Background and AimCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.MethodsCOVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.ResultsA total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).ConclusionA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

Primary pulmonary venous malformation is rare and usually presents as single or multiple round masses or nodules. Here, we present the first report of a case of venous malformation presenting as Mauritia arabica-like bronchial wall thickness that was initially misdiagnosed as bronchiectasis. A Chinese man in his late 20s presented complaining of hemoptysis for 10 days. Computed tomography demonstrated bronchiectasis and M. arabica-like bronchial wall thickening in the left lower lobe. He was unresponsive to medical treatment for bronchiectasis and underwent thoracoscopic left lower lobectomy. Histopathological examination revealed venous malformation around the bronchial walls with no bronchiectasis. Venous malformation should be considered in the differential diagnosis of bronchiectasis, especially in patients with the following triad of signs: no response to antibiotics, M. arabica-like bronchial wall thickness, and normal accompanying arteries.

Background and AimCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). ConclusionA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

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