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Severe SARS-CoV-2 Infection in a Cat with Hypertrophic Cardiomyopathy.

ABSTRACT: Coronavirus disease 19 (COVID-19), has claimed millions of human lives worldwide since the emergence of the zoonotic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019. Notably, most severe and fatal SARS-CoV-2 infections in humans have been associated with underlying clinical conditions, including diabetes, hypertension and heart diseases. Here, we describe a case of severe SARS-CoV-2 infection in a domestic cat (Felis catus) that presented with hypertrophic cardiomyopathy (HCM), a chronic heart condition that has been described as a comorbidity of COVID-19 in humans and that is prevalent in domestic cats. The lung and heart of the affected cat presented clear evidence of SARS-CoV-2 replication, with histological lesions similar to those observed in humans with COVID-19 with high infectious viral loads being recovered from these organs. The study highlights the potential impact of comorbidities on the outcome of SARS-CoV-2 infection in animals and provides important information that may contribute to the development of a feline model with the potential to recapitulate the clinical outcomes of severe COVID-19 in humans.

SUBMITTER: Carvallo FR

PROVIDER: S-EPMC8402861 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.

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Severe SARS-CoV-2 Infection in a Cat with Hypertrophic Cardiomyopathy.

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