Patient-reported outcome measures have become an important part of routine care. The aim of this study was to determine if Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be used to create patient subgroups for individuals seeking orthopaedic care. This was a cross-sectional study of patients from Duke University Department of Orthopaedic Surgery clinics (14 ambulatory and four hospital-based). There were two separate cohorts recruited by convenience sampling (i.e. patients were included in the analysis only if they completed PROMIS measures during a new patient visit). Cohort #1 (n = 12,141; December 2017 to December 2018,) included PROMIS short forms for eight domains (Physical Function, Pain Interference, Pain Intensity, Depression, Anxiety, Sleep Quality, Participation in Social Roles, and Fatigue) and Cohort #2 (n = 4,638; January 2019 to August 2019) included PROMIS Computer Adaptive Testing instruments for four domains (Physical Function, Pain Interference, Depression, and Sleep Quality). Cluster analysis (K-means method) empirically derived subgroups and subgroup differences in clinical and sociodemographic factors were identified with one-way analysis of variance.Aims
Methods
There is widespread variation in the management of rare orthopaedic disease, in a large part owing to uncertainty. No individual surgeon or hospital is typically equipped to amass sufficient numbers of cases to draw robust conclusions from the information available to them. The programme of research will establish the British Orthopaedic Surgery Surveillance (BOSS) Study; a nationwide reporting structure for rare disease in orthopaedic surgery. The BOSS Study is a series of nationwide observational cohort studies of pre-specified orthopaedic disease. All relevant hospitals treating the disease are invited to contribute anonymised case details. Data will be collected digitally through REDCap, with an additional bespoke software solution used to regularly confirm case ascertainment, prompt follow-up reminders and identify potential missing cases from external sources of information (i.e. national administrative data). With their consent, patients will be invited to enrich the data collected by supplementing anonymised case data with patient reported outcomes. The study will primarily seek to calculate the incidence of the rare diseases under investigation, with 95% confidence intervals. Descriptive statistics will be used to describe the case mix, treatment variations and outcomes. Inferential statistical analysis may be used to analyze associations between presentation factors and outcomes. Types of analyses will be contingent on the disease under investigation.Introduction
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