The variability in pain drawing styles and analysis methods has raised concerns about the reliability of pain drawings as a screening tool for nonpain symptoms. In this study, a data-driven approach to pain drawing analysis has been used to enhance the reliability. The aim was to identify distinct clusters of pain patterns by using latent class analysis (LCA) on 46 predefined anatomical areas of a freehand digital pain drawing. Clusters were described in the clinical domains of activity limitation, pain intensity, and psychological factors. A total of 21,123 individuals were included from 2 subgroups by primary pain complaint (low back pain (LBP) [n = 15,465]) or midback/neck pain (MBPNP) [n = 5658]). Five clusters were identified for the LBP subgroup: LBP and radiating pain (19.9%), radiating pain (25.8%), local LBP (24.8%), LBP and whole leg pain (18.7%), and widespread pain (10.8%). Four clusters were identified for the MBPNP subgroup: MBPNP bilateral posterior (19.9%), MBPNP unilateral posterior + anterior (23.6%), MBPNP unilateral posterior (45.4%), and widespread pain (11.1%). The clusters derived by LCA corresponded to common, specific, and recognizable clinical presentations. Statistically significant differences were found between these clusters in every self-reported health domain. Similarly, for both LBP and MBPNP, pain drawings involving more extensive pain areas were associated with higher activity limitation, more intense pain, and more psychological distress. This study presents a versatile data-driven approach for analyzing pain drawings to assist in managing spinal pain.

中文翻译：

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数据驱动识别不同的疼痛绘图模式及其与临床和心理因素的关联：一项针对 21,123 名脊柱疼痛患者的研究。

疼痛绘图风格和分析方法的可变性引起了人们对疼痛绘图作为非疼痛症状筛查工具的可靠性的担忧。在这项研究中，使用数据驱动的疼痛绘图分析方法来提高可靠性。目的是通过对手绘数字疼痛图的 46 个预定义解剖区域使用潜在类别分析 (LCA) 来识别不同的疼痛模式簇。集群在活动限制、疼痛强度和心理因素的临床领域进行描述。根据原发性疼痛主诉（腰痛 (LBP) [n = 15,465]）或中背/颈部疼痛 (MBPNP) [n = 5658]），共有 21,123 名受试者被纳入 2 个亚组。腰痛亚组被确定为五个组：腰痛和放射痛（19.9%）、放射痛（25.8%）、局部腰痛（24.8%）、腰痛和全腿疼痛（18.7%）以及广泛疼痛（10.8%）。 MBPNP 亚组被确定为四个簇：MBPNP 双侧后部 (19.9%)、MBPNP 单侧后部 + 前部 (23.6%)、MBPNP 单侧后部 (45.4%) 和广泛疼痛 (11.1%)。 LCA 得出的簇对应于常见的、特定的和可识别的临床表现。在每个自我报告的健康领域中，这些集群之间都存在统计上的显着差异。同样，对于 LBP 和 MBPNP，涉及更广泛疼痛区域的疼痛图与更高的活动限制、更剧烈的疼痛和更多的心理困扰相关。这项研究提出了一种通用的数据驱动方法，用于分析疼痛图，以帮助管理脊柱疼痛。