Paolo Giaccone, Elisabetta de Rinaldis, Federico D'Antoni, Fabrizio Russo, Luca Ambrosio, Giorgia Petrucci, Mario Merone, Leandro Pecchia, Sergio Iavicoli, Gianluca Vadalà, Rocco Papalia, Vincenzo Denaro
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Hierarchical clustering was employed to identify distinct phenotypes based on patient-reported outcome measures (PROMs), including the Oswestry Disability Index (ODI), Visual Analog Scale (VAS), Work Ability Index (WAI), Nordic score, and Patient Health Questionnaire-2 (PHQ-2). Independent <i>t</i> tests and Mann–Whitney <i>U</i> tests were used for phenotype profiling, distinguishing between continuous and categorical responses, respectively, to assess the most discriminative queries and highlight the most significantly different features (<i>p</i> < 0.05).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A total of 304 patients were included in the analysis. The AI-driven phenotyping approach identified two distinct clusters, representing 51% (Cluster 1) and 49% (Cluster 2) of the dataset. Compared to Cluster 1, Cluster 2 exhibited significantly higher absenteeism (17.00 vs. 5.22 days, <i>p <</i> 0.05), lower WAI (33.34 <i>±</i> 6.84 vs. 38.96 <i>±</i> 4.31, <i>p <</i> 0.05), worse pain-related outcomes in terms of higher VAS (5.98 <i>±</i> 2.06 vs. 4.48 <i>±</i> 2.48, <i>p <</i> 0.05) and ODI (33.52 <i>±</i> 16.56 vs. 20.08 <i>±</i> 13.59, <i>p <</i> 0.05), more frequent occupational exposure to manual handling of loads (84% vs. 16%) and higher psychological distress assessed through PHQ-2 (70% vs. 30%).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Our study identified the most relevant PROMs differentiating between cLBP clusters of patients, emphasizing different levels of absenteeism and pain-related outcomes.</p>\n \n <p>These findings contributed to unravel the data-driven AI potential in suggesting personalized interventions targeting specific biopsychosocial profiles, which may improve clinical outcomes and occupational functioning in workers with cLBP, ultimately enhancing their overall well-being.</p>\n </section>\n </div>","PeriodicalId":14876,"journal":{"name":"JOR Spine","volume":"8 3","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442893/pdf/","citationCount":"0","resultStr":"{\"title\":\"Exploring the Biopsychosocial Impact of Chronic Low Back Pain in Workers Through Artificial Intelligence-Driven Phenotyping\",\"authors\":\"Paolo Giaccone, Elisabetta de Rinaldis, Federico D'Antoni, Fabrizio Russo, Luca Ambrosio, Giorgia Petrucci, Mario Merone, Leandro Pecchia, Sergio Iavicoli, Gianluca Vadalà, Rocco Papalia, Vincenzo Denaro\",\"doi\":\"10.1002/jsp2.70110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Study Design</h3>\\n \\n <p>Cross-sectional retrospective cohort study.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>Chronic low back pain (cLBP) is a major cause of disability worldwide, significantly affecting return to work (RTW). 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引用次数: 0
摘要
研究设计：横断面回顾性队列研究。目的：慢性腰痛（cLBP）是世界范围内致残的主要原因，严重影响重返工作岗位（RTW）。本研究旨在利用人工智能（AI）数据驱动的患者表型分析，评估影响cLBP工人职业功能的生物心理社会因素。方法：通过人口统计学、临床和职业因素的综合评估，收集受cLBP影响的工人的资料。基于患者报告的结果测量（PROMs），包括Oswestry残疾指数（ODI）、视觉模拟量表（VAS）、工作能力指数（WAI）、北欧评分和患者健康问卷-2 (PHQ-2)，采用分层聚类来识别不同的表型。独立t检验和Mann-Whitney U检验用于表型分析，分别区分连续反应和分类反应，以评估最具判别性的查询并突出最显著的不同特征(p)结果：共有304名患者被纳入分析。人工智能驱动的表型方法确定了两个不同的集群，分别占数据集的51%（集群1）和49%（集群2）。与集群1相比，集群2的旷工率显著高于集群1 (17.00 vs. 5.22天，p 0.05)， WAI(33.34±6.84 vs. 38.96±4.31,p 0.05)，疼痛相关结果(VAS（5.98±2.06 vs. 4.48±2.48,p 0.05）和ODI（33.52±16.56 vs. 20.08±13.59,p 0.05）更差，更频繁的职业暴露于手工处理负荷（84% vs. 16%），通过PHQ-2评估的心理困扰（70% vs. 30%）更高。结论：我们的研究确定了与cLBP患者群最相关的PROMs，强调了不同程度的缺勤和疼痛相关的结果。这些发现有助于揭示数据驱动的人工智能潜力，提出针对特定生物心理社会特征的个性化干预措施，这可能会改善cLBP患者的临床结果和职业功能，最终提高他们的整体幸福感。
Exploring the Biopsychosocial Impact of Chronic Low Back Pain in Workers Through Artificial Intelligence-Driven Phenotyping
Study Design
Cross-sectional retrospective cohort study.
Objectives
Chronic low back pain (cLBP) is a major cause of disability worldwide, significantly affecting return to work (RTW). This study aimed to assess the biopsychosocial factors influencing occupational functioning in workers with cLBP using artificial intelligence (AI) data-driven patient phenotyping.
Methods
Data of workers affected by cLBP were collected through a comprehensive assessment of demographic, clinical, and occupational factors. Hierarchical clustering was employed to identify distinct phenotypes based on patient-reported outcome measures (PROMs), including the Oswestry Disability Index (ODI), Visual Analog Scale (VAS), Work Ability Index (WAI), Nordic score, and Patient Health Questionnaire-2 (PHQ-2). Independent t tests and Mann–Whitney U tests were used for phenotype profiling, distinguishing between continuous and categorical responses, respectively, to assess the most discriminative queries and highlight the most significantly different features (p < 0.05).
Results
A total of 304 patients were included in the analysis. The AI-driven phenotyping approach identified two distinct clusters, representing 51% (Cluster 1) and 49% (Cluster 2) of the dataset. Compared to Cluster 1, Cluster 2 exhibited significantly higher absenteeism (17.00 vs. 5.22 days, p < 0.05), lower WAI (33.34 ± 6.84 vs. 38.96 ± 4.31, p < 0.05), worse pain-related outcomes in terms of higher VAS (5.98 ± 2.06 vs. 4.48 ± 2.48, p < 0.05) and ODI (33.52 ± 16.56 vs. 20.08 ± 13.59, p < 0.05), more frequent occupational exposure to manual handling of loads (84% vs. 16%) and higher psychological distress assessed through PHQ-2 (70% vs. 30%).
Conclusion
Our study identified the most relevant PROMs differentiating between cLBP clusters of patients, emphasizing different levels of absenteeism and pain-related outcomes.
These findings contributed to unravel the data-driven AI potential in suggesting personalized interventions targeting specific biopsychosocial profiles, which may improve clinical outcomes and occupational functioning in workers with cLBP, ultimately enhancing their overall well-being.
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