Development and validation of a clinical prediction model for the diagnostic management of acute pulmonary embolism: an individual participant data meta-analysis

Abstract

Abstract Background/Introduction Risk stratification is used for decisions about imaging in patients with clinically suspected acute pulmonary embolism (PE). Purpose To develop a clinical prediction model that provides an individualized, accurate probability estimate for acute pulmonary embolism (PE) based on readily available clinical items and D-dimer concentrations. Methods An individual participant data meta-analysis was performed based on sixteen studies with data from 28,305 adult patients with clinically suspected PE from various clinical settings. A multilevel logistic regression model was built and validated including ten a priori defined objective candidate predictors to predict PE at baseline or during follow-up of 30-90 days. Multiple imputation was used for missing data. Backward elimination was performed with a P-value < 0.10. Discrimination (c-statistic with 95% CI and prediction intervals [PI]) and calibration (outcome:expected [O:E] ratio and calibration plot) were evaluated based on internal-external cross-validation. The accuracy of the model was compared to algorithms based on the Wells score and D-dimer testing. Results The final model included age (in years), sex, previous VTE, recent surgery/immobilization, hemoptysis, cancer, clinical signs of DVT, inpatient status, D-dimer (in μg/L), and an interaction term between age and D-dimer. The pooled c-statistic was 0.87 (95%-CI, 0.85-0.89; 95%-PI, 0.77-0.93) and overall calibration was very good (pooled O:E ratio, 0.99; 95% CI, 0.87-1.14; 95% PI, 0.55-1.79). In the lower range of probabilities, the model slightly overestimated VTE probability. Discrimination of the current model in the validation data sets was better than that of the Wells score combined with a D-dimer threshold based on age (c- statistic 0.73; 95%-CI, 0.70-0.75) or structured clinical pretest probability (c-statistic 0.79; 95%-CI, 0.76- 0.81). Conclusion(s) The present model provides an absolute, individualized probability of PE presence in a broad population of patients with suspected PE, with very good discrimination and calibration. Its clinical utility needs to be evaluated in a prospective management or impact study.Calibration plot of the new model