Virtual autopsy in SARS-CoV-2 breakthrough infection: a case report

It is well documented that COVID-19 vaccines are effective tools for limiting the pandemic. Unfortunately, as is true for all vaccines, SARS-CoV-2 infection in vaccinated individuals is still possible. We present an autopsy case of SARS-CoV-2 infection after vaccination (“breakthrough infection”) in an elderly man with several comorbidities where post-mortem CT scan was performed. The death was histologically attributed to cardio-respiratory arrest due to ischemic heart failure related to superinfected COVID-19 pneumonia and pre-existing comorbidities. For the first time in the literature, PMCT imaging related to a fatal, autopsy case of breakthrough SARS-CoV-2 infection is reported. PMCT of the lungs, in accordance with histopathological results, showed few signs of COVID-19 pneumonia, large area of consolidation in the right lower lobe, interpreted as bronco-pneumonic focus, and hypostasis. These findings were well-correlated with the previously reported literature about both PMCT and clinical CT imaging of the lungs in non-vaccinated individuals with early COVID-19 pneumonia and about pulmonary clinical CT imaging in COVID-19 pneumonia in breakthrough SARS-COV-2 infections. Further studies are needed to cover the whole spectrum of PMCT lung imaging in fatal breakthrough SARS-CoV-2 infection, however, this case represent a first step for exploring this difficult challenge during SARS-CoV-2 pandemic using virtual autopsy.

Postmortem CT pulmonary findings in SARS-CoV-2-positive cases: correlation with lung histopathological findings and autopsy results.

INTRODUCTION/PURPOSE: Postmortem computed tomography (PMCT) is a valuable tool for analyzing the death of patients with SARS-CoV-2 infection. The purpose of this study was to investigate the correlation between PMCT lung findings in autopsy cadavers positive for SARS-CoV-2 infection and the severity of COVID-19 lung disease by histopathological analysis. MATERIALS AND METHODS: We reviewed chest PMCT findings, paying particular attention to the lung parenchyma, in 8 autopsy cases positive for SARS-CoV-2. Correlations between chest PMCT and histopathological findings were assessed. Clinical conditions and comorbidities were also recorded and discussed. The primary cause of death was finally considered. RESULTS: In 6/8 cases, pulmonary PMCT findings were massive consolidation (4/8) and bilateral diffuse mixed densities with a crazy-paving pattern (2/8). These cases showed severe pulmonary signs of COVID-19 at histopathological analysis. In the remaining 2/8 cases, pulmonary PMCT findings were scant antideclive ground-glass opacities in prevalent gradient densities attributed to hypostasis. In 4/8 cases with massive consolidations, important comorbidities were noted. In 6/8 cases with severe pulmonary histopathological signs of lung COVID-19, autopsy found that the cause of death was cardiorespiratory failure. In the remaining 2/8 cases, histopathological analysis revealed lung alterations due to edema and some signs of SARS-CoV-2 infection; the cause of death was not attributed to SARS-CoV-2 infection (Table 1). DISCUSSION AND CONCLUSION: Chest PMCT findings correlate with the severity of COVID-19 lung disease at histopathology examination. According to our results, there may also be a relationship between cause of death and PMCT findings in COVID-19, which must be critically analyzed considering clinical antemortem data.

A real-time integrated framework to support clinical decision making for covid-19 patients.

BACKGROUND: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS: The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS: The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION: The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Use of post-mortem chest computed tomography in Covid-19 pneumonia.

BACKGROUND AND AIM: COVID-19 is an extremely challenging disease, both from a clinical and forensic point of view, and performing autopsies of COVID-19 deceased requires adequately equipped sectorial rooms and exposes health professionals to the risk of contagion. Among one of the categories that are most affected by SARS-Cov-2 infection are the elderly residents. Despite the need for prompt diagnoses, which are essential to implement all isolation measures necessary to contain the infection spread, deceased subjects in long-term care facilities are still are often diagnosed post-mortem. In this context, our study focuses on the use of post-mortem computed tomography for the diagnosis of COVID-19 infection, in conjunction with post-mortem swabs. The aim of this study was to assess the usefulness of post-mortem whole CT-scanning in identifying COVID-19 pneumonia as a cause of death, by comparing chest CT-findings of confirmed COVID-19 fatalities to control cases. MATERIALS AND METHODS: The study included 24 deceased subjects: 13 subjects coming from long-term care facility and 11 subjects died at home. Whole body CT scans were performed within 48 h from death in all subjects to evaluate the presence and distribution of pulmonary abnormalities typical of COVID-19-pneumonia, including: ground-glass opacities (GGO), consolidation, and pleural effusion to confirm the post-mortem diagnosis. RESULTS: Whole-body CT scans was feasible and allowed a complete diagnosis in all subjects. In 9 (69%) of the 13 cases from long-term care facility the cause of death was severe COVID 19 pneumonia, while GGO were present in 100% of the study population. CONCLUSION: In the context of rapidly escalating COVID-19 outbreaks, given that laboratory tests for the novel coronavirus is time-consuming and can be falsely negative, the post-mortem CT can be considered as a reliable and safe modality to confirm COVID-19 pneumonia. This is especially true for specific postmortem chest CT-findings that are rather characteristic of COVID-19 fatalities.

Claimed medical malpractice in fatal SARS-CoV-2 infections: the importance of combining ante- and post-mortem radiological data and autopsy findings for correct forensic analysis

We present the case of an elderly woman who died from COVID-19 during the first wave of the pandemic. The physicians in charge of the patient were later accused of medical malpractice resulting in the death of the patient. The article reviews the comprehensive medico-legal investigations into this case that included an analysis of the medical history, clinical imaging, post-mortem imaging, autopsy, histopathology, and microbiology as well as an assessment of the medical knowledge regarding transmission of the SARS-CoV-2 virus and the management of COVID-19 at the time of the patient's death. The investigation resulted in a verdict of not guilty. This case highlights the value of clinical and post-mortem imaging as well as various challenges of medico-legal investigations of COVID-19 related deaths.

Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review.

Due to its pandemic diffusion, SARS- CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection represents a global threat. Despite a multiorgan involvement has been described, pneumonia is the most common manifestation of COVID-19 (Coronavirus disease 2019) and it is associated with a high morbidity and a considerable mortality. Especially in the areas with high disease burden, chest imaging plays a crucial role to speed up the diagnostic process and to aid the patient management. The purpose of this comprehensive review is to understand the diagnostic capabilities and limitations of chest X-ray (CXR) and high-resolution computed tomography (HRCT) in defining the common imaging features of COVID-19 pneumonia and correlating them with the underlying pathogenic mechanisms. The evolution of lung abnormalities over time, the uncommon findings, the possible complications, and the main differential diagnosis occurring in the pandemic phase of SARS-CoV-2 infection are also discussed.

Longitudinal study of interventional radiology activity in a large metropolitan Italian tertiary care hospital: how the COVID-19 pandemic emergency has changed our activity.

OBJECTIVES: To retrospectively analyze interventional radiology (IR) activity changes in the COVID-19 era and to describe how to safely and effectively reorganize IR activity. METHODS: All IR procedures performed between January 30 and April 8, 2020 (COVID-era group) and the same 2019 period (non-COVID-era group) were retrospectively included and compared. A sub-analysis for the lockdown period (LDP: 11 March-8 April) was also conducted. Demographic, hospitalization, clinical, and procedural data were obtained for both groups and statistically compared with univariable analysis. RESULTS: A total of 1496 procedures (non-COVID era, 825; COVID era, 671) performed in 1226 patients (64.9 ± 15.1 years, 618 women) were included. The number of procedures decreased by 18.6% between 2019 and 2020 (825 vs 671, p < .001), with a reduction by 48.2% in LDP (188 vs 363, p < .0001). In the LDP COVID era, bedside procedures were preferred (p = .013), with an increase in procedures from the intensive care unit compared with the emergency department and outpatients (p = .048), and an increased activity for oncological patients (p = .003). No incidents of cross-infection of non-infected from infected patients and no evidence of COVID-19 infection of healthcare workers in the IR service was registered. CONCLUSIONS: Coronavirus disease outbreak changed the interventional radiology activity with an overall reduction in the number of procedures. However, this study confirms that interventional radiology continuum of care can be safely performed also during the pandemic, following defined measures and protocols, taking care of all patients. KEY POINTS: • Coronavirus disease pandemic determined a reduction of interventional radiology activity as compared to the same period of the previous year. • Interventional radiology procedures for life-threatening conditions and non-deferrable oncologic treatments were prioritized as opposed to elective procedures. • Strict adoption of safe procedures allowed us to have until now no incidents of cross-infection of non-infected from infected patients and no evidence of COVID-19 infection of HCWs in the IR service.