ArticleBMC pulmonary medicine2024
Development of a predictive nomogram for early identification of pulmonary embolism in hospitalized patients: a retrospective cohort study.
Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHospitalized 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.
methodsData 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).
resultsThe 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).
conclusionOur 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.
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