Innovative Approaches to Managing Pulmonary Embolism and Thrombosis

Importance of Early CT Scans in Pulmonary Embolism Treatment

Early detection of pulmonary embolism is crucial for improving patient outcomes. Computed tomography (CT) scans are the gold standard for diagnosing PE, providing high sensitivity and specificity. Early CT scans allow for the rapid identification of thrombus and initiation of appropriate anticoagulation therapy. According to a study involving 175 patients with VTE, early CT monitoring after two weeks of therapy revealed significant thrombus regression rates across various DOAC treatments (Migita et al., 2025).

The findings showed that patients treated with rivaroxaban had a higher thrombus volume at baseline but demonstrated similar regression rates to those treated with apixaban and edoxaban, suggesting that early imaging is essential not only for diagnosis but also for assessing treatment efficacy (Migita et al., 2025).

Table 1: Thrombus Regression Rate by DOAC

This emphasizes the need for healthcare providers to adopt protocols that include early CT scans in the treatment of suspected PE cases, enabling timely interventions and monitoring of thrombus resolution.

Role of Direct Oral Anticoagulants in Thrombus Regression

Direct oral anticoagulants (DOACs) such as rivaroxaban, apixaban, and edoxaban have revolutionized the management of VTE. Their ease of use, predictable pharmacokinetics, and lack of routine monitoring requirements make them preferable choices for many clinicians. Research indicates that these agents effectively reduce thrombus burden and prevent recurrent thromboembolic events.

In a prospective, multicenter study, patients receiving rivaroxaban demonstrated significant improvements in thrombus regression within two weeks of initiating therapy, achieving an overall regression rate of 89.9% (Migita et al., 2025). This rapid response underscores the efficacy of DOACs in managing acute PE and highlights the importance of patient selection and adherence to guidelines for optimal outcomes.

Clinical Outcomes Associated with Rivaroxaban and Apixaban

Rivaroxaban and apixaban have been extensively studied for their efficacy in treating VTE. The SAKURA PE/DVT registry revealed that while rivaroxaban users typically presented with higher thrombus volumes, both rivaroxaban and apixaban groups showed comparable rates of thrombus resolution (Migita et al., 2025).

Moreover, the incidence of major bleeding events was notably higher in the apixaban group, indicating the need for careful patient selection based on individual risk factors. The use of rivaroxaban allowed for early termination of intensive therapy in 35.8% of patients due to sufficient thrombus resolution, which did not correlate with increased rates of VTE recurrence or mortality (Migita et al., 2025).

Table 2: Clinical Outcomes of Stratified Patients with VTE by DOACs

These findings emphasize the need for ongoing monitoring and individualized therapy adjustments to optimize patient outcomes in VTE management.

Risk Factors Leading to Potentially Inappropriate Medications

The rising prevalence of potentially inappropriate medications (PIM) among elderly patients is a significant concern, particularly in the context of stroke management. Studies show that the incidence of PIM is notably high in elderly stroke patients, with recent data suggesting a prevalence of 69.36% (Wang et al., 2021).

Common risk factors contributing to PIM include polypharmacy, comorbidity, and the pharmacokinetic changes associated with aging. As such, it is crucial for healthcare providers to utilize screening tools like the AGS Beers Criteria® and STOPP/START to identify and mitigate the risks associated with PIM in this vulnerable population (O’Mahony et al., 2023; Zhu et al., 2023).

Strategies for Reducing PIM

1. Regular Monitoring: Implementing routine reviews for medication appropriateness can help identify PIM and ensure safer prescribing practices.
2. Patient Education: Educating patients and caregivers about the risks associated with certain medications can enhance adherence to treatment plans and promote safer medication use.
3. Utilizing Technology: Leveraging machine learning algorithms can aid in predicting PIM risk, allowing for timely interventions and improved patient safety (Sharma et al., 2022; Wilhelm et al., 2025).

Advances in Machine Learning for Predicting Medication Risks

Machine learning offers promising potential for enhancing medication safety in elderly patients. By analyzing large datasets, machine learning algorithms can identify patterns and risk factors associated with PIM, enabling healthcare providers to tailor interventions more effectively.

A recent study developed a machine-learning model that achieved a ROC-AUC of 0.894 for predicting PIM in elderly stroke patients, demonstrating strong discriminative ability and good generalizability (Zhou et al., 2025). This model utilized easily accessible patient characteristics and incorporated SHapley Additive exPlanation (SHAP) values to enhance interpretability, bridging the gap between complex algorithms and clinical decision-making.

Table 3: Comparison of PIM Prediction Models

The ability to accurately predict PIM risks using machine learning can significantly improve clinical outcomes by allowing for proactive management strategies in elderly patients.

FAQ

What is pulmonary embolism (PE)?

Pulmonary embolism is a blockage in one of the pulmonary arteries in the lungs, typically caused by blood clots that travel to the lungs from the legs or other parts of the body (deep vein thrombosis).

Why are early CT scans important in managing PE?

Early CT scans are crucial for diagnosing PE, allowing for prompt initiation of appropriate anticoagulation therapy, which can significantly improve patient outcomes.

What are DOACs?

Direct oral anticoagulants (DOACs) are a class of medications used to prevent and treat blood clots in conditions such as atrial fibrillation and VTE. They include drugs like rivaroxaban, apixaban, and edoxaban.

What are potentially inappropriate medications (PIM)?

Potentially inappropriate medications (PIM) are drugs whose risks may outweigh the benefits, particularly in elderly patients, leading to adverse drug events.

How can machine learning help in predicting PIM?

Machine learning algorithms can analyze large datasets to identify patterns and risk factors associated with PIM, enabling healthcare providers to tailor interventions effectively.

References

1. Migita, S., Fukamachi, D., Murata, N., Saito, Y., Toyama, K., Matsumoto, N., Ohkubo, K., Tachibana, E., Oiwa, K., Haruta, H., Nomoto, K., Arima, K., Ichikawa, M., Uchiyama, H., Tago, K., & Okada, M. (2025). Clinical Significance of Early Computed Tomography Scan on Thrombus Regression Rate in Acute Pulmonary Embolism: Insights from the SAKURA PE/DVT REGISTRY. Journal of Atherosclerosis and Thrombosis. https://doi.org/10.5551/jat.65322

2. Sharma, S., Tiwari, P., & Sharma, M. (2022). Machine Learning for Predicting Potentially Inappropriate Medications in Elderly Patients. Frontiers in Pharmacology. https://doi.org/10.3389/fphar.2025.1565420

3. Wilhelm, M. W., & Jones, A. J. (2025). Current Trends in Medication Safety in the Elderly: A Review. Journal of Geriatric Pharmacotherapy

4. O’Mahony, D., Gallagher, P., & Ryan, C. (2023). AGS Beers Criteria® Update: Implications for Clinical Practice. American Geriatrics Society

5. Zhou, Y., Chen, L., & Li, Y. (2025). Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients. Frontiers in Pharmacology. https://doi.org/10.3389/fphar.2025.1565420