Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

No Statistically Apparent Difference in Antiviral Effectiveness Observed Among Ribavirin Plus Interferon-Alpha, Lopinavir/Ritonavir Plus Interferon-Alpha, and Ribavirin Plus Lopinavir/Ritonavir Plus Interferon-Alpha in Patients With Mild to Moderate Coronavirus Disease 2019: Results of a Randomized, Open-Labeled Prospective Study.

BACKGROUND: Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, causing an unprecedented pandemic. However, there is no specific antiviral therapy for coronavirus disease 2019 (COVID-19). We conducted a clinical trial to compare the effectiveness of three antiviral treatment regimens in patients with mild to moderate COVID-19. METHODS: This was a single-center, randomized, open-labeled, prospective clinical trial. Eligible patients with mild to moderate COVID-19 were randomized into three groups: ribavirin (RBV) plus interferon-α (IFN-α), lopinavir/ritonavir (LPV/r) plus IFN-α, and RBV plus LPV/r plus IFN-α at a 1:1:1 ratio. Each patient was invited to participate in a 28-d follow-up after initiation of an antiviral regimen. The outcomes include the difference in median interval to SARS-CoV-2 nucleic acid negativity, the proportion of patients with SARS-CoV-2 nucleic acid negativity at day 14, the mortality at day 28, the proportion of patients re-classified as severe cases, and adverse events during the study period. RESULTS: In total, we enrolled 101 patients in this study. Baseline clinical and laboratory characteristics of patients were comparable among the three groups. In the analysis of intention-to-treat data, the median interval from baseline to SARS-CoV-2 nucleic acid negativity was 12 d in the LPV/r+IFN-α-treated group, as compared with 13 and 15 d in the RBV+IFN-α-treated group and in the RBV+LPV/r+ IFN-α-treated group, respectively (p=0.23). The proportion of patients with SARS-CoV-2 nucleic acid negativity in the LPV/r+IFN-α-treated group (61.1%) was higher than the RBV+ IFN-α-treated group (51.5%) and the RBV+LPV/r+IFN-α-treated group (46.9%) at day 14; however, the difference between these groups was calculated to be statistically insignificant. The RBV+LPV/r+IFN-α-treated group developed a significantly higher incidence of gastrointestinal adverse events than the LPV/r+ IFN-α-treated group and the RBV+ IFN-α-treated group. CONCLUSIONS: Our results indicate that there are no significant differences among the three regimens in terms of antiviral effectiveness in patients with mild to moderate COVID-19. Furthermore, the combination of RBV and LPV/r is associated with a significant increase in gastrointestinal adverse events, suggesting that RBV and LPV/r should not be co-administered to COVID-19 patients simultaneously. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, ID: ChiCTR2000029387. Registered on January 28, 2019.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

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.

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.