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Review
. 2020 Sep 21.
doi: 10.1111/ijd.15196. Online ahead of print.

Overlap between hemophagocytic lymphohistiocytosis and drug reaction and eosinophilia with systemic symptoms: a review

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Review

Overlap between hemophagocytic lymphohistiocytosis and drug reaction and eosinophilia with systemic symptoms: a review

Jason J Yang et al. Int J Dermatol. .

Abstract

Drug reaction and eosinophilia with systemic symptoms (DRESS), also known as drug-induced hypersensitivity syndrome (DIHS), shares features with hemophagocytic lymphohistiocytosis (HLH), most notably fever, rash, and internal organ involvement. However, there is increasing recognition of drug-induced (secondary) HLH and biopsy-proven hemophagocytosis in DRESS, suggesting that HLH and DRESS not only overlap but also may be diseases on the same spectrum of immune dysfunction. To characterize existing literature on HLH/DRESS overlap, we queried the PubMed/MEDLINE database for 23 cases of HLH-DRESS codiagnosis. Average time-to-onset of rash after exposure to inciting drug was 2.7 weeks. Fourteen cases (61%) clinically worsened despite initial therapy, prompting a workup with diagnosis of HLH on average 2.3 weeks after diagnosing DRESS. Nine cases met HLH diagnostic criteria and had a RegiSCAR score ≥4. Nine cases met one set of criteria with a presentation suggestive of the other. Five cases met neither criteria. A patient presenting with fever, generalized rash, bicytopenia, and internal organ involvement after drug exposure was most predictive of meeting diagnostic criteria for both HLH and DRESS. Treatment was highly variable, although most initiated systemic corticosteroids with/without IVIG, plasmapheresis, or etoposide. Patients with poor outcomes in this review were treated using steroid monotherapy and had viral reactivation. Dermatologists should consider the possibility of HLH in any patient presenting with fever, rash, internal organ involvement, and cytopenia. Additional studies will be necessary to further characterize HLH and DRESS overlap and determine optimal management.

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