Veterans Health Administration COVID-19 (VACO) Index for COVID-19 Mortality

Estimate 30-day mortality risk for patients with COVID-19 using the validated Veterans Health Administration COVID-19 (VACO) Index. Free online calculator for COVID-19 mortality risk assessment, risk stratification, and clinical decision support.

VACO Index for COVID-19 Mortality

Estimate 30-day mortality risk for patients with COVID-19 using the Veterans Health Administration COVID-19 (VACO) Index.

Age (years) *

Age is the strongest predictor of mortality risk

Sex *

MaleFemale

Charlson Comorbidity Index (CCI) *

CCI assesses comorbidity burden. Common scores: 0 (none), 1-2 (low), 3-4 (moderate), ≥5 (high)

History of Myocardial Infarction (MI) or Peripheral Vascular Disease (PVD)

History of MI or PVD increases mortality risk

Disclaimer: The VACO Index is a clinical decision support tool and should not be used as the sole basis for treatment decisions. This calculator is for educational purposes only and does not replace professional medical judgment. Always consider individual patient factors, clinical presentation, and local guidelines when making decisions about COVID-19 care. The VACO Index was developed using data from the Veterans Health Administration and may have different performance characteristics in other populations.

Veterans Health Administration COVID-19 (VACO) Index for COVID-19 Mortality

The VACO Index is a clinical prediction tool designed to estimate the 30-day mortality risk for patients diagnosed with COVID-19. It was developed and validated using data from over 13,000 patients within the U.S. Veterans Health Administration between February and August 2020. The index utilizes readily available patient data, including age, sex, and pre-existing comorbidities, to provide individualized risk assessments.

Purpose: Estimate 30-day mortality risk for patients with COVID-19 to guide clinical decision-making, resource allocation, and treatment planning

VACO Index Components

Risk Factor	Description	Impact
Age	Patient age in years	Strongest predictor of mortality risk. Risk increases significantly with advancing age, with exponential increase in older age groups (≥70 years)
Sex	Male or female	Male sex is associated with higher COVID-19 mortality risk compared to female sex
Charlson Comorbidity Index (CCI)	Weighted index of comorbidity burden based on 19 conditions	Higher CCI scores (≥5) are associated with significantly increased mortality risk. Common scores: 0 (none), 1-2 (low), 3-4 (moderate), ≥5 (high burden)
History of MI or PVD	History of myocardial infarction (MI) or peripheral vascular disease (PVD)	Presence of MI or PVD history further elevates mortality risk, independent of other factors

Risk Stratification

Low Risk

30-Day Mortality Risk: <5%

Interpretation: Lower predicted 30-day mortality risk

Recommendation: Standard COVID-19 care and monitoring. Consider outpatient management if clinically appropriate. Continue preventive measures and vaccination.

Moderate Risk

30-Day Mortality Risk: 5-15%

Interpretation: Moderate predicted 30-day mortality risk

Recommendation: Close monitoring and frequent clinical assessment. Consider hospitalization for close observation. Early intervention for complications.

High Risk

30-Day Mortality Risk: 15-30%

Interpretation: High predicted 30-day mortality risk

Recommendation: Hospitalization with close monitoring recommended. Consider ICU admission if clinically indicated. Aggressive supportive care and early intervention.

Very High Risk

30-Day Mortality Risk: ≥30%

Interpretation: Very high predicted 30-day mortality risk

Recommendation: Immediate hospitalization with intensive monitoring. Consider ICU admission and advanced life support. Aggressive supportive care and early intervention for complications.

Clinical Application

When to Use VACO Index

Patient with confirmed or suspected COVID-19
Risk stratification for 30-day mortality
Resource allocation and triage decisions
Informing treatment decisions and care planning
Facilitating discussions with patients and families regarding prognosis
Identifying high-risk patients who may benefit from early intervention
Guiding decisions about hospitalization and ICU admission

How to Calculate

Determine Age: Enter the patient's age in years. Age is the strongest predictor of mortality risk.
Identify Sex: Select male or female. Male sex is associated with higher risk.
Calculate Charlson Comorbidity Index (CCI): Assess the patient's comorbidity burden using the CCI. The CCI includes 19 conditions weighted by severity. Higher scores (≥5) indicate high comorbidity burden.
Assess MI or PVD History: Determine if the patient has a history of myocardial infarction (MI) or peripheral vascular disease (PVD).
Apply Logistic Regression Model: The VACO Index uses a logistic regression model that combines these factors to calculate a logit score.
Convert to Probability: The logit score is converted to a probability using the logistic function: P = 1 / (1 + e^(-logit))
Interpret Risk: The resulting probability represents the estimated 30-day mortality risk as a percentage.
Classify Risk Category: Risk categories: Low (<5%), Moderate (5-15%), High (15-30%), Very High (≥30%).

Key Clinical Features

Age as Primary Predictor

Age is the strongest predictor of COVID-19 mortality risk. The risk increases exponentially with advancing age, particularly in patients ≥70 years. This reflects the impact of immunosenescence and age-related comorbidities.

Comorbidity Burden

The Charlson Comorbidity Index (CCI) captures the cumulative burden of pre-existing conditions. Higher CCI scores (≥5) are strongly associated with increased mortality risk, reflecting the impact of multiple comorbidities on COVID-19 outcomes.

Sex Differences

Male sex is associated with higher COVID-19 mortality risk compared to female sex. This may reflect differences in immune response, hormonal factors, or behavioral factors that influence disease severity.

Cardiovascular Risk

History of myocardial infarction (MI) or peripheral vascular disease (PVD) independently increases mortality risk. This reflects the impact of cardiovascular disease on COVID-19 outcomes, particularly in the context of systemic inflammation.

Validation and Performance

Development and Validation

The VACO Index was developed and validated using data from over 13,000 COVID-19 patients within the U.S. Veterans Health Administration between February and August 2020. The model demonstrated strong predictive performance:

Development Cohort: Area under the receiver operating characteristic curve (AUC) of 0.79
Validation Cohort 1: AUC of 0.81
Validation Cohort 2: AUC of 0.84

The VACO Index has been externally validated in diverse patient populations, including those in academic medical centers and Medicare beneficiaries, confirming its utility across different healthcare settings.

Clinical Utility

The VACO Index provides individualized mortality risk estimates that can help clinicians:

Identify high-risk patients who may benefit from early intervention and close monitoring
Prioritize healthcare resources, such as ICU beds and ventilators, for patients with higher predicted risks
Facilitate discussions with patients and families regarding prognosis and care planning
Guide treatment decisions and resource allocation during the COVID-19 pandemic
Support triage decisions in resource-limited settings

Important Limitations

• The VACO Index is a clinical tool and should not be used in isolation
• Assessment requires clinical judgment and experience
• Should be interpreted in the context of complete clinical picture
• Requires correlation with patient history, physical examination, laboratory values, and imaging
• The index was developed using data from the Veterans Health Administration and may have different performance characteristics in other populations
• COVID-19 variants, vaccination status, and treatment availability may affect mortality risk and are not included in the original model
• The model was developed during the early phase of the pandemic (February-August 2020) and may not reflect current outcomes with improved treatments
• Not a substitute for clinical judgment or comprehensive evaluation
• Local guidelines and institutional protocols should be considered
• Results may vary based on patient population, clinical setting, and available treatments
• The VACO Index predicts 30-day mortality risk and does not account for longer-term outcomes or complications
• Consider other factors such as vaccination status, variant type, and treatment availability when making clinical decisions

The Veterans Health Administration COVID-19 (VACO) Index represents a significant advancement in clinical prediction tools for COVID-19 mortality risk assessment. Developed through rigorous analysis of data from the U.S. Veterans Health Administration healthcare system, this index provides clinicians with an evidence-based method to estimate 30-day mortality risk for patients diagnosed with COVID-19. The tool's development was driven by the urgent need during the early phase of the pandemic to identify patients at highest risk of poor outcomes, enabling more effective resource allocation and clinical decision-making.

Historical Context and Development

The VACO Index was developed during a critical period in the COVID-19 pandemic, specifically using data collected between February and August 2020. This timeframe represents the initial wave of the pandemic in the United States, when healthcare systems were grappling with unprecedented challenges in patient triage, resource allocation, and treatment planning. The development team, comprising researchers from the U.S. Department of Veterans Affairs, Yale University, and other leading institutions, recognized the need for a practical, validated tool that could be rapidly deployed in clinical settings.

The development cohort included over 13,000 patients with confirmed or suspected COVID-19 within the Veterans Health Administration system. This large, diverse patient population provided a robust foundation for model development and validation. The Veterans Health Administration healthcare system is particularly well-suited for this type of research due to its comprehensive electronic health records, standardized care protocols, and diverse patient population that includes individuals from various demographic backgrounds, geographic regions, and socioeconomic statuses.

Methodological Foundation

The VACO Index is built upon a logistic regression model, a statistical approach that is well-established in clinical prediction research. Logistic regression is particularly appropriate for binary outcomes such as mortality, as it can handle multiple predictor variables while accounting for their interactions and relative contributions to the outcome. The model development process involved several key steps, including variable selection, model fitting, internal validation, and external validation across multiple independent cohorts.

The selection of predictor variables was guided by both clinical knowledge and statistical analysis. The development team considered numerous potential predictors, including demographic factors, comorbidities, laboratory values, vital signs, and clinical presentation features. Through rigorous statistical analysis, four key variables emerged as the most predictive of 30-day mortality: age, sex, Charlson Comorbidity Index (CCI), and history of myocardial infarction (MI) or peripheral vascular disease (PVD).

Key Components of the VACO Index

Age as the Primary Predictor

Age stands as the strongest predictor of COVID-19 mortality risk in the VACO Index, a finding that aligns with extensive epidemiological data from the pandemic. The relationship between age and mortality risk is not linear but rather exponential, with risk increasing dramatically in older age groups. This pattern reflects several age-related factors that influence COVID-19 outcomes, including immunosenescence (age-related decline in immune function), increased prevalence of comorbidities, reduced physiological reserve, and age-related changes in organ function.

In the VACO Index model, age contributes to the logit score through a complex relationship that accounts for the exponential increase in risk with advancing age. Patients under 50 years of age generally have lower baseline risk, while those over 70 years demonstrate substantially increased mortality risk. The model captures this relationship through age-specific coefficients that increase progressively with each decade of life, particularly for patients aged 70 and above.

Sex Differences in COVID-19 Mortality

Sex represents another important component of the VACO Index, with male sex associated with higher mortality risk compared to female sex. This finding is consistent with global epidemiological data showing that men have higher COVID-19 mortality rates than women across most age groups and populations. Several biological and behavioral factors may contribute to this sex difference.

Biological factors include differences in immune response, with women generally demonstrating stronger immune responses to viral infections. Hormonal factors, particularly the role of estrogen, may also play a protective role in women. Additionally, sex differences in the expression of angiotensin-converting enzyme 2 (ACE2) receptors, which serve as the entry point for SARS-CoV-2, may contribute to differential susceptibility and disease severity. Behavioral factors, such as higher rates of smoking and certain comorbidities in men, may also contribute to the observed sex differences in mortality.

Charlson Comorbidity Index

The Charlson Comorbidity Index (CCI) is a well-validated tool for quantifying comorbidity burden that has been widely used in clinical research and prediction models. Originally developed to predict one-year mortality in hospitalized patients, the CCI assigns weighted scores to 19 different medical conditions based on their association with mortality. Conditions included in the CCI range from relatively minor conditions (such as peptic ulcer disease) to severe conditions (such as metastatic solid tumor).

In the context of COVID-19, the CCI serves as a comprehensive measure of a patient's baseline health status and vulnerability to severe disease. Higher CCI scores indicate greater comorbidity burden, which is strongly associated with increased COVID-19 mortality risk. The VACO Index incorporates CCI as a categorical variable, with different risk contributions for patients with low (0-2), moderate (3-4), or high (≥5) comorbidity burden.

Common conditions included in the CCI that are particularly relevant to COVID-19 outcomes include cardiovascular disease, chronic kidney disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), and malignancy. Each of these conditions has been independently associated with increased COVID-19 severity and mortality in numerous studies.

History of Myocardial Infarction or Peripheral Vascular Disease

The inclusion of history of myocardial infarction (MI) or peripheral vascular disease (PVD) as a specific predictor in the VACO Index reflects the significant impact of cardiovascular disease on COVID-19 outcomes. While cardiovascular conditions are already captured within the CCI, the VACO Index model identified MI and PVD history as having additional independent predictive value beyond their contribution to the overall comorbidity burden.

This finding aligns with the understanding that COVID-19 is not merely a respiratory disease but a systemic illness that can affect multiple organ systems, particularly the cardiovascular system. SARS-CoV-2 infection can lead to direct myocardial injury, myocarditis, arrhythmias, and exacerbation of pre-existing cardiovascular conditions. Patients with a history of MI or PVD may have reduced cardiovascular reserve, making them more vulnerable to the cardiovascular complications of COVID-19.

Model Performance and Validation

The VACO Index has demonstrated strong predictive performance across multiple validation cohorts. In the development cohort, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.79, indicating good discriminative ability. More importantly, the model maintained strong performance in independent validation cohorts, with AUC values of 0.81 and 0.84 in two separate validation sets. These validation results provide confidence that the model performs well in populations beyond the original development cohort.

The external validation of the VACO Index has extended beyond the Veterans Health Administration system. The model has been validated in diverse patient populations, including those in academic medical centers and Medicare beneficiaries. This broad validation demonstrates the model's generalizability and suggests that it can be useful across different healthcare settings and patient populations.

Clinical Applications

Risk Stratification

One of the primary applications of the VACO Index is risk stratification, allowing clinicians to categorize patients into different risk groups based on their predicted 30-day mortality risk. The model typically categorizes patients into four risk groups: low risk (mortality risk <5%), moderate risk (5-15%), high risk (15-30%), and very high risk (≥30%). This risk stratification can guide numerous clinical decisions, including the level of monitoring required, the need for hospitalization, and the intensity of care.

For patients identified as low risk, standard COVID-19 care and monitoring may be appropriate, with consideration of outpatient management if clinically feasible. These patients may benefit from close follow-up and monitoring for clinical deterioration, but may not require immediate hospitalization or intensive care resources. Moderate-risk patients typically require closer monitoring and more frequent clinical assessment, with consideration of hospitalization for close observation and early intervention for complications.

High-risk patients generally require hospitalization with close monitoring, and consideration of intensive care unit (ICU) admission if clinically indicated. These patients may benefit from aggressive supportive care, early intervention for complications, and optimization of comorbidity management. Very high-risk patients typically require immediate hospitalization with intensive monitoring, consideration of ICU admission and advanced life support, and aggressive supportive care with early intervention for complications.

Resource Allocation

During the COVID-19 pandemic, healthcare systems have faced unprecedented challenges in resource allocation, particularly regarding ICU beds, ventilators, and specialized personnel. The VACO Index can assist in these difficult decisions by identifying patients who are most likely to benefit from intensive care resources. While resource allocation decisions should never be based solely on a prediction model, the VACO Index can provide valuable information to inform these decisions alongside clinical judgment, patient preferences, and ethical considerations.

In resource-limited settings, the VACO Index can help prioritize patients for hospitalization, ICU admission, and specialized treatments. However, it is crucial to emphasize that the model should be used as one component of a comprehensive decision-making process that includes clinical assessment, patient values, and ethical principles. The model should not be used to deny care to patients, but rather to help optimize the allocation of limited resources to maximize overall benefit.

Treatment Planning

The VACO Index can inform treatment planning by helping clinicians identify patients who may benefit from early, aggressive intervention. High-risk patients, as identified by the VACO Index, may be candidates for early antiviral therapy, monoclonal antibody treatment, or other targeted interventions that have been shown to improve outcomes in COVID-19. The model can also help guide decisions about the intensity of monitoring, frequency of clinical assessments, and need for specialized consultations.

For patients with high predicted mortality risk, clinicians may consider more aggressive supportive care measures, early intervention for complications, and optimization of all comorbidities. These patients may also benefit from early advance care planning discussions, allowing patients and families to express their preferences regarding goals of care and end-of-life decisions. The VACO Index can help identify patients who may benefit from palliative care consultation, particularly those with very high predicted mortality risk.

Communication with Patients and Families

The VACO Index can facilitate discussions with patients and families regarding prognosis and care planning. By providing a quantitative estimate of mortality risk, the model can help clinicians communicate prognosis in a clear, understandable manner. This information can support shared decision-making, allowing patients and families to make informed choices about treatment options, goals of care, and advance care planning.

However, it is essential that clinicians use the VACO Index results as a starting point for discussions rather than as definitive prognostic information. The model provides population-level risk estimates, which may not perfectly reflect individual patient outcomes. Clinicians should emphasize that the model is a tool to inform discussions, not a definitive prediction of individual patient outcomes. Factors such as response to treatment, development of complications, and individual patient characteristics can significantly influence actual outcomes.

Integration with Clinical Practice

The VACO Index is designed to be practical and easy to use in clinical settings. The model requires only four readily available pieces of information: age, sex, Charlson Comorbidity Index, and history of MI or PVD. This simplicity makes the tool accessible to clinicians across various healthcare settings, from primary care offices to emergency departments to intensive care units.

The Charlson Comorbidity Index, while requiring calculation, is a well-established tool that many healthcare systems already incorporate into their electronic health records. For systems that do not automatically calculate CCI, numerous online calculators and clinical decision support tools are available to assist with this calculation. The other components of the VACO Index (age, sex, and MI/PVD history) are typically readily available from patient records or clinical assessment.

Electronic health record systems can be configured to automatically calculate the VACO Index when relevant patient data is available, providing real-time risk estimates to clinicians. This integration can help ensure that the tool is used consistently and that risk estimates are available when needed for clinical decision-making. Some healthcare systems have developed clinical decision support tools that display VACO Index results alongside other clinical information, helping clinicians incorporate risk estimates into their decision-making process.

Limitations and Considerations

While the VACO Index is a valuable clinical tool, it is essential to recognize its limitations. The model was developed using data from the early phase of the COVID-19 pandemic (February-August 2020), and the clinical landscape of COVID-19 has evolved significantly since that time. The availability of effective treatments, including antiviral medications, monoclonal antibodies, and improved supportive care strategies, has changed the natural history of COVID-19 and may affect the model's predictive performance.

Additionally, the emergence of new SARS-CoV-2 variants with different virulence characteristics may affect mortality rates and the model's accuracy. The widespread availability of COVID-19 vaccines has also fundamentally changed the risk profile of COVID-19, with vaccinated individuals generally experiencing lower mortality rates than unvaccinated individuals. The original VACO Index model does not account for vaccination status, which is now a critical factor in COVID-19 risk assessment.

The model was developed using data from the Veterans Health Administration system, which has a predominantly male, older patient population with specific demographic and health characteristics. While the model has been validated in other populations, its performance may vary in different patient groups. Clinicians should consider the model's performance characteristics in their specific patient population and adjust their interpretation accordingly.

The VACO Index provides population-level risk estimates and cannot predict individual patient outcomes with certainty. Many factors beyond those included in the model can influence individual patient outcomes, including response to treatment, development of complications, individual patient resilience, and access to care. The model should be used as one component of a comprehensive clinical assessment, not as a replacement for clinical judgment.

It is also important to recognize that the model predicts 30-day mortality risk, which is a specific outcome that may not capture all aspects of COVID-19 severity. Some patients may survive beyond 30 days but experience significant morbidity, long-term complications, or reduced quality of life. The model does not provide information about these other important outcomes, which should be considered in clinical decision-making.

Future Directions and Research

As the COVID-19 pandemic continues to evolve, there is ongoing research to refine and update prediction models like the VACO Index. Future versions of the model may incorporate additional predictors, such as vaccination status, variant type, and specific laboratory values that have been shown to predict outcomes. Researchers are also exploring the use of machine learning approaches that may be able to identify complex patterns in patient data that traditional statistical models might miss.

There is also interest in developing models that predict outcomes beyond mortality, such as the need for mechanical ventilation, length of hospital stay, or long-term complications. These models could provide additional valuable information for clinical decision-making and resource planning. Additionally, researchers are exploring the use of dynamic models that update risk estimates as new information becomes available during a patient's clinical course.

The integration of prediction models into clinical decision support systems continues to evolve, with efforts to make these tools more user-friendly, accessible, and integrated into clinical workflows. As electronic health record systems become more sophisticated, there are opportunities to automatically calculate risk estimates and present them to clinicians in ways that support, rather than replace, clinical judgment.