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Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy
Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determi...
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Published in: | PloS one 2019-04, Vol.14 (4), p.e0215133-e0215133 |
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description | Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery. |
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Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0215133</identifier><identifier>PMID: 30947300</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Artificial intelligence ; Biology and Life Sciences ; Body weight ; Bone surgery ; Care and treatment ; Central nervous system diseases ; Classification ; Comorbidity ; Compression ; Computer and Information Sciences ; Data processing ; Decompression, Surgical - adverse effects ; Demographic variables ; Demographics ; Demography ; Engineering and Technology ; Female ; Humans ; Information management ; Intervertebral Disc Degeneration - surgery ; Learning algorithms ; Machine Learning ; Male ; Mathematical models ; Medical personnel ; Medical prognosis ; Medical research ; Medicine and Health Sciences ; Middle Aged ; Neurosurgery ; Optimization ; Patients ; Performance prediction ; Physical Sciences ; Physicians ; Postoperative Complications - diagnosis ; Postoperative Complications - etiology ; Prediction models ; Predictive Value of Tests ; Prospective Studies ; Quality of life ; Research and Analysis Methods ; Risk factors ; Sensitivity analysis ; Signs and symptoms ; Smoking ; Spinal cord ; Spinal cord diseases ; Spinal Cord Diseases - surgery ; Spine ; Surgery ; Surgical outcomes ; Training ; Treatment Outcome</subject><ispartof>PloS one, 2019-04, Vol.14 (4), p.e0215133-e0215133</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Merali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.</description><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Biology and Life Sciences</subject><subject>Body weight</subject><subject>Bone surgery</subject><subject>Care and treatment</subject><subject>Central nervous system diseases</subject><subject>Classification</subject><subject>Comorbidity</subject><subject>Compression</subject><subject>Computer and Information Sciences</subject><subject>Data processing</subject><subject>Decompression, Surgical - adverse effects</subject><subject>Demographic variables</subject><subject>Demographics</subject><subject>Demography</subject><subject>Engineering and Technology</subject><subject>Female</subject><subject>Humans</subject><subject>Information management</subject><subject>Intervertebral Disc Degeneration - surgery</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medical personnel</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Neurosurgery</subject><subject>Optimization</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Physical Sciences</subject><subject>Physicians</subject><subject>Postoperative Complications - 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Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30947300</pmid><doi>10.1371/journal.pone.0215133</doi><tpages>e0215133</tpages><orcidid>https://orcid.org/0000-0002-2528-1684</orcidid><orcidid>https://orcid.org/0000-0002-5722-6364</orcidid><orcidid>https://orcid.org/0000-0002-3999-7632</orcidid><oa>free_for_read</oa></addata></record> |
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recordid | cdi_plos_journals_2204193750 |
source | Publicly Available Content Database (Proquest) (PQ_SDU_P3); PubMed Central Free |
subjects | Analysis Artificial intelligence Biology and Life Sciences Body weight Bone surgery Care and treatment Central nervous system diseases Classification Comorbidity Compression Computer and Information Sciences Data processing Decompression, Surgical - adverse effects Demographic variables Demographics Demography Engineering and Technology Female Humans Information management Intervertebral Disc Degeneration - surgery Learning algorithms Machine Learning Male Mathematical models Medical personnel Medical prognosis Medical research Medicine and Health Sciences Middle Aged Neurosurgery Optimization Patients Performance prediction Physical Sciences Physicians Postoperative Complications - diagnosis Postoperative Complications - etiology Prediction models Predictive Value of Tests Prospective Studies Quality of life Research and Analysis Methods Risk factors Sensitivity analysis Signs and symptoms Smoking Spinal cord Spinal cord diseases Spinal Cord Diseases - surgery Spine Surgery Surgical outcomes Training Treatment Outcome |
title | Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T14%3A11%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20a%20machine%20learning%20approach%20to%20predict%20outcome%20after%20surgery%20for%20degenerative%20cervical%20myelopathy&rft.jtitle=PloS%20one&rft.au=Merali,%20Zamir%20G&rft.date=2019-04-04&rft.volume=14&rft.issue=4&rft.spage=e0215133&rft.epage=e0215133&rft.pages=e0215133-e0215133&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0215133&rft_dat=%3Cgale_plos_%3EA581258472%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c758t-f35a4b5154400f1c17d4924ce26eabc3a6e62146f991ab0fdba875090411e2c73%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2204193750&rft_id=info:pmid/30947300&rft_galeid=A581258472&rfr_iscdi=true |