Baroncini Alice, Boissiere Louis, Larrieu Daniel, Haddad Sleiman, Pellisé Ferran, Alanay Ahmet, Kleinstueck Frank, Pizones Javier, Bourghli Anouar, Obeid Ibrahim
IRCCS Ospedale Galeazzi Sant'Ambrogio, Milano, Italy.
Spine Surgery Unit 1, Bordeaux University Pellegrin Hospital, Bordeaux, France.
Spine (Phila Pa 1976). 2025 Jul 15;50(14):975-980. doi: 10.1097/BRS.0000000000005173. Epub 2024 Oct 1.
Multicentric, retrospective analysis of prospectively collected data.
To utilize machine learning (ML) for clustering and management prediction (conservative vs . operative) in surgically treated adult spine deformity (ASD) patients, and to compare the attainment of the minimum clinically important difference (MCID) between predicted surgical and conservative patients.
Management choice in ASD is complex. ML can identify patient clusters and predict treatment, but it is unclear whether patients treated according to the prediction also show better clinical outcomes.
ASD patients (2-yr follow-up) were divided into groups using k-means clustering. Management choice was predicted among operated patients in each cluster. The MCID for the Oswestry Disability Index (ODI) and the Scoliosis Research Society-22 (SRS-22) were calculated and compared between patients with and without surgical prediction.
In cluster 1 (idiopathic scoliosis, n=675, 150 surgeries), 57% of patients had a conservative prediction. Of these, 52% and 49% achieved MCID for ODI and SRS-22, respectively, compared with 68% and 75% for those with surgical predictions [odds ratio (OR)=2 and 3.1, respectively].In cluster 2 (moderate sagittal imbalance, n=561, 200 surgeries), 12% had a conservative prediction. Of these, 29% and 46% achieved MCID for ODI and SRS-22, respectively, compared with 47% and 56% for those with surgical predictions.In cluster 3 (significant sagittal imbalance, n=537, 197 surgeries), 17% had a conservative prediction. Of these, 12% and 15% achieved MCID for ODI and SRS-22, respectively, compared with 37% and 45% for those with surgical predictions (OR=4.2 and 4.5, respectively).
Patients with concordant surgical prediction and management had higher odds of achieving the MCID, indicating a good correlation between prediction and clinical outcomes. In cluster 3, the low percentage of patients with conservative prediction achieving the MCID suggests that ML could well identify patients with poor clinical outcomes.
对前瞻性收集的数据进行多中心回顾性分析。
利用机器学习（ML）对接受手术治疗的成人脊柱畸形（ASD）患者进行聚类和管理预测（保守治疗与手术治疗），并比较预测接受手术和保守治疗的患者在达到最小临床重要差异（MCID）方面的情况。
ASD的管理选择很复杂。ML可以识别患者聚类并预测治疗方案，但尚不清楚根据预测进行治疗的患者是否也能获得更好的临床结果。
将ASD患者（随访2年）使用k均值聚类法分组。对每个聚类中接受手术的患者预测管理选择。计算并比较有和没有手术预测的患者在Oswestry功能障碍指数（ODI）和脊柱侧弯研究学会-22（SRS-22）方面的MCID。
在聚类1（特发性脊柱侧弯，n = 675，150例手术）中，57%的患者有保守治疗预测。其中，分别有52%和49%的患者在ODI和SRS-22方面达到MCID，而有手术预测的患者这一比例分别为68%和75%[优势比（OR）分别为2和3.1]。在聚类2（中度矢状面失衡，n = 561，200例手术）中，12%的患者有保守治疗预测。其中，分别有29%和46%的患者在ODI和SRS-22方面达到MCID，而有手术预测的患者这一比例分别为47%和56%。在聚类3（显著矢状面失衡，n = 537，197例手术）中，17%的患者有保守治疗预测。其中，分别有12%和15%的患者在ODI和SRS-22方面达到MCID，而有手术预测的患者这一比例分别为37%和45%（OR分别为4.2和4.5）。
手术预测与管理一致的患者达到MCID的几率更高，表明预测与临床结果之间具有良好的相关性。在聚类3中，保守治疗预测的患者达到MCID的比例较低，这表明ML能够很好地识别临床结果较差的患者。
