Exploring Multimorbidity in Patients With Spinal Pain

Overview

Spinal pain is frequently accompanied by other chronic conditions (multimorbidity) and the predicted rise in multimorbidity prevalence emphasizes the need for studies to understand its impact on patients with chronic pain conditions. Therefore the aims of the two studies are to: Work package 1 - Determine prevalence of multimorbidity among patients with spinal pain referred to hospital outpatient clinics. Examine associations with relevant health-related factors and cover the significance of multimorbidity in the diagnostic process, referral patterns and healthcare utilization. Work package 2: Examine the association between treatment burden arising from multimorbidity and patient prognosis in structured rehabilitation. Across both work packages data will be derived from individuals initially referred to the Department of Rheumatology at Aalborg University Hospital (AaUH) or the Medical Spine Clinic in Silkeborg (MSCS).

Background: Spinal pain is frequently accompanied by other chronic conditions (multimorbidity) and the predicted rise in multimorbidity prevalence emphasizes the need for studies to understand its impact on patients with chronic pain conditions. Objectives: Work package 1: Determine prevalence of multimorbidity among patients with spinal pain referred to hospital outpatient clinics. Examine associations with various health-related factors and cover the significance of multimorbidity in the diagnostic process, referral patterns and healthcare utilization. Work package 2: Examine the association between treatment burden arising from multimorbidity and patient prognosis in structured rehabilitation. Methods: The project consists of two work packages: A cross sectional study (Work package 1) and a prospective observational cohort study (Work package 2). Across both work packages data will be derived from individuals initially referred to the Department of Rheumatology at Aalborg University Hospital (AaUH) or the Medical Spine Clinic in Silkeborg (MSCS). In work package 1 patients with scheduled appointments at the Departments are identified via the hospital's electronic booking system. Secure questionnaires are sent via E-Boks. Data for the work packages will be collected through the hospital's electronic journal systems, patient-reported outcome measures (PROMs) and external danish registers. PROMS will be collected with REDCap at baseline and 3, 6, and 12 months after the initial consultation in the outpatient clinics. The patients' unique civil registration number (CPR) will be linked to external Danish registries facilitated by the Danish Civil Registration System (CRS). Variables: Electronic questionnaires: Name, CPR number, Marital status, Height and weight, Smoking and alcohol habits, and PROMS (elaborated elsewhere) Medical records: Past and current medication use, referral source (general practitioner, specialist, hospital), Past treatment attempts (physiotherapy, chiropractic, etc.), Information on any scans performed (if any), Signs of radiculopathy, Number of consultations. External registries: * "The Danish Civil Registration System": Age, sex, Migration status, Vital status. * "The Danish National Patient Registry": Concurrent diseases (ICD-10 codes), Period from referral date to treatment start date, Start and end dates of outpatient treatment. * "The Danish National Prescription Registry": All dispensed medications from Danish pharmacies (ATC codes). * "The Population Education Register": Highest completed level of education (short, medium, long) * "DREAM database": Income status and public financial support. Statistical considerations: Work package 1: A cross-sectional study Prevalence of multimorbidity and frequency of other diseases will be reported as percentages and total counts and presented in a table. Regression analysis will be emplyed to determine whether the number of chronic conditions or specific clusters of chronic conditions are associated with the primary outcome, EQ-5D-5L, other patient-reported measures, and use of healthcare services. Additionally, patients will be divided into groups categorized by the Multimorbidity Treatment Burden Questionnaire following the classification established by Duncan et al. These categories will divide participants into four distinct categories: no burden (score 0), low burden (score \<10), medium burden (score 10-22), and high burden (score ≥22). Analysis of Covariance (ANCOVA) will be employed to determine whether there are statistically significant differences in EQ-5D-5L scores between the groups while accounting for potential confounding covariates. Individual plots will be generated to visually depict the distribution of patient scores on EQ-5D-5L and Brief Pain Inventory across the four groups. Work package 2: An observational prospective cohort study As in work package 1, patients will be categorized into groups based on their baseline treatment burden. Mixed model with repeated measures will be utilized to determine whether there are statistically significant differences in EQ-5D-5L scores and BPI-scores between groups over time (baseline, 3-6-12 months follow-up), while accounting for potential confounding covariates. Line graphs with error bars will be generated to visually depict the changes in EQ-5D-5L and BPI across the four groups. For both workpackages missing data will be addressed by multiple imputation or other means if not deemed appropiate. The imputation model will be informed by existing literature and by a dropout analysis. In addition, the selection of covariates for inclusion in the statistical models will be guided by current evidence and clinical reasoning to account for potential confounding factors. In assessing the prevalence of multimorbidity, it is defined as the presence of two concurrent chronic diseases in a single individual. To discern which ICD-10 codes correspond to chronic health conditions, a unique script has been devised. This script draws from previous studies, particularly those focused on determining multimorbidity prevalence, and incorporates recommendations from international consensus on multimorbidity measurement in research. The validation of ICD-10 codes in the Danish National Patient Registry (DNPR) and the Primary Care Referral Registry (PCRR) has been systematically conducted across various diagnoses and studies, affirming their accuracy for research purposes.