Model Simulations Challenge Reductionist Research Approaches for Studying Chronic Low Back Pain

STUDY DESIGN. Analytical and numerical simulations. BACKGROUND. Traditionally, low back pain (LBP) is studied using a reductionist approach, in which the factors contributing to the clinical presentation of LBP are studied in isolation to identify the primary pathology or condition linked to LBP. We argue that reductionism may not be suitable for studying LBP considering the complex, multi-factorial nature of this condition. OBJECTIVES. To quantify the likelihood of successfully subclassifying LBP patients and effectively targeting treatment based on a single, dominant factor contributing to LBP. METHODS. Both the analytical and numerical simulations (Monte Carlo) of one million patients with LBP were performed. A number of factors contributing to LBP were randomly assigned to each individual. The following outcome measures were computed based on a number of factors: (1) The percentage of individuals that could be subclassified by identifying a single factor exceeding a certain threshold and (2) The average reduction in LBP when a treatment eliminates the largest contributing factor versus a multimodal treatment that eliminates a number of the randomly selected factors. RESULTS. With an increasing number of factors, the probability of subclassifying an individual to a subgroup based on a single factor tends to zero. A multimodal treatment arbitrarily addressing any two or more factors was more effective than diagnosing and treating a single factor that maximally contributed to LBP. CONCLUSION. Results suggest that reductionism is not appropriate for subclassifying LBP or targeting treatment. The use of reductionist approaches may explain some of the challenges with creating LBP classification system and designing more effective treatment interventions.