6 / 28
Journal of the American Academy of Dermatology
Take-home message
•
In this cross-sectional study of 259 patients admitted through the emergency
department between 2010 and 2012 with a diagnosis of lower extremity cel-
lulitis, 30.5%were ultimately found to have beenmisdiagnosed with cellulitis.
Variables associated with a true diagnosis of cellulitis included asymmetric
involvement, leukocytosis, tachycardia, and an age >70. These variables
were converted into a predictive model named the ALT-70 cellulitis score
(asymmetry, 3 points; leukocytosis, 1 point; tachycardia, 1 point; age >70 years,
2 points). A score of 0–2 points indicates >83.3% likelihood of pseudocellulitis,
and a score of >5 points indicates >82.2% likelihood of cellulitis.
•
A novel model incorporating the four variables of asymmetry, leukocytosis,
tachycardia, and age >70 years is capable of predicting pseudocellulitis
and cellulitis with a likelihood of >80%. This model may be useful in the
emergency department for early identification and may reduce healthcare
costs by precluding hospital admissions for pseudocellulitis.
Abstract
BACKGROUND
Cellulitis has many clinical mimickers (pseudocellulitis), which leads to fre-
quent misdiagnosis.
OBJECTIVE
To create a model for predicting the likelihood of lower extremity cellulitis.
METHODS
A cross-sectional review was performed of all patients admitted with a diag-
nosis of lower extremity cellulitis through the emergency department at a large hospital
between 2010 and 2012. Patients discharged with diagnosis of cellulitis were categorized
as having cellulitis, while those given an alternative diagnosis were considered to have
pseudocellulitis. Bivariate associations between predictor variables and final diagnosis
were assessed to develop a 4-variable model.
RESULTS
In total, 79 (30.5%) of 259 patients were misdiagnosed with lower extremity
cellulitis. Of the variables associated with true cellulitis, the 4 in the final model were
asymmetry (unilateral involvement), leukocytosis (white blood cell count ≥10,000/uL),
tachycardia (heart rate ≥90 bpm), and age ≥70 years. We converted these variables into
a points system to create the ALT-70 cellulitis score as follows: Asymmetry (3 points),
Leukocytosis (1 point), Tachycardia (1 point), and age ≥70 (2 points). With this score, 0–2
points indicate ≥83.3% likelihood of pseudocellulitis, and ≥5 points indicate ≥82.2% like-
lihood of true cellulitis.
LIMITATIONS
Prospective validation of this model is needed before widespread clinical use.
CONCLUSION
Asymmetry, leukocytosis, tachycardia, and age ≥70 are predictive of lower
extremity cellulitis. This model might facilitate more accurate diagnosis and improve
patient care.
A predictive model for diagnosis of lower extremity cellulitis: a cross-sectional study.
J Am Acad Dermatol
2017 Feb 16;[EPub Ahead of Print], AB Raff, QY Weng, JM Cohen,
et al.
A predictive model for diagnosis of lower
extremity cellulitis
COMMENT
By Boris D Lushniak
MD, MPH
T
he word diagnosis is derived through Latin
from Greek from a word meaning “to dis-
cern, distinguish.” Since the time of our
initial training in medical school, we have been
primed to follow an odyssey to become mas-
ter diagnosticians. The goal to “getting it right”
is critical because of the myriad repercussions
of being wrong. For example, incorrect diagno-
ses can lead to unnecessary treatment (one of
the concerns in the issue of antimicrobial resist-
ance) or a pathway of delayed treatment and
potential increased morbidity or mortality. Using
the skills of history taking, the physical exam,
diagnostic procedures, and laboratory tests, we
are at times awed by our acumen and success
in getting it right and perhaps humbled by the
opposite. In the practice of medicine, we are
committed to the ongoing pursuit to improve our
diagnostic skills using any variety of new tools.
In this endeavour to do our jobs better, we have
been blessed with high tech (and oftentimes very
costly) advancements and cutting-edge labora-
tory techniques. Yet, there is plenty to learn from
population-based pattern recognition, which then
allows us to establish diagnostic criteria.
In this article, the authors delineate the impact
of this common infection as well as the impact
of misdiagnosis, which is attributed to a lack of
“accurate or reliable diagnostic studies.” Using
a retrospective, cross-sectional chart review of
emergency department patients, comparisons
were made between admitting and discharge
diagnoses. The end product of the analyses
was a point system taking into account asym-
metry, leukocytosis, tachycardia, and age ≥70
(ALT-70), with proposed cutoff points and clini-
cal interpretation.
So, can we add this to our diagnostic tool kit?
Well, consider it a work in progress. Two points to
consider: 1) the threshold is set at 80% positive or
negative predictive value – that is, nothing is per-
fect; and 2) further validation is needed before
widespread clinical use. Yet this shows that pop-
ulation-based pattern recognition can play a part
in expanding our diagnostic tools.
Dr Lushniak is Professor and Chair
of the Department of Preventive
Medicine and Biostatistics
and Professor of Dermatology,
F. Edward Hébert School of
Medicine at the Uniformed
Services University of the Health
Sciences in Bethesda, Maryland.
EDITOR’S PICKS
6
PRACTICEUPDATE DERMATOLOGY