A differential diagnosis model for patients with COVID-19 and non-COVID-19 pneumonia based on laboratory indicators

OBJECTIVE: To retrospectively analyze the clinical characteristics, imaging features and laboratory indexes of the patients with COVID-19 and non-COVID-19 so as to seek for differential diagnosis indexes. METHODS: A total of 66 patients with COVID-19 and 40 non-COVID-19 patients were recruited as study subjects who were treated in the hospital from Jan 2020 to Apr 2020. The demographic data, clinical symptoms, underlying diseases, imaging features, length of hospital stay and laboratory test indexes at the admission were statistically analyzed. RESULTS: The white blood cell(WBC),albumin(ALB) and prealbumin(PALB) of the COVID-19 patients were remarkably lower than those of the non-COVID-19 patients;while the length of hospital stay, aspartate aminotransferase(AST), international normalized ratio(INR), fibrinogen(Fbg), lactate dehydrogenase(LDH), tumor specific growth factor(TSGF) and ferritin(Ferritin) of the COVID-19 group were remarkably higher than those of the non-COVID-19 group. The COVID-19 patients had a higher frequency of air bronchogram, reticular pattern, number of affected lobes and number of affected segments, but a lower frequency of centrilobular nodules than did the non-COVID-19 patients. The length of hospital stay of the COVID-19 patients was positively correlated with the age but was negatively correlated with LYM and ALB, and the length of hospital stay of the patients complicated with diabetes mellitus and hypertension was longer than the patients with other complications. Receiver operating characteristic(ROC) curve analysis showed that the areas under curves of WBC, TSGF, LDH and Ferritin were more than 75% in distinguishing between COVID-19 and non-COVID-19. Multivariate logistic regression analysis showed that TSGF, LDH and Ferritin were the independent factors for distinguishing between COVID-19 and non-COVID-19, and the area under curve of the joint detection of the three indexes was 0.9181. CONCLUSION: The ordinary COVID-19 patients and non-COVID-19 patients vary in some clinical characteristic, imaging features and clinical laboratory indexes. The joint diagnosis model of TSGF, LDH and Ferritin may be used as an effective indicator for distinguishing between ordinary COVID-19 and non-COVID-19.

Investment in energy resources, natural resources and environment: Evidence from China

Energy resource investment has been critical for a sustainable economy. Its significance, however, intensified, notably post COVID-19. The function of energy efficiency in ensuring a sustainable environment has been undervalued. However, unlike previous studies, this study aims to analyze the role of energy efficiency, industrial production and investment in energy on carbon emissions for China employing data from 1990 to 2020. The study uses updated time series econometric methods such as Narayan and Pop unit root test, Bayer-Hanck cointegration method, fully modified, dynamic and canonical cointegration analysis. The results confirmed cointegrating relationship among variables via Bayer-Hanck cointegration approach. The study also revealed that energy efficiency and carbon emissions had an inverse connection. Meanwhile, China's industrial production, energy investment, and gross domestic product have all been found to increase emissions. Consequently, natural resource rents harm the ecosystem. Based on the empirical findings, this study recommends the adoption of energy-efficient technologies and the use of energy from efficient sources in order to assist China to lessen environmental deterioration.

An increased pretreatment neutrophil-to-lymphocyte ratio predicts severe novel coronavirus-infected pneumonia (preprint)

Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19).Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 at the Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. Logistic regression analysis was performed to identify the independent risk factors that predicted severe COVID-19. The receiver-operating characteristic (ROC) curve of independent risk factors was calculated, and the area under the curve (AUC) was used to evaluate the efficiency of the prediction of severe COVID-19.Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP) and the serum albumin (ALB) level (P<0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CRP but was negatively correlated with the ALB level (P<0.05). Multivariate logistic regression analysis showed that the NLR and ALB level were independent risk factors for severe COVID-19 (OR=1.319, 95% CI: 1.043-1.669, P=0.021; OR=0.739, 95% CI: 0.616-0.886, P=0.001), with AUCs of 0.851 and 0.128, respectively. An NLR of 4.939 corresponded to the maximum joint sensitivity and specificity according to the ROC curve (0.700 and 0.917, respectively).Conclusion An increased NLR can serve as an early warning sign of severe COVID-19.

Increased pretreatment C-reactive protein-to-albumin ratio predicts severe coronavirus disease 2019 (preprint)

Objective The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000; P =0.008; P=0.000; P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008; OR=1.512, P =0.007; OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features (preprint)

Purpose: To identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage, and to identify the most valuable features in the differential diagnosis. Materials and Method: A consecutive cohort of 73 COVID-19 and 48 influenza pneumonia patients were retrospectively recruited from five independent institutions. The courses of both diseases were confirmed to be in the early stages (mean 2.66 (SD 2.62) days for COVID-19 and mean 2.19 (SD 2.10) days for influenza pneumonia after onset). The chi-square test, student`s t-test, and Kruskal-Wallis H-test were performed to compare CT imaging and clinical features between the two groups. Spearman or Kendall correlation tests between feature metrics and diagnosis outcomes were also assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was evaluated with univariate analysis. The corresponding area under the curve (AUC), accuracy, specificity, sensitivity and threshold were reported. Results: The ground-glass opacification (GGO) was the most common imaging feature in COVID-19, including pure-GGO (75.3%) and mixed-GGO (78.1%), mainly in peripheral distribution. For clinical features, most COVID-19 patients presented normal white blood cell (WBC) count (89.04%) and neutrophil count (84.93%). Twenty imaging features and 6 clinical features were identified to be significantly different between the two diseases. The diagnosis outcomes correlated significantly with the WBC count (r=-0.526, P<0.001) and neutrophil count (r=-0.500, P<0.001). Four CT imaging features had absolute correlations coefficients higher than 0.300 (P<0.001), including crazy-paving pattern, mixed-GGO in peripheral area, pleural effusions, and consolidation. Conclusions: Among a total of 1537 lesions and 62 imaging and clinical features, 26 features were demonstrated to be significantly different between COVID-19 and influenza pneumonia. The crazy-paving pattern was recognized as the most powerful imaging feature for the differential diagnosis in the early stage, while WBC count yielded the highest diagnostic efficacy in clinical manifestations.