Alice Baroncini, Louis Boissiere, Daniel Larrieu, Sleiman Haddad, Ferran Pellisé, Ahmet Alanay, Frank Kleinstueck, Javier Pizones, Anouar Bourghli, Ibrahim Obeid
{"title":"成人脊柱畸形 (ASD) 患者群之间有无预测手术指征患者的比较。","authors":"Alice Baroncini, Louis Boissiere, Daniel Larrieu, Sleiman Haddad, Ferran Pellisé, Ahmet Alanay, Frank Kleinstueck, Javier Pizones, Anouar Bourghli, Ibrahim Obeid","doi":"10.1097/BRS.0000000000005173","DOIUrl":null,"url":null,"abstract":"<p><strong>Study design: </strong>Multicentric, retrospective analysis of prospectively collected data.</p><p><strong>Objective: </strong>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.</p><p><strong>Summary of background data: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":"975-980"},"PeriodicalIF":2.6000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of Patients With and Without Predicted Surgical Indication Between Clusters of Adult Spine Deformity (ASD) Patients.\",\"authors\":\"Alice Baroncini, Louis Boissiere, Daniel Larrieu, Sleiman Haddad, Ferran Pellisé, Ahmet Alanay, Frank Kleinstueck, Javier Pizones, Anouar Bourghli, Ibrahim Obeid\",\"doi\":\"10.1097/BRS.0000000000005173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>Multicentric, retrospective analysis of prospectively collected data.</p><p><strong>Objective: </strong>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.</p><p><strong>Summary of background data: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":22193,\"journal\":{\"name\":\"Spine\",\"volume\":\" \",\"pages\":\"975-980\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/BRS.0000000000005173\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/BRS.0000000000005173","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Comparison of Patients With and Without Predicted Surgical Indication Between Clusters of Adult Spine Deformity (ASD) Patients.
Study design: Multicentric, retrospective analysis of prospectively collected data.
Objective: 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.
Summary of background data: 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.
Materials and methods: 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.
Results: 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).
Conclusion: 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.
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Recognized internationally as the leading journal in its field, Spine is an international, peer-reviewed, bi-weekly periodical that considers for publication original articles in the field of Spine. It is the leading subspecialty journal for the treatment of spinal disorders. Only original papers are considered for publication with the understanding that they are contributed solely to Spine. The Journal does not publish articles reporting material that has been reported at length elsewhere.
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