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Research paper
Proposal of new clinical diagnostic criteria for POEMS syndrome

Abstract

Objective To propose the optimal diagnostic criteria for polyneuropathy, organomegaly, endocrinopathy, M-protein and skin changes (POEMS) syndrome using appropriate statistical methods and disease controls.

Methods This retrospective cohort study included 104 consecutive patients with suspected POEMS syndrome, among whom a gold standard group of 60 patients with definitive POEMS syndrome diagnosis were followed for at least 12 months to strictly exclude other disorders mimicking POEMS syndrome and to confirm response to POEMS syndrome-specific treatment. Thirty patients with chronic inflammatory demyelinating polyradiculoneuropathy (demyelinating polyradiculoneuropathy controls) and 30 with multiple myeloma or immunoglobulin light chain amyloidosis (monoclonal plasma cell proliferation controls) were also included. Logistic regression analyses were performed to determine optimal combination of clinical and laboratory abnormalities, characteristic of POEMS syndrome.

Results The diagnostic criteria were statistically defined as the presence of the three major criteria (polyneuropathy (typically demyelinating), monoclonal plasma cell proliferative disorder and elevated vascular endothelial growth factor) and at least two of the four minor criteria (oedema/effusion, skin changes, organomegaly and sclerotic bone lesions), based on best performance by area under the receiver operating characteristic curve analyses. The sensitivity and specificity were 100% and 100%, respectively; the diagnostic accuracy of the proposed criteria was equivalent to somewhat complicated previous criteria.

Conclusions The statistically defined, simple diagnostic criteria for POEMS syndrome could accelerate early diagnosis and treatment, thereby contribute to better outcome in patients with this serious disease. Prospective larger studies are required to confirm the validity.

  • POEMS syndrome
  • diagnostic criteria
  • sensitivity
  • specificity

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