Assessing the Categories and Factors Influencing Treatment Decision Conflict in Patients With Lumbar Disc Herniation: A Latent Profile Analysis.

BACKGROUND: Lumbar disc herniation (LDH) represents a prevalent degenerative spinal disorder, characterized by severe pain and functional limitations that significantly diminish patients' quality of life. Despite the availability of conservative and surgical treatment options, patients frequently encounter considerable decisional conflict (DC). Current research often neglects the inherent heterogeneity within this patient population. To ensure that the research questions and outcome measures reflect patients' real-world priorities, we involved patients with LDH in the study design phase. This study employs latent profile analysis (LPA) to identify distinct patterns in treatment decision-making and examine their underlying factors, thereby providing a scientific basis for personalized clinical decision support.

METHODS: This cross-sectional study involved 743 patients diagnosed with LDH. Patients and public were involved in refining the questionnaire content and validating the relevance of outcome measures. The data collection tools included a General Information Questionnaire, the Decisional Conflict Scale (DCS), the Health Belief Scale, and the Treatment Expectancy Questionnaire. Influential factors were identified through univariate analyses and multinomial logistic regression. LPA was used to identify latent profiles and delineate their unique characteristics.

RESULTS: Three distinct latent profiles emerged: the low-DC group (23.55%), the moderate DC group (44.55%), and the high-DC group (31.90%). Multinomial logistic regression analysis indicated that factors such as gender, marital status, educational attainment, type of medical insurance, employment status, annual household income, place of residence, and clinical history (including duration since onset and prior consultations) significantly influenced membership in these profiles. Additionally, treatment expectations and health beliefs were identified as critical psychological predictors of DC.

CONCLUSION: Patients with LDH exhibit substantial heterogeneity in treatment DC, classified into low, moderate, and high-conflict groups via latent profile analysis, with over 70% experiencing moderate to high DC. DC in patients with LDH is influenced by multidimensional factors, including sociodemographic characteristics, clinical features, health beliefs, and treatment expectations, with health beliefs and treatment expectations acting as core protective factors. These findings highlight the need to replace the one-size-fits-all model with personalized decision-support strategies for patients with LDH based on conflict profiles and individual characteristics.