Maarseveen Tjardo Daniël, Glas Herman Kasper, Veris-van Dieren Josien, van den Akker Erik, Knevel Rachel
Department of Rheumatology, Leiden University Medical Center, Leiden, Zuid-Holland, the Netherlands.
Rheumatology outpatient clinics, Reumazorg Zuid West Nederland, Goes, Zeeland, the Netherlands.
NPJ Digit Med. 2025 Feb 14;8(1):98. doi: 10.1038/s41746-025-01495-4.
Musculoskeletal complaints account for 30% of GP consultations, with many referred to rheumatology clinics via letters. This study developed a Machine Learning (ML) pipeline to prioritize referrals by identifying rheumatoid arthritis (RA), osteoarthritis, fibromyalgia, and patients requiring long-term care. Using 8044 referral letters from 5728 patients across 12 clinics, we trained and validated ML models in two large centers and tested their generalizability in the remaining ten. The models were robust, with RA achieving an AUC-ROC of 0.78 (CI: 0.74-0.83), osteoarthritis 0.71 (CI: 0.67-0.74), fibromyalgia 0.81 (CI: 0.77-0.85), and chronic follow-up 0.63 (CI: 0.61-0.66). The RA-classifier outperformed manual referral systems, as it prioritised RA over non-RA cases (P < 0.001), while the manual referral system could not differentiate between the two. The other classifiers showed similar prioritisation improvements, highlighting the potential to enhance care efficiency, reduce clinician workload, and facilitate earlier specialized care. Future work will focus on building clinical decision-support tools.
肌肉骨骼疾病主诉占全科医生会诊病例的30%，许多患者通过信件被转诊至风湿病诊所。本研究开发了一种机器学习（ML）流程，通过识别类风湿性关节炎（RA）、骨关节炎、纤维肌痛以及需要长期护理的患者来对转诊进行优先级排序。我们使用来自12家诊所5728名患者的8044封转诊信，在两个大型中心对ML模型进行了训练和验证，并在其余十个中心测试了其通用性。这些模型性能强劲，RA的曲线下面积（AUC-ROC）为0.78（置信区间：0.74 - 0.83），骨关节炎为0.71（置信区间：0.67 - 0.74），纤维肌痛为0.81（置信区间：0.77 - 0.85），慢性随访为0.63（置信区间：0.61 - 0.66）。RA分类器优于人工转诊系统，因为它将RA病例的优先级置于非RA病例之上（P < 0.001），而人工转诊系统无法区分两者。其他分类器也显示出类似的优先级提升，凸显了提高护理效率、减轻临床医生工作量以及促进早期专科护理的潜力。未来的工作将专注于构建临床决策支持工具。
