Distinct Laboratory Test Result Profiles Between SARS-CoV-2 and Seasonable Influenza Infected Patients.

OBJECTIVE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses are contagious respiratory pathogens with similar symptoms but require different treatment and management strategies. This study investigated the differences in laboratory test result profiles between SARS-CoV-2 and influenza infected patients upon presentation to emergency department (ED). METHODS: Laboratory test results and demographic information from 723 influenza positive (2018/1/1 to 2020/3/15) and 1,281 SARS-CoV-2 positive (2020/3/11 to 2020/6/30) ED patients were retrospectively analyzed. The dataset was randomly divided into a training/validation set (2/3) and a test set (1/3) with the same SARS-CoV-2/influenza ratio. Four machine learning models in differentiating the laboratory profiles of RT-PCR confirmed SARS-CoV-2 and influenza positive patients were evaluated. The Shapley Additive Explanations technique was employed to visualize the impact of laboratory tests on the overall differentiation. Furthermore, the model performance was also evaluated in a new test dataset including 519 SARS-CoV-2 ED patients (2020/12/1 to 2021/2/28) and the previous influenza positive patients (2018/1/1 to 2020/3/15). RESULTS: A laboratory test result profile consisting of 15 blood tests, together with patient age, gender, and race can discriminate the two types of viral infections using a random forest (RF) model. The RF model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 in the test set. Among the profile of 15 laboratory tests, the serum total calcium level exhibited the greatest contribution to the overall differentiation. Furthermore, the model achieved an AUC of 0.81 in a new test set. CONCLUSION: We developed a laboratory tests-based RF model differentiating SARS-CoV-2 from influenza, which may be useful for the preparedness of overlapping COVID-19 resurgence and future seasonal influenza.

Reassessment of disease severity routine laboratory tests in the COVID-19 infection

Introduction. Publications demonstrate some limitations of National Early Warning Score 2 (NEWS-2) accuracy in assessment on coronavirus infection severity. The purpose of this study was to determine the value of the patient’s age and routine laboratory parameters in the assessment of patient’s general condition in coronavirus pneumonia and their relation to NEWS-2 scale parameters. Materials and methods. 50 case reports of patients with COVID-19 infection observed in the Sechenov University in January–March 2021 were analyzed. 34 % of patients were males aged 31 to 89 years (average age 55 years) and 66 % — females aged 40 to 91 (mean age 63). The diagnosis of pneumonia was confirmed by computed tomography. NEWS-2 scale total score was assessed. Results. According to the physician’s subjective assessment the condition was significantly more often assessed as moderate and severe. There was only a weak correlation between the blood oxygen saturation and the total NEWS-2 score (r = 0.165, α = 0.1). We found a mild correlation (r = 0.341, α = 0.1) between the patient’s age and NEWS-2 score. Among the most significantly interrelated parameters were age, neutrophil count, serum creatinine, CRP, fibrinogen level. Seven interrelated parameters (age, body temperature, blood oxygen saturation, the neutrophils count, creatinine, CRP, fibrinogen), for which a reliable relation with other tests has been shown, were assigned with its special index according to their contribution to the assessment of the overall condition severity. An aggregated score (criterion X) was proposed for assessment of disease severity according to equation. The proportions of mild, moderate, and severe cases according to criterion X were 12 %, 64 % and 24 %. Conclusion. The preliminary results obtained in the study emphasize the importance of routine laboratory tests in assessment of coronavirus infection severity. An evident discrepancy between NEWS-2 score and X criterion may be very important for practice. © Russian Journal of Neurosurgery.All rights reserved.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Prealbumin as a Predictor of Prognosis in Patients With Coronavirus Disease 2019.

Background: The predictive value of prealbumin for the prognosis of coronavirus disease 2019 (COVID-19) has not been extensively investigated. Methods: A total of 1,115 patients with laboratory-confirmed COVID-19 were enrolled at Tongji hospital from February to April 2020 and classified into fatal (n = 129) and recovered (n = 986) groups according to the patient's outcome. Prealbumin and other routine laboratory indicators were measured simultaneously. Results: The level of prealbumin on admission was significantly lower in fatal patients than in recovered patients. For predicting the prognosis of COVID-19, the performance of prealbumin was better than most routine laboratory indicators, such as albumin, lymphocyte count, neutrophil count, hypersensitive C-reactive protein, d-dimer, lactate dehydrogenase, creatinine, and hypersensitive cardiac troponin I. When a threshold of 126 mg/L was used to discriminate between fatal and recovered patients, the sensitivity and specificity of prealbumin were, respectively, 78.29 and 90.06%. Furthermore, a model based on the combination of nine indexes showed an improved performance in predicting the death of patients with COVID-19. Using a cut-off value of 0.19, the prediction model was able to distinguish between fatal and recovered individuals with a sensitivity of 86.82% and a specificity of 90.37%. Conclusions: A lower level of prealbumin on admission may indicate a worse outcome of COVID-19. Immune and nutritional status may be vital factors for predicting disease progression in the early stage of COVID-19.

Can routine laboratory tests discriminate SARS-CoV-2-infected pneumonia from other causes of community-acquired pneumonia?

BACKGROUND: The clinical presentation of SARS-CoV-2-infected pneumonia (COVID-19) resembles that of other etiologies of community-acquired pneumonia (CAP). We aimed to identify clinical laboratory features to distinguish COVID-19 from CAP. METHODS: We compared the hematological and biochemical features of 84 patients with COVID-19 at hospital admission and 221 patients with CAP. Parameters independently predictive of COVID-19 were calculated by multivariate logistic regression. The receiver operating characteristic (ROC) curves were generated and the area under the ROC curve (AUC) was measured to evaluate the discriminative ability. RESULTS: Most hematological and biochemical indexes of patients with COVID-19 were significantly different from patients with CAP. Nine laboratory parameters were identified to be predictive of a diagnosis of COVID-19. The AUCs demonstrated good discriminatory ability for red cell distribution width (RDW) with an AUC of 0.87 and hemoglobin with an AUC of 0.81. Red blood cell, albumin, eosinophil, hematocrit, alkaline phosphatase, and mean platelet volume had fair discriminatory ability. Combinations of any two parameters performed better than did the RDW alone. CONCLUSIONS: Routine laboratory examinations may be helpful for the diagnosis of COVID-19. Application of laboratory tests may help to optimize the use of isolation rooms for patients when they present with unexplained febrile respiratory illnesses.