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BACKGROUND: Thoracic empyema and malignant pleural mesothelioma (MPM) are distinct medical conditions with similar symptoms, including cough, chest pain, and breathing difficulty. We present a rare MPM case mimicking thoracic empyema. Physicians must consider MPM risks for patients exposed to building material who exhibit lobulated pleural effusions, indicating thoracic empyema. CASE SUMMARY: A 68-year-old retired male construction worker suffered from shortness of breath and chest tightness over 10 d, particularly during physical activity. A poor appetite and 4 kg weight loss over the past 3 wk were also reported. Chest images and laboratory data concluded a tentative impression of empyema thoracis (right). Video-assisted thoracic surgery with decortication and delobulation (right) was conducted. The pathological report yielded an MPM diagnosis. Refractory pleural bilateral effusions and respiratory failure developed postoperatively, and the patient died three weeks after the operation. CONCLUSION: Thoracic empyema and MPM are distinct medical conditions that can present similar symptoms, and video-assisted thoracic surgery facilitates an accurate diagnosis. Empyema-mimicking presentations and postoperative refractory pleural effusion may indicate a poor MPM outcome.
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During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients suspected of COVID-19, of which 27 had confirmed COVID-19. The clinical characteristics of these patients were used to train artificial intelligence (AI) models such as support vector machine (SVM), decision tree, random forest, and artificial neural network for diagnosing COVID-19. When analyzing the performance of the models, SVM showed the highest sensitivity (SVM vs. decision tree vs. random forest vs. artificial neural network: 100% vs. 42.86% vs. 28.57% vs. 71.43%), while decision tree and random forest had the highest specificity (SVM vs. decision tree vs. random forest vs. artificial neural network: 88.37% vs. 100% vs. 100% vs. 94.74%) in the diagnosis of COVID-19. With the aid of AI models, physicians may identify COVID-19 patients earlier, even with few baseline data available, and segregate infected patients earlier to avoid hospital cluster infections and to ensure the safety of medical professionals and ordinary patients in the hospital.
RESUMO
PURPOSE: This cohort study evaluated the effectiveness of noninvasive heart rate variability (HRV) analysis to assess the risk of cardiovascular disease over a period of 8 years. METHODS: Personal and working characteristics were collected before biochemistry examinations and 5 min HRV tests from the Taiwan Bus Driver Cohort Study (TBDCS) in 2005. This study eventually identified 161 drivers with cardiovascular disease (CVD) and 627 without between 2005 and 2012. Estimation of the hazard ratio was analyzed by using the Cox proportional-hazards model. RESULTS: Subjects with CVD had an overall lower standard deviation of NN intervals (SDNN) than their counterparts did. The SDNN index had a strong association with CVD, even after adjusting for risk factors. Using a median split for SDNN, the hazard ratio of CVD was 1.83 (95% CI = 1.10-3.04) in Model 1 and 1.87 (95% CI = 1.11-3.13) in Model 2. Furthermore, the low-frequency (LF) index was associated with a risk of CVD in the continuous approach. For hypertensive disease, the SDNN index was associated with increased risks in both the continuous and dichotomized approaches. When the root-mean-square of the successive differences (RMSSDs), high frequency (HF), and LF were continuous variables, significant associations with hypertensive disease were observed. CONCLUSIONS: This cohort study suggests that SDNN and LF levels are useful for predicting 8 year CVD risk, especially for hypertensive disease. Further research is required to determine preventive measures for modifying HRV dysfunction, as well as to investigate whether these interventions could decrease CVD risk among professional drivers.