The value of clinical features in the diagnosis of acute pulmonary embolism: systematic review and meta-analysis

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1 Q J Med 2007; 100: doi: /qjmed/hcm113 The value of clinical features in the diagnosis of acute pulmonary embolism: systematic review and meta-analysis J. WEST 1, S. GOODACRE 2 and F. SAMPSON 2 From the 1 Emergency Department, Sheffield Teaching Hospitals Trust and 2 Medical Care Research Unit, University of Sheffield, Sheffield, UK Received 31 August 2007 and in revised form 5 November 2007 Summary Background: Clinical assessment of patients with suspected pulmonary embolus (PE) is used to estimate the probability of PE and determine what (if any) diagnostic testing is required. Aim: We aimed to estimate the diagnostic value of individual clinical features used to determine the pre-test probability of acute PE. Design: Systematic review and meta-analysis. Methods: We searched electronic databases (1966 to May 2007) and the bibliographies of retrieved articles for any article that reported the diagnostic performance of clinical features compared to a diagnostic test in patients with suspected acute pulmonary embolism. s were calculated for each feature and pooled using a random effects model, as implemented by MetaDiSc statistical software. Introduction Suspected acute pulmonary embolism (PE) is a common cause for acute hospital attendance and admission. Clinical assessment is necessary to estimate a pre-test probability of PE and determine what (if any) diagnostic testing is required. Clinical assessment may be used in an unstructured manner to generate a pre-test estimate of probability or may be used in a formal clinical probability score to categorize patients into (typically) low, intermediate or Results: We identified 18 studies for inclusion with a total of 5997 patients. The most useful features (pooled likelihood ) for ruling in PE were syncope (2.38), shock (4.07), thrombophlebitis (2.20), current DVT (2.05), leg swelling (2.11), sudden dyspnoea (1.83), active cancer (1.74), recent surgery (1.63), haemoptysis (1.62) and leg pain (1.60); while the most useful features for ruling out PE were the absence of sudden dyspnoea (0.430), any dyspnoea (0.521) and tachypnea (0.561). All other clinical features had likelihood s near to one. Many of the analyses involved pooling results that had significant, so these estimates should be used with caution. Conclusions: Individual clinical features only slightly raise or lower the probability of PE. In isolation, they have limited diagnostic value and none can be used to rule in or rule out PE without further testing. high-risk groups. 1 In the former approach, a likelihood can provide an estimate of the diagnostic value of each clinical feature and help estimate the clinical probability of PE in a Bayesian manner. 2 In the latter each valuable clinical feature can be ascribed a score, or weight, that contributes to the overall scoring, or categorization, of clinical probability. Whichever approach is taken, we need to know how diagnostically useful each element of clinical Address correspondence to Professor Steve Goodacre, Medical Care Research Unit, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, UK. s.goodacre@sheffield.ac.uk! The Author Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please journals.permissions@oxfordjournals.org

2 764 J. West et al. assessment is. Although there have been many studies examining the diagnostic value of individual clinical features, and meta-analyses have examined clinical probability scores, 1,3 we are not aware of any studies that have systematically synthesized data to provide an overall estimate of the diagnostic value of each clinical feature used to diagnose PE. It is recognized that diagnostic decision-making should be based upon systematic identification, selection and synthesis of the available literature. 4 We aimed to systematically review the literature and synthesize existing data to estimate the value of individual clinical features in the diagnosis of acute PE. Methods We undertook a systematic review and metaanalysis of diagnostic cohort studies evaluating the value of clinical features for PE in patients presenting with suspected acute PE. Search strategy We searched the following electronic databases (1966 to May 2007): Medline, EMBASE, NAHL, Web of Science, Cochrane Database of Systematic Reviews, Cochrane Controlled Trials Register and Database of Reviews of Effectiveness. The bibliographies of all retrieved articles were scanned for potentially relevant articles that were not identified by the original search. Selection of studies for inclusion Two reviewers (J.W. and F.S.) screened the titles and abstracts of all articles identified by the search strategy and independently determined whether the article could potentially be reporting a cohort study that measured the diagnostic value of clinical features or clinical score compared to a test (pulmonary angiography, lung perfusion scanning (with or without ventilation scanning), CT pulmonary angiography, lower limb ultrasound or D-dimer), although for this study we only ultimately analysed those reporting clinical features. Full copies of all selected articles were retrieved. Two further reviewers (J.W. and S.G.) then independently reviewed the full articles to determine whether they really did meet the criteria outlined above. A Kappa score was calculated for agreement between the two reviewers and disagreements were resolved by discussion. We specifically excluded studies that measured the risk of developing PE after recording clinical characteristics, rather than the probability of PE being present at the time of assessment; case control studies, in which patients were selected on the basis of having, or not having, PE; and studies with less than ten patients. We included studies published in English, French and Spanish, but excluded studies published in other languages. Assessment of study quality We assessed study quality by determining whether application of the was independent of the findings of clinical assessment, whether observers who were blind to the results undertook clinical assessment, and whether observers who were blind to the results of clinical assessment interpreted the. Empirical evidence suggests that failure to meet these criteria is associated with over-estimation of diagnostic accuracy. 5 Data extraction We extracted the following data from each article: the setting for recruitment, groups excluded from the study, population characteristics (mean/median age, gender balance), prevalence of PE, whether clinical data were extracted from clinical notes or collected on a ized proforma by the clinician, who recorded the clinical data, the used, and the number of true positives, true negatives, false positives and false negatives for each clinical feature (either as reported or calculated from the reported data). Data analysis We used a random effects model, as implemented by MetaDiSc statistical software, 6 to estimate pooled likelihood s for the presence and absence of each clinical feature. A Chi-square test for is reported for each clinical feature. Results The flow of articles selected for review is shown in Figure 1. We identified 46 studies examining clinical diagnosis of PE, 28 of these only reported the results of composite clinical scores, leaving 18 that reported individual clinical features The characteristics of the studies included in the review are shown in Table 1. The 18 studies included a total 5997 patients with suspected pulmonary embolism (median sample size 171, range ), the majority presenting to an emergency department. The prevalence of PE ranged from 9% to 55% (median 38%). The mean age of each study cohort ranged from 44 to 69 years (median 57) and the proportion of male subjects ranged from 30% to 59% (median 41%).

3 Diagnosis of pulmonary embolism 765 Potentially relevant studies identified and screened for retrieval, n=2328 Studies retrieved for more detailed evaluation, n=104 (Kappa=0.60) Plus 8 additional studies identified from lists Potentially suitable studies to be included in the metaanalysis, n=46 Studies reporting clinical features included in the metaanalysis, n=18 In most studies, the was applied independently of the results of clinical assessment. The exceptions were studies that augmented a D-dimer with further testing based on clinical probability. Clinical assessments were blinded in the majority of cases, but reporting of the blinding of the was generally poor. Each study reported only a selection of the clinical features. The last column of Table 1 shows which clinical features were reported by each study. The codes for clinical features are outlined in Tables 2 and 3. A few studies reported clinical features in such a way that the relevant data could not be extracted, for example reporting means for continuous data such as tachycardia. Tables 2 and 3 show the summary likelihood s for individual clinical features, along with a confidence interval and a. The most useful features (likelihood ) for ruling in PE are syncope (2.38), shock (4.07), thrombophlebitis (2.20), current DVT (2.05), leg swelling (2.11), sudden dyspnoea (1.83), active cancer (1.74), surgery (1.63), haemoptysis (1.62) and leg pain (1.60); while the most useful features for ruling out PE are the absence of sudden dyspnoea (0.430), any dyspnoea (0.521) and tachypnea (0.561). Discussion Studies excluded, n=2224 Studies excluded from the meta-analysis: Duplicated data, n=16 Unable to extract data, n=23 Retrospective, n=8 Others, n=9 Studies of clinical scores not reporting individual clinical features, n=28 Figure 1. Flow diagram of studies considered for the review. Clinical assessment should be based upon systematic identification, selection and synthesis of the available literature. 4 To our knowledge this is the first published meta-analysis of key features of the clinical history and examination used to diagnose PE. We have shown that a history of syncope, thrombophlebitis, current DVT, leg swelling, sudden dyspnoea, active cancer, surgery, haemoptysis or leg pain each slightly increase the likelihood of PE, as does shock on examination. The absence of dyspnoea or tachypnea each slightly reduces the probability of PE. All these features have likelihood s relatively near to one, so no clinical feature can be used in isolation to rule in or rule out PE. It is also apparent that a number of clinical features that may commonly be used to diagnose PE, such as pleuritic chest pain does not appear to be supported by the pooled data presented here. We did not include studies of composite clinical probability scores or studies of overall unstructured clinical estimates of PE in our analysis. Chunilal et al. 1 recently examined these studies and found that clinical probability scores and unstructured probability estimates categorized patients into high, medium and low risk of PE with similar accuracy. High probability estimates by clinical probability score were associated with likelihood s ranging from 8.6 to 66 in derivation studies and from 1.4 to 29 in validation studies, while unstructured high probability estimates were associated with likelihood s ranging from 1.9 to 12. Low probability estimates by clinical probability score were associated with likelihood s ranging from 0.05 to 0.31 in derivation studies and from 0.19 to 0.93 in validation studies, while likelihood s for unstructured low probability estimates ranged from 0.13 to Derivation studies of clinical scores are likely to overestimate diagnostic accuracy so it seems reasonable to conclude that clinical scores and overall unstructured estimates provide more useful diagnostic assessment than isolated clinical features but are not sufficiently accurate to rule in or rule out PE without further testing. The clinical probability scores assessed by Chunilal were the Wells score, 25 the Geneva score 26 and the PISA PED score. 27 There is no clear difference in diagnostic performance between the three scores but the Wells score has been more extensively validated. The characteristics used in the scores reflect, to a certain extent, the findings of our analysis. The simplified Wells score uses features of DVT (likelihood 2.05), recent surgery (1.63), haemoptysis (1.62) and cancer (1.74). It also uses tachycardia and past history of DVT/PE, neither of which appeared to be diagnostically useful in our analysis. The Geneva score uses previous DVT/PE, recent surgery (replaced by malignancy in the Modified Geneva score) and tachycardia, alongside age, chest radiograph and arterial blood gas results. The PISA PED score uses sudden dyspnoea (1.83),

4 Table 1 Characteristics of the study cohorts First author Year N % with PE Mean age % male Setting Reference Independent Hildner FJ NS NS IP Isotope lung scan and PA clinical assessment Features assessed (see Tables 2 and 3 for codes) Yes Yes Unclear H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, H12, H14, H17, H21, E1, E2, E3, E4, E6 Guidotti TL ED V/Q scan and PA Yes Unclear Unclear H1, H2, H3, H5, H6, H7, H8, H9, H12, H14, H15, H19, H20, H21, E1, E2, E4 Hull RD ED Venography, impedence plethysmography, V/Q scan and PA Yes Yes Yes H1, H2, H4, H5, H6, H7, H11, H14, H18, H19, E2, E5 Celi A ED þ IP LPS Yes Yes Yes H1, H4, H11, H21, H23, E3 Gray HW NS V/Q scan and PA Yes Yes Yes H1, H2, H3, H5, H7, H15, H20 Nazeyrollas P OP V/Q scan and PA Yes Unclear Unclear H1, H2, H5, H7, H13, H17 Susec O ED V/Q scan and PA Yes Yes Unclear H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, H11, H16, H20, H22, H23, H24 Grifoni S ED LPS and PA Yes Yes Yes H1, H2, H3, H4, H5, H6, H11, H10, H13, H14, H15, H16, H18, H19, E6 Leveau P ED CUS, phlebography, LPS and PA Yes Yes Unclear H1, H2, H7, H9, H11, H12, H13, E5 Stollberger C IP LPS and PA No Unclear Unclear H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, H13, E1, E2, E4, E5, E6 Wells PS ED D-dimer, CUS, V/Q No Yes Yes H3, H4, H6 scan and PA Miniati M IP LPS and PA Yes Yes Yes H3, H4, H6, H8, H10, H12, H18, H22 Kline JA ED CUS, venogram, V/Q scan, CT and PA Yes Unclear Unclear H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, H11, H16, H17, H19, H20, H23, H24 Miniati M IP þ other LPS and PA Yes Yes Yes H3, H4, H6, H8, H10, H11, H12, H J. West et al. (continued)

5 Diagnosis of pulmonary embolism 767 Table 1 Continued Features assessed (see Tables 2 and 3 for codes) clinical assessment First author Year N % with PE Mean age % male Setting Reference Independent Miniati M NS 45.3 NS LPS and PA Yes Yes Unclear H1, H2, H3, H4, H6, H7, H8, H12, H15, H17, H18, H22, H24, E1, E3 Miniati M NS 45.6 IP LPS and PA Yes Yes Yes H1, H2, H3, H4, H7, H8, H10, H12, H13, E1, E3 No Yes Yes H3, H4, H5, H6 Anderson DR ED D-dimer, CUS, CTPA and PA Hatipoglu O NS LPS Yes Yes Unclear H1, H2, H7, H17 NS ¼ not stated; ED ¼ emergency department; IP ¼ inpatient; OP ¼ outpatient; LPS ¼ lung perfusion scan; V/Q scan ¼ ventilation-perfusion scan; PA ¼ pulmonary angiography; CUS ¼ compression ultrasound (lower limb). chest pain (not useful), fever (not useful) and syncope (2.38), alongside age, gender previous cardiovascular, respiratory disease or thrombolphlebitis, electrocardiogram and chest radiograph. The inconsistency among these scores, and between these scores and our findings, suggest that an optimal score has yet to be identified or that characteristics may vary between settings. Some limitations in our analysis need to be appreciated before using these likelihood s in clinical practice. We did not search for unpublished data and studies in languages other than English, French and Spanish were excluded. However, it is unlikely that the poor diagnostic performance of individual features can be attributed to either publication or language bias. A more salient issue is that the primary studies included in our analysis may have used certain clinical features to select patients for inclusion. For example, if patients with pleuritic chest pain or previous venous thromboembolism were preferentially included in the studies over those with non-pleuritic chest pain or no previous venous thromboembolism then this may explain why these features did not appear to be diagnostically useful. Diagnostic studies of patients with suspected PE can only tell us what clinical features increase or decrease the likelihood of PE once PE has been suspected, they cannot tell us whether to suspect PE in the first place. Another important limitation is that many of our analyses were subject to substantial. This is a common feature of meta-analysis of diagnostic data and there is some controversy as to whether data should be synthesized in the presence of significant. We have chosen to synthesize data because an overall summary estimate provides clinicians with a practical, usable best estimate. However, these summary estimates should be used with caution. The observed is most likely to be due to differences in population characteristics or clinician interpretation of features, so in specific circumstances a clinical feature may be more, or less, useful than our likelihood s suggest. Research is required to identify the causes of in estimates of the diagnostic value of clinical features. In particular, are there specific patient groups in which certain clinical features are more, or less, useful? In conclusion, this meta-analysis has shown that individual clinical features have limited value in the diagnosis of PE. The presence of syncope, current DVT, leg swelling, sudden dyspnoea, active cancer, surgery, haemoptysis, leg pain or shock each slightly increase the probability of PE, while the absence of dyspnoea or tachypnea each slightly reduce the probability of PE.

6 768 J. West et al. Table 2 Feature Summary likelihood s for features from clinical history Code Number of studies Feature present Feature absent Chest pain H < <0.001 Dyspnoea H < <0.001 Immobilization H Surgery H < Active cancer H < <0.001 Haemoptysis H Past history of venous H < thromboembolism Oestrogen therapy H Syncope H <0.001 Trauma H Cough H Thrombophlebitis H < <0.001 Current DVT H < Leg swelling H Congestive heart H failure Sudden dyspnoea H < Smoking H Cardiovascular H disease Chronic pulmonary H disease Pleuritic chest pain H Leg pain H Pulmonary disease H Cardiac disease H Pregnancy H <0.001 Table 3 Summary likelihood s for features from clinical examination Feature Code Number of studies Feature present Feature absent Tachycardia E Cyanosis E Fever E < <0.001 Pulmonary rales E Tachypnoea E Shock E Acknowledgements We thank the Information Resources staff in the University of Sheffield School of Health for their help with literature searches. References 1. Chunilal SD, Eikelboom JW, Attia J, Miniati M, Panju AA, Simel DL, et al. Does this patient have pulmonary embolism? JAMA 2003; 290:

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