Padua Prediction Score Calculator- Free VTE Risk Assessment Tool

About This Padua Prediction Score VTE Risk Calculator

This Padua Prediction Score calculator is designed for healthcare professionals, including physicians, nurses, pharmacists, and physical therapists, who need to assess venous thromboembolism (VTE) risk in hospitalized medical (nonsurgical) patients. The tool calculates the cumulative Padua score by evaluating 11 clinical risk factors for deep vein thrombosis (DVT) and pulmonary embolism (PE), providing instant risk classification to guide thromboprophylaxis decisions at the bedside or through clinical workflow integration.

The calculator implements the Padua Prediction Score risk assessment model (RAM) published by Barbar et al. in 2010, which is recommended by the American College of Chest Physicians (ACCP) 9th edition guidelines for antithrombotic therapy and prevention of thrombosis. Risk factors are weighted according to the original scoring system: 3 points each for active cancer, previous VTE, reduced mobility, and known thrombophilia; 2 points for recent trauma or surgery; and 1 point each for age 70+, heart or respiratory failure, acute MI or ischemic stroke, acute infection or rheumatological disorder, obesity, and ongoing hormonal treatment. The binary classification at a threshold of 4 points determines whether pharmacological thromboprophylaxis should be considered.

The visual risk spectrum bar provides an intuitive display of where the patient’s score falls relative to the low-risk and high-risk zones, with a clear threshold marker at the ACCP-recommended cutoff of 4 points. The risk factor contribution tab shows the relative weight of each selected factor, while the VTE prophylaxis guide tab provides evidence-based thromboprophylaxis recommendations for both low-risk and high-risk patients, including options for patients with bleeding contraindications. This tool should always be used alongside clinical judgment and bleeding risk assessment for balanced prophylaxis decision-making.

Padua Prediction Score: A Complete Guide to VTE Risk Assessment in Hospitalized Medical Patients

Venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE), remains one of the most significant preventable causes of morbidity and mortality among hospitalized patients worldwide. Despite the availability of effective thromboprophylaxis, a substantial proportion of at-risk medical inpatients do not receive appropriate preventive measures. The Padua Prediction Score was developed to address this critical gap by providing clinicians with a simple, validated risk assessment model (RAM) for identifying hospitalized medical patients at elevated risk of VTE. Published in 2010 by Barbar and colleagues at the University of Padua in Italy, this scoring system has since been adopted by major clinical guidelines, including those from the American College of Chest Physicians (ACCP), as a recommended tool for VTE risk stratification in nonsurgical hospitalized patients.

Understanding the Padua Prediction Score is essential for healthcare professionals involved in the care of hospitalized medical patients. This comprehensive guide covers the scoring criteria, clinical interpretation, evidence base, limitations, and practical application of the Padua Prediction Score in modern clinical practice. Whether you are a physician, nurse, pharmacist, or physical therapist, this tool can help guide thromboprophylaxis decisions and ultimately reduce the burden of hospital-acquired VTE.

What Is the Padua Prediction Score?

The Padua Prediction Score (PPS) is a validated clinical risk assessment tool designed to estimate the risk of venous thromboembolism in hospitalized medical (nonsurgical) patients. It was developed through a prospective cohort study conducted at the Department of Internal Medicine at the University of Padua in Padova, Italy, and published in the Journal of Thrombosis and Haemostasis in 2010. The score evaluates 11 independent risk factors for VTE, each weighted according to its relative contribution to thrombotic risk.

The primary purpose of the Padua Prediction Score is to stratify hospitalized medical patients into two risk categories: low risk (score less than 4) and high risk (score of 4 or greater). Patients identified as high risk are candidates for pharmacological or mechanical thromboprophylaxis, while those at low risk may not require prophylactic measures. This binary classification provides a straightforward decision point for clinicians, making the tool practical and easy to implement at the bedside or through electronic medical record systems.

Unlike risk assessment models designed for surgical patients (such as the Caprini RAM), the Padua Prediction Score specifically targets the medical inpatient population, a group in which VTE prophylaxis has historically been underutilized. Studies have consistently shown that while surgical patients frequently receive appropriate thromboprophylaxis, medical patients are often overlooked despite carrying a significant VTE risk.

The 11 Risk Factors and Scoring Criteria

The Padua Prediction Score evaluates 11 clinical variables, each assigned a specific point value based on its strength of association with VTE risk. Understanding each criterion is essential for accurate risk assessment.

Clinical Interpretation of Padua Prediction Scores

The Padua Prediction Score uses a straightforward binary classification system. A total score below 4 places the patient in the low-risk category, while a score of 4 or higher places them in the high-risk category. This threshold was established in the original derivation study and has been maintained in subsequent clinical guideline recommendations.

In the original prospective cohort study by Barbar et al. (2010), 1,180 consecutive patients admitted to an internal medicine department over a two-year period were classified according to the Padua Prediction Score and followed for up to 90 days after admission. Among 469 patients (39.7%) classified as high risk, VTE developed in 11.0% of those who did not receive thromboprophylaxis compared to only 2.2% in those who received appropriate prophylaxis. Among 711 low-risk patients (60.3%), VTE occurred in only 0.3%. These findings demonstrated a 32-fold increased risk of VTE in high-risk patients without prophylaxis compared to low-risk patients.

The clinical implications are clear: patients scoring 4 or above should be considered for pharmacological thromboprophylaxis (typically low-molecular-weight heparin or unfractionated heparin), provided there are no significant contraindications such as active bleeding or severe thrombocytopenia. For patients with contraindications to anticoagulant prophylaxis, mechanical measures such as graduated compression stockings or intermittent pneumatic compression devices should be considered. Patients scoring below 4 are generally considered at low enough risk that the benefits of prophylaxis do not outweigh the potential bleeding risks.

Evidence Base and Original Study Design

The Padua Prediction Score was derived from a prospective cohort study conducted at the Second Division of Internal Medicine at the University of Padua in Padova, Italy. The study enrolled 1,180 consecutive patients admitted between 2006 and 2008, excluding those younger than 18 years, those already on anticoagulant therapy, those with VTE at admission, and those admitted for fewer than 48 hours. Each patient was assessed for the 11 risk factors at admission and followed for up to 90 days to monitor the development of symptomatic VTE, confirmed by objective diagnostic tests (compression ultrasonography for DVT and computed tomographic pulmonary angiography for PE).

The key findings of the derivation study were notable. Of the 469 high-risk patients, 186 received adequate thromboprophylaxis while 283 did not. VTE developed in 4 of 186 (2.2%) treated patients compared to 31 of 283 (11.0%) untreated patients, yielding a hazard ratio of 0.13 (95% CI: 0.04 to 0.40). Bleeding occurred in only 3 of 186 (1.6%) treated high-risk patients, suggesting a favorable risk-benefit profile for thromboprophylaxis in this group. These results formed the foundation for the ACCP 9th edition guidelines recommending the Padua Prediction Score for VTE risk assessment in medical inpatients.

It is important to note that the Padua Prediction Score was derived from a single-center study in an Italian population. While this does not invalidate the tool, it means that external validation across diverse populations and healthcare settings has been an important area of subsequent research. Some validation studies have confirmed the discriminative ability of the score, while others have raised questions about its sensitivity compared to alternative risk assessment models.

Guideline Recommendations and Clinical Adoption

The Padua Prediction Score has been endorsed by several major clinical guidelines for VTE prevention in hospitalized medical patients. The American College of Chest Physicians (ACCP) 9th edition guidelines on antithrombotic therapy and prevention of thrombosis specifically recommend the Padua Prediction Score as the preferred tool for identifying medical inpatients at increased risk of VTE. The American Heart Association (AHA) has also included the Padua Prediction Score in its call to action for preventing VTE in hospitalized patients.

The American Society of Hematology (ASH) VTE guidelines for medical patients recommend identifying high-risk patients through validated risk assessment models, with the Padua Prediction Score identified as one of the two most widely externally validated tools, alongside the IMPROVE-VTE RAM. The American Physical Therapy Association (APTA) includes the Padua Prediction Score in its 2022 evidence-based clinical practice guideline for the management of individuals at risk for or diagnosed with VTE, recommending its use by physical therapists to assess VTE risk in patients with reduced mobility.

Beyond the United States, the Padua Prediction Score has been adopted in various national and institutional protocols worldwide. In some healthcare systems, it has been implemented as a mandatory risk assessment tool upon admission for nonsurgical patients, with computerized clinical decision support systems automatically prompting clinicians to perform the assessment and recommend appropriate prophylaxis based on the score.

Comparison with Other VTE Risk Assessment Models

Several alternative risk assessment models exist for VTE risk stratification in hospitalized patients, each with different characteristics, strengths, and limitations. Understanding how the Padua Prediction Score compares to these alternatives helps clinicians choose the most appropriate tool for their practice setting.

The Caprini Risk Assessment Model is one of the most widely studied alternatives. Originally developed for both surgical and medical patients, the Caprini RAM incorporates 39 individual risk factors (compared to the Padua Prediction Score’s 11), providing a more granular risk stratification with multiple risk categories (very low, low, moderate, and high risk). Studies comparing the two models have generally found that the Caprini RAM offers higher sensitivity for identifying VTE cases (up to 84% compared to approximately 49% for the Padua Prediction Score), but at the cost of lower specificity and greater complexity. The main practical limitation of the Caprini RAM is that its 39-item format makes it more time-consuming to complete.

The IMPROVE-VTE Risk Assessment Model was developed from an international registry of over 15,000 patients from 52 hospitals across 12 countries, giving it a broader derivation population than the Padua Prediction Score. The IMPROVE-VTE RAM assesses seven items (previous VTE, thrombophilia, lower extremity paralysis, active cancer, immobilization over one week, ICU admission, and age 60 or older) and classifies patients into three risk groups (low, intermediate, and high). The ASH guidelines identify both the Padua Prediction Score and the IMPROVE-VTE RAM as the two most widely validated tools for medical inpatient VTE risk assessment.

The Geneva Risk Score is another validated tool that includes 19 criteria and has been shown to compare favorably with the Padua Prediction Score in some validation studies, particularly for identifying low-risk patients who do not require prophylaxis. However, its greater complexity (19 criteria versus 11) limits its practical adoption in many clinical settings.

Validation Across Diverse Populations

Since its publication in 2010, the Padua Prediction Score has been evaluated in various populations and clinical settings worldwide. External validation studies have been conducted in North American, European, Asian, and Middle Eastern populations, providing insights into the tool’s performance across different ethnic groups and healthcare systems.

A large retrospective case-control study in China involving 1,804 medical inpatients found that the VTE risk increased significantly with higher Padua Prediction Scores, with high-risk classification associated with a 5.01-fold increased VTE risk. However, the study also noted that the Padua Prediction Score could identify only 49.1% of VTE cases as high risk, compared to 84.3% identified by the Caprini RAM. This finding suggested that some important risk factors may not be adequately captured by the Padua Prediction Score’s 11-item framework.

A study in Japanese medical inpatients evaluated the performance of both the Padua and IMPROVE-VTE RAMs, finding moderate discriminative ability for both models (C-statistic of approximately 0.64 for each). Both models tended to underpredict VTE risk in the Japanese population, with observed event rates generally exceeding predicted rates. These findings highlight the importance of considering population-specific factors when applying the Padua Prediction Score.

An Israeli validation study using electronic medical records in over 5,000 hospitalized nonsurgical patients found a very low overall symptomatic VTE incidence (0.27%) with no significant difference between high-risk and low-risk groups as defined by the Padua Prediction Score. The investigators suggested that the tool’s predictive ability may vary across different healthcare settings and patient populations, and that continuous risk reassessment beyond admission may be necessary.

These validation studies collectively suggest that while the Padua Prediction Score provides useful risk stratification in many populations, its performance is not uniform across all clinical settings. Healthcare providers should be aware of these limitations and consider supplementing the score with clinical judgment, especially when managing patients from populations underrepresented in the original derivation study.

Practical Application: Using the Padua Prediction Score

Implementing the Padua Prediction Score in clinical practice involves a systematic assessment of each of the 11 risk factors at the time of hospital admission for medical patients. The process is straightforward and can be completed in minutes, making it practical for busy clinical environments.

The first step is to identify the clinical indication for risk assessment. The Padua Prediction Score is specifically designed for hospitalized medical (nonsurgical) patients. Surgical patients should be assessed using surgical-specific tools such as the Caprini RAM. The assessment should be performed upon admission and may need to be reassessed if the patient’s clinical status changes significantly during hospitalization.

For each of the 11 risk factors, the clinician reviews the patient’s medical history, current clinical presentation, laboratory results, and medication list. Active cancer status requires checking for current malignancy with local or distant metastases, or recent chemotherapy or radiotherapy within 6 months. Previous VTE requires a documented history of DVT or PE (excluding superficial venous thrombosis). Reduced mobility is assessed based on anticipated bed rest with bathroom privileges only for at least 3 days. Known thrombophilia requires documented laboratory confirmation of hereditary or acquired thrombophilic conditions.

The remaining factors are generally assessable from routine clinical information: recent trauma or surgery within one month, age 70 or older, cardiac or respiratory failure, acute myocardial infarction or ischemic stroke, acute infection or active rheumatological disease, obesity (BMI 30 kg/m2 or greater), and current hormonal therapy use. Once all applicable risk factors are identified and their points summed, the total score determines the risk category and guides prophylaxis decisions.

Thromboprophylaxis Recommendations Based on Risk Stratification

The clinical utility of the Padua Prediction Score lies in its ability to guide appropriate thromboprophylaxis decisions. For patients classified as high risk (score of 4 or greater), current guidelines recommend pharmacological thromboprophylaxis, typically with low-molecular-weight heparin (LMWH) such as enoxaparin 40 mg subcutaneously once daily, unfractionated heparin (UFH) 5,000 units subcutaneously two or three times daily, or fondaparinux 2.5 mg subcutaneously once daily. The choice of agent depends on patient-specific factors including renal function, body weight, and contraindications.

For high-risk patients who have contraindications to pharmacological prophylaxis (such as active bleeding, severe thrombocytopenia, or recent hemorrhagic stroke), mechanical prophylaxis with graduated compression stockings (GCS) or intermittent pneumatic compression (IPC) devices should be considered. Some guidelines suggest combining pharmacological and mechanical prophylaxis for patients at very high risk, although the evidence for this approach in medical patients is less robust than in surgical populations.

For patients classified as low risk (score below 4), the current evidence does not support routine pharmacological thromboprophylaxis, as the potential bleeding risks may outweigh the modest VTE risk reduction. These patients should still receive general preventive measures, including early mobilization, adequate hydration, and education about VTE symptoms. Ongoing clinical reassessment is important, as a patient’s risk status may change during hospitalization due to new clinical developments such as acute infection, immobilization, or procedural interventions.

Limitations of the Padua Prediction Score

While the Padua Prediction Score is a valuable clinical tool, it has several important limitations that clinicians should be aware of when applying it in practice. Understanding these limitations is crucial for appropriate use and interpretation of the score.

The most significant limitation is that the Padua Prediction Score was derived from a single-center study in an Italian population, raising questions about the generalizability of the scoring weights and risk threshold across different populations and healthcare settings. While the ACCP guidelines endorsed the tool, the 9th edition guidelines noted that further validation was needed. Subsequent external validation studies have yielded mixed results, with some confirming the tool’s utility and others questioning its sensitivity, particularly in Asian populations.

The binary classification (low risk vs. high risk) at the threshold of 4 may oversimplify the continuous nature of VTE risk. Patients with a score of 3 may have a meaningfully different risk from those with a score of 0 or 1, yet all are classified as low risk. Some researchers have suggested that a lower threshold (such as 3) or a multi-tier classification system might improve the clinical utility of the score, though this modification has not been widely adopted.

The Padua Prediction Score does not include some risk factors that other models have identified as important, such as family history of VTE, central venous catheterization, ICU admission, lower extremity paralysis, and specific laboratory markers (such as D-dimer levels). The exclusion of these factors may contribute to the lower sensitivity reported in some comparative studies. Additionally, the score was not designed to assess risk dynamically; it provides a snapshot at admission and does not account for changing risk factors during hospitalization.

The score also does not incorporate bleeding risk assessment. While identifying patients at high VTE risk is important, the decision to prescribe anticoagulant prophylaxis must also consider the patient’s bleeding risk. Separate tools, such as the IMPROVE Bleeding Risk Score, should be used in conjunction with the Padua Prediction Score to make balanced prophylaxis decisions.

Special Populations and Considerations

Certain patient populations require special consideration when applying the Padua Prediction Score. Cancer patients represent a particularly high-risk group for VTE, and the Padua Prediction Score appropriately assigns the highest point value (3 points) to active cancer. However, cancer patients may benefit from more specialized VTE risk assessment tools such as the Khorana Score, which was specifically designed for ambulatory cancer patients receiving chemotherapy. For hospitalized cancer patients, the Padua Prediction Score remains applicable, and the combination of active cancer with other risk factors frequently results in high-risk classification.

Elderly patients (70 years and older) receive 1 point in the Padua Prediction Score, reflecting the well-documented age-dependent increase in VTE risk. However, elderly patients are also at increased risk of bleeding complications from anticoagulant prophylaxis, particularly those with renal impairment, low body weight, or concomitant antiplatelet therapy. Careful balancing of VTE and bleeding risks is essential in this population, and dose adjustments of prophylactic anticoagulants may be necessary.

Patients with known thrombophilia receive 3 points, but this requires prior documentation of a thrombophilic condition. Routine thrombophilia testing at admission is not recommended for risk assessment purposes, as the results would not be available in time to guide initial prophylaxis decisions. The thrombophilia criterion is therefore most useful for patients with previously established diagnoses.

Pregnant patients are typically excluded from the Padua Prediction Score assessment, as pregnancy-associated VTE risk is managed through separate guidelines. Similarly, patients admitted for psychiatric conditions, pediatric patients, and patients in rehabilitation facilities may not be well represented in the original derivation cohort, and the applicability of the score to these populations is uncertain.

Integration with Electronic Health Records

One of the strengths of the Padua Prediction Score is its amenability to integration with electronic health record (EHR) systems. Several healthcare institutions worldwide have implemented computerized clinical decision support (CDS) systems that automatically calculate the Padua Prediction Score based on data already captured in the EHR, including diagnoses, laboratory results, medications, and mobility assessments. These systems can generate alerts when a patient is identified as high risk, prompting clinicians to consider thromboprophylaxis.

The simplicity of the 11-item scoring system makes it particularly well-suited for electronic implementation compared to more complex models. However, accurate automated scoring depends on the quality and completeness of data entry in the EHR. Some risk factors, such as reduced mobility and known thrombophilia, may require specific documentation that is not always captured in structured data fields, potentially leading to underscoring of risk. Healthcare systems implementing automated Padua Prediction Score calculations should ensure that data capture processes adequately support all 11 criteria.

Studies of EHR-integrated risk assessment systems have shown that computerized alerts based on the Padua Prediction Score can increase rates of appropriate thromboprophylaxis in medical inpatients. A landmark study by Kucher et al. demonstrated that electronic alerts significantly increased the use of prophylaxis and reduced VTE rates in hospitalized patients, supporting the value of systematic risk assessment and clinical decision support in this domain.

Understanding Venous Thromboembolism: Deep Vein Thrombosis and Pulmonary Embolism

To fully appreciate the importance of VTE risk assessment, it is helpful to understand the conditions that the Padua Prediction Score aims to prevent. Venous thromboembolism encompasses two related but clinically distinct conditions: deep vein thrombosis (DVT) and pulmonary embolism (PE).

Deep vein thrombosis occurs when a blood clot (thrombus) forms in a deep vein, most commonly in the lower extremities (legs). Risk factors include venous stasis (reduced blood flow due to immobility), endothelial injury (damage to the blood vessel lining), and hypercoagulability (an increased tendency to form clots), collectively known as Virchow’s triad. DVT can cause leg swelling, pain, warmth, and redness, but it may also be asymptomatic. The most serious complication of DVT is embolization of the clot to the pulmonary vasculature, resulting in pulmonary embolism.

Pulmonary embolism occurs when a blood clot travels through the venous system to the lungs, where it lodges in one or more pulmonary arteries, obstructing blood flow. PE can range from clinically silent to rapidly fatal, depending on the size and location of the embolus and the patient’s cardiopulmonary reserve. Symptoms may include sudden shortness of breath, chest pain (particularly with breathing), rapid heart rate, and, in severe cases, cardiovascular collapse. PE is estimated to cause or contribute to approximately 100,000 to 300,000 deaths annually in the United States alone, and it is a leading cause of preventable hospital death worldwide.

Hospitalized medical patients are at particular risk for VTE due to the convergence of multiple risk factors including acute illness, immobility, age, and comorbid conditions. The Padua Prediction Score captures many of these risk factors in a structured format, allowing clinicians to identify patients who would benefit most from preventive interventions.

The Role of Early Mobilization and Non-Pharmacological Prevention

While the Padua Prediction Score focuses primarily on guiding pharmacological thromboprophylaxis decisions, non-pharmacological prevention strategies play an important complementary role in VTE prevention for all hospitalized patients, regardless of risk category. Early mobilization, in particular, has been recognized as a key preventive measure by multiple clinical guidelines and professional organizations.

Physical therapists play a vital role in VTE prevention through mobility assessment and intervention. The American Physical Therapy Association (APTA) 2022 clinical practice guidelines recommend that physical therapists assess VTE risk in patients with reduced mobility, with the Padua Prediction Score identified as a useful tool for this purpose. Physical therapists can contribute to VTE prevention by promoting early ambulation, prescribing appropriate exercise programs, and educating patients about the importance of mobility and hydration during hospitalization.

Mechanical prophylaxis devices, including graduated compression stockings and intermittent pneumatic compression devices, provide non-pharmacological VTE prevention by promoting venous return and preventing venous stasis. These devices are particularly valuable for patients who have contraindications to anticoagulant therapy or as adjunctive measures in very high-risk patients. Current guidelines generally recommend mechanical prophylaxis only when pharmacological prophylaxis is contraindicated, as the evidence for mechanical prophylaxis alone in medical patients is more limited than for pharmacological measures.

General preventive measures applicable to all hospitalized patients include adequate hydration to maintain proper blood viscosity, leg elevation when appropriate, avoidance of prolonged immobility, and patient education about VTE risk factors and warning signs. These measures should be implemented as part of standard hospital care regardless of the patient’s Padua Prediction Score.

Future Directions in VTE Risk Assessment

The field of VTE risk assessment continues to evolve, with ongoing research focused on improving the accuracy and clinical utility of risk prediction tools. Several areas of active investigation may shape the future landscape of VTE risk assessment in hospitalized medical patients.

Machine learning and artificial intelligence approaches are being explored for VTE risk prediction, leveraging the vast amounts of clinical data available in electronic health records to develop more accurate and personalized risk models. These approaches can incorporate hundreds of clinical variables simultaneously and may identify non-obvious risk factor combinations that traditional scoring systems cannot capture. Early studies have shown promising results, with machine learning models potentially outperforming traditional RAMs in some settings, though challenges remain in terms of interpretability, external validation, and clinical implementation.

Biomarker-based risk assessment is another area of active research. D-dimer levels, for example, have been shown to correlate with VTE risk in hospitalized patients and may enhance risk stratification when combined with clinical scoring systems. Other biomarkers under investigation include microparticles, coagulation factor levels, and inflammatory markers. Integration of biomarker data with clinical risk scores could potentially improve the sensitivity and specificity of VTE risk assessment, though standardized testing protocols and validated thresholds are needed before widespread clinical adoption.

Genomic risk profiling represents a longer-term opportunity for personalized VTE risk assessment. As understanding of the genetic determinants of VTE risk improves, it may become possible to incorporate genetic information into risk assessment algorithms, identifying patients with hereditary thrombophilic tendencies who may benefit from prophylaxis even in the absence of other clinical risk factors. However, the clinical utility and cost-effectiveness of genomic VTE risk profiling remain to be established.

Dynamic risk assessment models that continuously update risk estimates based on evolving clinical data during hospitalization represent another promising direction. Unlike the Padua Prediction Score, which provides a single assessment at admission, dynamic models could alert clinicians to increasing VTE risk as new risk factors emerge during a patient’s hospital stay, enabling timely initiation or intensification of prophylactic measures.

Frequently Asked Questions

Conclusion

The Padua Prediction Score represents an important advance in the prevention of hospital-acquired venous thromboembolism among medical inpatients. Its simplicity, with just 11 risk factors and a clear binary classification at a threshold of 4 points, makes it practical for everyday clinical use while providing meaningful risk stratification supported by prospective clinical evidence. Since its publication in 2010, the score has been adopted by major clinical guidelines including those from the ACCP, AHA, and ASH, and has been implemented in healthcare systems worldwide.

While the Padua Prediction Score has limitations, including its derivation from a single-center study and variable performance across different populations, it remains a valuable tool when used as part of a comprehensive approach to VTE prevention. Clinicians should combine the Padua Prediction Score with bleeding risk assessment, clinical judgment, and institutional protocols to make individualized thromboprophylaxis decisions. As the field continues to evolve with advances in machine learning, biomarker research, and dynamic risk assessment, the fundamental principle underlying the Padua Prediction Score will remain unchanged: systematic risk assessment is essential for identifying and protecting hospitalized patients at risk for this preventable and potentially fatal condition.