Effects of medical insurance system on the hospitalization cost of acute myocardial infarction patients.

BACKGROUND: Acute myocardial infarction is still a burden on Chinese patients. Whether different medical insurance system have any influence on the hospitalization cost and therapeutic effect of acute myocardial infarction patient needs further investigation. METHOD: In this study, 600 patients were stratified by health insurance status to investigate the cost effectiveness. RESULT: Compared with free medical care, patients with other health insurance status have a significantly lower age (P Ë‚ 0.05-0.001), the youngest of which is new rural cooperative medical system. The hospital expense, nursing fee, length of stay, daily hospitalization cost, daily drug cost, daily nursing cost and percent of nursing cost of different health insurance status were statistically significant. ANCOVA analyses controlling for age showed that the differences of hospital expenses, nursing fee, length of stay and daily hospitalization cost were still statistically significant. Further studies found that health insurance status was the leading factors influencing length of stay (ß = - 0.305, P = 0.0000001), nursing costs (ß = - 0.319, P = 0.004), daily hospitalization costs (ß = 0.296, P = 0.0001) and occurrence of clinical events (ß = - 0.186, OR = 0.830, 95% CI 0.694-0.993, P = 0.041). CONCLUSIONS: The hospitalization cost, length of stay, nursing work and therapeutic effect of acute myocardial infarction patients are affected by different health insurance status and age.

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19's endemic and pandemic.

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Then, we divided the data into two independent radiomic cohorts for training (70 COVID-19 patients and 70 other pneumonias patients), and validation (20 COVID-19 patients and 20 other pneumonias patients) by using support vector machine (SVM). This model used 20 rounds of tenfold cross-validation for training. Finally, single-shot testing of the final model was performed on the independent validation cohort. In the COVID-19 patients, correlation analysis (multiple comparison correction-Bonferroni correction, P < 0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The final model showed good discrimination on the independent validation cohort, with an accuracy of 89.83%, sensitivity of 94.22%, specificity of 85.44%, and AUC of 0.940. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some textural features were positively correlated with WBC, and NE, and also negatively related to SPO2H and NE. Our results showed that radiomic features can classify COVID-19 patients and other pneumonias patients. The SVM model can achieve an excellent diagnosis of COVID-19.

Correlation between CT findings and outcomes in 46 patients with coronavirus disease 2019.

The aim of this study was to analyze initial chest computed tomography (CT) findings in COVID-19 pneumonia and identify features associated with poor prognosis. Patients with RT-PCR-confirmed COVID-19 infection were assigned to recovery group if they made a full recovery and to death group if they died within 2 months of hospitalization. Chest CT examinations for ground-glass opacity, crazy-paving pattern, consolidation, and fibrosis were scored by two reviewers. The total CT score comprised the sum of lung involvement (5 lobes, scores 1-5 for each lobe, range; 0, none; 25, maximum). 40 patients who recovered from COVID-19 and six patients who died were enrolled. The initial chest CTs showed 27 (58.7%) patients had ground-glass opacity, 19 (41.3%) had ground glass and consolidation, and 35 (76.1%) patients had crazy-paving pattern. None of the patients who died had fibrosis in contrast to six (15%) patients who recovered from COVID-19. Most patients had subpleural lesions (89.0%) as well as bilateral (87.0%) and lower (93.0%) lung lobe involvement. Diffuse lesions were present in four (67%) patients who succumbed to coronavirus but only one (2.5%) patient who recovered (p < 0.001). In the death group of patients, the total CT score was higher than that of the recovery group (p = 0.005). Patients in the death group had lower lymphocyte count and higher C-reactive protein than those in the recovery group (p = 0.011 and p = 0.041, respectively). A high CT score and diffuse distribution of lung lesions in COVID-19 are indicative of disease severity and short-term mortality.