes: moderate/mild (patients who did not die or were not intubated) and severe (patients who were intubated and/or died). ROC curve analysis was applied to identify the cut-off point maximizing the Youden index in the prediction of the outcome. Clinical and laboratory data were analyzed through Boruta and Random Forest classifiers. Results: The agreement between the two radiologist scores was substantial (kappa = 0.76). A radiological score ≥ 9 predicted a severe class: sensitivity = 0.67, specificity = 0.58, accuracy = 0.61, PPV = 0.40, NPV = 0.81, F1 score = 0.50, AUC = 0.65. Such performance was improved to sensitivity = 0.80, specificity = 0.86, accuracy = 0.84, PPV = 0.73, NPV = 0.90, F1 score = 0.76, AUC= 0.82, combining two clinical variables (oxygen saturation [SpO2]), the ratio of arterial oxygen partial pressure to fractional inspired oxygen [P/F ratio] and three laboratory test results (C-reactive protein, lymphocytes [%], hemoglobin). Conclusion: Our CXR severity score assigned by the two radiologists, who read the CXRs combined with some specific clinical data and laboratory results, has the potential role in predicting the outcome of COVID-19 patients.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Sudden neck swelling with rash as late manifestation of COVID-19: a case report.

BACKGROUND: Although there are reports of otolaryngological symptoms and manifestations of CoronaVirus Disease 19 (COVID-19), there have been no documented cases of sudden neck swelling with rash in patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection described in literature. CASE PRESENTATION: We report a case of a sudden neck swelling and rash likely due to late SARS-CoV-2 in a 64-year-old woman. The patient reported COVID-19 symptoms over the previous three weeks. Computed Tomography (CT) revealed a diffuse soft-tissue swelling and edema of subcutaneous tissue, hypodermis, and muscular and deep fascial planes. All the differential diagnoses were ruled out. Both the anamnestic history of the patient's husband who had died of COVID-19 with and the collateral findings of pneumonia and esophageal wall edema suggested the association with COVID-19. This was confirmed by nasopharyngeal swab polymerase chain reaction. The patient was treated with lopinavir/ritonavir, hydroxychloroquine and piperacillin/tazobactam for 7 days. The neck swelling resolved in less than 24 h, while the erythema was still present up to two days later. The patient was discharged after seven days in good clinical condition and with a negative swab. CONCLUSION: Sudden neck swelling with rash may be a coincidental presentation, but, in the pandemic context, it is most likely a direct or indirect complication of COVID-19.

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab.

OBJECTIVES: We aimed to assess the diagnostic performance of CT in patients with a negative first RT-PCR testing and to identify typical features of COVID-19 pneumonia that can guide diagnosis in this case. METHODS: Patients suspected of COVID-19 with a negative first RT-PCR testing were retrospectively revalued after undergoing CT. CT was reviewed by two radiologists and classified as suspected COVID-19 pneumonia, non-COVID-19 pneumonia or negative. The performance of both first RT-PCR result and CT was evaluated by using sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) and by using the second RT-PCR test as the reference standard. CT findings for confirmed COVID-19 positive or negative were compared by using the Pearson chi-squared test (P values < 0.05) RESULTS: Totally, 337 patients suspected of COVID-19 underwent CT and nasopharyngeal swabs in March 2020. Eighty-seven out of 337 patients had a negative first RT-PCR result; of these, 68 repeated RT-PCR testing and were included in the study. The first RT-PCR test showed SE 0, SP = 100%, PPV = NaN, NPV = 70%, AUC = 50%, and CT showed SE = 70% SP = 79%, PPV = 86%, NPV = 76%, AUC = 75%. The most relevant CT variables were ground glass opacity more than 50% and peripheral and/or perihilar distribution. DISCUSSION: Negative RT-PCR test but positive CT features should be highly suggestive of COVID-19 in a cluster or community transmission scenarios, and the second RT-PCR test should be promptly requested to confirm the final diagnosis.