Back

Clinical features and outcomes of 2019 novel coronavirus-infected patients with high plasma BNP levels

liu, y.; Liu, D.; Song, H.; chen, C.; lv, M.; pei, X.; Hu, Z.; Qin, Z.; Li, J.

2020-04-02 cardiovascular medicine
10.1101/2020.03.31.20047142 medRxiv
Show abstract

AimsTo explore clinical features and outcome of 2019 novel coronavirus(2019-nCoV)-infected patients with high BNP levels Methods and resultsData were collected from patients medical records, and we defined high BNP according to the plasma BNP was above > 100 pg/mL. In total,34 patients with corona virus disease 2019(COVID-19)were included in the analysis. Ten patients had high plasma BNP level. The median age for these patients was 60.5 years(interquartile range, 40-80y), and 6/10 (60%) were men. Underlying comorbidities in some patients were coronary heart disease (n=2, 20%), hypertesion (n=3,30%), heart failure (n=1,10%)and diabetes (n=2, 20%). Six (60%) patients had a history of Wuhan exposure. The most common symptoms at illness onset in patients were fever (n=7, 70%), cough (n=3, 30%), headache or fatigue(n=4,40%). These patients had higher aspartate aminotransferase(AST), troponin I, C reactive protein and lower hemoglobin, and platelet count,compared with patients with normal BNP, respectively. Compared with patients with normal BNP, patients with high BNP were more likely to develop severe pneumonia, and receive tracheal cannula, invasive mechanical ventilation, continuous renal replacement therapy, extracorporeal membrane oxygenation, and be admitted to the intensive care unit. One patient with high BNP died during the study. ConclusionHigh BNP is a common condition among patients infected with 2019-nCoV. Patients with high BNP showed poor clinical outcomes

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.