Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

Cross-cultural adaptation, validity, and reliability of the Chinese version of the Nurses' Perceptions of Disaster Core Competencies Scale (NPDCC).

BACKGROUND: In recent years, disasters occurred frequently all over the world, and the role of nurses in public health emergencies and disaster emergencies was highlighted under the background of the covid19 epidemic. However, there was a lack of education and evaluation. Our study aims to cross-cultural adapt the Nurses' Perceptions of Disaster Core Competencies Scale (NPDCC) and evaluate the reliability and validity of the Chinese version. METHODS: We translated the scale following the translation-integration-back translation-expert review procedure, adapted according to Chinese culture. We evaluated the reliability and validity of the scale, and a total sample of 911 nurse data from the Yangtze River Delta Regional Nursing Alliance Hospital was gathered. RESULTS: The Chinese version of NPDCC included 45 items, 5 factors (critical thinking skills, special diagnostic skills, general diagnostic skills, technical skills, and communication skills) were extracted from the analysis, which could explain the 68.289% of the total variance. The content validity index was 0.925. The Cronbach's α of the total NPDCC score was 0.978, and 0.884-0.945 for every factor. The split-half for the scale was 0.930, and every factor was 0.861-0.894. CONCLUSIONS: The Chinese version of NPDCC has excellent reliability and validity, and it is suitable to measure nurses' perceptions of disaster core competencies in China. The next step is to promote the application in a large scale.

A prediction model based on machine learning for diagnosing the early COVID-19 patients (preprint)

With the dramatically fast spread of COVID-9, real-time reverse transcription polymerase chain reaction (RT-PCR) test has become the gold standard method for confirmation of COVID-19 infection. However, RT-PCR tests are complicated in operation andIt usually takes 5-6 hours or even longer to get the result. Additionally, due to the low virus loads in early COVID-19 patients, RT-PCR tests display false negative results in a number of cases. Analyzing complex medical datasets based on machine learning provides health care workers excellent opportunities for developing a simple and efficient COVID-19 diagnostic system. This paper aims at extracting risk factors from clinical data of early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including logistic regression(LR), support vector machine(SVM), decision tree(DT), random forest(RF) and a deep learning-based method for diagnosis of early COVID-19. The results show that the LR predictive model presents a higher specificity rate of 0.95, an area under the receiver operating curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyze the contribution of the factors to the predictive models. Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.