Development and multicenter performance evaluation of fully automated SARS-CoV-2 IgM and IgG immunoassays.

Objectives: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread globally. The laboratory diagnosis of SARS-CoV-2 infection has relied on nucleic acid testing; however, it has some limitations, such as low throughput and high rates of false negatives. Tests of higher sensitivity are needed to effectively identify infected patients. Methods: This study has developed fully automated chemiluminescent immunoassays to determine IgM and IgG antibodies to SARS-CoV-2 in human serum. The assay performance has been evaluated at 10 hospitals. Clinical specificity was evaluated by measuring 972 hospitalized patients and 586 donors of a normal population. Clinical sensitivity was assessed on 513 confirmed cases of SARS-CoV-2 by RT-PCR. Results: The assays demonstrated satisfied assay precision with coefficient of variation of less than 4.45%. Inactivation of specimen did not affect assay measurement. SARS-CoV-2 IgM showed clinical specificity of 97.33 and 99.49% for hospitalized patients and the normal population respectively, and SARS-CoV-2 IgG showed clinical specificity of 97.43 and 99.15% respectively. SARS-CoV-2 IgM showed clinical sensitivity of 82.54, 92.93, and 84.62% before 7 days, 7-14 days, and after 14 days respectively, since onset of symptoms, and SARS-CoV-2 IgG showed clinical sensitivity of 80.95, 97.98, and 99.15% respectively at the same time points above. Conclusions: We have developed fully automated immunoassays for detecting SARS-CoV-2 IgM and IgG antibodies in human serum. The assays demonstrated high clinical specificity and sensitivity, and add great value to nucleic acid testing in fighting against the global pandemic of the SARS-CoV-2 infection.

Construction and comparison of short-term prognosis prediction model based on machine learning in acute ischemic stroke.

Objective: To construct and compared the short-term prognosis prediction models of acute ischemic stroke (AIS) by machine learning (ML). Methods: Retrospectively study. The group W (mRS≤3) was clustered, and combined with group P (mRS>3) to form the post-clustering dataset for modeling. The "glmnet", "rpart", "xgboost", "randomForest", "neuralnet" packages were used to construct ML models. The accuracy, sensitivity, specificity, positive predict value (PPV), negative predict value (NPV) among the models were compared. Four external clinical datasets were used for external clinical validation. The optimal prediction model was determined by variable screening ability, model visualization, and external clinical validation performance. Results: The post-clustering dataset contains 139 patients (group W) and 122 patients (group P). The neutrophil multiplied by D-dimer (NDM) has predictive value in all ML prediction models in this study. In the decision tree model, NDMQ occupies the first tree node, When NDM≤5.62 and the age<74.5, the probability of poor prognosis of AIS is less than 20 %. When NDM>5.62 and accompanied by pneumonia, the incidence of poor prognosis of AIS is about 90 %. In the Random Forest (RF) model, NDMQ had the highest Gini index. The variable combination screened by the RF model had the best performance in the neural network, and the accuracy, sensitivity, specificity, PPV, and NPV of the external validation were 0.800, 0.774, 0.833, 0.857, and 0.741, respectively. The RF model had the best performance in the external clinical validation datasets, with accuracies of 0.646, 0.697, 0.695, and 0.713, respectively. Conclusions: NDM shows predictive value for AIS short-term prognosis in all ML models in this study. The optimal model in screening characteristic variables and the performance of in external clinical datasets was RF model. In the analysis of medical data with small sample size and outcome as categorical variables, RF could be used as the main algorithm to build a model.

Neutrophil count multiplied by D-dimer combined with pneumonia may better predict short-term outcomes in patients with acute ischemic stroke.

OBJECTIVE: To investigate the predictive value of neutrophil, D-dimer and diseases associated with stroke for short-term outcomes of acute ischemic stroke (AIS). METHODS: By collecting the subitems of laboratory data especially routine blood and coagulation test in AIS patients, and recording their clinical status, the correlation, regression and predictive value of each subitem with the short-term outcomes of AIS were analyzed. The predict model was constructed. RESULTS: The neutrophil count multiplied by D-dimer (NDM) had the best predictive value among the subitems, and the area under the receiver operating characteristic (ROC) curve reached 0.804. When clinical information was not considered, the Youden index of NDM was calculated to be 0.48, corresponding to an NDM value of 7.78, a diagnostic sensitivity of 0.79, specificity of 0.69, negative predictive value of 96%. NDM were divided into 5 quintiles, the five grade of NDM (quintile) were < = 1.82, 1.83-2.41, 2.42-3.27, 3.28-4.49, 4.95+, respectively. The multivariate regression analysis was conducted between NDM (quintile), Babinski+, pneumonia, cardiac disease and poor outcomes of AIS. Compared with the first grade of NDM (quintile), the second grade of NDM (quintile) was not significant, but the third grade of NDM (quintile) showed 7.061 times, the fourth grade of NDM (quintile) showed 11.776 times, the fifth grade of NDM (quintile) showed 23.394 times in short-term poor outcomes occurrence. Babinski sign + showed 1.512 times, pneumonia showed 2.995 times, cardiac disease showed 1.936 times in short-term poor outcomes occurrence compared with those negative patients. CONCLUSIONS: NDM combined with pneumonia may better predict short-term outcomes in patients with AIS. Early prevention, regular examination and timely intervention should be emphasized for patients, which may reduce the risk of short-term poor outcomes.

D-dimer daily continuous tendency predicts the short-term prognosis for COVID-19 independently: A retrospective study from Northeast China.

BACKGROUND AND OBJECTIVE: To study whether D-dimer daily continuous tendency could predict the short-term prognosis of COVID-19. PATIENTS AND METHODES: According to the short-term prognosis, 81 COVID-19 patients were divided into two groups, one of worse prognosis (Group W) and the other of better prognosis (Group B). The slope of D-dimer linear regression during hospitalization (SLOPE) was calculated as an indicator of D-dimer daily continuous tendency. The SLOPE difference between Group W and Group B was compared. The difference between the discharge results and the 3-month follow-up results was also compared. COX regression analysis was used to analyze the relationship between SLOPE and short-term prognosis of COVID-19. RESULTS: There were 16 patients in Group W and 65 patients in Group B. Group W had more critical proportion (p < 0.0001), indicating that the symptoms of its patients were more severe during hospitalization. ARDS, the most visible cause of worse prognosis, accounted for up to 68.75%, and many symptoms merged and resulted in worse prognosis. The D-dimer levels of Group W not only were significantly higher (p < 0.0001), but also showed an increasing trend. In addition, the D-dimer levels at discharge were significantly higher than those at follow-up (p = 0.0261), and the mean difference was as high as 0.7474. SLOPE significantly correlated with the short-term prognosis of COVID-19 independently (RR: 1.687, 95% CI: 1.345-2.116, P < 0.0001). The worst prognosis occurred most likely during the first month after COVID-19 diagnosis. CONCLUSION: Our study found that D-dimer daily continuous tendency independently correlates with worse prognosis and can be used as an independent predictor of the short-term prognosis for COVID-19.