Impact of CT Scan Phenotypes in Clinical Manifestations, Management and Outcomes of Hospitalised Patients with COVID-19

COVID-19 is such a heterogeneous disease that a one-size-fits-all approach is not recommended, so the management of patients has been based on their clinical and laboratory characteristics. We therefore investigated possible homogeneous groups presenting similar features of lung involvement based on chest CT and laboratory results. We designed a study to identify a possible correlation between CT scan phenotypes, laboratory exams, and clinical outcomes. We retrospectively analysed 120 adult patients with COVID-19 5who underwent chest CT scan during hospitalisation, between March and December 2020 at our COVID-19 Hospital in two different wards: Respiratory Intensive Care Unit (RICU) and Intensive Care Unit (ICU). The analysis of CT scans resulted in the identification of three radiological phenotypes by two blinded pulmonologists (Cohen's κ = 0.9 for Phenotype 1, 0.9 for Phenotype 2 and 0.89 for Phenotype 3), in accordance with what previously described by Robba et al. “Phenotype 1” (PH1) is characterised by modest interstitial oedema with presentation on chest CT of diffuse ground glass opacities (GGO). “Phenotype 2” (PH2) shows predominant consolidation at lung lobes. “Phenotype 3” (PH3) shows a typical CT pattern of moderate-to-severe ARDS, with alveolar oedema. Based on our results, we could hypothesise that phenotype 2 shows a different trend from all the others and would seem to be more related to a coagulopathy, although we cannot exclude the hypothesis that one phenotype evolves from the other. Further studies might focus on the predictive role of D-dimer, and its cut-offs, in delineating the PH2 patients, that could require an early CT scan to avoid excessive pressure support and finally prevent VILI. To further understand the exact basis of the different CT scan phenotype, a longer longitudinal analysis of clinical and laboratory features (e.g., timing of weaning, pressures and FiO2 delivered) in each phenotype and a comparison among them is needed

Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in obstructive sleep apnea (OSA)

Continuous positive airway pressure (CPAP) is the “gold-standard” therapy for obstructive sleep apnea (OSA), but the main problem is the poor adherence. Therefore, we have searched for the causes of poor adherence to CPAP therapy by applying predictive machine learning (ML) methods. The study was conducted on OSAs in nighttime therapy with CPAP. An outpatient follow-up was planned at 3, 6, 12 months. We collected several parameters at the baseline visit and after dividing all patients into two groups (Adherent and Non-adherent) according to therapy adherence, we compared them. Statistical differences between the two groups were not found according to baseline characteristics, except gender (P< .01). Therefore, we applied ML to predict CPAP adherence, and these predictive models showed an accuracy and sensitivity of 68.6% and an AUC (area under the curve) of 72.9% through the SVM (support vector machine) classification method. The identification of factors predictive of long-term CPAP adherence is complex, but our proof of concept seems to demonstrate the utility of ML to identify subjects poorly adherent to therapy. Therefore, application of these models to larger samples could aid in the careful identification of these subjects and result in important savings in healthcare spending

The Role of Transthoracic Ultrasound in the Study of Interstitial Lung Diseases: High-Resolution Computed Tomography Versus Ultrasound Patterns: Our Preliminary Experience

Transthoracic ultrasound (TUS) is a readily available imaging tool that can provide a quick real-time evaluation. The aim of this preliminary study was to establish a complementary role for this imaging method in the approach of interstitial lung diseases (ILDs). TUS examination was performed in 43 consecutive patients with pulmonary fibrosis and TUS findings were compared with the corresponding high-resolution computed tomography (HRCT) scans. All patients showed a thickened hyperechoic pleural line, despite no difference between dominant HRCT patterns (ground glass, honeycombing, mixed pattern) being recorded (p > 0.05). However, pleural lines’ thickening showed a significant difference between different HRCT degree of fibrosis (p 3 B-lines and subpleural nodules was also assessed in a large number of patients, although they did not demonstrate any particular association with a specific HRCT finding or fibrotic degree. Results allow us to suggest a complementary role for TUS in facilitating an early diagnosis of ILD or helping to detect a possible disease progression or eventual complications during routine clinical practice (with pleural line measurements and subpleural nodules), although HRCT remains the gold standard in the definition of ILD pattern, disease extent and follow-up

Chylothorax found in a patient with COVID-19

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its clinical spectrum ranges from mild to moderate or severe illness. A 78-year-old male was presented at emergency department with dyspnoea, dry cough and severe asthenia. The nasopharyngeal swab by real-time polymerase chain reaction confirmed a SARS-CoV-2 infection. The x-ray and the thoracic ultrasound revealed right pleural effusion. A diagnostic-therapeutic thoracentesis drained fluid identified as chylothorax. Subsequently, the patient underwent a chest computed tomography which showed the radiological hallmarks of COVID-19 and in the following weeks he underwent a chest magnetic resonance imaging to obtain a better view of mediastinal and lymphatic structures, which showed a partial thrombosis affecting the origin of superior vena cava and the distal tract of the right subclavian vein. For this reason, anticoagulant therapy was optimized and in the following weeks the patient was discharged for clinical and radiological improvement. This case demonstrates chylothorax as a possible and uncommon complication of COVID-19

Consequences of the COVID-19 pandemic on admissions to general hospital psychiatric wards in Italy: Reduced psychiatric hospitalizations and increased suicidality

Aims: The present investigation aimed at evaluating differences in psychiatric hospitalizations in Italy during and after the lockdown due to the novel coronavirus disease 2019 (COVID-19), compared to the same periods in 2018 and 2019. Methods: We obtained and analyzed anonymized data on psychiatric admissions (n = 4550) from 12 general hospital psychiatric wards (GHPWs) in different Italian regions (catchment area = 3.71 millions of inhabitants). Using a mixed-effects Poisson regression model, we compared admission characteristics across three periods: (a) March 1-June 30, 2018 and 2019; (b) March 1-April 30, 2020 (i.e., lockdown); and (c) May 1-June 30, 2020 (i. e., post-lockdown). Results: During the COVID-19 lockdown, there was a 41% reduction (IRR = 0.59; p 0.001, CI: 0.45-0.79) in psychiatric admissions in the enrolled GHPWs with respect to the 2018 and 2019 control period. Conversely, admission rates in the post-lockdown period were similar to those observed in the control period. Notably, a consistent and significant reduction in psychiatric hospitalizations of older patients (aged 65 years) was observed in the lockdown (40%; IRR = 0.60; 95% CI: 0.44-0.82) and post-lockdown (28%; IRR = 0.72; 95% CI: 0.54-0.96) periods. Long-stay admissions (>14 days) increased (63%; IRR = 1.63; 95% CI: 1.32-2.02) during the lockdown and decreased by 39% thereafter (IRR = 0.61; 95% CI: 0.49-0.75). A significant 35% increase in patients reporting suicidal ideation was observed in the post-lockdown period, compared to the rate observed in the 2018 and 2019 control period (IRR = 1.35; 95% CI: 1.01-1.79). Conclusion: The COVID-19 lockdown was associated with changes in the number of psychiatric admissions, particularly for older patients and long-stay hospitalizations. Increased admission of patients reporting suicida

Consequences of the COVID-19 pandemic on admissions to general hospital psychiatric wards in Italy: Reduced psychiatric hospitalizations and increased suicidality

Aims: The present investigation aimed at evaluating differences in psychiatric hospitalizations in Italy during and after the lockdown due to the novel coronavirus disease 2019 (COVID-19), compared to the same periods in 2018 and 2019. Methods: We obtained and analyzed anonymized data on psychiatric admissions (n = 4550) from 12 general hospital psychiatric wards (GHPWs) in different Italian regions (catchment area = 3.71 millions of inhabitants). Using a mixed-effects Poisson regression model, we compared admission characteristics across three periods: (a) March 1-June 30, 2018 and 2019; (b) March 1-April 30, 2020 (i.e., lockdown); and (c) May 1-June 30, 2020 (i.e., post-lockdown). Results: During the COVID-19 lockdown, there was a 41% reduction (IRR = 0.59; p 65 years) was observed in the lockdown (40%; IRR = 0.60; 95% CI: 0.44-0.82) and post-lockdown (28%; IRR = 0.72; 95% CI: 0.54-0.96) periods. Long-stay admissions (>14 days) increased (63%; IRR = 1.63; 95% CI: 1.32-2.02) during the lockdown and decreased by 39% thereafter (IRR = 0.61; 95% CI: 0.49-0.75). A significant 35% increase in patients reporting suicidal ideation was observed in the post-lockdown period, compared to the rate observed in the 2018 and 2019 control period (IRR = 1.35; 95% CI: 1.01-1.79). Conclusion: The COVID-19 lockdown was associated with changes in the number of psychiatric admissions, particularly for older patients and long-stay hospitalizations. Increased admission of patients reporting suicidal ideation in the post-lockdown period merits special attention. Further studies are required to gain insight into the observed phenomena