Prediction of Pulmonary Embolism in the Emergency Department:
The Revised Geneva Score
Gre ´goire Le Gal, MD; Marc Righini, MD; Pierre-Marie Roy, MD; Olivier Sanchez, MD; Drahomir Aujesky, MD, MSc; Henri Bounameaux, MD;
and Arnaud Perrier, MD
Background: Diagnosis of pulmonary embolism requires clinical
probability assessment. Implicit assessment is accurate but is not
standardized, and current prediction rules have shortcomings.
Objective: To construct a simple score based entirely on clinical
variables and independent from physicians’ implicit judgment.
Design: Derivation and external validation of the score in 2 inde-
pendent management studies on pulmonary embolism diagnosis.
Setting: Emergency departments of 3 university hospitals in Europe.
Patients: Consecutive patients admitted for clinically suspected pul-
Measurements: Collected data included demographic characteris-
tics, risk factors, and clinical signs and symptoms suggestive of
venous thromboembolism. The variables statistically significantly as-
sociated with pulmonary embolism in univariate analysis were in-
cluded in a multivariate logistic regression model. Points were as-
signed according to the regression coefficients. The score was then
externally validated in an independent cohort.
Results: The score comprised 8 variables (points): age older than
65 years (1 point), previous deep venous thrombosis or pulmonary
embolism (3 points), surgery or fracture within 1 month (2 points),
active malignant condition (2 points), unilateral lower limb pain (3
points), hemoptysis (2 points), heart rate of 75 to 94 beats/min (3
points) or 95 beats/min or more (5 points), and pain on lower-limb
deep venous palpation and unilateral edema (4 points). In the
validation set, the prevalence of pulmonary embolism was 8% in
the low-probability category (0 to 3 points), 28% in the interme-
diate-probability category (4 to 10 points), and 74% in the high-
probability category (?11 points).
Limitations: Interobserver agreement for the score items was not
Conclusions: The proposed score is entirely standardized and is
based on clinical variables. It has sustained internal and external
validation and should now be tested for clinical usefulness in an
Ann Intern Med. 2006;144:165-171.
For author affiliations, see end of text.
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 pa-
tients 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 as-
sessment 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 fac-
tors, physical examination, and results of simple tests. Al-
though 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, in-
vestigators developed prediction rules, the most widely val-
idated of which are the Wells score (13) and the Geneva
score (14). Both rules were compared with implicit judg-
ment and demonstrated similar accuracy (15). However,
those rules also have limitations. Computation of the Ge-
neva 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
n recent years, clinical probability assessment into 3 cat-
egories has become an important component of strategies
whether an alternative diagnosis is more likely than a pul-
monary embolism diagnosis (13). That criterion carries a
major weight in this score and can obviously not be stan-
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 de-
partment with suspected pulmonary embolism (16).
Editors’ Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
Editorial comment. . . . . . . . . . . . . . . . . . . . . . . . . . 210
Related articles. . . . . . . . . . . . . . . . . . . . . 157 and 201
Conversion of figures and tables into slides
Annals of Internal Medicine
© 2006 American College of Physicians 165
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 measure-
ment, lower-limb venous ultrasonography, and helical
computed tomography (CT) in the emergency department
(7). Briefly, all consecutive patients admitted to the emer-
gency departments of 3 general and teaching hospitals
(Geneva University Hospital, Switzerland; University Hos-
pital, Lausanne, Switzerland; and Angers University Hos-
pital, 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 , or preg-
nancy), 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
All patients underwent a standardized sequential diag-
nostic work-up. At admission, the physician in charge of
the patient in the emergency department performed a clin-
ical evaluation of the patient and filled out a standardized
data form. The form recorded demographic characteristics;
risk factors; clinical signs and symptoms of venous throm-
boembolism; results of arterial blood gas analysis, electro-
cardiography, 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 informa-
tion, 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, available at www.annals.org). We ruled out pul-
monary embolism by 1) a D-dimer level (by enzyme-linked
immunosorbent assay [Vidas DD New Assay, bioMe ´rieux,
Marcy l’Etoile, France]) less than the cutoff value of 500
?g/L; 2) negative results on lower-limb venous compres-
sion 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 re-
sults on CT, either by a normal pulmonary angiogram; a
normal ventilation–perfusion lung scan; or the combina-
tion of a low-probability ventilation–perfusion scan, a low
clinical probability, and negative results on venous ultra-
sonography. We diagnosed pulmonary embolism by 1) a
proximal deep venous thrombosis found on lower-limb ul-
trasonography, 2) a positive helical CT scan, 3) a positive
pulmonary angiogram in patients with high clinical prob-
ability, 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 thrombo-
embolic 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%).
We evaluated all of the clinical variables in our data-
base that are known to be potentially associated with pul-
monary embolism except dyspnea and chest pain, which
were the inclusion criteria in the study. We excluded hor-
mone 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 radiog-
raphy. 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
We performed univariate analyses to select predictor
variables for the multivariate model and to determine the
significance and strength of the association between each
Using clinical findings to estimate the probability of dis-
ease can help guide management decisions.
These investigators used data from 2 independent studies
to derive and validate an 8-item score for predicting pul-
monary embolism in patients seen in the emergency de-
partment for suspected embolism. Items addressed age,
previous thrombosis, recent surgery or fracture, malignant
condition, unilateral leg pain, unilateral leg edema, he-
moptysis, and heart rate. The prevalence of pulmonary
embolism in patients with low, intermediate, and high
scores was 8%, 28%, and 74%, respectively.
We should now assess whether using this prediction rule
affects patient outcomes.
Revised Geneva Score for PE
166 7 February 2006 Annals of Internal Medicine Volume 144 • Number 3
candidate predictor and pulmonary embolism. We assessed
significance by using the chi-square test for nominal cate-
gorical variables and the Mann–Whitney U test for contin-
uous 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 signifi-
cantly associated with pulmonary embolism in univariate
analysis in a multivariate logistic regression model. We
then removed non–statistically significant variables and cal-
culated a regression coefficient for each statistically signifi-
cant 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 corre-
sponding 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 approxi-
mately 10% and 2) a high-probability group with a prev-
alence of pulmonary embolism more than 60%. We as-
sessed the predictive accuracy of the final score categories
by the proportion of patients with pulmonary embolism in
We performed a cross-validation procedure by split-
ting the sample randomly into 10 equal groups. We per-
formed 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
Table 1. Characteristics of the Study Sample (Derivation Sample)*
Characteristic Missing Values, n (%)Value
Patients with confirmed PE
0 222 (23.0%)
0 60.6 y (SD, 19.4)
72.6 kg (SD, 16.1)
Patients with family history of DVT or PE
Patients with personal history of DVT or PE
Patients with known congestive heart failure
Patients with previous stroke
Patients with COPD
Patients who had surgery, fracture, or both within 1 mo
Patients who were immobile within 1 mo
Patients with active malignant condition
Patients currently using oral contraceptive
Pregnant or postpartum patients
Patients with syncope
Patients with recent cough
Patients with hemoptysis
Patients with dyspnea
Patients with chest pain
Patients with unilateral lower-limb pain
Mean central temperature
Mean heart rate
Mean respiratory rate
Mean systolic blood pressure
Mean diastolic blood pressure
Signs related to PE
Patients with chronic venous insufficiency
Patients with varicose veins
Patients with unilateral edema and pain on deep venous palpation
Patients with abnormal chest auscultation
Patients with neck vein distention
36.9 °C (SD, 0.8)
86.3 beats/min (SD, 19.7)
20.2 cycles/min (SD, 7.0)
140 mm Hg (SD, 23)
81 mm Hg (SD, 15)
* COPD ? chronic obstructive pulmonary disease; DVT ? deep venous thrombosis; PE ? pulmonary embolism.
Revised Geneva Score for PE
7 February 2006 Annals of Internal Medicine Volume 144 • Number 3 167
cross-validation subset each time. We also performed ROC
We validated the score in an independent cohort of
patients included in a prospective outcome study that as-
sessed the performances of a diagnostic strategy based on
clinical probability, plasma D-dimer measurement, lower-
limb venous ultrasonography, and multidetector helical
CT for pulmonary embolism (16). We included consecu-
tive patients admitted to the emergency departments of 3
teaching hospitals (Geneva University Hospital; Angers
University Hospital; and Ho ˆpital Europe ´en Georges-Pom-
pidou, Paris, France) for suspected pulmonary embolism
between September 2002 and October 2003. The diagnos-
tic algorithm was similar to that of the derivation sample
study, and the diagnostic criteria for the presence and ab-
sence of pulmonary embolism were identical to those of
the derivation sample (7). Appendix Figure 2 (available at
www.annals.org) shows the flow of the study in the valida-
tion set. All patients were also followed for 3 months. The
risk for thromboembolic events during follow-up in pa-
tients who met the criteria for absence of pulmonary em-
bolism and who were not receiving anticoagulant therapy
was 1.0% (CI, 0.4% to 2.2%). The data collection form
was identical to that used in the derivation study, allowing
retrospective calculation of the score for each patient.
We assessed the calibration of the score (that is, its
ability to predict a probability of pulmonary embolism cor-
responding to the observed proportion of confirmed cases
of pulmonary embolism) by using the Hosmer–Lemeshow
goodness-of-fit statistic. Briefly, we sorted patients from
the validation sample in increasing order of their estimated
probability of pulmonary embolism and then divided them
into 10 groups of approximately equal numbers of pa-
tients. We compared the numbers of observed and pre-
dicted confirmed cases of pulmonary embolism in the 10
groups by using a chi-square test. A P value more than 0.05
indicated a non–statistically significant discrepancy be-
tween observed and predicted events. We assessed the dis-
crimination ability of the score in the external validation
data set by an ROC curve analysis. We computed the area
under the ROC curve and its 95% CI. We assessed the
predictive accuracy of the score by calculating the preva-
lence of pulmonary embolism in the 3 clinical probability
categories (low, intermediate, and high). We performed
statistical analyses by using SPSS software, version 12.0
(SPSS Inc., Chicago, Illinois).
Role of the Funding Sources
The 2 studies that we used in our paper were sup-
ported by grants from the Hirsch Fund of the University of
Geneva, the Swiss National Research Foundation (grant
32-61773.00), the Royal College of Physicians and Sur-
geons of Canada (grants 97/4-T10 and 00/4-T9), La Fon-
dation Que ´be ´coise pour le Progre `s de la Me ´decine Interne
and Les Internistes et Rhumatologues Associe ´s de l’Ho ˆpital
du Sacre ´-Cœur, and the Direction of Clinical Research of
the Angers University Hospital (grant 2001/021). The
funding sources had no role in the design, analysis, or
reporting of the study or in the decision to submit the
paper for publication.
Table 1 presents the general characteristics of the der-
ivation sample and the collected clinical variables. The
overall prevalence of pulmonary embolism was 23.0% (222
of 965 patients).
Table 2. The Revised Geneva Score*
Age ? 65 y
Previous DVT or PE
Surgery (under general anesthesia)
or fracture (of the lower limbs)
within 1 mo
Active malignant condition (solid or
condition, currently active or
considered cured ? 1 y)
Unilateral lower-limb pain
Pain on lower-limb deep venous
palpation and unilateral edema
* DVT ? deep venous thrombosis; PE ? pulmonary embolism.
Table 3. Calibration Assessment: Observed and Predicted
Patients with and without Pulmonary Embolism in 10
Groups of Patients with Increasing Score*
GroupPatients with PE, nPatients without PE, nTotal
* PE ? pulmonary embolism.
Revised Geneva Score for PE
168 7 February 2006 Annals of Internal Medicine Volume 144 • Number 3
We found a statistically significant association with the
diagnosis of pulmonary embolism for 10 variables: age,
previous venous thromboembolism, recent surgery, active
malignant condition, hemoptysis, unilateral lower-limb
pain, heart rate, chronic venous insufficiency, varicose
veins, and unilateral edema associated with pain provoked
by deep venous palpation. Among the numerical variables,
age was categorized into 2 groups (?65 years and ?65
years) and heart rate was categorized into 3 groups (?75
beats/min, 75 to 94 beats/min, and ?95 beats/min). We
included all 10 variables in a multivariate logistic regression
model. In the multivariate analysis, varicose veins and
chronic venous insufficiency were not independently asso-
ciated with pulmonary embolism. We assigned points for
the score according to the regression coefficients obtained
from the final model, including the 8 variables that inde-
pendently predicted pulmonary embolism in multivariate
analysis (Table 2). A regression coefficient of 0.3 corre-
sponded approximately to 1 point. Table 2 presents the
final score (the revised Geneva score) and the optimal cut-
off values to reach the predefined prevalence of pulmonary
embolism in each clinical probability category (see Meth-
In the derivation sample, we retrospectively computed
the revised Geneva score in 956 of the 965 patients (values
were missing for 9 patients [0.9%]); the area under the
ROC curve was 0.74 (CI, 0.70 to 0.78). In the internal
cross-validation, the area under the ROC curve was 0.73
(CI, 0.69 to 0.77). In the external validation set, we could
calculate the score for 749 of the 756 patients (values were
missing for 7 patients [0.9%]); the area under the ROC
curve was 0.74 (CI, 0.70 to 0.78).
The score also demonstrated good calibration. We
split patients into 10 groups with increasing predicted
probability of pulmonary embolism. The Hosmer–Leme-
show goodness-of-fit test result was nonsignificant (P ?
0.55), indicating that the observed and predicted numbers
of patients with and without pulmonary embolism did not
statistically significantly differ (Table 3). The Figure shows
the prevalence of pulmonary embolism according to the
number of points in the derivation and validation sets. As
shown in Table 4, the accuracy of the score and the pro-
portion of patients classified into each clinical probability
category were similar in the derivation and the validation
In our analysis, we present a clinical prediction rule
derived from a large cohort of consecutive emergency de-
partment outpatients with suspected pulmonary embolism.
The score is standardized and relies exclusively on clinical
information. It allows physicians to classify patients into 3
categories corresponding to an increasing prevalence of
pulmonary embolism. In the validation sample, the preva-
lence of pulmonary embolism was 8%, 29%, and 74% in
the low, intermediate, and high clinical probability catego-
ries, respectively (Table 3).
Is this score clinically credible? We considered all of
the known main risk factors and clinical signs of venous
thromboembolism for inclusion in the model. Some po-
tentially relevant data, such as pregnancy or the postpar-
tum period, were not statistically significantly associated
with pulmonary embolism, probably because few patients
in our sample had those characteristics. However, we be-
lieve that no obvious items are missing and that our score
may apply to a large proportion of patients with suspected
pulmonary embolism. Moreover, all the data required for
the score are routinely collected in the context of suspected
pulmonary embolism and are available from the patient’s
history and physical examination. Therefore, the score may
be easy to compute. Of interest, the clinical characteristics
entering in our score are very similar to those of the Cana-
dian prediction rule for pulmonary embolism developed by
Wells and colleagues (13), although we derived our score
in an entirely distinct population from western Europe,
hence adding content validity.
We derived our score by using a recommended
method (20, 21). We clearly identified pulmonary embo-
lism, the outcome of interest, and defined it by accepted
diagnostic criteria verified by a formal 3-month follow-up.
A commonly accepted rule requires that at least 10 out-
come events per independent variable should be included
in a prediction rule (22). In our derivation sample, 222
patients had confirmed pulmonary embolism and the final
score comprised only 8 variables. Furthermore, because pa-
tients were consecutive outpatients admitted to emergency
departments for suspected pulmonary embolism or were
self-referred because of symptoms suggestive of pulmonary
Figure. Prevalence of pulmonary embolism (PE) according to
the revised Geneva score in the derivation and validation
Patients with scores ?12 were pooled because of small numbers.
Revised Geneva Score for PE
7 February 2006 Annals of Internal Medicine Volume 144 • Number 3 169
embolism, generalization of this score seems possible. We
did not provide an explicit evaluation method for each
collected item and we did not test interrater reproducibility
of those items, which are limitations of our score. How-
ever, we precisely defined the predictors used to build the
score, and they are reasonably straightforward. We used
classical statistical methods: univariate analysis to screen for
the association between candidate variables and pulmonary
embolism, followed by a multivariate analysis that included
all statistically significant predictors.
Is this score valid and accurate? Different clinicians,
partially in different centers, performed external validation
of the score in a cohort of patients that was entirely distinct
from the derivation sample (16). As shown in Table 3, the
score performs as well in the derivation and validation sets
and allows physicians to stratify patients into 3 clinical
probability groups with an increasing prevalence of pulmo-
nary embolism. Generalizability of our score might be a
concern. Indeed, data collection, diagnostic criteria, and
the diagnostic algorithm in the validation data set were
similar or identical to those in the derivation data set. Also,
validation data came from 2 of the 3 hospitals involved in
the derivation study. In fact, more than 200 residents ro-
tating in the emergency department collected data, and the
likelihood that the same physicians worked up patients in
both the derivation and validation sets is extremely low.
Standardization of the diagnostic algorithm and criteria
minimizes misclassification bias. We applied the score to the
subset of 234 patients who were recruited in the only center
that was not involved in the derivation study, and we obtained
nearly identical results (data not shown). The score’s predic-
tive accuracy is at least as good as that of implicit clinical
judgment (2, 8, 11). It is also similar to the accuracy of the
Wells rule (13), although the score does not incorporate any
subjective judgment, such as the likelihood of an alternative
The patients from the derivation and the validation
sets came from management rather than diagnostic studies.
Therefore, not all patients received a diagnosis by a gold
standard criterion, and our score could be considered ac-
curate for predicting which patients can be safely left un-
treated rather than for predicting pulmonary embolism.
This is probably not a major issue. First, a diagnostic study
in which pulmonary angiography would be performed in
every patient is no longer feasible and would only result in
major selection bias. Second, our diagnostic criteria are
now widely accepted. Third, the advent of increasingly
sensitive techniques, such as the 64–detector row CT, en-
tails the risk for detecting very small peripheral clots of
questionable clinical significance and, hence, overtreat-
ment. Therefore, the relevant clinical question is not
whether a patient has pulmonary embolism but whether
anticoagulant treatment is required. In that sense, our score
is appropriate. Finally, incorporation bias might be a con-
cern because patients were worked up according to clinical
assessment based on the original Geneva score, which in-
cluded 4 items of the revised score. However, clinicians
collected all of the relevant clinical variables in suspected
pulmonary embolism and not only those necessary to com-
pute the original Geneva score. Therefore, we tested all
clinical variables for association with pulmonary embolism.
Moreover, as shown in Appendix Figure 1 and Appendix
Figure 2 (available at www.annals.org), clinical assessment
changed the diagnostic approach only in patients with a
high clinical probability and negative results on lower-limb
ultrasonography and thoracic CT, which constitute less
than 1% of patients.
The score should be clinically useful since it identifies
a large group of low-risk patients in whom the prevalence
of pulmonary embolism is less than 10% and who can
usually be managed entirely by noninvasive tests. Con-
versely, in the high-probability group with a proportion of
confirmed pulmonary embolism of more than 60%, our
score justifies the use of invasive tests to rule out pulmo-
nary embolism when less sensitive noninvasive test results
are negative. The proportion of patients who were classi-
fied by the score as having a low clinical probability is
lower than that in recent Canadian studies (1, 23), but this
is probably due to the higher prevalence of pulmonary
embolism (about 25%) in our sample. This does not have
a major bearing on clinical decision making, however,
since patients with a low or intermediate clinical probabil-
ity are managed in the same way in recent algorithms in-
cluding CT (2, 7), provided a highly sensitive D-dimer
Table 4. Proportion of Patients Classified by the Geneva Revised Score in Each Clinical Probability Category and Predictive
Accuracy of the Revised Geneva Score*
Derivation SetValidation Set
Patients, n (%)†
Patients with PE, n Patients with Confirmed PE
(95% CI), %
Patients, n (%)Patients with PE, nPatients with Confirmed PE
(95% CI), %
* PE ? pulmonary embolism.
† The percentages do not add up to 100% because of rounding.
Revised Geneva Score for PE
170 7 February 2006 Annals of Internal Medicine Volume 144 • Number 3
assay is used. Such assays have a sensitivity of approxi-
mately 97% and a specificity of approximately 40% (24).
Considering an average prevalence of pulmonary embolism
of 21% in patients with a low to intermediate probability
(Table 3), the post-test probability after a negative D-dimer
result would be only about 2%.
Nevertheless, although we collected the clinical data in
the validation sample prospectively, we calculated this new
score retrospectively. Hence, to be considered fully vali-
dated, the revised Geneva score should now be used pro-
spectively in a formal outcome study with patient follow-
up. In addition to verifying the score’s accuracy and
clinical usefulness, the outcome study should evaluate cli-
nicians’ adherence to it. In studies that used the previous
Geneva score (7), clinicians chose to override score assess-
ment by implicit judgment in 20% of the patients (15).
Whether this is inevitable when an entirely standardized
score is proposed for clinical decision making or whether
our revised score might be more acceptable to clinicians
remains to be demonstrated.
In summary, we developed a new prediction score for
emergency department outpatients with suspected pulmo-
nary embolism that is entirely standardized and is based on
simple clinical variables. This revised Geneva score is clin-
ically relevant, is easy to compute, and has sustained inter-
nal and external validation. It should now be tested for
clinical usefulness in a formal outcome study.
From Brest University Hospital, Brest, France; Geneva University Hos-
pital, Geneva, Switzerland; Angers University Hospital, Angers, France;
Ho ˆpital Europe ´en Georges-Pompidou, Paris, France; and Centre Hospi-
talier Universitaire Vaudois, Lausanne, Switzerland.
Grant Support: By the Hirsch Fund of the University of Geneva, the
Swiss National Research Foundation (grant 32-61773.00), the Royal
College of Physicians and Surgeons of Canada (grants 97/4-T10 and
00/4-T9), La Fondation Que ´be ´coise pour le Progre `s de la Me ´decine
Interne and Les Internistes et Rhumatologues Associe ´s de l’Ho ˆpital du
Sacre ´-Cœur, and the Direction of Clinical Research of the Angers Uni-
versity Hospital (grant 2001/021).
Potential Financial Conflicts of Interest: None disclosed.
Requests for Single Reprints: Gre ´goire Le Gal, MD, EA 3878, De ´par-
tement de Me ´decine Interne et Pneumologie, CHU de la Cavale
Blanche, 29609 Brest Cedex, France; e-mail, email@example.com.
Current author addresses and author contributions are available at www
1. Wells PS, Anderson DR, Rodger M, Stiell I, Dreyer JF, Barnes D, et al.
Excluding pulmonary embolism at the bedside without diagnostic imaging: man-
agement of patients with suspected pulmonary embolism presenting to the emer-
gency department by using a simple clinical model and d-dimer. Ann Intern
Med. 2001;135:98-107. [PMID: 11453709]
2. Musset D, Parent F, Meyer G, Maı ˆtre S, Girard P, Leroyer C, et al. Diag-
nostic strategy for patients with suspected pulmonary embolism: a prospective
multicentre outcome study. Lancet. 2002;360:1914-20. [PMID: 12493257]
3. Perrier A, Bounameaux H. Cost-effective diagnosis of deep vein thrombosis
and pulmonary embolism. Thromb Haemost. 2001;86:475-87. [PMID:
4. British Thoracic Society guidelines for the management of suspected acute
pulmonary embolism. Thorax. 2003;58:470-83. [PMID: 12775856]
5. Guidelines on diagnosis and management of acute pulmonary embolism. Task
Force on Pulmonary Embolism, European Society of Cardiology. Eur Heart J.
2000;21:1301-36. [PMID: 10952823]
6. Kruip MJ, Leclercq MG, van der Heul C, Prins MH, Bu ¨ller HR. Diagnostic
strategies for excluding pulmonary embolism in clinical outcome studies. A sys-
tematic review. Ann Intern Med. 2003;138:941-51. [PMID: 12809450]
7. Perrier A, Roy PM, Aujesky D, Chagnon I, Howarth N, Gourdier AL, et al.
Diagnosing pulmonary embolism in outpatients with clinical assessment,
D-dimer measurement, venous ultrasound, and helical computed tomography: a
multicenter management study. Am J Med. 2004;116:291-9. [PMID:
8. Perrier A, Desmarais S, Miron MJ, de Moerloose P, Lepage R, Slosman D,
et al. Non-invasive diagnosis of venous thromboembolism in outpatients. Lancet.
1999;353:190-5. [PMID: 9923874]
9. Kelly J, Hunt BJ. A clinical probability assessment and D-dimer measurement
should be the initial step in the investigation of suspected venous thromboembo-
lism. Chest. 2003;124:1116-9. [PMID: 12970044]
10. Perrier A, Nendaz MR, Sarasin FP, Howarth N, Bounameaux H. Cost-
effectiveness analysis of diagnostic strategies for suspected pulmonary embolism
including helical computed tomography. Am J Respir Crit Care Med. 2003;167:
39-44. [PMID: 12502474]
11. Value of the ventilation/perfusion scan in acute pulmonary embolism. Results
of the prospective investigation of pulmonary embolism diagnosis (PIOPED).
The PIOPED Investigators. JAMA. 1990;263:2753-9. [PMID: 2332918]
12. Perrier A, Bounameaux H, Morabia A, de Moerloose P, Slosman D, Didier
D, et al. Diagnosis of pulmonary embolism by a decision analysis-based strategy
including clinical probability, D-dimer levels, and ultrasonography: a manage-
ment study. Arch Intern Med. 1996;156:531-6. [PMID: 8604959]
13. Wells PS, Anderson DR, Rodger M, Ginsberg JS, Kearon C, Gent M, et al.
Derivation of a simple clinical model to categorize patients probability of pulmo-
nary embolism: increasing the models utility with the SimpliRED D-dimer.
Thromb Haemost. 2000;83:416-20. [PMID: 10744147]
14. Wicki J, Perneger TV, Junod AF, Bounameaux H, Perrier A. Assessing
clinical probability of pulmonary embolism in the emergency ward: a simple
score. Arch Intern Med. 2001;161:92-7. [PMID: 11146703]
15. Chagnon I, Bounameaux H, Aujesky D, Roy PM, Gourdier AL, Cornuz J,
et al. Comparison of two clinical prediction rules and implicit assessment among
patients with suspected pulmonary embolism. Am J Med. 2002;113:269-75.
16. Perrier A, Roy PM, Sanchez O, Le Gal G, Meyer G, Gourdier AL, et al.
Multidetector-row computed tomography in suspected pulmonary embolism. N
Engl J Med. 2005;352:1760-8. [PMID: 15858185]
17. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum
creatinine. Nephron. 1976;16:31-41. [PMID: 1244564]
18. Hersh AL, Stefanick ML, Stafford RS. National use of postmenopausal
hormone therapy: annual trends and response to recent evidence. JAMA. 2004;
291:47-53. [PMID: 14709575]
19. McNeil BJ, Hanley JA, Funkenstein HH, Wallman J. Paired receiver oper-
ating characteristic curves and the effect of history on radiographic interpretation.
CT of the head as a case study. Radiology. 1983;149:75-7. [PMID: 6611955]
20. Wasson JH, Sox HC. Clinical prediction rules. Have they come of age?
[Editorial] JAMA. 1996;275:641-2. [PMID: 8594248]
21. Wyatt JC, Altman DG. Commentary: prognostic models: clinically useful or
quickly forgotten? BMJ. 1995;311:1539-41.
22. Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules.
Applications and methodological standards. N Engl J Med. 1985;313:793-9.
23. Wells PS, Ginsberg JS, Anderson DR, Kearon C, Gent M, Turpie AG, et
al. Use of a clinical model for safe management of patients with suspected pul-
monary embolism. Ann Intern Med. 1998;129:997-1005. [PMID: 9867786]
24. Stein PD, Hull RD, Patel KC, Olson RE, Ghali WA, Brant R, et al.
D-dimer for the exclusion of acute venous thrombosis and pulmonary embolism:
a systematic review. Ann Intern Med. 2004;140:589-602. [PMID: 15096330]
Revised Geneva Score for PE
7 February 2006 Annals of Internal Medicine Volume 144 • Number 3 171
Current Author Addresses: Dr. Le Gal: EA 3878, De ´partement de
Me ´decine Interne et Pneumologie, CHU de la Cavale Blanche, 29609
Brest Cedex, France.
Drs. Righini, Bounameaux, and Perrier: Geneva University Hospital,
Rue Micheli du Crest 24, 1211 Geneva, Switzerland.
Dr. Roy: Emergency Service, CHU, 4 Rue Larrey, 49033 Angers,
Dr. Sanchez: Service of Pneumology, Ho ˆpital Europe ´en Georges-Pom-
pidou, 20 Rue Leblanc, 75015 Paris, France.
Dr. Aujesky: Centre Hospitalier Universitaire Vaudois, Rue du Bugnon
46, 1011 Lausanne, Switzerland.
Author Contributions: Conception and design: G. Le Gal, M. Righini,
H. Bounameaux, A. Perrier.
Analysis and interpretation of the data: G. Le Gal, M. Righini, A. Per-
Drafting of the article: G. Le Gal, A. Perrier.
Critical revision of the article for important intellectual content: M.
Righini, D. Aujesky, H. Bounameaux.
Final approval of the article: G. Le Gal, M. Righini, P.-M. Roy, O.
Sanchez, D. Aujesky, H. Bounameaux, A. Perrier.
Provision of study materials or patients: M. Righini, P.-M. Roy, O.
Sanchez, D. Aujesky.
Statistical expertise: G. Le Gal.
Obtaining of funding: H. Bounameaux.
Administrative, technical, or logistic support: O. Sanchez, H. Bou-
Collection and assembly of data: P.-M. Roy, O. Sanchez.
Annals of Internal Medicine
7 February 2006 Annals of Internal Medicine Volume 144 • Number 3 W-27
Appendix Figure 1. Diagnostic flow chart of the derivation set.
ELISA ? enzyme-linked immunosorbent assay; PE ? pulmonary embolism; Rx ? anticoagulant therapy; VTE ? venous thromboembolism. Adapted
from Perrier et al. (7), © 2004, with permission of the Massachusetts Medical Society.
W-28 7 February 2006 Annals of Internal Medicine Volume 144 • Number 3
Appendix Figure 2. Diagnostic flow chart of the validation set.
CT ? computed tomography; ELISA ? enzyme-linked immunosorbent assay; PE ? pulmonary embolism; Rx ? anticoagulant therapy; US ?
ultrasonography; V/Q ? ventilation–perfusion; VTE ? venous thromboembolism. Adapted from Perrier et al. (16), © 2005, with permission of the
Massachusetts Medical Society.
7 February 2006 Annals of Internal Medicine Volume 144 • Number 3 W-29