Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed;however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer 'LungCTAnalyzer';and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

The effect of health literacy on vaccine hesitancy among Italian anticoagulated population during COVID-19 pandemic: the moderating role of health engagement.

Assessing vaccine hesitancy and its determinants is pivotal to optimize vaccine acceptance in anticoagulated patients, given that this population has been described to have a higher risk of severe COVID-19-related complications. This study assessed the moderator role of patients' health engagement on the relationship between health literacy and vaccine hesitancy. A web-based survey was performed in Italy during the first wave (June-August 2020) and the second wave (October 2020-March 2021) of the COVID-19 pandemic, enrolling 288 patients. The rates of vaccine hesitancy reported during the first pandemic wave were 38.4% and 30.8% during the second wave (when a vaccine was available) (p = .164). A moderation analysis was performed to assess the role of health engagement in influencing the relationship from health literacy to vaccine hesitancy. Patients' health engagement enhanced the effects of health literacy on decreasing vaccine hesitancy (p < .001), suggesting that co-construction strategies for communicative action are pivotal.

Managing anticoagulation in the COVID-19 era between lockdown and reopening phases.

Patients on anticoagulant treatment are constantly increasing, with an estimated prevalence in Italy of 2% of the total population. The recent spreadout of the COVID-19 pandemic requires a re-organization of Anticoagulation Clinics to prevent person-to-person viral diffusion and continue to offer the highest possible quality of assistance to patients. In this paper, based on the Italian Federation of Anticoagulation Clinics statements, we offer some advice aimed at improving patient care during COVID-19 pandemic, with particular regard to the lockdown and reopening periods. We give practical guidance regarding the following points: (1) re-thinking the AC organization, (2) managing patients on anticoagulants when they become infected by the virus, (3) managing anticoagulation surveillance in non-infected patients during the lockdown period, and (4) organizing the activities during the reopening phases.