RESUMO
Development of back pain is multifactorial, and it is not well understood which factors are the main drivers of the disease. We therefore applied a machine-learning approach to an existing large cohort study data set and sought to identify and rank the most important contributors to the presence of back pain amongst the documented parameters of the cohort. Data from 399 participants in the KORA-MRI (Cooperative health research in the region Augsburg-magnetic resonance imaging) (Cooperative Health Research in the Region Augsburg) study was analyzed. The data set included MRI images of the whole body, including the spine, metabolic, sociodemographic, anthropometric, and cardiovascular data. The presence of back pain was one of the documented items in this data set. Applying a machine-learning approach to this preexisting data set, we sought to identify the variables that were most strongly associated with back pain. Mediation analysis was performed to evaluate the underlying mechanisms of the identified associations. We found that depression and anxiety were the 2 most selected predictors for back pain in our model. Additionally, body mass index, spinal canal width and disc generation, medium and heavy physical work as well as cardiovascular factors were among the top 10 most selected predictors. Using mediation analysis, we found that the effects of anxiety and depression on the presence of back pain were mainly direct effects that were not mediated by spinal imaging. In summary, we found that psychological factors were the most important predictors of back pain in our cohort. This supports the notion that back pain should be treated in a personalized multidimensional framework. PERSPECTIVE: This article presents a wholistic approach to the problem of back pain. We found that depression and anxiety were the top predictors of back pain in our cohort. This strengthens the case for a multidimensional treatment approach to back pain, possibly with a special emphasis on psychological factors.
Assuntos
Dor Lombar , Humanos , Estudos de Coortes , Dor Lombar/psicologia , Depressão/diagnóstico por imagem , Dor nas Costas/diagnóstico por imagem , Dor nas Costas/epidemiologia , Imageamento por Ressonância Magnética , Ansiedade/diagnóstico por imagem , Ansiedade/epidemiologia , Vértebras Lombares/patologiaRESUMO
PURPOSE: Despite undergoing allogeneic hematopoietic stem cell transplantation (HCT), patients with acute myeloid leukemia (AML) with internal tandem duplication mutation in the FMS-like tyrosine kinase 3 gene (FLT3-ITD) have a poor prognosis, frequently relapse, and die as a result of AML. It is currently unknown whether a maintenance therapy using FLT3 inhibitors, such as the multitargeted tyrosine kinase inhibitor sorafenib, improves outcome after HCT. PATIENTS AND METHODS: In a randomized, placebo-controlled, double-blind phase II trial (SORMAIN; German Clinical Trials Register: DRKS00000591), 83 adult patients with FLT3-ITD-positive AML in complete hematologic remission after HCT were randomly assigned to receive for 24 months either the multitargeted and FLT3-kinase inhibitor sorafenib (n = 43) or placebo (n = 40 placebo). Relapse-free survival (RFS) was the primary endpoint of this trial. Relapse was defined as relapse or death, whatever occurred first. RESULTS: With a median follow-up of 41.8 months, the hazard ratio (HR) for relapse or death in the sorafenib group versus placebo group was 0.39 (95% CI, 0.18 to 0.85; log-rank P = .013). The 24-month RFS probability was 53.3% (95% CI, 0.36 to 0.68) with placebo versus 85.0% (95% CI, 0.70 to 0.93) with sorafenib (HR, 0.256; 95% CI, 0.10 to 0.65; log-rank P = .002). Exploratory data show that patients with undetectable minimal residual disease (MRD) before HCT and those with detectable MRD after HCT derive the strongest benefit from sorafenib. CONCLUSION: Sorafenib maintenance therapy reduces the risk of relapse and death after HCT for FLT3-ITD-positive AML.