A47: Progress Report on the Development of New Classification Criteria for Adult and Juvenile Idiopathic Inflammatory Myopathies
Authors: Pilkington Clarissa , Tjärnlund Anna , Bottai Matteo , Rider Lisa G , Werth Victoria P , Visser Marianne de , Alfredsson Lars , Amato Anthony A , Barohn Richard J , Liang Matthew H , Singh Jasvinder A , Miller Frederick W , Lundberg Ingrid E. ,   On behalf of the members of the IMCCP International Myositis Classification Criteria Project
Published: March 2014

Background/Purpose:

Inadequate classification criteria for IIM are a fundamental limitation in clinical studies. An international, multidisciplinary collaboration, the International Myositis Classification Criteria Project (IMCCP), supported by ACR and EULAR, was established to address this problem.

Methods:

Identification and definition of potential criterion

Candidate variables to be included in classification criteria were assembled from published criteria and inclusion criteria in controlled trials of myositis and refined using Nominal Group Technique. Comparator groups confused with IIM were defined.

Data collection

Within this retrospective case control study, clinical and laboratory data from IIM and comparator patients were collected from 47 rheumatology, dermatology, neurology and pediatrics clinics worldwide from 2008–2011.

Analysis

Crude pair‐wise associations among all variables measured and between each variable and clinician's diagnosis were assessed. Three approaches for derivation of classification criteria were explored:

Traditional: case defined by specified number of items from a set

Risk score: patient assigned a probability risk score by summing score‐points associated with the variables (Probability model 1 and 2)

Classification tree: case defined by a decision tree

A random forest algorithm explored the most important variables. Results obtained with each approach were utilized to improve others iteratively.

Validation

Internal validation using bootstrap methods was performed. External validation using extracted data from the Euromyositis register and an UK juvenile myositis register was performed.

Results:

Data from 973 IIM patients (74% adults;26% children), representing subgroups of IIM (245 polymyositis, 239 dermatomyositis, 176 inclusion body myositis and 246 juvenile dermatomyositis cases) and 629 comparators (81% adults; 19% children) were obtained. The comparators include other myopathies and systemic rheumatic diseases.

Two probability score models were developed (Table ): Model 1 comprised clinical variables on muscles, skin, and laboratory measures; Model 2 additionally comprised muscle biopsy variables. Model 1 performed nearly as well as Model 2 and both models performed as well as and often better than, the classification tree that was developed and published criteria.

External validation using data on 2363 myositis patients in the Euromyositis register resulted in >99% sensitivity, and using 332 juvenile myositis cases resulted in 100% sensitivity for both probability models.

PERFORMANCE OF NEW AND EXISITING CLASSIFICATION/DIAGNOSTIC CRITERIA FOR IIM

Probability model

Performance(%)
Without muscle biopsy data(Model 1)
With muscle biopsy data (Model 2)
Classification Tree
Peter & Bohan [1]
Tanimoto et al. [2]
Targoff et al.[3]
Dalakas & Hohlfeld [4]
Hoogendijk et al.[5]

Sensitivity
87
88
88
98
96
93
6
51

Specificity
88
89
72
55
31
88
99
96

Correctly classified
87
88
84
86
79
91
45
70

Cut point for probability: 55%

Definite and probable polymyositis and dermatomyositis

New model for classification criteria for IIM and performance of criteria

Probability model

Variable
Score points

18 ≤ Age of onset of first symptom < 40
1.6

Age of onset of first symptom ≥ 40
2.3

Clinical Muscle Variables

Objective symmetric weakness, usually progressive, of the proximal upper extremities
0.7

Objective symmetric weakness, usually progressive, of the proximal lower extremities
0.6

Neck flexors are relatively weaker than neck extensors
1.6

In the legs proximal muscles are relatively weaker than distal muscles
1.5

Skin Variables

Heliotrope rash
3.3

Gottronxs papules
2.3

Gottron's sign
3.4

Other Clinical Variables

Dysphagia or esophageal dysmotility
0.7

Laboratory Variables

Serum creatine kinase activity (CK) activity
1.2

Serum lactate dehydrogenase (LDH) activity or Serum aspartate aminotransferase (ASAT/AST/SGOT) activity or Serum alanine aminotransferase (ALAT/ALT/SGPT) activity

Anti‐Jo‐1 (anti‐His) antibody positivity
4.2

Score‐sum from above items
0.9

Muscle Biopsy Variables

Endomysial infiltration of mononuclear cells surrounding, but not invading, myofibers
1.4

Perimysial and/or perivascular infiltration of mononuclear cells
1.2

Perifascicular atrophy
1.6

Rimmed vacoules
2.2

When muscle biopsies are available, multiply the score‐sum of all other variables by 0.9 and then add the scores of the positive biopsies. Probability of disease can be obtained using the web calculator: www.imm.ki.se/biostatistics/calculators/iim

Conclusion:

New classification criteria for IIM with readily clinically assessable measurements and symptoms have been developed. They generally show superior performance compared with existing criteria.