Pulmonary Embolism Mortality Risk - Medical Algorithm

Description

Aujesky et al developed a clinical prediction rule for distinguishing low from high risk patients with a pulmonary embolism. This can help identify patients who may benefit from more aggressive management. The authors are from the University of Pittsburgh, University of Lausanne, University of Geneva, and University of Angers.

Parameters:

(1) age of the patient

(2) history of cancer

(3) history of heart failure

(4) history of chronic lung disease

(5) history of chronic renal disease

(6) history of cerebrovascular disease (transient ischemic attack or stroke)

(7) pulse

(8) systolic blood pressure

(9) mental status

(10) arterial oxygen saturation

Parameter	Finding	Points
age of the patient	< 70 years of age	0
	>= 70 years of age	1
history of cancer	none	0
	present	1
history of heart failure	none	0
	present	1
history of chronic lung disease	none	0
	present	1
history of chronic renal disease	none	0
	present	1
history of cerebrovascular disease	none	0
	present	1
pulse	< 110 beats per minute	0
	>= 110 beats per minute	1
systolic blood pressure	>= 100 mm Hg	0
	< 100 mm Hg	1
mental status	normal	0
	altered (disorientation, lethargy. stupor, coma)	1
arterial oxygen saturation	>= 90%	0
	< 90%	1

total score =

= SUM(points for all 10 parameters)

Interpretation:

• minimum score: 0

• maximum score: 10

• A score = 0 indicates a low risk for short-term (30 day) mortality and complications.

• A score >= 1 indicates a high risk for short-term mortality and complications.

Performance:

• The sensitivity for 30-day mortality was high (97-100%) but specificity was low (23-35%.

• The negative predictive value was high (98%+) while the positive predictive value was low (<= 11%).

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Description

Specialty

Objective

ICD-10

Clinical Prediction Rule of Aujesky et al for Identifying the Level of Risk for a Patient with a Pulmonary Embolism

Purpose: To identify a patient with an acute pulmonary embolism (PE) at high risk for 30 day mortality and complications using the prediction rule of Aujesky et al.

Description

Specialty

Objective

ICD-10