A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm

BACKGROUND

Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.

METHODS

This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.

RESULTS

The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.

CONCLUSIONS

Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Assessment of the Utilization of Validated Diagnostic Predictive Tools and D-Dimer in the Evaluation of Pulmonary Embolism: A Single-Center Retrospective Cohort Study from a Public Hospital in New York City

INTRODUCTION

A significant increase in the use of computed tomography with pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) has been observed in the past twenty years. We aimed to investigate whether the validated diagnostic predictive tools and D-dimers were adequately utilized in a large public hospital in New York City.

METHODS

We conducted a retrospective review of patients who underwent CTPA for the specific indication of ruling out PE over a period of one year. Two independent reviewers, blinded to each other and to the CTPA and D-dimer results, estimated the clinical probability (CP) of PE using Well's score, the YEARS algorithm, and the revised Geneva score. Patients were classified based on the presence or absence of PE in the CTPA.

RESULTS

A total of 917 patients were included in the analysis (median age: 57 years, female: 59%). The clinical probability of PE was considered low by both independent reviewers in 563 (61.4%), 487 (55%), and 184 (20.1%) patients based on Well's score, the YEARS algorithm, and the revised Geneva score, respectively. D-dimer testing was conducted in less than half of the patients who were deemed to have low CP for PE by both independent reviewers. Using a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off in patients with a low CP of PE would have missed only a small number of mainly subsegmental PE. All three tools, when combined with D-dimer < 500 ng/mL or <age-adjusted cut-off, yielded a NPV of > 95%.

CONCLUSION

All three validated diagnostic predictive tools were found to have significant diagnostic value in ruling out PE when combined with a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off. Excessive use of CTPA was likely secondary to suboptimal use of diagnostic predictive tools.