Did Malpractice Risk Kill V/Q Exams?

CT perfusion angiography exams have largely replaced nuclear medicine-based ventilation/perfusion (V/Q) studies for detecting pulmonary embolism. But a new article in Academic Radiology suggests that CT’s rise wasn’t entirely based on clinical efficacy – fears of malpractice risk may have played a role. 

V/Q studies can help diagnose PE by enabling clinicians to visualize lung perfusion, showing defects such as blockages in pulmonary vessels. The scans are typically performed in three phases … 

  1. An albumin injection to show pulmonary vasculature.
  2. A radiopharmaceutical that’s inhaled and imaged with a gamma camera.
  3. A chest radiograph to correlate findings. 

The scans dominated PE imaging in the 1980s, but the rise of CT saw radiology facilities begin to shift.

  • CTPA was seen as having higher spatial resolution and was easier to perform than nuclear medicine exams. 

But the new article suggests that there were other forces at work as well – in particular, fear of malpractice risk from PEs that weren’t adequately followed after inconclusive V/Q exams.

  • The problem originated with clinical guidelines for V/Q reporting that classified some 20% of V/Q studies as “low probability” for PE when they probably would have better been classified as “inconclusive” or “non-diagnostic.”

As a result, a number of “low probability” patients weren’t followed up adequately, with tragic results that later figured into medical malpractice cases …

  • A patient who was diagnosed with pneumonia after an inconclusive V/Q exam, sent home, and died one day later of a “massive” PE.
  • A patient with leg and chest pain who was given heparin after a negative V/Q scan and later suffered internal hemorrhage; fortunately she survived.
  • A patient with “vague symptoms” who had an inconclusive V/Q scan and later died of an undiagnosed PE that some claimed would have been detected on CTPA.

Indeed, the theme of PE malpractice cases began to shift over time, from failure to diagnose V/Q scans to failure to order CTPA exams – which were soon seen as the clinical gold standard.

The Takeaway

Given the fast pace of development in radiology, it’s inevitable that some technologies that were once clinical staples fall by the wayside. But the new article offers a fascinating look at how clinical language can lead to medico-legal concerns that influence physician behavior – often in ways that are impossible to detect as they happen.

AI Detects Incidental PE

In one of the most famous quotes about radiology and artificial intelligence, Curtis Langlotz, MD, PhD, once said that AI will not replace radiologists, but radiologists with AI will replace those without it. A new study in AJR illustrates his point, showing that radiologists using a commercially available AI algorithm had higher rates of detecting incidental pulmonary embolism on CT scans. 

AI is being applied to many clinical use cases in radiology, but one of the more promising is for detecting and triaging emergent conditions that might have escaped the radiologist’s attention on initial interpretations.

  • Pulmonary embolism is one such condition. PE can be life-threatening and occurs in 1.3-2.6% of routine contrast-enhanced CT exams, but radiologist miss rates range from 10-75% depending on patient population.

AI can help by automatically analyzing CT scans and alerting radiologists to PEs when they can be treated quickly; the FDA has authorized several algorithms for this clinical use. 

  • In the new paper, researchers conducted a prospective real-world study of Aidoc’s BriefCase for iPE Triage at the University of Alabama at Birmingham. 

Researchers tracked rates of PE detection in 4.3k patients before and after AI implementation in 2021, finding … 

  • Radiologists saw their sensitivity for PE detection go up after AI implementation (80% vs. 96%) 
  • Specificity was unchanged (99.1% vs. 99.9%, p=0.58)
  • The PE incidence rate went up (1.4% vs. 1.6%)
  • There was no statistically significant difference in report turnaround time before and after AI (65 vs. 78 minutes, p=0.26)

The study echoes findings from 2023, when researchers from UT Southwestern also used the Aidoc algorithm for PE detection, in that case finding that AI cut times for report turnaround and patient waits. 

The Takeaway

While studies showing AI’s value to radiologists are commonplace, many of them are performed under controlled conditions that don’t translate to the real world. The current study is significant because it shows that with AI, radiologists can achieve near-perfect detection of a potentially life-threatening condition without a negative impact on workflow.

Viz.ai Adds PE Stratification

Viz.ai announced the FDA clearance of its new RV/LV ratio algorithm, adding an important risk stratification feature to its pulmonary embolism AI module, while representing an interesting example of how triage AI solutions might evolve.

Triage + Stratification + Coordination Viz PE becomes far more comprehensive with its new RV/LV integration, helping radiologists detect/prioritize PE cases and assess right heart strain (a major cause of PE mortality), while equipping PE response teams with more actionable information. 

  • This addition might also improve clinicians’ experience with Viz PE, noting the risk of developing AI “alert fatigue” when all severity levels are treated the same.

Viz.ai is So On-Trend – Signify Research recently forecast that AI leaders will increasingly expand into new clinical segments, enhance their current solutions, and leverage platform / marketplace strategies, as AI evolves from point solutions to comprehensive workflows. Those trends are certainly evident within Viz.ai’s recent PE strategy…

  • Viz PE’s late 2021 launch was a key step in Viz.ai’s expansion beyond neuro/stroke
  • Adding RV/LV risk stratification certainly enhances Viz PE’s detection capabilities
  • Viz PE was developed by Avicenna.AI, arguably making Viz.ai a platform vendor
  • Viz PE’s workflow now combines detection, assessment, and care coordination

The same could be said for Aidoc, which previously added Imbio’s RV/LV algorithm to its PE AI solution (and also supports incidental PE), although few other triage AI workflows are this advanced for PE or other clinical areas.

The Takeaway

Viz.ai’s PE and RV/LV integration is a great example of how detection-focused AI tools can evolve through risk/severity stratification and workflow integration — and it might prove to be a key step in Viz.ai’s expansion beyond stroke AI.

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