Takotsubo syndrome and respiratory diseases: a systematic review

Takotsubo syndrome (TTS) is a rare cardiovascular condition characterized by reversible ventricular dysfunction and a presentation resembling that of acute myocardial infarction. An increasing number of studies has shown the association of respiratory diseases with TTS. Here, we comprehensively reviewed the literature and examined the available evidence for this association. After searching PubMed, EMBASE, and Cochrane Library databases, two investigators independently reviewed 3117 studies published through May 2021. Of these studies, 99 met the inclusion criteria (n = 108 patients). In patients with coexisting respiratory disease and TTS, the most common TTS symptom was dyspnoea (70.48%), followed by chest pain (24.76%) and syncope (2.86%). The most common type of TTS was apical, accounting for 81.13% of cases, followed by the midventricular (8.49%), basal (8.49%), and biventricular (1.89%) types. Among the TTS cases, 39.82% were associated with obstructive lung disease and 38.89% were associated with pneumonia. Coronavirus disease 2019 (COVID-19), which has been increasingly reported in patients with TTS, was identified in 29 of 42 (69.05%) patients with pneumonia. The overall mortality rate for patients admitted for respiratory disease complicated by TTS was 12.50%. Obstructive lung disease and pneumonia are the most frequently identified respiratory triggers of TTS. Medications and invasive procedures utilized in managing respiratory diseases may also contribute to the development of TTS. Furthermore, the diagnosis of TTS triggered by these conditions can be challenging due to its atypical presentation. Future prospective studies are needed to establish appropriate guidelines for managing respiratory disease with concurrent TTS. Graphical Graphical

NLR combined with SaO2 predict severe illness among COVID-19 patients: a currently updated model (preprint)

Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.

The Proteomic Characteristics of Airway Mucus from Critical Ill COVID-19 Patients (preprint)

Background: The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood.Methods: In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively.Results: Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change > 1.5 or < 0.67 and a p-value < 0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways.Conclusions: This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy.Funding Statement: This work was supported by grants from the National Key R&D Program of China (2016YFC0903700), the National Natural Science Foundation of China (81520108001 and 81770043), and grant specific for COVID-19 study from Guangzhou Institute of Respiratory Health.Declaration of Interests: The authors have no conflict of interest to declare.Ethics Approval Statement: All the procedures were approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2020-65). Verbal informed consent were obtained from all participants because the family members were in quarantine.

NLR combined with SaO2 predict severe illness among COVID-19 patients: a currently updated model (preprint)

Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.

The proteomic characteristics of airway mucus from critical ill COVID-19 patients (preprint)

Background: The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood. Methods: : In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively. Results: : Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change > 1.5 or < 0.67 and a p-value < 0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways. Conclusions: : This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy.