Optimizing CTPA Use in Pulmonary Embolism: Can Clinical Algorithms Outperform Instinct?

Background:

Pulmonary embolism (PE) is a life-threatening diagnosis that often prompts urgent imaging with CT Pulmonary Angiography (CTPA). While CTPA is highly sensitive, its liberal use in low-risk patients raises concerns around unnecessary radiation, contrast exposure, and resource strain. This study investigates whether validated clinical algorithms can outperform clinicians' judgment in guiding CTPA use.

Methods:

We retrospectively reviewed all patients who underwent CTPA for suspected PE at a major tertiary center in Bahrain between December 2019 and August 2024. Pre-test probability was retrospectively calculated using the Wells score, and D-dimer testing was analyzed for compliance with NICE-recommended diagnostic pathways.

Results:

Among the 743 patients evaluated, only 18.7% had CTPA-confirmed PE; this was significantly lower than the 15.4–37% diagnostic yield benchmark recommended by the Royal College of Radiologists. Notably, 47.3% of patients with a low Wells score and negative D-dimer underwent CTPA despite being eligible to avoid imaging per the existing guidelines. Additionally, the Wells score was undocumented in nearly two-thirds of cases, indicating that clinical intuition frequently overrode structured decision-making tools.

Conclusion:

Our data reveal a substantial gap between guideline-based diagnostic pathways and real-world clinical practice. The overuse of CTPA not only inflates healthcare costs but also exposes patients to avoidable harm. This study supports the integration of algorithm-based tools into electronic ordering systems to standardize decision-making, reduce low-yield scans, and enhance diagnostic accuracy. As imaging technology advances, so too must our frameworks for using it wisely—not as a reflex, but as a deliberate, evidence-informed step in patient care.