Clinical characteristics of 82 cases of death from COVID-19

Zhang, Bicheng; Zhou, Xiaoyang; Qiu, Yanru; Song, Yanxing; Feng, Fan; Feng, Jia; Song, Qibin; Jia, Qingzhu; Wang, Jun

doi:10.1371/journal.pone.0235458

Cited by 445 publications

(446 citation statements)

References 21 publications

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“…Zhang and colleagues 9 reported that the median age in a small cohort of COVID-19 non-survivors was 72.5, similar to our ndings. In addition, both Zhang and we found that male COVID-19 patients accounted for the majority of the non-survivors.…”

Section: Discussionsupporting

confidence: 91%

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

Qiao

Zhang

et al. 2020

Preprint

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BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups as well as between survivors and non-survivors. In addition, we developed a decision tree classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

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Section: Discussionsupporting

confidence: 91%

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

Qiao

Zhang

et al. 2020

Preprint

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“…These factors could increase the likelihood of the development of endothelial dysfunction and platelet aggregation promoting the formation of occlusive thrombus in the heart and lungs in COVID-19 patients with diabetes. In patients with COVID-19 however there are increased levels of fibrinogen, CRP, and D-dimer (14,58), with elevated D-dimer levels being a risk factor for mortality (53) these findings indicate that there appears to be an increase not only in coagulation but also in fibrinolysis. An imbalance or an impairment of coordination between coagulation and fibrinolysis appears to be responsible for much of the pathology observed in COVID-19 (59).…”

Section: Obesity Diabetes Affect the Host Response To Sars-cov-2mentioning

confidence: 99%

Obesity, Diabetes and COVID-19: An Infectious Disease Spreading From the East Collides With the Consequences of an Unhealthy Western Lifestyle

et al. 2020

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The pandemic of COVID-19, caused by the coronavirus, SARS-CoV-2, has had a global impact not seen for an infectious disease for over a century. This acute pandemic has spread from the East and has been overlaid onto a slow pandemic of metabolic diseases of obesity and diabetes consequent from the increasing adoption of a Western-lifestyle characterized by excess calorie consumption with limited physical activity. It has become clear that these conditions predispose individuals to a more severe COVID-19 with increased morbidity and mortality. There are many features of diabetes and obesity that may accentuate the clinical response to SARS-CoV-2 infection: including an impaired immune response, an atherothrombotic state, accumulation of advanced glycation end products and a chronic inflammatory state. These could prime an exaggerated cytokine response to viral infection, predisposing to the cytokine storm that triggers progression to septic shock, acute respiratory distress syndrome, and multi-organ failure. Infection leads to an inflammatory response and tissue damage resulting in increased metabolic activity and an associated increase in the mechanisms by which cells ingest and degrade tissue debris and foreign materials. It is becoming clear that viruses have acquired an ability to exploit these mechanisms to invade cells and facilitate their own life-cycle. In obesity and diabetes these mechanisms are chronically activated due to the deteriorating metabolic state and this may provide an increased opportunity for a more profound and sustained viral infection.

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“…While most patients are asymptomatic or display only mild symptoms such as fatigue, fever, and dry coughs, some individuals develop pneumonia, severe acute respiratory distress syndrome, sepsis, and septic shock with an overall case fatality rate of 5% (4)(5)(6). In a subset of patients, COVID-19 is associated with a so-called cytokine storm, lymphopenia, and dysregulation of the immune system, yet the underlying mechanisms and cellular sources of these immunological complications remain to be fully understood (7,8). First studies have shown that reduced T cell counts correlated with disease severity in COVID-19 patients (9).…”

Section: Introductionmentioning

confidence: 99%