This study aims to explore the effect of hypertension on disease progression and prognosis in patients with coronavirus disease 2019 (COVID-19). A total of 310 patients diagnosed with COVID-19 were studied. A comparison was made between two groups of patients, those with hypertension and those without hypertension. Their demographic data, clinical manifestations, laboratory indicators, and treatment methods were collected and analyzed. A total of 310 patients, including 113 patients with hypertension and 197 patients without hypertension, were included in the analysis. Compared with patients without hypertension, patients with hypertension were older, were more likely to have diabetes and cerebrovascular disease, and were more likely to be transferred to the intensive care unit. The neutrophil count and lactate dehydrogenase, fibrinogen, and D-dimer levels in hypertensive patients were significantly higher than those in nonhypertensive patients (P < 0.05). However, multivariate analysis (adjusted for age and sex) failed to show that hypertension was an independent risk factor for COVID-19 mortality or severity. COVID-19 patients with hypertension were more likely than patients without hypertension to have severe pneumonia, excessive inflammatory reactions, organ and tissue damage, and deterioration of the disease. Patients with hypertension should be given additional attention to prevent worsening of their condition.

Background Coronavirus disease 2019 (COVID-19) pneumonia tends to affect cardiovascular system and cause cardiovascular damage. This study aimed to explore the prevalence of myocardial injury and risk factors for mortality in patients with COVID-19 pneumonia. Methods 224 consecutive patients with confirmed diagnosis of SARS-CoV-2 infection and definite outcomes (discharge or death) were retrospectively analyzed. Laboratory results including myocardial biomarkers, oxygen saturation, inflammatory indicators and coagulation function were compared between survivors and non-survivors. Univariate and multivariate logistic regression model were used to explore risk factors for in-hospital mortality, and a chart with different combinations of risk factors was constructed to predict mortality. Results 203 patients were included in the final analysis, consisting of 145 patients who recovered and 58 patients who died. Compared with survivors, non-survivors were older, with more comorbidities, more severe inflammation and active coagulation function, higher levels of myocardial biomarkers and lower SaO 2 . 28 (50%) non-survivors and 9 (6%) survivors developed myocardial injury, which was associated with disease severity at admission. Elevated d-dimer (OR = 9.51, 95% CI [3.61–25.0], P < 0.001), creatinine kinase-myocardial band (OR = 6.93, 95% CI [1.83–26.2], P = 0.004), Troponin I (OR = 10.1, 95% CI [3.1–32.8], P < 0.001) and C-reactive protein (OR = 15.1, 95% CI [1.7–129.3], P = 0.013) were risk factors for mortality. Patients with abnormal levels of d-dimer, Troponin I and CRP were predicted to have significantly higher probability of death. Conclusions Our results suggest that SARS-CoV-2 infection may induce myocardial injury and consequently exacerbate the clinical course and worsen prognosis. Abnormal d-dimer, CK-MB, Troponin I and CRP are risk factors for short-term mortality.

Background Limited studies have investigated the accuracy of therapeutic decision-making using machine learning-based coronary computed tomography angiography (ML-CCTA) compared with CCTA. Purpose To investigate the performance of ML-CCTA for therapeutic decision compared with CCTA. Material and Methods The study population consisted of 322 consecutive patients with stable coronary artery disease. The SYNTAX score was calculated with an online calculator based on ML-CCTA results. Therapeutic decision-making was determined by ML-CCTA results and the ML-CCTA-based SYNTAX score. The therapeutic strategy and the appropriate revascularization procedure were selected using ML-CCTA, CCTA, and invasive coronary angiography (ICA) independently. Results The sensitivity, specificity, positive predictive value, negative predictive value, accuracy of ML-CCTA and CCTA for selecting revascularization candidates were 87.01%, 96.43%, 95.71%, 89.01%, 91.93%, and 85.71%, 87.50%, 86.27%, 86.98%, 86.65%, respectively, using ICA as the standard reference. The area under the receiver operating characteristic curve (AUC) of ML-CCTA for selecting revascularization candidates was significantly higher than CCTA (0.917 vs. 0.866, P = 0.016). Subgroup analysis showed the AUC of ML-CCTA for selecting percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) candidates was significantly higher than CCTA (0.883 vs. 0.777, P < 0.001, 0.912 vs. 0.826, P = 0.003, respectively). Conclusion ML-CCTA could distinguish between patients who need revascularization and those who do not. In addition, ML-CCTA showed a slightly superior to CCTA in making an appropriate decision for patients and selecting a suitable revascularization strategy.

Objective This study aimed to compare quantifiable radiologic findings and their dynamic change throughout the clinical course of common and severe coronavirus disease 2019 (COVID-19), and to provide valuable evidence for radiologic classification of the two types of this disease. Methods 112 patients with laboratory-confirmed COVID-19 were retrospectively analyzed. Volumetric percentage of infection and density of the lung were measured by a computer-aided software. Clinical parameters were recorded to reflect disease progression. Baseline data and dynamic change were compared between two groups and a decision-tree algorithm was developed to determine the cut-off value for classification. Results 93 patients were finally included and were divided into common group ( n = 76) and severe group ( n = 17) based on current criteria. Compared with common patients, severe patients experienced shorter advanced stage, peak time and plateau, but longer absorption stage. The dynamic change of volume and density coincided with the clinical course. The interquartile range of volumetric percentage of the two groups were 1.0–7.2% and 11.4–31.2%, respectively. Baseline volumetric percentage of infection was significantly higher in severe group, and the cut-off value of it was 10.10%. Conclusions Volumetric percentage between severe and common patients was significantly different. Because serial CT scans are systemically performed in patients with COVID-19 pneumonia, this quantitative analysis can simultaneously provide valuable information for physicians to evaluate their clinical course and classify common and severe patients accurately.