ABSTRACT
Background:The coronavirus disease 2019 (COVID-19) is a highly infectious respiratory disease. There is no recommended antiviral treatment approved for COVID-19 in Sierra Leone, and supportive care and protection of vital organ function are performed for the patients. This study summarized the clinical characteristics, drug treatments, and risk factors for the severity and prognosis of COVID-19 in Sierra Leone to provide evidence for the treatment of COVID-19.Methods:Data of 180 adult COVID-19 patients from the 34th Military Hospital in Freetown Sierra Leone between March 31, 2020 and August 11, 2020 were retrospectively collected. Patients with severe and critically ill are classified in the severe group, while patients that presented asymptomatic, mild, and moderate disease were grouped in the non-severe group. The clinical and laboratory information was retrospectively collected to assess the risk factors and treatment strategies for severe COVID-19. Demographic information, travel history, clinical symptoms and signs, laboratory detection results, chest examination findings, therapeutics, and clinical outcomes were collected from each case file. Multivariate logistic analysis was adopted to identify the risk factors for deaths. Additionally, the clinical efficacy of dexamethasone treatment was investigated.Results:Seventy-six (42.22%) cases were confirmed with severe COVID-19, while 104 patients (57.78%) were divided into the non-severe group. Fever (56.67%, 102/180) and cough (50.00%, 90/180) were the common symptoms of COVID-19. The death rate was 18.89% (34/180), and severe pneumonia (44.12%, 15/34) and septic shock (23.53%, 8/34) represented the leading reasons for deaths. The older age population, a combination of hypertension and diabetes, the presence of pneumonia, and high levels of inflammatory markers were significantly associated with severity of COVID-19 development (P < 0.05 for all). Altered level of consciousness [odds ratio (OR) = 56.574, 95% confidence interval (CI) 5.645-566.940, P = 0.001], high levels of neutrophils (OR = 1.341, 95%CI 1.109-1.621, P = 0.002) and C-reactive protein (CRP) (OR = 1.014, 95%CI 1.003-1.025, P = 0.016) might be indicators for COVID-19 deaths. Dexamethasone treatment could reduce mortality [30.36% (17/56) vs. 50.00% (10/20)] among severe COVID-19 cases, but the results were not statistically significant (P > 0.05).Conclusions:The development and prognosis of COVID-19 may be significantly correlated with consciousness status, and the levels of neutrophils and CRP. © 2022 Journal of Bone and Joint Surgery Inc.. All rights reserved.
ABSTRACT
OBJECTIVE: Since the coronavirus disease 2019 (COVID-19) outbreak in Wuhan in 2019, the virus has spread rapidly. We investigated the clinical and computed tomography (CT) characteristics of different clinical types of COVID-19. MATERIALS AND METHODS: We retrospectively analyzed clinical and chest CT findings of 89 reverse transcription polymerase chain reaction confirmed cases from five medical centers in China. All the patients were classified into the common (n = 65), severe (n = 18), or fatal (n = 6) type. CT features included lesion distribution, location, size, shape, edge, density, and the ratio of lung lesions to extra-pulmonary lesions. A COVID-19 chest CT analysis tool (uAI-discover-COVID-19) was used to calculate the number of infections from the chest CT images. RESULTS: Fatal type COVID-19 is more common in older men, with a median age of 65 years. Fever was more common in the severe and fatal type COVID-19 patients than in the common type patients. Patients with fatal type COVID-19 were more likely to have underlying diseases. On CT examination, common type COVID-19 showed bilateral (68%), patchy (83%), ground-glass opacity (48%), or mixed (46%) lesions. Severe and fatal type COVID-19 showed bilateral multiple mixed density lesions (56%). The infection ratio (IR) increased in the common type (2.4 [4.3]), severe type (15.7 [14.3]), and fatal type (36.9 [14.2]). The IR in the inferior lobe of both lungs was statistically different from that of other lobes in common and severe type patients (P < 0.05). However, in the fatal type group, only the IR in the right inferior lung (RIL) was statistically different from that in the right superior lung(RUL), right middle lung (RML), and the left superior lung (LSL) (P < 0.05). CONCLUSION: The CT findings and clinical features of the various clinical types of COVID-19 pneumonia are different. Chest CT findings have unique characteristics in the different clinical types, which can facilitate an early diagnosis and evaluate the clinical course and severity of COVID-19.
ABSTRACT
BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.