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Predicting Poor Outcome of COVID-19 Patients on the Day of Admission with the COVID-19 Score
Luke Tseng1, Erin Hittesdorf2, Mitchell F Berman2
1Columbia University Vagelos College of Physicians & Surgeons, New York, NY, USA.
Insights
A simple COVID-19 admission score using laboratory tests can predict patient deterioration. This score helps identify high-risk individuals for early intervention and improved outcomes.
Area of Science:
- Biomarkers and Clinical Chemistry
- Infectious Diseases and Epidemiology
- Critical Care Medicine
Background:
- Coronavirus disease 2019 (COVID-19) poses a significant risk of multiorgan failure and mortality.
- Early identification of patients at high risk for deterioration is crucial for timely medical intervention and resource allocation.
- Objective admission scoring systems can aid in stratifying COVID-19 patients upon hospital entry.
Purpose of the Study:
- To develop and validate a predictive model for COVID-19 severity using readily available laboratory parameters.
- To create a simple, objective score to identify patients likely to experience adverse outcomes.
- To improve patient triage and monitoring strategies for COVID-19 cases.
Main Methods:
- Retrospective cohort study of 2545 COVID-19 patients admitted to a New York City academic medical center.
- Analysis of laboratory tests and demographic data available at admission.
- Development of a multivariate logistic regression model incorporating significant predictors of the primary endpoint (intubation, stage 3 acute kidney injury, or death).
- Internal validation using a 30% subset of the patient cohort.
Main Results:
- The final predictive model included age, albumin, creatinine, C-reactive protein (CRP), and lactate dehydrogenase (LDH).
- The model demonstrated strong predictive performance with an area under the ROC curve of 0.850.
- The model achieved a sensitivity of 0.800 and a specificity of 0.761 in identifying patients with adverse outcomes.
Conclusions:
- A predictive score based on a few key laboratory tests can accurately identify COVID-19 patients at risk of poor outcomes.
- This objective scoring system is valuable for risk stratification and clinical decision-making upon patient admission.
- The developed model offers a practical tool for enhancing patient care during the COVID-19 pandemic.
Background:
COVID-19 may result in multiorgan failure and death. Early detection of patients at risk may allow triage and more intense monitoring. The aim of this study was to develop a simple, objective admission score, based on laboratory tests, that identifies patients who are likely going to deteriorate.
Methods:
This is a retrospective cohort study of all COVID-19 patients admitted to a tertiary academic medical center in New York City during the COVID-19 crisis in spring 2020. The primary combined endpoint included intubation, stage 3 acute kidney injury (AKI), or death. Laboratory tests available on admission in at least 70% of patients (and age) were included for univariate analysis. Tests that were statistically or clinically significant were then included in a multivariate binary logistic regression model using stepwise exclusion. 70% of all patients were used to train the model, and 30% were used as an internal validation cohort. The aim of this study was to develop and validate a model for COVID-19 severity based on biomarkers.
Results:
Out of 2545 patients, 833 (32.7%) experienced the primary endpoint. 53 laboratory tests were analyzed, and of these, 47 tests (and age) were significantly different between patients with and without the endpoint. The final multivariate model included age, albumin, creatinine, C-reactive protein, and lactate dehydrogenase. The area under the ROC curve was 0.850 (CI [95%]: 0.813, 0.889), with a sensitivity of 0.800 and specificity of 0.761. The probability of experiencing the primary endpoint can be calculated as p=e (-2.4475+0.02492age - 0.6503albumin+0.81926creat+0.00388CRP+0.00143LDH)/1+e (-2.4475+ 0.02492age - 0.6503albumin+0.81926creat+0.00388CRP+0.00143LDH).
Conclusions:
Our study demonstrated that poor outcome in COVID-19 patients can be predicted with good sensitivity and specificity using a few laboratory tests. This is useful for identifying patients at risk during admission.
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