This retrospective study evaluated clinical, laboratory, and simple CT parameters of normotensive patients with acute PE from admission. The Bova score and 2019 ESC algorithm were used for risk stratification. The outcomes of interest were defined as the occurrence of adverse outcomes within 30 days after admission into hospital. Eligible patients were randomized 2:1 into derivation and validation cohorts. The derivation cohort was used to develop and evaluate a multivariable logistic regression model for predicting the outcomes of interest. The discriminatory power was evaluated by comparing the nomogram to the established risk stratification systems. The consistency of the nomogram was evaluated using the validation cohort. The investigators independently collected the data regarding clinical, laboratory, and CT parameters as well as data regarding the risk stratification scores and outcomes of interest. This research was approved by the Institutional Review Board of the Shengjing Hospital of China Medical University (No. 2020PS522K), and informed consent was exempted due to the absence of treatment intervention in patients.
Normotensive patients with acute PE were evaluated if they were treated at the Shengjing Hospital of China Medical University between January 2011 and May 2020. The diagnosis and management of acute PE was based on the 2019 ESC guidelines . The inclusion criteria were an age of ≥18 years and a PE diagnosis based on CT pulmonary angiography. The exclusion criteria were pregnancy, reception of reperfusion treatment before admission, and missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.
The patients’ medical records were reviewed to collect their demographic characteristics and baseline data from their admission regarding heart rate, systolic pressure, history of disease, arterial oxyhemoglobin saturation, c-Tn I concentration (μg/L), and NT-pro BNP concentration (pg/mL).
Assessing RV dysfunction
Within 24 hours after admission, RV dysfunction determined a transthoracic echocardiography using an IE Elite ultrasound machine (Philips) equipped with an S 5–1 transducer (frequency conversion1–5 MHz) by ultrasound specialist as following criteria: RV dilation (end-diastolic diameter>30mm, evaluated at 4-chamber view or parasternal view), an increased RV/left ventricle(LV) end-diastolic diameter ratio>0.9 at 4-chamber view, hypokinesia of the free RV wall, increased velocity of the jet of tricuspid regurgitation at apical 4-chamber view, decreased tricuspid annulus plane systolic, anyone or combinations of the condition above [2, 14].
Risk stratification was based on the 2019 ESC algorithm  and Bova score , with classifications as “low risk,” “intermediate-low risk,” and “intermediate-high risk” (Additional file 1: Table S1 and Additional file 2: Table S2). The 2019 ESC algorithm evaluated c-Tn I (cutoff: 0.04 μg/L ), NT-pro BNP (cutoff: 600 pg/mL) levels, RV dysfunction, and the simple PE severity index . The Bova score was calculated based on c-Tn I (cutoff: 0.05 µg/L), RV dysfunction, heart rate (cutoff: 110 beats/min), and systolic pressure (cutoff: 90–100 mmHg).
Outcomes of interest
The outcomes of interest were defined as the occurrence of adverse outcomes within 30 days after admission. Adverse outcomes were defined as PE-related deaths, the need for mechanical ventilation, the need for cardiopulmonary resuscitation, and the need for life-saving vasopressor and reperfusion treatment [9, 15].
Measurement of CT parameters
Three simple CT parameters were selected for the analysis. The first factor was thrombus location, which was categorized as within the central pulmonary artery (CPA embolism) [15, 16], spanning both sides of the bifurcation (saddle-CPA embolism) , and outside the CPA (non-CPA embolism) (Additional file 3: Figure S1 a, b and c). The second factor was the RV and LV diameters in the short-axis plane, which were measured as the maximal diameter from the cardiac intima to the interventricular septum , as well as the relative ratio of the RV/LV short-axis diameters (Additional file 4: Figure S2 a). The third factor was the maximum chamber diameters, which were measured using a 4-chamber view perpendicular to the atrial and interventricular septum (Additional file 4: Figure S2 b), as well as the relative ratios of the RV/LV and right atrium (RA)/left atrium (LA) 4-chamber diameters. All CT parameters were measured using the Mimics Medical software (version 19.0, Mimics Medical software, Leuven, Belgium).
Development of the model and risk-scoring tool
The model was developed based on three steps: (a) identifying relevant prognostic factors; (b) developing and validating the model; (c) evaluating the model’s discriminatory power relative to the 2019 ESC algorithm and Bova score.
In the first step, eligible patients were randomized 2:1 into derivation and validation cohorts based on the TRIPOD standard . All clinical, laboratory, and CT parameters were included into a classification and regression tree (CART) to identify relevant prognostic factors with importance . All potential decisional factors for adverse outcomes were evaluated and chosen into splits providing the optimal separations by binomial data until the splits reached a minimum size or no improvement could be made. All the chosen binomial parameters from CART were used to develop the model.
In the second step, univariate and multivariate logistic regression analyses were used to investigate binomial prognostic factors using the derivation cohort, and a nomogram was created by converting each regression coefficient from the multivariate logistic regression onto a scale of 0 points (low) to 100 points (high). The total scores for all variables were summed , and the different risk groups were separated based on their total nomogram scores via another CART analysis. A validation cohort was used to evaluate the model’s consistency relative to the observed outcomes . A calibration curve was used to assess the consistency between actual incidence and predicted incidence of the nomogram in the derivation and validation cohorts.
In the third step, the models’ abilities to predict adverse outcomes were compared to the 2019 ESC algorithm  and the Bova score  based on the receiver-operating characteristic curve (ROC) and decision curve analysis (DCA). The final risk-scoring tool was published as a free web-based calculator.
Continuous variables were expressed as mean ± standard deviation and compared using the Student’s t test. Categorical variables were presented as numbers (%) and compared using the χ2 test. By a recursive partitioning analysis, CART was used to dichotomize each variable while controlling for confounders and divide the derivation cohort into different risk groups according to the total nomogram score . Univariate and multivariate logistic regression analyses were used to evaluate the different factors, and the results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). The nomograms’ predictive performances were evaluated based on the concordance index (C-index) and calibration with 1,000 bootstrap resampling . The ROC curves were used to evaluate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC). A calibration curve was used to assess the consistency between the actual incidence and predicted incidence of the nomogram . Clinical utility was evaluated based on net benefit from the DCA. DeLong’s test was used to compare AUC values . Differences were considered significant at p-values of < 0.05, and all analyses were performed using R software (version 4.0.1; R Foundation, https://www.r-project.org).