Reliability of patient self-reports to clinician-assigned functional scores of inclusion body myositis

Background: Sporadic inclusion body myositis (IBM) is a debilitating disease which leads to impaired ambulation and loss of hand function. Yale IBM Registry (IBMR) was launched in November 2016 to address the knowledge gap in IBM natural history data. The registry interface provides an IBM personalized index calculator (IBM-PIC) based on the IBM-functional rating scale (IBM-FRS). While the calculator is based on the IBM-FRS, it has not been directly compared to the IBM-FRS score. Therefore, in this study, we compared the patient-reported IBM-PIC score from this calculator with the physician-obtained IBM-FRS score. Method: IBM-FRS was administered over the phone within two weeks of their most recent IBM-PIC entry in the IBMR to 35 participants. To compare the agreement between IBM-FRS and IBM-PIC scores, Interrater Correlation Coefficient (ICC) analysis was performed. For individual questions, Fleiss Kappa statistics was used. Results: Thirty-five active IBM-PIC users participated. Eighty percent of the participants were men, and 91% were White Caucasians. The reported IBM-FRS score of this group was 23.5 +/- 7.4 (range 1-38). The Interrater Correlation Coefficient (ICC) between the physician-administered IBM-FRS score and the IBM-PIC was 0.98 (0.96-0.99). There was moderate to substantial agreement on all the questions on IBM-FRS except for handwriting and fine motor skills. Discussion: IBM-PIC is a reliable indicator of the IBM-FRS score obtained by the physician. It is anticipated that this online platform will be a valuable tool for assessing IBM severity and monitoring disease progression remotely both in clinical practice and research studies.

Automated summary

This study compared the patient-reported IBM-PIC score with the physician-obtained IBM-FRS score and found that IBM-PIC is a reliable indicator of IBM-FRS score. The online platform is anticipated to be a valuable tool for assessing IBM severity and monitoring disease progression remotely.