The Use of a Clinical Prediction Model and D-Dimer (DD) Testing in the Diagnosis of Deep Vein Thrombosis (DVT): A Systematic Review.

Abstract Background: The diagnosis of DVT can be made by determining pretest probability of disease and using this information in combination with DD testing and ultrasound imaging. A number of studies have evaluated the use of clinical probability but this literature has not been summarized. Purpose: To systematically review trials that evaluated DVT prevalence using clinical prediction rules either with or without DD for the diagnosis of DVT. Data Sources: English and French language studies were identified from a MEDLINE search from 1990 to March 2004 and were supplemented by a review of all relevant bibliographies. Study Selection: Prospective management studies of symptomatic outpatients with suspected DVT in which patients were followed for a minimum of 3 months were selected. Clinical prediction rules had to be employed prior to DD and diagnostic tests. Studies were excluded if patients with a history of prior DVT were enrolled or if insufficient information was presented to calculate the prevalence of DVT for each of the 3 clinical probability estimates (low, moderate and high risk). Data Extraction: Two reviewers assessed each study for inclusion/exclusion criteria and collected data on prevalence and on sensitivity, specificity and likelihood ratios of DD in each of the 3 clinical probability estimates (low, moderate and high risk). Data Synthesis: 14 management studies involving a clinical prediction model in the diagnosis of DVT in over 8000 patients were included, of which 11 utilized DD in the diagnostic algorithm. All studies employed the same clinical prediction rule. The inverse variance weighted average prevalence of DVT in the low, moderate and high probability subgroups were 4.9% (95% CI= 4.2% to 5.7%), 17.4% (95% CI= 16.2% to 18.8%), and 53.6% (95% CI= 51.1% to 56.2%), respectively. The overall weighted prevalence was 18.3% (95% CI= 17.4% to 19.2%). The sensitivity of DD for the diagnosis of DVT in the low, moderate and high probability subgroups were 90.4% (95% CI= 84.7% to 94.2%), 92.0 % (95% CI= 89.1% to 94.2%), 93.6% (95% CI= 91.2% to 94.3%); and the specificities were 69.9% (95% CI= 68.0% to 71.8%), 52.4% (95% CI= 49.8% to 55.0%), and 43.2% (95% CI= 38.8% to 47.6%), respectively. The Mantel-Haenszel pooled estimates for diagnostic odds ratios (DOR) were 17.4 (95%CI=10.4–29.1), 10.2 (95% CI=7.1–14.6), and 10.1 (95% CI=6.9–14.9) in low, moderate and high groups respectively. Conclusion: Accurate estimates of the prevalence of DVT can be achieved using the same clinical prediction rule. Using this rule, it is unlikely that low probability patients have a DVT probability of more than 5%. Specificity of the DD seems to have clinically relevant differences depending on pretest probability but the DORs (which incorporate sensitivity and specificity) are similar. The data suggest that DVT can be excluded if patients are low probability even when DDs of lower sensitivity are employed and that DD testing has lower utility in high probability patients since false positives are common.

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