Could EKGs help doctors use AI to detect pulmonary embolisms?

To make diagnoses easier and more accessible, researchers have spent more than 20 years developing advanced computer programs, or algorithms, designed to help doctors determine whether at-risk patients are actually experiencing pulmonary embolisms. The results have been mixed. For example, algorithms that used EHRs have produced a wide range of success rates for accurately detecting clots and can be labor-intensive. Meanwhile, the more accurate ones depend heavily on data from the CTPAs.

In this study the researchers found that fusing algorithms that rely on EKG and EHR data may be an effective alternative, because EKGs are widely available and relatively easy to administer.

The researchers created and tested out various algorithms on data from 21,183 Mount Sinai Health System patients who showed moderate to highly suspicious signs of having pulmonary embolisms. While some algorithms were designed to use EKG data to screen for pulmonary embolisms, others were designed to use EHR data. In each situation, the algorithm learned to identify a pulmonary embolism case by comparing either EKG or EHR data with corresponding results from CTPAs. Finally, a third, fusion algorithm was created by combining the best-performing EKG algorithm with the best-performing EHR one.

The results showed that the fusion model not only outperformed its parent algorithms but was also better at identifying specific pulmonary embolism cases than the Wells' Criteria Revised Geneva Score and three other currently used screening tests. The researchers estimated that the fusion model was anywhere from 15 to 30 percent more effective at accurately screening acute embolism cases, and the model performed best at predicting the most severe cases. Furthermore, the fusion model's accuracy remained consistent regardless of whether race or sex was tested as a factor, suggesting it may be useful for screening a variety of patients.

According to the authors, these results support the theory that EKG data may be effectively incorporated into new pulmonary embolism screening algorithms. They plan to further develop and test these algorithms out for potential utility in the clinic.