Prediction of Pulmonary Embolism in the Emergency Department: The Revised Geneva Score

Context Using clinical findings to estimate the probability of disease can help guide management decisions. Contribution These investigators used data from 2 independent studies to derive and validate an 8-item score for predicting pulmonary embolism in patients seen in the emergency department for suspected embolism. Items addressed age, previous thrombosis, recent surgery or fracture, malignant condition, unilateral leg pain, unilateral leg edema, hemoptysis, and heart rate. The prevalence of pulmonary embolism in patients with low, intermediate, and high scores was 8%, 28%, and 74%, respectively. Implications We should now assess whether using this prediction rule affects patient outcomes. The Editors In recent years, clinical probability assessment into 3 categories has become an important component of strategies for optimal diagnosis of pulmonary embolism (1-7) using noninvasive tests (3, 6). For instance, highly sensitive d-dimer assays safely rule out pulmonary embolism in patients with a low or intermediate clinical probability (7, 8), while less sensitive assays have been validated only in low-probability patients in outcome studies (1, 9). Clinical assessment has been shown to be useful for reducing the requirement for invasive tests in outcome studies (1, 2, 7, 8) and to be cost-effective (10). The landmark Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study (11) determined the clinical probability of pulmonary embolism by clinicians' implicit global judgment on the basis of history, risk factors, physical examination, and results of simple tests. Although proven valid in subsequent management studies (2, 8, 12), that evaluation has an important limitation in its implicitness, particularly in environments where patients are managed by less experienced physicians. Therefore, investigators developed prediction rules, the most widely validated of which are the Wells score (13) and the Geneva score (14). Both rules were compared with implicit judgment and demonstrated similar accuracy (15). However, those rules also have limitations. Computation of the Geneva score (14) requires arterial blood gas values while breathing room air, a variable that was not available in 15% of the patients in the external validation sample (15). The Wells score includes the clinician's judgment of whether an alternative diagnosis is more likely than a pulmonary embolism diagnosis (13). That criterion carries a major weight in this score and can obviously not be standardized. Therefore, we derived a new prediction rule that is entirely based on clinical variables and is independent of physicians' implicit judgment by using a large multicenter cohort of patients admitted to the emergency department for clinically suspected pulmonary embolism (7). We also performed an external retrospective validation of this new rule in a distinct cohort of patients in the emergency department with suspected pulmonary embolism (16). Methods Derivation Step Patients and Study Design The prediction rule was derived from a multicenter prospective outcome study that was designed to evaluate a diagnostic strategy for pulmonary embolism combining clinical probability assessment, plasma d-dimer measurement, lower-limb venous ultrasonography, and helical computed tomography (CT) in the emergency department (7). Briefly, all consecutive patients admitted to the emergency departments of 3 general and teaching hospitals (Geneva University Hospital, Switzerland; University Hospital, Lausanne, Switzerland; and Angers University Hospital, Angers, France) were eligible for the study if they had suspected pulmonary embolism, defined as acute onset of new or worsening shortness of breath or chest pain without any other obvious cause. Exclusion criteria were 1) ongoing anticoagulant treatment, 2) contraindication to CT (known allergy to contrast iodine agents or risk for allergic reaction, creatinine clearance <0.50 mL/s [<30 mL/min] calculated by the Cockcroft-Gault formula [17], or pregnancy), 3) suspected massive pulmonary embolism with shock, or 4) estimated life expectancy less than 3 months. We obtained written informed consent from all patients. The ethics committee at each study site approved the study. Between October 2000 and June 2002, we screened 1280 patients with suspected pulmonary embolism. We excluded patients because of CT (n= 258) or protocol violations (n= 67). The total study sample consisted of 965 patients. All patients underwent a standardized sequential diagnostic work-up. At admission, the physician in charge of the patient in the emergency department performed a clinical evaluation of the patient and filled out a standardized data form. The form recorded demographic characteristics; risk factors; clinical signs and symptoms of venous thromboembolism; results of arterial blood gas analysis, electrocardiography, and chest radiography; and the likelihood of an alternative diagnosis compared with that of pulmonary embolism. It contained definitions or explanations only for potentially equivocal items. On the basis of that information, the physician assigned each patient into a clinical probability category by using the Geneva prediction rule (14) with possible override by implicit assessment (15). The physician then performed sequential tests (Appendix Figure 1). We ruled out pulmonary embolism by 1) a d-dimer level (by enzyme-linked immunosorbent assay [Vidas DD New Assay, bioMrieux, Marcy l'Etoile, France]) less than the cutoff value of 500 g/L; 2) negative results on lower-limb venous compression ultrasonography and helical CT in patients without high clinical probability and with a positive d-dimer test result; and 3) a negative result on pulmonary angiography in patients with high clinical probability. Finally, we ruled out pulmonary embolism in patients with inconclusive results on CT, either by a normal pulmonary angiogram; a normal ventilation-perfusion lung scan; or the combination of a low-probability ventilation-perfusion scan, a low clinical probability, and negative results on venous ultrasonography. We diagnosed pulmonary embolism by 1) a proximal deep venous thrombosis found on lower-limb ultrasonography, 2) a positive helical CT scan, 3) a positive pulmonary angiogram in patients with high clinical probability, and 4) a high-probability ventilation-perfusion lung scan in patients with inconclusive results on CT. We followed all patients for 3 months. The risk for thromboembolic events during follow-up in patients who met the criteria for absence of pulmonary embolism and who were not receiving anticoagulant therapy was 1.0% (95% CI, 0.5% to 2.1%). Appendix Figure 1. Diagnostic flow chart of the derivation set. Score Derivation We evaluated all of the clinical variables in our database that are known to be potentially associated with pulmonary embolism except dyspnea and chest pain, which were the inclusion criteria in the study. We excluded hormone replacement therapy because of the recent dramatic reduction in its prescription (18). To obtain a rule entirely based on clinical variables, we did not use the results of blood gas analysis, electrocardiography, and chest radiography. We also left out the likelihood of an alternative diagnosis compared with that of pulmonary embolism to achieve an entirely standardized score. Finally, we did not evaluate variables for which more than 2% of data were missing, namely body weight, temperature, and respiratory rate. We performed univariate analyses to select predictor variables for the multivariate model and to determine the significance and strength of the association between each candidate predictor and pulmonary embolism. We assessed significance by using the chi-square test for nominal categorical variables and the Mann-Whitney U test for continuous variables. A 2-tailed P value less than 0.05 indicated statistical significance. We then categorized the continuous variables that were statistically significantly associated with pulmonary embolism, choosing the most discriminative cutoff point or points. We included variables that were statistically significantly associated with pulmonary embolism in univariate analysis in a multivariate logistic regression model. We then removed non-statistically significant variables and calculated a regression coefficient for each statistically significant variable in the final model. We assigned points for the score according to the regression coefficients, with 1 point corresponding to a value close to the smallest regression coefficient and serving as the least common denominator for assigning point values for the score items. We then computed the score for each patient, performed a receiver-operating characteristic (ROC) curve analysis (19), and computed the area under the ROC curve and its corresponding 95% CI. Finally, we chose the cutoff value that discriminated among the low-, intermediate-, and high-probability groups to identify 1) a low-probability group with a prevalence of pulmonary embolism of approximately 10% and 2) a high-probability group with a prevalence of pulmonary embolism more than 60%. We assessed the predictive accuracy of the final score categories by the proportion of patients with pulmonary embolism in each group. Validation Step Internal Validation We performed a cross-validation procedure by splitting the sample randomly into 10 equal groups. We performed the logistic regression model on 9 groups, and we applied the resulting prediction equation to the 10th group. We repeated this procedure 10 times, rotating the cross-validation subset each time. We also performed ROC curve analysis. External Validation We validated the score in an independent cohort of patients included in a prospective outcome study that assessed the performances of a diagnostic strategy based on clinical probability, plasma d-dimer measurement, lower-limb venous ultrasonography, and multidetector helical CT for pulmonary