{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T02:45:27Z","timestamp":1785725127694,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: This study evaluated whether exploratory spinopelvic compensation patterns are associated with 12-month patient-reported recovery after total knee arthroplasty (TKA), with explicit separation between baseline-only prediction and contemporaneous association-based risk stratification. Materials and Methods: This retrospective single-center cohort included 700 complete cases selected from 723 primary TKA records after 23 exclusions for incomplete key radiographic alignment data and\/or unavailable 12-month patient-reported outcome measures (PROMs). The primary endpoint was a strict composite MCID-based endpoint, defined as failure to achieve the prespecified minimal clinically important difference (MCID) for either the Oxford Knee Score (OKS) or WOMAC at 12 months. OKS- and WOMAC-specific MCID failures were reported as component sensitivity outcomes. K-means clustering was used to define exploratory compensation patterns rather than validated preoperative phenotypes. Predictor timing was separated into a baseline-only model and a contemporaneous model that additionally used 12-month alignment-change variables. Results: The strict composite MCID endpoint occurred in 537\/700 patients (76.7%). Exploratory patterns showed increasing endpoint prevalence: Pattern A, 153\/215 (71.2%); Pattern B, 195\/258 (75.6%); and Pattern C, 189\/227 (83.3%). The baseline-only ridge logistic model showed moderate discrimination (out-of-fold AUC 0.715, 95% CI 0.670\u20130.758). The contemporaneous model showed higher internal discrimination (out-of-fold AUC 0.864, 95% CI 0.834\u20130.894), but it included alignment changes measured in the same follow-up window as the outcome. Conclusions: Baseline-only prediction was modest, whereas stronger model performance was observed only in a contemporaneous association model. The exploratory patterns should not be interpreted as stable clinical phenotypes or used for individual-level decisions without external validation.<\/jats:p>","DOI":"10.3390\/medicina62081481","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T01:43:27Z","timestamp":1785721407000},"page":"1481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploratory Spinopelvic Compensation Patterns Are Associated with a Strict Composite 12-Month MCID Endpoint After Total Knee Arthroplasty: A Retrospective Risk-Stratification Study"],"prefix":"10.3390","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0152-1065","authenticated-orcid":false,"given":"\u0130brahim","family":"Altun","sequence":"first","affiliation":[{"name":"Department of Orthopedics and Traumatology, Kayseri City Hospital SUAM, University of Health Sciences, 38080 Kayseri, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammed","family":"Melez","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, Memorial Health Group Kayseri Hospital, 38080 Kayseri, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1007\/s11420-018-9614-8","article-title":"Patient satisfaction after total knee replacement: A systematic review","volume":"14","author":"Kahlenberg","year":"2018","journal-title":"HSS J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Klem, N.R., Smith, A., O\u2019Sullivan, P., Dowsey, M.M., Sch\u00fctze, R., Kent, P., Choong, P.F., and Bunzli, S. (2021). What influences patient satisfaction after total knee replacement? A qualitative long-term follow-up study. BMJ Open, 11.","DOI":"10.1136\/bmjopen-2021-050385"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s00264-013-2081-4","article-title":"Anterior knee pain after total knee arthroplasty: A narrative review","volume":"38","author":"Petersen","year":"2014","journal-title":"Int. Orthop."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.knee.2021.02.027","article-title":"Relationships between the changes in flexion contracture and standing flexion angle of the knee and sagittal spinal alignment after total knee arthroplasty","volume":"29","author":"Han","year":"2021","journal-title":"Knee"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7348","DOI":"10.12998\/wjcc.v10.i21.7348","article-title":"Effect of osteoarthritic knee flexion deformity correction by total knee arthroplasty on sagittal spinopelvic alignment in Indian population","volume":"10","author":"Puthiyapura","year":"2022","journal-title":"World J. Clin. Cases"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2910","DOI":"10.1016\/j.arth.2025.05.011","article-title":"Type IIB spino-pelvic imbalance is an independent risk factor for flexion contracture after total knee arthroplasty","volume":"40","author":"Innocenti","year":"2025","journal-title":"J. Arthroplast."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9","DOI":"10.31616\/asj.2019.0359","article-title":"Spinopelvic alignment and low back pain before and after total knee arthroplasty","volume":"15","author":"Kitagawa","year":"2021","journal-title":"Asian Spine J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1002\/ksa.70047","article-title":"Reduced spinopelvic mobility does not correlate with knee flexion deformity in patients undergoing total knee arthroplasty","volume":"34","author":"Pichler","year":"2025","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1007\/s00402-022-04462-w","article-title":"Total knee arthroplasty in patients with lumbar spinal fusion leads to significant changes in pelvic tilt and sacral slope","volume":"143","author":"Shichman","year":"2023","journal-title":"Arch. Orthop. Trauma. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.spinee.2024.08.025","article-title":"The spinopelvic alignment in patients with prior knee or hip arthroplasty undergoing elective lumbar surgery","volume":"25","author":"Hambrecht","year":"2025","journal-title":"Spine J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, S.C., Choi, H.G., Kim, J.S., Kim, T.W., and Lee, Y.S. (2021). Effects of total knee arthroplasty on coronal and sagittal whole-body alignments: Serial assessments using whole-body EOS. J. Clin. Med., 10.","DOI":"10.3390\/jcm10153242"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1055\/s-0042-1747947","article-title":"The influence of knee osteoarthritis on spinopelvic alignment and global sagittal balance","volume":"36","author":"Katsumi","year":"2023","journal-title":"J. Knee Surg."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, W.S., Cho, B.W., Kwon, H.M., Kim, T.H., Park, K.K., and Park, J.Y. (2025). Predictors of flexion contracture progression following total knee arthroplasty: Role of global sagittal alignment and patient age. Eur. J. Med. Res., 30.","DOI":"10.1186\/s40001-025-03225-8"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jain, M., Pal, D.K., Sahu, R.N., Meher, B.R., Patro, B.K., and Tripathy, S.K. (2025). Effect of total knee arthroplasty on the spinopelvic parameters: A systematic review and meta-analysis. World J. Orthop., 16.","DOI":"10.5312\/wjo.v16.i7.107869"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jouve, J.L., Choufani, E., Peltier, E., Khal, A., and Pesenti, S. (2024). Neuromonitoring for spine surgery in children. Orthop. Traumatol. Surg. Res., 110.","DOI":"10.1016\/j.otsr.2023.103780"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, Y., Lee, M.L., Hsu, W., and Liow, M.H.L. (2024). Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models. npj Digit. Med., 7.","DOI":"10.1038\/s41746-024-01265-8"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ribbons, K., Cochrane, J., Johnson, S., Wills, A., Ditton, E., Dewar, D., Broadhead, M., Chan, I., Dixon, M., and Dunkley, C. (2025). Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-88560-w"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mohammed, H., Huang, Y., Memtsoudis, S., Parks, M., Huang, Y., and Ma, Y. (2022). Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0263897"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(19)30037-6","article-title":"Reporting of artificial intelligence prediction models","volume":"393","author":"Collins","year":"2019","journal-title":"Lancet"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1097\/EDE.0b013e3181577511","article-title":"Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration","volume":"18","author":"Vandenbroucke","year":"2007","journal-title":"Epidemiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Collins, G.S., Moons, K.G.M., Dhiman, P., Riley, R.D., Beam, A.L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J.B., and van Smeden, M. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385.","DOI":"10.1136\/bmj-2023-078378"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"for the PROBAST Group. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST: Explanation and elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1016\/j.arth.2015.06.012","article-title":"WOMAC, EQ-5D and Knee Society Score thresholds for treatment success after total knee arthroplasty","volume":"30","author":"Giesinger","year":"2015","journal-title":"J. Arthroplast."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1007\/s00167-013-2776-5","article-title":"The minimal clinically important difference in the Oxford Knee Score and Short Form 12 score after total knee arthroplasty","volume":"22","author":"Clement","year":"2014","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Holtz, N., Hamilton, D.F., Giesinger, J.M., Jost, B., and Giesinger, K. (2020). Minimal important differences for the WOMAC osteoarthritis index and the Forgotten Joint Score-12 in total knee arthroplasty patients. BMC Musculoskelet. Disord., 21.","DOI":"10.1186\/s12891-020-03415-x"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Khal, A.A., Apostu, D., Cosnarovici, R., Pesenti, S., Jouve, J.L., and Mihu, R.C. (2024). Cross-cultural adaptation and validation of the Romanian Musculoskeletal Tumor Society Scoring System for patients with extremity bone sarcomas. Medicina, 60.","DOI":"10.3390\/medicina60050778"},{"key":"ref_29","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jclinepi.2015.12.005","article-title":"A calibration hierarchy for risk models was defined: From utopia to empirical data","volume":"74","author":"Nieboer","year":"2016","journal-title":"J. Clin. Epidemiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.recesp.2011.04.017","article-title":"Performance measures for prediction models and markers: Evaluation of predictions and classifications","volume":"64","author":"Steyerberg","year":"2011","journal-title":"Rev. Esp. Cardiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1136\/heartjnl-2011-301246","article-title":"Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new marker","volume":"98","author":"Moons","year":"2012","journal-title":"Heart"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Riley, R.D., Snell, K.I.E., Archer, L., Ensor, J., Debray, T.P.A., Van Calster, B., van Smeden, M., and Collins, G.S. (2024). Evaluation of clinical prediction models: Calculating the sample size required for an external validation study. BMJ, 384.","DOI":"10.1136\/bmj-2023-074821"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Collins, G.S., de Groot, J.A., Dutton, S., Omar, O., Shanyinde, M., Tajar, A., Voysey, M., Wharton, R., Yu, L.-M., and Moons, K.G. (2014). External validation of multivariable prediction models: A systematic review of methodological conduct and reporting. BMC Med. Res. Methodol., 14.","DOI":"10.1186\/1471-2288-14-40"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jclinepi.2015.04.005","article-title":"Prediction models need appropriate internal, internal-external, and external validation","volume":"69","author":"Steyerberg","year":"2016","journal-title":"J. Clin. Epidemiol."}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/62\/8\/1481\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T02:17:35Z","timestamp":1785723455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/62\/8\/1481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,1]]},"references-count":36,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["medicina62081481"],"URL":"https:\/\/doi.org\/10.3390\/medicina62081481","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,1]]}}}