Diagnostic prediction models for suspected pulmonary embolism: systematic review and independent external validation in primary care

Abstract

Objective: To validate all diagnostic prediction models for ruling out pulmonary embolism that are easily applicable in primary care.

Design: Systematic review followed by independent external validation study to assess transportability of retrieved models to primary care medicine.

Setting: 300 general practices in the Netherlands.

Participants: Individual patient dataset of 598 patients with suspected acute pulmonary embolism in primary care.

Main outcome measures: Discriminative ability of all models retrieved by systematic literature search, assessed by calculation and comparison of C statistics. After stratification into groups with high and low probability of pulmonary embolism according to pre-specified model cut-offs combined with qualitative D-dimer test, sensitivity, specificity, efficiency (overall proportion of patients with low probability of pulmonary embolism), and failure rate (proportion of pulmonary embolism cases in group of patients with low probability) were calculated for all models.

Results: Ten published prediction models for the diagnosis of pulmonary embolism were found. Five of these models could be validated in the primary care dataset: the original Wells, modified Wells, simplified Wells, revised Geneva, and simplified revised Geneva models. Discriminative ability was comparable for all models (range of C statistic 0.75-0.80). Sensitivity ranged from 88% (simplified revised Geneva) to 96% (simplified Wells) and specificity from 48% (revised Geneva) to 53% (simplified revised Geneva). Efficiency of all models was between 43% and 48%. Differences were observed between failure rates, especially between the simplified Wells and the simplified revised Geneva models (failure rates 1.2% (95% confidence interval 0.2% to 3.3%) and 3.1% (1.4% to 5.9%), respectively; absolute difference -1.98% (-3.33% to -0.74%)). Irrespective of the diagnostic prediction model used, three patients were incorrectly classified as having low probability of pulmonary embolism; pulmonary embolism was diagnosed only after referral to secondary care.

Conclusions: Five diagnostic pulmonary embolism prediction models that are easily applicable in primary care were validated in this setting. Whereas efficiency was comparable for all rules, the Wells rules gave the best performance in terms of lower failure rates.

Fig 1 Flow scheme of diagnostic pathway in suspected pulmonary embolism in primary care

Fig 2 Overview of selection of studies that developed or validated prediction models for diagnosis of pulmonary embolism, based on literature search in PubMed and Embase. PERC=pulmonary embolism rule-out criteria

Fig 3 Forest plot of failure rates in development and validation studies of diagnostic prediction models, if combined with D-dimer testing