Point-of-Care Guides

Applying a Clinical Prediction Rule to Distinguish Lower Extremity Cellulitis from Its Mimics

 

Am Fam Physician. 2021 Sep ;104(2):309-310.

Author disclosure: No relevant financial affiliations.

Clinical Question

How can a clinician best determine whether a patient with lower leg erythema has cellulitis?

Evidence Summary

Lower extremity cellulitis typically presents with acutely expanding erythema, warmth, edema, and tenderness. These signs and symptoms are nonspecific, making it difficult for clinicians to distinguish between true cellulitis and its mimics (pseudocellulitis), many of which do not need treatment with antibiotics. Common mimics include contact dermatitis, venous stasis dermatitis, superficial thrombophlebitis, gout, and lymphedema but can also include more serious mimics such as deep venous thrombosis, necrotizing fasciitis, and septic arthritis. Studies have shown that about 30% of people admitted to the hospital for treatment of cellulitis are mis-diagnosed,13 resulting in unnecessary hospitalizations and intravenous antibiotic therapy. Dermatology consultation increases diagnostic accuracy for cellulitis1 and is often used as the criterion standard in cellulitis studies but is not feasible in every patient.

A study of 73 adults diagnosed clinically with cellulitis in the emergency department found that a temperature difference of 0.85°F (0.47°C) or more between affected and corresponding unaffected areas, measured by thermal imaging, was 100% sensitive and 50% specific for cellulitis.2 Thus, thermal imaging had a negative predictive value of 100% but required specialized equipment that is often not available.

The seven-point ALT-70 score for cellulitis (https://www.mdcalc.com/alt-70-score-cellulitis) is a clinical prediction rule developed using a retrospective chart review of 259 patients, of whom 70% had a final diagnosis of cellulitis (Table 1).4 It can be used to help differentiate cellulitis from pseudocellulitis at presentation and had similar predictive value for inpatients at 24 and 48 hours after presentation, based on a retrospective chart review.4,5

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TABLE 1.

ALT-70 Predictive Model for Lower Extremity Cellulitis in the Emergency Department

Step 1: Calculate the score (0 to 7)

Clinical variable

Points


Asymmetry (unilateral leg involvement)

3

Leukocytosis (white blood cell count ≥ 10,000 per μL [10 × 109 per L])

1

Tachycardia (heart rate ≥ 90 beats per minute)

1

70 years or older

2

Total: ______

Step 2: Apply the score to a patient with suspected cellulitis

ALT-70 score

Likelihood of cellulitis (95% CI)

Clinical recommendation


0 to 2

9% (0 to 28%)

Cellulitis unlikely; reassess diagnosis

3 to 4

72% (57% to 87%)

Indeterminate; consider consultation

5 to 7

95% (85% to 100%)

Cellulitis likely; treat empirically


Adapted with permission from Raff AB, Weng QY, Cohen JM, et al. A predictive model for diagnosis of lower extremity cellulitis: a cross-sectional study. J Am Acad Dermatol. 2017;76(4):625.e1.

TABLE 1.

ALT-70 Predictive Model for Lower Extremity Cellulitis in the Emergency Department

Step 1: Calculate the score (0 to 7)

Clinical variable

Points


Asymmetry (unilateral leg involvement)

3

Leukocytosis (white blood cell count ≥ 10,000 per μL [10 × 109 per L])

1

Tachycardia (heart rate ≥ 90 beats per minute)

1

70 years or older

2

Total: ______

Step 2: Apply the score to a patient with suspected cellulitis

ALT-70 score

Likelihood of cellulitis (95% CI)

Clinical recommendation


0 to 2

9% (0 to 28%)

Cellulitis unlikely; reassess diagnosis

3 to 4

72% (57% to 87%)

Indeterminate; consider consultation

5 to 7

95% (85% to 100%)

Cellulitis likely; treat empirically


Adapted with permission from Raff AB, Weng QY, Cohen JM, et al. A predictive model for diagnosis of lower extremity cellulitis: a cross-sectional study. J Am Acad Dermatol. 2017;76(4):625.e1.

The ALT-70 clinical prediction rule has been validated prospectively, and it outperformed thermal imaging for the diagnosis of lower extremity cellulitis in the emergency department.6 In that study of 67 patients, with a dermatology consultation as the criterion standard, using a cutoff of three or more points had very high sensitivity (97.8%) but only moderate specificity (47.6%),

Address correspondence to Lia Pierson Bruner, MD, at lia.bruner@uga.edu. Reprints are not available from the author.

Author disclosure: No relevant financial affiliations.

References

show all references

1. Ko LN, Garza-Mayers AC, St John J, et al. Effect of dermatology consultation on outcomes for patients with presumed cellulitis: a randomized clinical trial. JAMA Dermatol. 2018;154(5):529–536....

2. Ko LN, Raff AB, Garza-Mayers AC, et al. Skin surface temperatures measured by thermal imaging aid in the diagnosis of cellulitis. J Invest Dermatol. 2018;138(3):520–526.

3. Weng QY, Raff AB, Cohen JM, et al. Costs and consequences associated with misdiagnosed lower extremity cellulitis. JAMA Dermatol. 2017;153(2):141–146.

4. Raff AB, Weng QY, Cohen JM, et al. A predictive model for diagnosis of lower extremity cellulitis: a cross-sectional study. J Am Acad Dermatol. 2017;76(4):618–625.e2.

5. Singer S, Li DG, Gunasekera N, et al. The ALT-70 cellulitis model maintains predictive value at 24 and 48 hours after presentation. J Am Acad Dermatol. 2019;81(6):1252–1256.

6. Li DG, Dewan AK, Xia FD, et al. The ALT-70 predictive model outperforms thermal imaging for the diagnosis of lower extremity cellulitis: a prospective evaluation. J Am Acad Dermatol. 2018;79(6):1076–1080.e1.

7. Edwards G, Freeman K, Llewelyn MJ, et al. What diagnostic strategies can help differentiate cellulitis from other causes of red legs in primary care? BMJ. 2020;368:m54.

8. Ezaldein HH, Waldman A, Grunseich K, et al. Risk stratification for cellulitis versus noncellulitic conditions of the lower extremity: a retrospective review of the NEW HAvUN criteria. Cutis. 2018;102(1):E8–E12.

This guide is one in a series that offers evidence-based tools to assist family physicians in improving their decision-making at the point of care.

This series is coordinated by Mark H. Ebell, MD, MS, deputy editor for evidence-based medicine.

A collection of Point-of-Care Guides published in AFP is available at https://www.aafp.org/afp/poc.

 

 

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