Thromb Haemost 2008; 99(01): 229-234
DOI: 10.1160/TH07-05-0321
New Technologies, Diagnostic Tools and Drugs
Schattauer GmbH

Further validation and simplification of the Wells clinical decision rule in pulmonary embolism

Nadine S Gibson
1   Department of Vascular Medicine, Academical Medical Center, Amsterdam, the Netherlands
,
Maaike Sohne
1   Department of Vascular Medicine, Academical Medical Center, Amsterdam, the Netherlands
,
Marieke J. H. A Kruip
2   Department of Hematology, Erasmus Medical Center, Rotterdam, the Netherlands
,
Lidwine W Tick
3   Department of Internal Medicine, Meander Medical Center, Amersfoort, the Netherlands
,
Victor E Gerdes
1   Department of Vascular Medicine, Academical Medical Center, Amsterdam, the Netherlands
,
Patrick M Bossuyt
4   Department of Clinical Epidemiology & Biostatistics, Academical Medical Center, Amsterdam, the Netherlands
,
Philip S Wells
5   Department of Medicine, University of Ottawa, Ottawa Hospital & Ottawa Health Research Unit, Ottawa, Ontario, Canada
,
Harry R Buller
1   Department of Vascular Medicine, Academical Medical Center, Amsterdam, the Netherlands
,
the Christopher study investigators › Author Affiliations
Further Information

Correspondence to:

Nadine Gibson, MD
Department of Vascular Medicine
Academical Medical Center
Meibergdreef 9
1105 AZ Amsterdam
Phone: +31 20 566 7516   
Fax: +31 20 696 8833   

Publication History

Received: 03 May 2007

Accepted after major revision: 12 November 2007

Publication Date:
24 November 2017 (online)

 

Summary

The Wells rule is a widely applied clinical decision rule in the diagnostic work-up of patients with suspected pulmonary embolism (PE).The objective of this study was to replicate, validate and possibly simplify this rule. We used data collected in 3,306 consecutive patients with clinically suspected PE to recalculate the odds ratios for the variables in the rule, to calculate the proportion of patients with PE in the probability categories, the area under the ROC curve and the incidence of venous thromboembolism during follow-up. We compared these measures with those for a modified and a simplified version of the decision rule. In the replication, the odds ratios in the logistic regression model were found to be lower for each of the seven individual variables (p=0.02) but the proportion of patients with PE in the probability categories in our study group were comparable to those in the original derivation and validation groups. The area under the ROC of the original, modified and simplified decision rule was similar: 0.74 (p=0.99; p=0.07).The venous thromboembolism incidence at three months in the group of patients with a Wells score ≤ 4 and a normal D-dimer was 0.5%, versus 0.3% with a modified rule and 0.5% with a simplified rule. The proportion of patients safely excluded for PE was 32%, versus 31% and 30%, respectively. This study further validates the diagnostic utility of theWells rule and indicates that the scoring system can be simplified to one point for each variable.


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  • References

  • 1 Celi A, Palla A, Petruzzelli S. et al. Prospective study of a standardized questionnaire to improve clinical estimate of pulmonary embolism. Chest 1989; 95: 332-337.
  • 2 Hoellerich VL, Wigton RS. Diagnosing pulmonary embolism using clinical findings. Arch Intern Med 1986; 146: 1699-1704.
  • 3 Michel BC, Kuijer PM, McDonnell J. et al. The role of a decision rule in symptomatic pulmonary embolism patients with a non-high probability ventilation-perfusion scan. Thromb Haemost 1997; 78: 794-798.
  • 4 Petruzzelli S, Palla A, Citi M. et al. Improvement of screening for pulmonary embolism with a standardized questionnaire. Respiration 1990; 57: 329-337.
  • 5 Miniati M, Prediletto R, Formichi B. et al. Accuracy of clinical assessment in the diagnosis of pulmonary embolism. Am J Respir Crit Care Med 1999; 159: 864-781.
  • 6 ten Wolde M, Hagen PJ, Macgillavry MR. et al. Non-invasive diagnostic work-up of patients with clinically suspected pulmonary embolism; results of a management study. J Thromb Haemost 2004; 2: 1110-1117.
  • 7 Sanson BJ, Lijmer JG, Mac Gillavry MR. et al. Comparison of a clinical probability estimate and two clinical models in patients with suspected pulmonary embolism. ANTELOPE-Study Group. Thromb Haemost 2000; 83: 199-203.
  • 8 Kabrhel C, Camargo Jr. CA, Goldhaber SZ. Clinical gestalt and the diagnosis of pulmonary embolism: does experience matter?. Chest 2005; 127: 1627-1630.
  • 9 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: 416-420.
  • 10 Chunilal SD, Eikelboom JW, Attia J. et al. Does this patient have pulmonary embolism?. J Am Med Assoc 2003; 290: 2849-2858.
  • 11 Righini M, Aujesky D, Roy PM. et al. Clinical usefulness of D-dimer depending on clinical probability and cutoff value in outpatients with suspected pulmonary embolism. Arch Intern Med 2004; 164: 2483-2487.
  • 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: 98-107.
  • 13 Wolf SJ, McCubbin TR, Feldhaus KM. et al. Prospective validation of Wells Criteria in the evaluation of patients with suspected pulmonary embolism. Ann Emerg Med 2004; 44: 503-510.
  • 14 Clinical policy: critical issues in the evaluation and management of adult patients presenting with suspected pulmonary embolism. Ann Emerg Med 2003; 41: 257-270.
  • 15 British Thoracic Society guidelines for the management of suspected acute pulmonary embolism. Thorax 2003; 58: 470-483.
  • 16 Charlson ME, Ales KL, Simon R. et al. Why predictive indexes perform less well in validation studies. Is it magic or methods? Arch Intern Med 1987; 147: 2155-2161.
  • 17 Copas JB. Using regression models for prediction: shrinkage and regression to the mean. Stat Methods Med Res 1997; 6: 167-183.
  • 18 Writing Group for the Christopher Study Investigators. Effectiveness of Managing Suspected Pulmonary Embolism Using an Algorithm Combining Clinical Probability, D-Dimer Testing, and Computed Tomography. J Am Med Assoc 2006; 295: 172-179.
  • 19 Metz CE, Herman BA, Roe CA. Statistical comparison of two ROC-curve estimates obtained from partially- paired datasets. Med Decis Making 1998; 18: 110-121.
  • 20 Dawes RM. The robust beauty of improper linear models in decision making. Am Psychol 1979; 34: 571-582.
  • 21 Wicki J, Perneger TV, Junod AF. et al. Assessing clinical probability of pulmonary embolism in the emergency ward: a simple score. Arch Intern Med 2001; 161: 92-97.
  • 22 Patil S, Henry JW, Rubenfire M. et al. Neural network in the clinical diagnosis of acute pulmonary embolism. Chest 1993; 104: 1685-1689.
  • 23 Wyatt J. Nervous about artificial neural networks?. Lancet 1995; 346: 1175-1177.
  • 24 Wyatt JC, Altman DG. Commentary: Prognostic models: clinically useful or quickly forgotten?. Br Med J 1995; 311: 1539-1541.
  • 25 Kruip MJ, Slob MJ, Schijen JH. et al. Use of a clinical decision rule in combination with D-dimer concentration in diagnostic workup of patients with suspected pulmonary embolism: a prospective management study. Arch Intern Med 2002; 162: 1631-1635.

Correspondence to:

Nadine Gibson, MD
Department of Vascular Medicine
Academical Medical Center
Meibergdreef 9
1105 AZ Amsterdam
Phone: +31 20 566 7516   
Fax: +31 20 696 8833   

  • References

  • 1 Celi A, Palla A, Petruzzelli S. et al. Prospective study of a standardized questionnaire to improve clinical estimate of pulmonary embolism. Chest 1989; 95: 332-337.
  • 2 Hoellerich VL, Wigton RS. Diagnosing pulmonary embolism using clinical findings. Arch Intern Med 1986; 146: 1699-1704.
  • 3 Michel BC, Kuijer PM, McDonnell J. et al. The role of a decision rule in symptomatic pulmonary embolism patients with a non-high probability ventilation-perfusion scan. Thromb Haemost 1997; 78: 794-798.
  • 4 Petruzzelli S, Palla A, Citi M. et al. Improvement of screening for pulmonary embolism with a standardized questionnaire. Respiration 1990; 57: 329-337.
  • 5 Miniati M, Prediletto R, Formichi B. et al. Accuracy of clinical assessment in the diagnosis of pulmonary embolism. Am J Respir Crit Care Med 1999; 159: 864-781.
  • 6 ten Wolde M, Hagen PJ, Macgillavry MR. et al. Non-invasive diagnostic work-up of patients with clinically suspected pulmonary embolism; results of a management study. J Thromb Haemost 2004; 2: 1110-1117.
  • 7 Sanson BJ, Lijmer JG, Mac Gillavry MR. et al. Comparison of a clinical probability estimate and two clinical models in patients with suspected pulmonary embolism. ANTELOPE-Study Group. Thromb Haemost 2000; 83: 199-203.
  • 8 Kabrhel C, Camargo Jr. CA, Goldhaber SZ. Clinical gestalt and the diagnosis of pulmonary embolism: does experience matter?. Chest 2005; 127: 1627-1630.
  • 9 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: 416-420.
  • 10 Chunilal SD, Eikelboom JW, Attia J. et al. Does this patient have pulmonary embolism?. J Am Med Assoc 2003; 290: 2849-2858.
  • 11 Righini M, Aujesky D, Roy PM. et al. Clinical usefulness of D-dimer depending on clinical probability and cutoff value in outpatients with suspected pulmonary embolism. Arch Intern Med 2004; 164: 2483-2487.
  • 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: 98-107.
  • 13 Wolf SJ, McCubbin TR, Feldhaus KM. et al. Prospective validation of Wells Criteria in the evaluation of patients with suspected pulmonary embolism. Ann Emerg Med 2004; 44: 503-510.
  • 14 Clinical policy: critical issues in the evaluation and management of adult patients presenting with suspected pulmonary embolism. Ann Emerg Med 2003; 41: 257-270.
  • 15 British Thoracic Society guidelines for the management of suspected acute pulmonary embolism. Thorax 2003; 58: 470-483.
  • 16 Charlson ME, Ales KL, Simon R. et al. Why predictive indexes perform less well in validation studies. Is it magic or methods? Arch Intern Med 1987; 147: 2155-2161.
  • 17 Copas JB. Using regression models for prediction: shrinkage and regression to the mean. Stat Methods Med Res 1997; 6: 167-183.
  • 18 Writing Group for the Christopher Study Investigators. Effectiveness of Managing Suspected Pulmonary Embolism Using an Algorithm Combining Clinical Probability, D-Dimer Testing, and Computed Tomography. J Am Med Assoc 2006; 295: 172-179.
  • 19 Metz CE, Herman BA, Roe CA. Statistical comparison of two ROC-curve estimates obtained from partially- paired datasets. Med Decis Making 1998; 18: 110-121.
  • 20 Dawes RM. The robust beauty of improper linear models in decision making. Am Psychol 1979; 34: 571-582.
  • 21 Wicki J, Perneger TV, Junod AF. et al. Assessing clinical probability of pulmonary embolism in the emergency ward: a simple score. Arch Intern Med 2001; 161: 92-97.
  • 22 Patil S, Henry JW, Rubenfire M. et al. Neural network in the clinical diagnosis of acute pulmonary embolism. Chest 1993; 104: 1685-1689.
  • 23 Wyatt J. Nervous about artificial neural networks?. Lancet 1995; 346: 1175-1177.
  • 24 Wyatt JC, Altman DG. Commentary: Prognostic models: clinically useful or quickly forgotten?. Br Med J 1995; 311: 1539-1541.
  • 25 Kruip MJ, Slob MJ, Schijen JH. et al. Use of a clinical decision rule in combination with D-dimer concentration in diagnostic workup of patients with suspected pulmonary embolism: a prospective management study. Arch Intern Med 2002; 162: 1631-1635.