Related Experiment Video
Updated: Oct 21, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients
Caidong Liu1, Ziyu Wang2, Wei Wu2
1Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Insights
This study developed a machine learning model using laboratory indicators to effectively distinguish COVID-19 from other pneumonias and stratify COVID-19 patients by risk. The model accurately predicts severity and mortality, aiding early diagnosis and patient management.
Area of Science:
- Medical diagnostics
- Machine learning in healthcare
- Infectious disease epidemiology
Background:
- Distinguishing COVID-19 from other viral pneumonias is crucial for appropriate patient management.
- Early risk stratification of COVID-19 patients is essential for optimizing treatment strategies and resource allocation.
Purpose of the Study:
- To develop a model for differentiating COVID-19 from non-COVID-19 viral pneumonia using laboratory indicators.
- To classify COVID-19 patients into low-risk and high-risk groups at admission based on laboratory findings.
- To predict the survival probability of COVID-19 patients using laboratory data.
Main Methods:
- Retrospective cohort study including 3,563 COVID-19 and 118 non-COVID-19 pneumonia patients.
- Utilized support vector machine and logistic regression models with laboratory indicators for risk stratification and survival prediction.
- Employed joint random sampling for model development and validation.
Main Results:
- A support vector machine model using neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen effectively stratified COVID-19 patients (AUC = 0.89).
- Laboratory indicators within the first week of admission accurately predicted mortality (AUC = 0.95), identifying key markers like eosinophil percentage and lactate dehydrogenase.
- Laboratory findings demonstrated utility in diagnosing COVID-19 and differentiating it from other viral pneumonias.
Conclusions:
- The developed risk-stratification model aids in early diagnosis, monitoring, and severity prediction of COVID-19.
- Laboratory indicators are valuable tools for distinguishing COVID-19 from non-COVID-19 viral pneumonia.
- This model supports clinical decision-making for COVID-19 patient care.
Abstract:
Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:30Pneumonia III: Complications and Assessment
01:25Transmission-based Precautions II: Airborne and Protective Environment
Airborne precautions:
Use airborne precautions when treating patients known or suspected to have diseases that spread through the air—for example, tuberculosis or measles. These organisms are present in smaller droplets expelled by an infected person and...
01:26Standard Precaution
Hand hygiene is the most crucial means to prevent the transmission of disease. Employers are legally required to provide their workers with personal protective equipment (PPE) to minimize exposure or contact with...
01:13Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
01:22Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test