Developing a scoring tool to estimate the risk of deterioration for normotensive patients with acute pulmonary embolism on admission

Figures

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  Fig. 1. Flowing chart of inclusion and exclusion criteria

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  Fig. 2. The nomogram for predicting the risk of adverse outcomes. c Tn-I cardiac troponin I; NT-pro BNP N-terminal pro-brain natriuretic peptide; RV right ventricle; LV left ventricle; RA right atrium; LA left atrium

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  Fig. 3. The receiver-operating characteristic curves for predicting adverse outcomes. The training dataset is shown using the red line and the validation dataset is shown using the green line. The area under the curve values for predicting adverse outcomes were 0.925 in the training dataset (95% confidence interval [CI] 0.900–0.946, p < 0.001) and 0.900 in the validation dataset (95% CI 0.883–0.948, p < 0.001)

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  Fig. 4. Calibration curves for the nomogram predicting adverse outcomes. The training dataset is shown using the red line, and the validation dataset is shown using the green line

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  Fig. 5. Decision curve analysis for the nomogram. A comparison of net benefit between the nomogram, the 2019 ESC algorithm, and the Bova score, which revealed that the nomogram was better in terms of net benefit (net benefit: 0.03–0.98). The nomogram is shown using the red line, Bove score is shown using the green line, and 2019 ESC algorithm is shown using green line. ESC European Society of Cardiology

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  Fig. 6. The web-based calculator for estimating the risk of adverse outcomes (https://gaoyzcmu.shinyapps.io/APE9AD/). The QR code in the lower right corner can be used to publish the result to mobile electronic equipment. NT-pro BNP N-terminal pro-brain natriuretic peptide; RV right ventricle; LV left ventricle; RA right atrium; LA left atrium