Download PDF
Thromb Haemost 2017; 117(11): 2176-2185
DOI: 10.1160/TH17-06-0395
Stroke, Systemic or Venous Thromboembolism
Schattauer GmbH Stuttgart

Clinical Decision Rules for Pulmonary Embolism in Hospitalized Patients: A Systematic Literature Review and Meta-analysis

Anne R. Bass
,
Kara G. Fields
,
Rie Goto
,
Gregory Turissini
,
Shirin Dey
,
Linda A. Russell
Further Information

Publication History

06 June 2017

10 August 2017

Publication Date:
30 November 2017 (online)

    Permissions and Reprints

Abstract

Background Clinical decision rules (CDRs) for pulmonary embolism (PE) have been validated in outpatients, but their performance in hospitalized patients is not well characterized.

Objectives The goal of this systematic literature review was to assess the performance of CDRs for PE in hospitalized patients.

Methods We performed a structured literature search using Medline, EMBASE and the Cochrane library for articles published on or before January 18, 2017. Two authors reviewed all titles, abstracts and full texts. We selected prospective studies of symptomatic hospitalized patients in which a CDR was used to estimate the likelihood of PE. The diagnosis of PE had to be confirmed using an accepted reference standard. Data on hospitalized patients were solicited from authors of studies in mixed populations of outpatients and hospitalized patients. Study characteristics, PE prevalence and CDR performance were extracted. The methodological quality of the studies was assessed using the QUADAS instrument.

Results Twelve studies encompassing 3,942 hospitalized patients were included. Studies varied in methodology (randomized controlled trials and observational studies) and reference standards used. The pooled sensitivity of the modified Wells rule (cut-off ≤ 4) in hospitalized patients was 72.1% (95% confidence interval [CI], 63.7–79.2) and the pooled specificity was 62.2% (95% CI, 52.6–70.9). The modified Wells rule (cut-off ≤ 4) plus D-dimer testing had a pooled sensitivity 99.7% (95% CI, 96.7–100) and pooled specificity 10.8% (95% CI, 6.7–16.9). The efficiency (proportion of patients stratified into the ‘PE unlikely’ group) was 8.4% (95% CI, 4.1–16.5), and the failure rate (proportion of low likelihood patients who were diagnosed with PE during follow-up) was 0.1% (95% CI, 0–5.3).

Conclusion In symptomatic hospitalized patients, use of the Wells rule plus D-dimer to rule out PE is safe, but allows very few patients to forgo imaging.

Keywords

pulmonary embolism - clinical decision support - inpatients - Wells rule - D-dimer

Supplementary Material

 

  • References

  • 1 Baglin TP, White K, Charles A. Fatal pulmonary embolism in hospitalised medical patients. J Clin Pathol 1997; 50 (07) 609-610
    CrossrefPubMedGoogle Scholar
  • 2 Sandler DA, Martin JF. Autopsy proven pulmonary embolism in hospital patients: are we detecting enough deep vein thrombosis?. J R Soc Med 1989; 82 (04) 203-205
    CrossrefPubMedGoogle Scholar
  • 3 Raymakers AJ, Mayo J, Marra CA, FitzGerald M. Diagnostic strategies incorporating computed tomography angiography for pulmonary embolism: a systematic review of cost-effectiveness analyses. J Thorac Imaging 2014; 29 (04) 209-216
    CrossrefPubMedGoogle Scholar
  • 4 Mitchell AM, Jones AE, Tumlin JA, Kline JA. Prospective study of the incidence of contrast-induced nephropathy among patients evaluated for pulmonary embolism by contrast-enhanced computed tomography. Acad Emerg Med 2012; 19 (06) 618-625
    CrossrefPubMedGoogle Scholar
  • 5 Turedi S, Erdem E, Karaca Y. , et al. The high risk of contrast induced nephropathy in patients with suspected pulmonary embolism despite three different prophylaxis: a randomized controlled trial. Acad Emerg Med 2016; 23 (10) 1136-1145
    CrossrefPubMedGoogle Scholar
  • 6 Einstein AJ, Henzlova MJ, Rajagopalan S. Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography. JAMA 2007; 298 (03) 317-323
    CrossrefPubMedGoogle Scholar
  • 7 Woo JK, Chiu RY, Thakur Y, Mayo JR. Risk-benefit analysis of pulmonary CT angiography in patients with suspected pulmonary embolus. AJR Am J Roentgenol 2012; 198 (06) 1332-1339
    CrossrefPubMedGoogle Scholar
  • 8 Carrier M, Righini M, Wells PS. , et al. Subsegmental pulmonary embolism diagnosed by computed tomography: incidence and clinical implications. A systematic review and meta-analysis of the management outcome studies. J Thromb Haemost 2010; 8 (08) 1716-1722
    CrossrefPubMedGoogle Scholar
  • 9 Donato AA, Khoche S, Santora J, Wagner B. Clinical outcomes in patients with isolated subsegmental pulmonary emboli diagnosed by multidetector CT pulmonary angiography. Thromb Res 2010; 126 (04) e266-e270
    CrossrefPubMedGoogle Scholar
  • 10 den Exter PL, van Es J, Klok FA. , et al. Risk profile and clinical outcome of symptomatic subsegmental acute pulmonary embolism. Blood 2013; 122 (07) 1144-1149 , quiz 1329
    CrossrefPubMedGoogle Scholar
  • 11 Wells PS, Anderson DR, Rodger M. , et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost 2000; 83 (03) 416-420
    Article in Thieme ConnectPubMedGoogle Scholar
  • 12 Wells PS, Anderson DR, Rodger M. , et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med 2001; 135 (02) 98-107
    CrossrefPubMedGoogle Scholar
  • 13 Lucassen W, Geersing GJ, Erkens PM. , et al. Clinical decision rules for excluding pulmonary embolism: a meta-analysis. Ann Intern Med 2011; 155 (07) 448-460
    CrossrefPubMedGoogle Scholar
  • 14 van Belle A, Büller HR, Huisman MV. , et al; Christopher Study Investigators. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. JAMA 2006; 295 (02) 172-179
    CrossrefPubMedGoogle Scholar
  • 15 Douma RA, Mos IC, Erkens PM. , et al; Prometheus Study Group. Performance of 4 clinical decision rules in the diagnostic management of acute pulmonary embolism: a prospective cohort study. Ann Intern Med 2011; 154 (11) 709-718
    CrossrefPubMedGoogle Scholar
  • 16 Dunne RM, Ip IK, Abbett S. , et al. Effect of evidence-based clinical decision support on the use and yield of CT pulmonary angiographic imaging in hospitalized patients. Radiology 2015; 276 (01) 167-174
    CrossrefPubMedGoogle Scholar
  • 17 Douma RA, Gibson NS, Gerdes VE. , et al. Validity and clinical utility of the simplified Wells rule for assessing clinical probability for the exclusion of pulmonary embolism. Thromb Haemost 2009; 101 (01) 197-200
    Article in Thieme ConnectPubMedGoogle Scholar
  • 18 Hogg K, Thomas D, Mackway-Jones K, Lecky F, Cruickshank K. Diagnosing pulmonary embolism: a comparison of clinical probability scores. Br J Haematol 2011; 153 (02) 253-258
    CrossrefPubMedGoogle Scholar
  • 19 Kline JA, Peterson CE, Steuerwald MT. Prospective evaluation of real-time use of the pulmonary embolism rule-out criteria in an academic emergency department. Acad Emerg Med 2010; 17 (09) 1016-1019
    CrossrefPubMedGoogle Scholar
  • 20 Hendriksen JM, Geersing GJ, Lucassen WA. , et al. Diagnostic prediction models for suspected pulmonary embolism: systematic review and independent external validation in primary care. BMJ 2015; 351: h4438
    PubMedGoogle Scholar
  • 21 Cohen AT, Spiro TE, Spyropoulos AC. , et al; MAGELLAN Study Group. D-dimer as a predictor of venous thromboembolism in acutely ill, hospitalized patients: a subanalysis of the randomized controlled MAGELLAN trial. J Thromb Haemost 2014; 12 (04) 479-487
    CrossrefPubMedGoogle Scholar
  • 22 Dindo D, Breitenstein S, Hahnloser D. , et al. Kinetics of D-dimer after general surgery. Blood Coagul Fibrinolysis 2009; 20 (05) 347-352
    CrossrefPubMedGoogle Scholar
  • 23 Sugimoto E, Kuroda T, Fujita Y, Namba Y, Mitani S. D-dimer testing cannot rule out thromboembolism after major lower extremity arthroplasties and thromboprophylaxis treatment. J Anesth 2015; 29 (05) 686-689
    CrossrefPubMedGoogle Scholar
  • 24 PRISMA. Available at: http://www.prisma-statement.org/Default.aspx accessed July 9, 2016
    PubMed
  • 25 PROSPERO. Available at: http://www.crd.york.ac.uk/PROSPERO/ accessed July 9, 2016
    PubMed
  • 26 Anderson DR, Barnes D. The use of leg venous ultrasonography for the diagnosis of pulmonary embolism. Semin Nucl Med 2008; 38 (06) 412-417
    CrossrefPubMedGoogle Scholar
  • 27 Whiting PF, Rutjes AW, Westwood ME. , et al; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155 (08) 529-536
    CrossrefPubMedGoogle Scholar
  • 28 Chu H, Cole SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 2006; 59 (12) 1331-1332 , author reply 1332–1333
    CrossrefPubMedGoogle Scholar
  • 29 Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 2001; 20 (19) 2865-2884
    CrossrefPubMedGoogle Scholar
  • 30 Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med 2003; 22 (17) 2693-2710
    CrossrefPubMedGoogle Scholar
  • 31 Bates SM, Takach Lapner S, Douketis JD. , et al. Rapid quantitative D-dimer to exclude pulmonary embolism: a prospective cohort management study. J Thromb Haemost 2016; 14 (03) 504-509
    CrossrefPubMedGoogle Scholar
  • 32 Kruip MJ, Söhne M, Nijkeuter M. , et al; Christopher Study Investigators. A simple diagnostic strategy in hospitalized patients with clinically suspected pulmonary embolism. J Intern Med 2006; 260 (05) 459-466
    CrossrefPubMedGoogle Scholar
  • 33 Mos IC, Douma RA, Erkens PM. , et al; Prometheus Study Group. Diagnostic outcome management study in patients with clinically suspected recurrent acute pulmonary embolism with a structured algorithm. Thromb Res 2014; 133 (06) 1039-1044
    CrossrefPubMedGoogle Scholar
  • 34 Kearon C, Ginsberg JS, Douketis J. , et al; Canadian Pulmonary Embolism Diagnosis Study (CANPEDS) Group. An evaluation of D-dimer in the diagnosis of pulmonary embolism: a randomized trial. Ann Intern Med 2006; 144 (11) 812-821
    CrossrefPubMedGoogle Scholar
  • 35 Rodger MA, Bredeson CN, Jones G. , et al. The bedside investigation of pulmonary embolism diagnosis study: a double-blind randomized controlled trial comparing combinations of 3 bedside tests vs ventilation-perfusion scan for the initial investigation of suspected pulmonary embolism. Arch Intern Med 2006; 166 (02) 181-187
    CrossrefPubMedGoogle Scholar
  • 36 Kline JA, Hogg MM, Courtney DM, Miller CD, Jones AE, Smithline HA. D-dimer threshold increase with pretest probability unlikely for pulmonary embolism to decrease unnecessary computerized tomographic pulmonary angiography. J Thromb Haemost 2012; 10 (04) 572-581
    CrossrefPubMedGoogle Scholar
  • 37 Posadas-Martínez ML, Vázquez FJ, Giunta DH, Waisman GD, de Quirós FG, Gándara E. Performance of the Wells score in patients with suspected pulmonary embolism during hospitalization: a delayed-type cross sectional study in a community hospital. Thromb Res 2014; 133 (02) 177-181
    CrossrefPubMedGoogle Scholar
  • 38 Stein PD, Fowler SE, Goodman LR. , et al; PIOPED II Investigators. Multidetector computed tomography for acute pulmonary embolism. N Engl J Med 2006; 354 (22) 2317-2327
    CrossrefPubMedGoogle Scholar
  • 39 Miniati M, Monti S, Bottai M. A structured clinical model for predicting the probability of pulmonary embolism. Am J Med 2003; 114 (03) 173-179
    CrossrefPubMedGoogle Scholar
  • 40 Miniati M, Bottai M, Monti S. Comparison of 3 clinical models for predicting the probability of pulmonary embolism. Medicine (Baltimore) 2005; 84 (02) 107-114
    CrossrefPubMedGoogle Scholar
  • 41 Miniati M, Bottai M, Monti S, Salvadori M, Serasini L, Passera M. Simple and accurate prediction of the clinical probability of pulmonary embolism. Am J Respir Crit Care Med 2008; 178 (03) 290-294
    CrossrefPubMedGoogle Scholar
  • 42 van Es N, van der Hulle T, van Es J. , et al. Wells Rule and d-Dimer Testing to Rule Out Pulmonary Embolism: A Systematic Review and Individual-Patient Data Meta-analysis. Ann Intern Med 2016; 165 (04) 253-261
    CrossrefPubMedGoogle Scholar
  • 43 PISA 1 calculator. Available at: https://www.ifc.cnr.it/pisamodel/pisamodel1/calcolo.html accessed July 9, 2016
    PubMed
  • 44 PISA 2 calculator . Available at: https://www.ifc.cnr.it/pisamodel/pisamodel2/calcolo2.html accessed July 9, 2016
    PubMed
  • 45 Leblanc M, Paul N. V/Q SPECT and computed tomographic pulmonary angiography. Semin Nucl Med 2010; 40 (06) 426-441
    CrossrefPubMedGoogle Scholar
  • 46 Karami-Djurabi R, Klok FA, Kooiman J, Velthuis SI, Nijkeuter M, Huisman MV. D-dimer testing in patients with suspected pulmonary embolism and impaired renal function. Am J Med 2009; 122 (11) 1050-1053
    CrossrefPubMedGoogle Scholar
  • 47 Schouten HJ, Geersing GJ, Koek HL. , et al. Diagnostic accuracy of conventional or age adjusted D-dimer cut-off values in older patients with suspected venous thromboembolism: systematic review and meta-analysis. BMJ 2013; 346: f2492
    CrossrefPubMedGoogle Scholar
  • 48 Kassim NA, Farid TM, Pessar SA, Shawkat SA. Performance evaluation of different d-dimer cutoffs in bedridden hospitalized elderly patients. Clin Appl Thromb Hemost 2016; 1076029616665165
    PubMedGoogle Scholar
  • 49 van der Hulle T, Cheung WY, Kooij S. , et al; YEARS study group. Simplified diagnostic management of suspected pulmonary embolism (the YEARS study): a prospective, multicentre, cohort study. Lancet 2017; 390 (10091): 289-297
    CrossrefPubMedGoogle Scholar
  • 50 Kline JA, Hogg MM, Courtney DM. , et al. D-dimer and exhaled CO2/O2 to detect segmental pulmonary embolism in moderate-risk patients. Am J Respir Crit Care Med 2010; 182 (05) 669-675
    CrossrefPubMedGoogle Scholar
  • 51 Ramme AJ, Iturrate E, Dweck E. , et al. End tidal carbon dioxide as a screening tool for computed tomography angiogram in postoperative orthopaedic patients suspected of pulmonary embolism. J Arthroplasty 2016; 31 (10) 2348-2352
    CrossrefPubMedGoogle Scholar
  • 52 Langhan ML, Li FY, Lichtor JL. Respiratory depression detected by capnography among children in the postanesthesia care unit: a cross-sectional study. Paediatr Anaesth 2016; 26 (10) 1010-1017
    CrossrefPubMedGoogle Scholar
  • 53 Agency for Healthcare Research and Quality. Postoperative pulmonary embolism or deep vein thrombosis: rate per 1,000 surgical discharges with an operating room procedure. Available at: https://www.qualitymeasures.ahrq.gov/summaries/summary/15381/postoperative-pulmonary-embolism-or-deep-vein-thrombosis-rate-per-1000-surgical-discharges-with-an-operating-room-procedure accessed October 2, 2016
    PubMedGoogle Scholar
  • 54 Konstantinides SV, Barco S, Lankeit M, Meyer G. Management of pulmonary embolism: an update. J Am Coll Cardiol 2016; 67 (08) 976-990
    PubMedGoogle Scholar
  • 55 Ghazvinian R, Gottsäter A, Elf J. Is it safe to withhold long-term anticoagulation therapy in patients with small pulmonary emboli diagnosed by SPECT scintigraphy?. Thromb J 2016; 14: 12-16
    CrossrefPubMedGoogle Scholar
  • 56 Voora D, Eby C, Linder MW. , et al. Prospective dosing of warfarin based on cytochrome P-450 2C9 genotype. Thromb Haemost 2005; 93 (04) 700-705
    Article in Thieme ConnectPubMedGoogle Scholar