{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T22:02:40Z","timestamp":1791756160418,"version":"4.3.4"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Hip fractures represent a significant global health burden, with high mortality rates. Accurate prediction of 30-d postoperative mortality is instrumental to optimize patient care. This study aimed to validate previously developed logistic regression and machine learning (ML) models for predicting 30-d mortality after hip fracture surgery measured from the date of the index operation (counting deaths occurring in-hospital or within 30\u00a0d after surgery) and compare their performance against one established calculator. We included 47\u2009276 patients aged \u226565\u00a0yr who underwent hip fracture surgery, excluding cancer, atypical, pathologic, and stress fractures. We applied previously derived logistic regression and ML equations from the 2011 to 2017 National Surgical Quality Improvement Program (NSQIP) dataset, to the updated 2018-2020 dataset. We developed 2 models for each, one derived on admission and one taking into account operative course. For each of these models, we also developed a full model and a parsimonious counterpart. We assessed discrimination using the area under the curve (AUC) metric. We compared AUCs for models with the DeLong test. We developed an online calculator for our most highly performing model. We also benchmarked our preoperative logistic and ML calculators against a previously published preoperative model. The logistic models demonstrated acceptable to good discrimination with AUCs as follows: preoperative full of 0.756, preoperative parsimonious of 0.748, postoperative full of 0.829, and postoperative parsimonious of 0.817. Machine learning models maintained good discrimination performance: preoperative full, 0.772; preoperative parsimonious, 0.751; postoperative full, 0.865; and postoperative parsimonious, 0.838. Machine learning models were slightly superior to logistic regression models. A previously published model by Harris et\u00a0al. had more variables and demonstrated no significant superiority. Both logistic regression and ML approaches offer clinically useful predictions for 30-d mortality after hip fracture surgery. Prospective validation in other populations and then integration of the parsimonious models into clinical practice may enhance perioperative decision-making and possibly patient outcomes.<\/jats:p>","DOI":"10.1093\/jbmrpl\/ziag079","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:27:00Z","timestamp":1777289220000},"source":"Crossref","is-referenced-by-count":1,"title":["Are machine learning models superior to logistic regression models to predict 30-day mortality post-hip fracture surgery?"],"prefix":"10.1093","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0174-7454","authenticated-orcid":false,"given":"Zeinab","family":"Issa","sequence":"first","affiliation":[{"name":"Division of Endocrinology, American University of Beirut Medical Center , Beirut 1107 2020,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fouad","family":"Trad","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, American University of Beirut , Beirut 1107 2020,","place":["Lebanon"]},{"name":"Electrical and Computer Engineering Department, Lebanese American University , Byblos P.O. Box 36 ,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9183-7395","authenticated-orcid":false,"given":"Ryan","family":"Yammine","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, American University of Beirut Medical Center , Beirut 1107 2020,","place":["Lebanon"]},{"name":"Calcium Metabolism and Osteoporosis Program, WHO Collaborating Center for Metabolic Bone Disorders, American University of Beirut Medical Center , Beirut 1107 2020 ,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1411-5121","authenticated-orcid":false,"given":"Caline","family":"Rhayem","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, American University of Beirut Medical Center , Beirut 1107 2020,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5727-2613","authenticated-orcid":false,"given":"Aya","family":"Ghosn","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Memorial Sloan Kettering , New York, NY 10065 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joudie","family":"Alwan","sequence":"additional","affiliation":[{"name":"Department of Pediatric Emergency Medicine, Children\u2019s Hospital of Michigan , Detroit, MI 48201 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani","family":"Tamim","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Clinical Research Institute, American University of Beirut Medical Center , Beirut 1107 2020 ,","place":["Lebanon"]},{"name":"College of Medicine, Alfaisal University , Riyadh 11533 ,","place":["Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachid","family":"Haidar","sequence":"additional","affiliation":[{"name":"Division of Orthopedic Surgery, American University of Beirut Medical Center , Beirut 1107 2020 ,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Chehab","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, American University of Beirut , Beirut 1107 2020,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghada","family":"El-Hajj Fuleihan","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, American University of Beirut Medical Center , Beirut 1107 2020,","place":["Lebanon"]},{"name":"Calcium Metabolism and Osteoporosis Program, WHO Collaborating Center for Metabolic Bone Disorders, American University of Beirut Medical Center , Beirut 1107 2020 ,","place":["Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"issue":"9","key":"2026062003062604200_ref1","doi-asserted-by":"crossref","first-page":"e580","DOI":"10.1016\/S2666-7568(21)00172-0","article-title":"Global, regional, and national burden of bone fractures in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019","volume":"2","author":"GBD 2019 Fracture Collaborators","year":"2021","journal-title":"Lancet Healthy Longev"},{"key":"2026062003062604200_ref2","doi-asserted-by":"publisher","first-page":"105832","DOI":"10.1016\/j.archger.2025.105832","article-title":"Global, regional, and national burdens of hip fractures in elderly individuals from 1990 to 2021 and predictions up to 2050: a systematic analysis of the Global Burden of Disease Study 2021","volume":"133","author":"Tian","year":"2025","journal-title":"Arch Gerontol Geriatr"},{"issue":"8","key":"2026062003062604200_ref3","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1016\/j.injury.2018.04.015","article-title":"Epidemiology and social costs of hip fracture","volume":"49","author":"Veronese","year":"2018","journal-title":"Injury"},{"key":"2026062003062604200_ref4","volume-title":"Osteoporosis Facts and Statistics","author":"International Osteoporosis Foundation"},{"issue":"11","key":"2026062003062604200_ref5","doi-asserted-by":"publisher","first-page":"3210","DOI":"10.1210\/clinem\/dgaf087","article-title":"Secular trends in hip fracture mortality and predictors of mortality from the NSQIP database","volume":"110","author":"Rhayem","year":"2025","journal-title":"J Clin Endocrinol Metab"},{"issue":"1","key":"2026062003062604200_ref6","doi-asserted-by":"publisher","first-page":"22922","DOI":"10.1038\/s41598-025-98713-6","article-title":"Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery","volume":"15","author":"Trad","year":"2025","journal-title":"Sci Rep"},{"issue":"1","key":"2026062003062604200_ref7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","article-title":"External validation of prognostic models: what, why, how, when and where?","volume":"14","author":"Ramspek","year":"2020","journal-title":"Clin Kidney J"},{"issue":"12","key":"2026062003062604200_ref8","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1097\/CORR.0000000000002294","article-title":"A tool to estimate risk of 30-day mortality and complications after hip fracture surgery: accurate enough for some but not all purposes? A study from the ACS-NSQIP database","volume":"480","author":"Harris","year":"2022","journal-title":"Clin Orthop Relat Res"},{"issue":"3","key":"2026062003062604200_ref9","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"issue":"3","key":"2026062003062604200_ref10","doi-asserted-by":"publisher","first-page":"100600","DOI":"10.1016\/j.jscai.2023.100600","article-title":"Assessment of TVT and STS risk score performances in patients undergoing transcatheter aortic valve replacement","volume":"2","author":"Al-Azizi","year":"2023","journal-title":"J Soc Cardiovasc Angiogr Interv"},{"issue":"1","key":"2026062003062604200_ref11","doi-asserted-by":"publisher","DOI":"10.1186\/s12871-023-02354-z","article-title":"Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study","volume":"23","author":"Michelsen","year":"2023","journal-title":"BMC Anesthesiol"},{"issue":"8","key":"2026062003062604200_ref12","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.injury.2010.04.023","article-title":"Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture","volume":"41","author":"Lin","year":"2010","journal-title":"Injury"},{"issue":"3","key":"2026062003062604200_ref13","doi-asserted-by":"publisher","first-page":"e233391","DOI":"10.1001\/jamanetworkopen.2023.3391","article-title":"Artificial intelligence for hip fracture detection and outcome prediction: a systematic review and meta-analysis","volume":"6","author":"Lex","year":"2023","journal-title":"JAMA Netw Open"}],"container-title":["JBMR Plus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jbmrplus\/advance-article-pdf\/doi\/10.1093\/jbmrpl\/ziag079\/68178076\/ziag079.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jbmrplus\/article-pdf\/10\/7\/ziag079\/68178076\/ziag079.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jbmrplus\/article-pdf\/10\/7\/ziag079\/68178076\/ziag079.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T07:06:35Z","timestamp":1781939195000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jbmrplus\/article\/doi\/10.1093\/jbmrpl\/ziag079\/8664005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":13,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jbmrpl\/ziag079","relation":{},"ISSN":["2473-4039"],"issn-type":[{"value":"2473-4039","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,7]]},"published":{"date-parts":[[2026,4,28]]},"article-number":"ziag079"}}