Supervised Machine Learning for Predicting Length of Stay After Lumbar Arthrodesis: A Comprehensive Artificial Intelligence Approach.
Introduction: Few studies have evaluated the utility of machine learning techniques to predict and classify outcomes, such as length of stay (LOS), for lumbar fusion patients. Six supervised machine learning algorithms may be able to predict and classify whether a patient will experience a short or...
| Publicado en: | Journal of the American Academy of Orthopaedic Surgeons Vol. 30; no. 3; pp. 125 - 133 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157728535&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157728535 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1067151X 541Z jtl: Journal of the American Academy of Orthopaedic Surgeons issn: 1067151X maglogo: N pubinfo: dt: Feb2022 vid: 30 iid: 3 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 157728535 157728535 NLM34928886 157728535 10.5435/JAAOS-D-21-00241 NLM34928886 157728535 ppf: 125 ppct: 8 formats: tig: atl: Supervised Machine Learning for Predicting Length of Stay After Lumbar Arthrodesis: A Comprehensive Artificial Intelligence Approach. aug: au: Etzel, Christine M Veeramani, Ashwin Zhang, Andrew S McDonald, Christopher L DiSilvestro, Kevin J Cohen, Eric M Daniels, Alan H affil: From the Department of Orthopedic Surgery (Etzel, Zhang, McDonald, DiSilvestro, Cohen, and Daniels), Warren Alpert Medical School of Brown University, Rhode Island Hospital, and the Division of Applied Mathematics (Veeramani), Brown University, Providence, RI sug: subj: Spinal Fusion Methods Retrospective Design Lumbar Vertebrae Surgery Length of Stay Artificial Intelligence Human ab: Introduction: Few studies have evaluated the utility of machine learning techniques to predict and classify outcomes, such as length of stay (LOS), for lumbar fusion patients. Six supervised machine learning algorithms may be able to predict and classify whether a patient will experience a short or long hospital LOS after lumbar fusion surgery with a high degree of accuracy.Methods: Data were obtained from the National Surgical Quality Improvement Program between 2009 and 2018. Demographic and comorbidity information was collected for patients who underwent anterior, anterolateral, or lateral transverse process technique arthrodesis procedure; anterior lumbar interbody fusion (ALIF); posterior, posterolateral, or lateral transverse process technique arthrodesis procedure; posterior lumbar interbody fusion/transforaminal lumbar interbody fusion (PLIF/TLIF); and posterior fusion procedure posterior spine fusion (PSF). Machine learning algorithmic analyses were done with the scikit-learn package in Python on a high-performance computing cluster. In the total sample, 85% of patients were used for training the models, whereas the remaining patients were used for testing the models. C-statistic area under the curve and prediction accuracy (PA) were calculated for each of the models to determine their accuracy in correctly classifying the test cases.Results: In total, 12,915 ALIF patients, 27,212 PLIF/TLIF patients, and 23,406 PSF patients were included in the algorithmic analyses. The patient factors most strongly associated with LOS were sex, ethnicity, dialysis, and disseminated cancer. The machine learning algorithms yielded area under the curve values of between 0.673 and 0.752 (PA: 69.6% to 80.1%) for ALIF, 0.673 and 0.729 (PA: 66.0% to 81.3%) for PLIF/TLIF, and 0.698 and 0.749 (PA: 69.9% to 80.4%) for PSF.Conclusion: Machine learning classification algorithms were able to accurately predict long LOS for ALIF, PLIF/TLIF, and PSF patients. Supervised machine learning algorithms may be useful in clinical and administrative settings. These data may additionally help inform predictive analytic models and assist in setting patient expectations.Level Iii: Diagnostic study, retrospective cohort study. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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