Pulmonary embolism tools

PERC and Wells address diagnosis (whether to test); PESI and sPESI address prognosis after PE is confirmed.

Side-by-side comparison of model characteristics
CharacteristicPERCWells PEPESIsPESI
Clinical questionCan PE be excluded without D-dimer testing in a low-risk patient?What is the pretest probability of pulmonary embolism?What is the 30-day mortality risk of a patient with confirmed acute PE?Is a patient with acute PE at low risk of 30-day mortality?
Outcome predictedExclusion of PEPretest probability of PEAll-cause mortalityAll-cause mortality
Time horizonCurrent episode (45-day follow-up in studies)Current episode30 days30 days
Intended populationUS emergency department patients evaluated for PE.Inpatients and outpatients with suspected PE (Canadian derivation).Patients with acute PE in Pennsylvania hospitals (derivation) with internal/external validation.Spanish RIETE registry and multicentre cohort.
Evidence levelStrong evidenceStrong evidenceStrong evidenceStrong evidence
Validation

PROPER cluster-randomised non-inferiority trial: 3-month thromboembolic events 0.1% with the PERC strategy vs 0% with usual care (non-inferior).

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Derivation cohort (Jiménez 2010): 30-day mortality 1.0% with sPESI 0 vs 10.9% with sPESI ≥ 1.

Discrimination————
Calibration————
Geographic applicability————
Guideline statusSupported by the 2018 ACEP clinical policy on suspected PE; evaluated in the PROPER randomised trial.Incorporated in the 2019 ESC acute PE guideline diagnostic algorithms.Used for risk stratification in the 2019 ESC acute PE guideline.Used alongside PESI in the 2019 ESC acute PE guideline.
Important limitations
  • Only valid with low gestalt pretest probability.
  • Performance is lower in high-prevalence settings.
  • 'PE most likely' is subjective and heavily weighted.
  • Requires adaptation in pregnancy.
  • Age contributes heavily, so older patients rarely score low risk.
  • Does not include RV dysfunction or biomarkers.
  • Binary output; does not replace assessment of RV function in intermediate-risk patients.

Inputs required

Which variables each model uses. Models with more inputs are not automatically better — validation in your population matters most.

Input variables used by each model
InputPERCWells PEPESIsPESI
Clinician pretest probability●used–not used–not used–not used
Age ≥ 50 years●used–not used–not used–not used
Heart rate ≥ 100 beats/min●used–not used–not used–not used
Oxygen saturation < 95% on room air●used–not used–not used–not used
Unilateral leg swelling●used–not used–not used–not used
Haemoptysis●used●used–not used–not used
Surgery or trauma within 4 weeks (requiring hospitalisation)●used–not used–not used–not used
Prior PE or DVT●used–not used–not used–not used
Hormone use (oral contraceptives, hormone replacement or oestrogen)●used–not used–not used–not used
Clinical signs and symptoms of DVT–not used●used–not used–not used
PE is the most likely diagnosis (or equally likely)–not used●used–not used–not used
Heart rate > 100 beats/min–not used●used–not used–not used
Immobilisation ≥ 3 days or surgery in the previous 4 weeks–not used●used–not used–not used
Previous, objectively diagnosed PE or DVT–not used●used–not used–not used
Malignancy (treatment within 6 months or palliative)–not used●used–not used–not used
Age–not used–not used●used–not used
Sex–not used–not used●used–not used
History of cancer–not used–not used●used●used
History of heart failure–not used–not used●used–not used
History of chronic lung disease–not used–not used●used–not used
Heart rate ≥ 110 beats/min–not used–not used●used●used
Systolic BP < 100 mmHg–not used–not used●used●used
Respiratory rate ≥ 30 breaths/min–not used–not used●used–not used
Temperature < 36 °C–not used–not used●used–not used
Altered mental status–not used–not used●used–not used
Arterial oxyhaemoglobin saturation < 90%–not used–not used●used–not used
Age > 80 years–not used–not used–not used●used
Chronic cardiopulmonary disease (heart failure or chronic lung disease)–not used–not used–not used●used
Oxyhaemoglobin saturation < 90%–not used–not used–not used●used

Comparison shows model characteristics only. Different models predict different outcomes in different populations, so their numerical results are not directly comparable.