{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:27:28Z","timestamp":1775251648941,"version":"3.50.1"},"reference-count":27,"publisher":"BMJ","issue":"9","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"unspecified","delay-in-days":4,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Higher Education Innovation Fund Project of Gansu","award":["2022B-109"],"award-info":[{"award-number":["2022B-109"]}]},{"name":"Lanzhou Talent Innovation and Entrepreneurship Project, Grant","award":["2022-3-25."],"award-info":[{"award-number":["2022-3-25."]}]},{"name":"Construction Project of National Famous Old Chinese Medicine Expert Inheritance Studio of Zhang Xiaogang","award":["(Chinese Medicine Education Letter [2022] No. 75)"],"award-info":[{"award-number":["(Chinese Medicine Education Letter [2022] No. 75)"]}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["23JRRA1192"],"award-info":[{"award-number":["23JRRA1192"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2023,8,8]]},"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Lumbar degenerative disease (LDD) is one of the most common reasons for patients to present with low back pain. Proper evaluation and treatment of patients with LDD are important, which clinicians perform using a variety of predictors for guidance in choosing the most appropriate treatment. Because evidence on which treatment is best for LDD is limited, the purpose of this study is to establish a clinical prediction model based on machine learning (ML) to accurately predict outcomes of patients with LDDs in the early stages by their clinical characteristics and imaging changes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>In this study, we develop and validate a clinical prognostic model to determine whether patients will experience complications within 6 months after percutaneous endoscopic lumbar discectomy (PELD). Baseline data will be collected from patients\u2019 electronic medical records. As of now, we have recruited a total of 580 participants (n=400 for development, n=180 for validation). The study\u2019s primary outcome will be the incidence of complications within 6 months after PELD. We will use an ML algorithm and a multiple logistic regression analysis model to screen factors affecting surgical efficacy. We will evaluate the calibration and differentiation performance of the model by the area under the curve. Sensitivity (Sen), specificity, positive predictive value and negative predictive value will be reported in the validation data set, with a target of 80% Sen. The results of this study could better illustrate the performance of the clinical prediction model, ultimately helping both clinicians and patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>Ethical approval was obtained from the medical ethics committee of the Affiliated Hospital of Gansu University of Traditional Chinese Medicine (Lanzhou, China; No. 2022-57). Findings and related data will be disseminated in peer-reviewed journals, at conferences, and through open scientific frameworks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration number<\/jats:title>\n                    <jats:p>\n                      Chinese Clinical Trial Register (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"www.chictr.org.cn\">www.chictr.org.cn<\/jats:ext-link>\n                      ) No. ChiCTR2200064421.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2023-072139","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T11:40:52Z","timestamp":1693914052000},"page":"e072139","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Development and validation of a multimodal feature fusion prognostic model for lumbar degenerative disease based on machine learning: a study protocol"],"prefix":"10.1136","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1857-9156","authenticated-orcid":false,"given":"Zhipeng","family":"Wang","sequence":"first","affiliation":[{"name":"Clinical College of Traditional Chinese Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"},{"name":"Department of Orthopedics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiyun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Clinical College of Traditional Chinese Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"},{"name":"Department of Orthopedics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzhen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daping","family":"Qin","sequence":"additional","affiliation":[{"name":"Clinical College of Traditional Chinese Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Qi","sequence":"additional","affiliation":[{"name":"Clinical College of Traditional Chinese Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Clinical College of Traditional Chinese Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1953-848X","authenticated-orcid":false,"given":"Xiaogang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Clinical College of Traditional Chinese Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"},{"name":"Department of Orthopedics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"2025120619150245000_13.9.e072139.1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)00733-9"},{"key":"2025120619150245000_13.9.e072139.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.spinee.2012.12.003"},{"key":"2025120619150245000_13.9.e072139.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmrv.2011.04.003"},{"key":"2025120619150245000_13.9.e072139.4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ijotn.2016.09.001","article-title":"Factors and concerns of patients that influence the decision for spinal surgery and implications for practice: a review of literature","volume":"25","author":"Lam","year":"2017","journal-title":"Int J Orthop Trauma Nurs"},{"key":"2025120619150245000_13.9.e072139.5","doi-asserted-by":"crossref","first-page":"327","DOI":"10.3340\/jkns.2016.59.4.327","article-title":"Decision-making algorithm for adult spinal deformity surgery","volume":"59","author":"Kim","year":"2016","journal-title":"J Korean Neurosurg Soc"},{"key":"2025120619150245000_13.9.e072139.6","doi-asserted-by":"publisher","DOI":"10.1007\/s00586-009-1155-x"},{"key":"2025120619150245000_13.9.e072139.7","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.spinee.2020.07.005","article-title":"A matched comparison of outcomes between percutaneous endoscopic lumbar discectomy and open lumbar microdiscectomy for the treatment of lumbar disc herniation: a 2-year retrospective cohort study","volume":"21","author":"Jarebi","year":"2021","journal-title":"Spine J"},{"key":"2025120619150245000_13.9.e072139.8","doi-asserted-by":"crossref","first-page":"e422","DOI":"10.1016\/j.wneu.2019.02.068","article-title":"Predictors for poor outcomes after percutaneous endoscopic lumbar discectomy: a retrospective study of 241 patients","volume":"126","author":"Shen","year":"2019","journal-title":"World Neurosurg"},{"key":"2025120619150245000_13.9.e072139.9","doi-asserted-by":"crossref","first-page":"304","DOI":"10.21037\/tlcr.2018.05.15","article-title":"Lung cancer prediction using machine learning and advanced imaging techniques","volume":"7","author":"Kadir","year":"2018","journal-title":"Transl Lung Cancer Res"},{"key":"2025120619150245000_13.9.e072139.10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ijmedinf.2017.11.010","article-title":"Evaluation of three machine learning models for self-referral decision support on low back pain in primary care","volume":"110","author":"Oude Nijeweme-d\u2019Hollosy","year":"2018","journal-title":"Int J Med Inform"},{"key":"2025120619150245000_13.9.e072139.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.03.008"},{"key":"2025120619150245000_13.9.e072139.12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s00586-018-5497-0","article-title":"Self-learning computers for surgical planning and prediction of postoperative alignment","volume":"27","author":"Lafage","year":"2018","journal-title":"Eur Spine J"},{"key":"2025120619150245000_13.9.e072139.13","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.115.001593"},{"key":"2025120619150245000_13.9.e072139.14","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.7150\/jca.34585","article-title":"Identification of a sixteen-gene prognostic biomarker for lung adenocarcinoma using a machine learning method","volume":"11","author":"Ma","year":"2020","journal-title":"J Cancer"},{"key":"2025120619150245000_13.9.e072139.15","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1038\/s41598-020-59115-y","article-title":"Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection","volume":"10","author":"Xu","year":"2020","journal-title":"Sci Rep"},{"key":"2025120619150245000_13.9.e072139.16","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1214\/10-STS330","article-title":"To explain or to predict","volume":"25","author":"Shmueli","year":"2010","journal-title":"Statist Sci"},{"key":"2025120619150245000_13.9.e072139.17","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"2025120619150245000_13.9.e072139.18","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9275"},{"key":"2025120619150245000_13.9.e072139.19","doi-asserted-by":"crossref","DOI":"10.1186\/s13063-021-05571-4","article-title":"Correction to: the role of machine learning in clinical research: transforming the future of evidence generation","volume":"22","author":"Weissler","year":"2021","journal-title":"Trials"},{"key":"2025120619150245000_13.9.e072139.20","doi-asserted-by":"crossref","DOI":"10.1155\/2016\/5978489","article-title":"Application of machine learning approaches for classifying sitting posture based on force and acceleration sensors","volume":"2016","author":"Zemp","year":"2016","journal-title":"Biomed Res Int"},{"key":"2025120619150245000_13.9.e072139.21","first-page":"603","article-title":"Use of artificial neural networks to decision making in patients with lumbar spinal canal stenosis","volume":"61","author":"Azimi","year":"2017","journal-title":"J Neurosurg Sci"},{"key":"2025120619150245000_13.9.e072139.22","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1097\/BRS.0000000000002442","article-title":"Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion","volume":"43","author":"Kim","year":"2018","journal-title":"Spine"},{"key":"2025120619150245000_13.9.e072139.23","doi-asserted-by":"publisher","DOI":"10.1016\/j.spinee.2018.11.009"},{"key":"2025120619150245000_13.9.e072139.24","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000014197","article-title":"Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department","volume":"98","author":"Chiew","year":"2019","journal-title":"Medicine (Baltimore)"},{"key":"2025120619150245000_13.9.e072139.25","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.bbagen.2016.05.019","article-title":"Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine","volume":"1860","author":"Lu","year":"2016","journal-title":"Biochim Biophys Acta"},{"key":"2025120619150245000_13.9.e072139.26","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"2025120619150245000_13.9.e072139.27","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2018-025611","article-title":"Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies","volume":"9","author":"Heus","year":"2019","journal-title":"BMJ Open"}],"container-title":["BMJ Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjopen-2023-072139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T03:15:13Z","timestamp":1765077313000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopen.bmj.com\/lookup\/doi\/10.1136\/bmjopen-2023-072139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9,5]]},"published-print":{"date-parts":[[2023,9]]}},"alternative-id":["10.1136\/bmjopen-2023-072139"],"URL":"https:\/\/doi.org\/10.1136\/bmjopen-2023-072139","relation":{},"ISSN":["2044-6055","2044-6055"],"issn-type":[{"value":"2044-6055","type":"print"},{"value":"2044-6055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}