Abstract
Background
Hospitalized patients often present with complex clinical conditions, but there is a lack of effective tools to assess their risk of pulmonary embolism (PE). Therefore, our study aimed to develop a nomogram model for better predicting PE in hospitalized populations.
Methods
Data from hospitalized patients (aged ≥ 15 years) who underwent computed tomography pulmonary angiography (CTPA) to confirm PE and non-PE were collected from December 2013 to April 2023. Univariate and multivariate stepwise logistic regression analyses were conducted to identify independent predictors of PE, followed by the construction of a predictive nomogram and internal validation. The efficiency and clinical utility of the nomogram model were assessed using receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC).
Results
The study included 313 PE and 339 non-PE hospitalized patients. Male gender, dyspnea or shortness of breath, interstitial lung disease, lower limb deep vein thrombosis, elevated fibrin degradation product (FDP), pulmonary arterial hypertension, and tricuspid regurgitation were identified as independent risk factors. The AUC of the predictive nomogram model was 0.956 (95% CI: 0.939–0.974), demonstrating superior performance compared with the simplified Wells score of 0.698 (95% CI: 0.654–0.741) and the modified Geneva score of 0.758 (95% CI: 0.717–0.799).
Conclusion
Our study demonstrated that challenges remain in the accuracy of the Wells score and revised Geneva score in assessing PE in hospitalized patients. Fortunately, the nomogram we developed has shown a favorable ability to discriminate PE cases, providing high reference value for clinical practice. However, given that this was a single-center study, we plan to expand efforts to collect data from additional centers to further validate our model.
Key Points
- Study Population: Included 652 hospitalized patients, divided into 313 PE cases and 339 non-PE controls, with data collected over nearly a decade.
- Independent Predictors: Seven key risk factors identified: male sex, dyspnea, interstitial lung disease, lower limb deep vein thrombosis (DVT), elevated fibrin degradation product (FDP), pulmonary arterial hypertension (PAH), and tricuspid regurgitation.
- Nomogram Model: Constructed using multivariate logistic regression, achieving high predictive accuracy with an AUC of 0.956 in the training set and 0.929 in the validation set.
- Comparison with Existing Tools: The model outperformed the Wells and revised Geneva scores, which showed lower AUC values, particularly in hospitalized settings.
- Model Validation: Calibration curves indicated strong consistency between predicted and observed outcomes, with decision curve analysis (DCA) demonstrating high clinical utility.
- Clinical Utility: At a threshold probability of 0.58, the model provided sensitivity of 87.8% and specificity of 90.6% in the training set.
- Improved Risk Stratification: The nomogram incorporated objective diagnostic measures (e.g., FDP and DVT) alongside clinical symptoms, making it more suitable for hospitalized patients.
- Limitations: Single-center, retrospective design, limited sample size, and exclusion of COVID-19-associated cases, which may affect generalizability.
- Future Directions: Emphasis on external validation, larger multicenter studies, and inclusion of COVID-19-related PE data for a more robust model.
- Clinical Implications: The nomogram could reduce unnecessary CTPA scans and improve early PE detection, potentially lowering mortality and enhancing management strategies.
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