Diagnostic management of acute pulmonary embolism: a prediction model based on a patient data meta-analysis

Abstract

Aims: Risk stratification is used for decisions regarding need for imaging in patients with clinically suspected acute pulmonary embolism (PE). The aim was to develop a clinical prediction model that provides an individualized, accurate probability estimate for the presence of acute PE in patients with suspected disease based on readily available clinical items and D-dimer concentrations.

Methods and results: An individual patient data meta-analysis was performed based on sixteen cross-sectional or prospective studies with data from 28 305 adult patients with clinically suspected PE from various clinical settings, including primary care, emergency care, hospitalized and nursing home patients. A multilevel logistic regression model was built and validated including ten a priori defined objective candidate predictors to predict objectively confirmed PE at baseline or venous thromboembolism (VTE) during follow-up of 30 to 90 days. Multiple imputation was used for missing data. Backward elimination was performed with a P-value <0.10. Discrimination (c-statistic with 95% confidence intervals [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 subsequently compared with algorithms based on the Wells score and D-dimer testing. The final model included age (in years), sex, previous VTE, recent surgery or immobilization, haemoptysis, cancer, clinical signs of deep vein thrombosis, 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). The model slightly overestimated VTE probability in the lower range of estimated probabilities. 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: 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.

Registration: PROSPERO ID 89366.

Keywords: D-dimer; Pulmonary embolism; diagnosis; prediction model; venous thromboembolism.

Structured Graphical Abstract

A clinical prediction model for the diagnostic management of acute pulmonary embolism was developed and validated using data from 28 305 patients across 16 studies. Eight clinical variables and quantitative D-dimer levels were included in the final model, which showed good discrimination and calibration. Overall performance was comparable to that of current diagnostic strategies, but, unlike traditional decision rules, the model can be used to calculate absolute probabilities of pulmonary embolism.

Figure 1

(A) Overall calibration of the new model. The dashed line indicates a situation of perfect calibration. The solid line reflects the actual correlation between estimated probabilities and observed prevalence of pulmonary embolism. The histogram below the plot shows the distribution of estimated probabilities in the study population. (B) Overall calibration of the new model for estimated risks between 0–10%. The dashed line indicates a situation of perfect calibration. The solid line reflects the actual correlation between estimated probabilities and observed prevalence of pulmonary embolism. Histogram below the plot shows distribution of estimated probabilities in the study population.

Figure 2

Efficiency and safety of currently used algorithms compared with the new model. Efficiency (x-axis) and failure rate (y-axis) of current diagnostic algorithms are plotted with 95% confidence intervals (dots with bars). The solid line shows the potential efficiency and safety of the new model based over the range of estimated probabilities, with the shaded area showing the 95% confidence intervals.

Figure 3

Distribution of risk estimated by the new model in patients categorized as ‘pulmonary embolism excluded’ based on the Wells score with D-dimer testing using the age-adjusted threshold (panel A) or a threshold based on clinical pretest probability (panel B).