Update of a Model to Predict Outcomes after Endovascular Aneurysm Repair
•An interactive model predicts outcomes after elective endovascular aneurysm repair (EVAR).•Major predictive factors included fitness, ASA, stroke, age and aneurysm angle.•Other factors were white cell count, respiratory disease, diabetes and statins.•The model informs patients of potential risks or...
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Veröffentlicht in: | Annals of vascular surgery 2021-08, Vol.75, p.430-444 |
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description | •An interactive model predicts outcomes after elective endovascular aneurysm repair (EVAR).•Major predictive factors included fitness, ASA, stroke, age and aneurysm angle.•Other factors were white cell count, respiratory disease, diabetes and statins.•The model informs patients of potential risks or likely outcomes after EVAR.•The model improves surgeon-patient discussions and decision-making before EVAR.
Risk assessment models must be continuously validated and updated to ensure that predictions remain valid. Here, the Endovascular Aneurysm Repair Risk Assessment Model, developed in 2008, is updated and improved.
We used prospectively collected data from Australian patients who underwent elective endovascular aneurysm repair between 2009 and 2013 (n = 695). Data were provided by treating surgeons and the National Death Index. Key outcomes were early and midterm survival, early complications (endoleak, operative, and graft-related) and late complications (endoleak and graft-related). Multinomial logistic regression determined which preoperative variables best predicted each outcome. Area under Receiver Operating Characteristic curve (AUROC), model P-value and internal validation statistics were used to select the best model.
Ten preoperative variables were included in the modeling for 10 key outcomes. The most valid outcomes with AUROC>0.7 were 1- and 3-year survival, 30 and 90-day mortality, early and late endoleak (types I, III and IV) and type II endoleak (with an increase in sac size ≥5 mm). The 10 preoperative variables that contributed to outcome models were self-reported fitness, American Society of Anesthesiologists physical status score, history of stroke/transient ischemic attack, age, aneurysm angle, infrarenal neck length, white cell count, respiratory assessment, diabetes and statin therapy. Fitness alone statistically significantly predicted 30 and 90-day deaths better than any other preoperative variable; achieving high AUROCs (0.78 and 0.80), and high odds ratios (12.8 [95% CI: 1.5–110.4] and 18.1 [95% CI: 2.2–149]).
An updated interactive predictive model of outcomes after endovascular aneurysm repair has been created. Many of the variables used in the 2008 model continued to be significant, however, new variables including fitness and respiratory assessment, improved the model. The new model uses variables routinely collected preoperatively, and hence can better support surgeon-patient discussions prior to operation. Informing patients of |
doi_str_mv | 10.1016/j.avsg.2021.02.045 |
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Risk assessment models must be continuously validated and updated to ensure that predictions remain valid. Here, the Endovascular Aneurysm Repair Risk Assessment Model, developed in 2008, is updated and improved.
We used prospectively collected data from Australian patients who underwent elective endovascular aneurysm repair between 2009 and 2013 (n = 695). Data were provided by treating surgeons and the National Death Index. Key outcomes were early and midterm survival, early complications (endoleak, operative, and graft-related) and late complications (endoleak and graft-related). Multinomial logistic regression determined which preoperative variables best predicted each outcome. Area under Receiver Operating Characteristic curve (AUROC), model P-value and internal validation statistics were used to select the best model.
Ten preoperative variables were included in the modeling for 10 key outcomes. The most valid outcomes with AUROC>0.7 were 1- and 3-year survival, 30 and 90-day mortality, early and late endoleak (types I, III and IV) and type II endoleak (with an increase in sac size ≥5 mm). The 10 preoperative variables that contributed to outcome models were self-reported fitness, American Society of Anesthesiologists physical status score, history of stroke/transient ischemic attack, age, aneurysm angle, infrarenal neck length, white cell count, respiratory assessment, diabetes and statin therapy. Fitness alone statistically significantly predicted 30 and 90-day deaths better than any other preoperative variable; achieving high AUROCs (0.78 and 0.80), and high odds ratios (12.8 [95% CI: 1.5–110.4] and 18.1 [95% CI: 2.2–149]).
An updated interactive predictive model of outcomes after endovascular aneurysm repair has been created. Many of the variables used in the 2008 model continued to be significant, however, new variables including fitness and respiratory assessment, improved the model. The new model uses variables routinely collected preoperatively, and hence can better support surgeon-patient discussions prior to operation. Informing patients of potential risks or likely outcomes following elective surgery can assist with preoperative shared decision-making.</description><identifier>ISSN: 0890-5096</identifier><identifier>EISSN: 1615-5947</identifier><identifier>DOI: 10.1016/j.avsg.2021.02.045</identifier><identifier>PMID: 33838242</identifier><language>eng</language><publisher>Netherlands: Elsevier Inc</publisher><subject>Aged ; Aged, 80 and over ; Aortic Aneurysm - diagnosis ; Aortic Aneurysm - mortality ; Aortic Aneurysm - surgery ; Australia ; Blood Vessel Prosthesis Implantation - adverse effects ; Blood Vessel Prosthesis Implantation - mortality ; Clinical Decision-Making ; Decision Making, Shared ; Decision Support Techniques ; Endovascular Procedures - adverse effects ; Endovascular Procedures - mortality ; Female ; Humans ; Male ; Middle Aged ; Patient Participation ; Postoperative Cognitive Complications - etiology ; Predictive Value of Tests ; Prospective Studies ; Risk Assessment ; Risk Factors ; Time Factors ; Treatment Outcome</subject><ispartof>Annals of vascular surgery, 2021-08, Vol.75, p.430-444</ispartof><rights>2021 Elsevier Inc.</rights><rights>Copyright © 2021 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c307t-f84efbaf47a6a77b520de9dfbd404b650829c4c9012c445681f467533ff87b5c3</cites><orcidid>0000-0002-4533-6320</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0890509621002752$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33838242$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cowled, Prue</creatorcontrib><creatorcontrib>Boult, Margaret</creatorcontrib><creatorcontrib>Barnes, Mary</creatorcontrib><creatorcontrib>Fitridge, Robert A</creatorcontrib><title>Update of a Model to Predict Outcomes after Endovascular Aneurysm Repair</title><title>Annals of vascular surgery</title><addtitle>Ann Vasc Surg</addtitle><description>•An interactive model predicts outcomes after elective endovascular aneurysm repair (EVAR).•Major predictive factors included fitness, ASA, stroke, age and aneurysm angle.•Other factors were white cell count, respiratory disease, diabetes and statins.•The model informs patients of potential risks or likely outcomes after EVAR.•The model improves surgeon-patient discussions and decision-making before EVAR.
Risk assessment models must be continuously validated and updated to ensure that predictions remain valid. Here, the Endovascular Aneurysm Repair Risk Assessment Model, developed in 2008, is updated and improved.
We used prospectively collected data from Australian patients who underwent elective endovascular aneurysm repair between 2009 and 2013 (n = 695). Data were provided by treating surgeons and the National Death Index. Key outcomes were early and midterm survival, early complications (endoleak, operative, and graft-related) and late complications (endoleak and graft-related). Multinomial logistic regression determined which preoperative variables best predicted each outcome. Area under Receiver Operating Characteristic curve (AUROC), model P-value and internal validation statistics were used to select the best model.
Ten preoperative variables were included in the modeling for 10 key outcomes. The most valid outcomes with AUROC>0.7 were 1- and 3-year survival, 30 and 90-day mortality, early and late endoleak (types I, III and IV) and type II endoleak (with an increase in sac size ≥5 mm). The 10 preoperative variables that contributed to outcome models were self-reported fitness, American Society of Anesthesiologists physical status score, history of stroke/transient ischemic attack, age, aneurysm angle, infrarenal neck length, white cell count, respiratory assessment, diabetes and statin therapy. Fitness alone statistically significantly predicted 30 and 90-day deaths better than any other preoperative variable; achieving high AUROCs (0.78 and 0.80), and high odds ratios (12.8 [95% CI: 1.5–110.4] and 18.1 [95% CI: 2.2–149]).
An updated interactive predictive model of outcomes after endovascular aneurysm repair has been created. Many of the variables used in the 2008 model continued to be significant, however, new variables including fitness and respiratory assessment, improved the model. The new model uses variables routinely collected preoperatively, and hence can better support surgeon-patient discussions prior to operation. Informing patients of potential risks or likely outcomes following elective surgery can assist with preoperative shared decision-making.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aortic Aneurysm - diagnosis</subject><subject>Aortic Aneurysm - mortality</subject><subject>Aortic Aneurysm - surgery</subject><subject>Australia</subject><subject>Blood Vessel Prosthesis Implantation - adverse effects</subject><subject>Blood Vessel Prosthesis Implantation - mortality</subject><subject>Clinical Decision-Making</subject><subject>Decision Making, Shared</subject><subject>Decision Support Techniques</subject><subject>Endovascular Procedures - adverse effects</subject><subject>Endovascular Procedures - mortality</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Patient Participation</subject><subject>Postoperative Cognitive Complications - etiology</subject><subject>Predictive Value of Tests</subject><subject>Prospective Studies</subject><subject>Risk Assessment</subject><subject>Risk Factors</subject><subject>Time Factors</subject><subject>Treatment Outcome</subject><issn>0890-5096</issn><issn>1615-5947</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEFLwzAYhoMobk7_gAfJ0UtrkiZtCl6GTCdMJuLOIU2-SEe7zKQd7N_bsenR03d53pf3exC6pSSlhOYP61Tv4lfKCKMpYSnh4gyNaU5FIkpenKMxkSVJBCnzEbqKcU0IZZLLSzTKMplJxtkYzVdbqzvA3mGN37yFBncevwewtenwsu-MbyFi7ToIeLaxfqej6Rsd8HQDfdjHFn_AVtfhGl043US4Od0JWj3PPp_myWL58vo0XSQmI0WXOMnBVdrxQue6KCrBiIXSuspywqtcEMlKw005TDWci1xSx_NCZJlzcqBNNkH3x95t8N89xE61dTTQNHoDvo-KCUpZVuaCDig7oib4GAM4tQ11q8NeUaIOBtVaHQyqg0FFmBoMDqG7U39ftWD_Ir_KBuDxCMDw5a6GoKKpYWMGYwFMp6yv_-v_ASW-gUM</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Cowled, Prue</creator><creator>Boult, Margaret</creator><creator>Barnes, Mary</creator><creator>Fitridge, Robert A</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4533-6320</orcidid></search><sort><creationdate>202108</creationdate><title>Update of a Model to Predict Outcomes after Endovascular Aneurysm Repair</title><author>Cowled, Prue ; Boult, Margaret ; Barnes, Mary ; Fitridge, Robert A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c307t-f84efbaf47a6a77b520de9dfbd404b650829c4c9012c445681f467533ff87b5c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aortic Aneurysm - diagnosis</topic><topic>Aortic Aneurysm - mortality</topic><topic>Aortic Aneurysm - surgery</topic><topic>Australia</topic><topic>Blood Vessel Prosthesis Implantation - adverse effects</topic><topic>Blood Vessel Prosthesis Implantation - mortality</topic><topic>Clinical Decision-Making</topic><topic>Decision Making, Shared</topic><topic>Decision Support Techniques</topic><topic>Endovascular Procedures - adverse effects</topic><topic>Endovascular Procedures - mortality</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Patient Participation</topic><topic>Postoperative Cognitive Complications - etiology</topic><topic>Predictive Value of Tests</topic><topic>Prospective Studies</topic><topic>Risk Assessment</topic><topic>Risk Factors</topic><topic>Time Factors</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cowled, Prue</creatorcontrib><creatorcontrib>Boult, Margaret</creatorcontrib><creatorcontrib>Barnes, Mary</creatorcontrib><creatorcontrib>Fitridge, Robert A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of vascular surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cowled, Prue</au><au>Boult, Margaret</au><au>Barnes, Mary</au><au>Fitridge, Robert A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Update of a Model to Predict Outcomes after Endovascular Aneurysm Repair</atitle><jtitle>Annals of vascular surgery</jtitle><addtitle>Ann Vasc Surg</addtitle><date>2021-08</date><risdate>2021</risdate><volume>75</volume><spage>430</spage><epage>444</epage><pages>430-444</pages><issn>0890-5096</issn><eissn>1615-5947</eissn><abstract>•An interactive model predicts outcomes after elective endovascular aneurysm repair (EVAR).•Major predictive factors included fitness, ASA, stroke, age and aneurysm angle.•Other factors were white cell count, respiratory disease, diabetes and statins.•The model informs patients of potential risks or likely outcomes after EVAR.•The model improves surgeon-patient discussions and decision-making before EVAR.
Risk assessment models must be continuously validated and updated to ensure that predictions remain valid. Here, the Endovascular Aneurysm Repair Risk Assessment Model, developed in 2008, is updated and improved.
We used prospectively collected data from Australian patients who underwent elective endovascular aneurysm repair between 2009 and 2013 (n = 695). Data were provided by treating surgeons and the National Death Index. Key outcomes were early and midterm survival, early complications (endoleak, operative, and graft-related) and late complications (endoleak and graft-related). Multinomial logistic regression determined which preoperative variables best predicted each outcome. Area under Receiver Operating Characteristic curve (AUROC), model P-value and internal validation statistics were used to select the best model.
Ten preoperative variables were included in the modeling for 10 key outcomes. The most valid outcomes with AUROC>0.7 were 1- and 3-year survival, 30 and 90-day mortality, early and late endoleak (types I, III and IV) and type II endoleak (with an increase in sac size ≥5 mm). The 10 preoperative variables that contributed to outcome models were self-reported fitness, American Society of Anesthesiologists physical status score, history of stroke/transient ischemic attack, age, aneurysm angle, infrarenal neck length, white cell count, respiratory assessment, diabetes and statin therapy. Fitness alone statistically significantly predicted 30 and 90-day deaths better than any other preoperative variable; achieving high AUROCs (0.78 and 0.80), and high odds ratios (12.8 [95% CI: 1.5–110.4] and 18.1 [95% CI: 2.2–149]).
An updated interactive predictive model of outcomes after endovascular aneurysm repair has been created. Many of the variables used in the 2008 model continued to be significant, however, new variables including fitness and respiratory assessment, improved the model. The new model uses variables routinely collected preoperatively, and hence can better support surgeon-patient discussions prior to operation. Informing patients of potential risks or likely outcomes following elective surgery can assist with preoperative shared decision-making.</abstract><cop>Netherlands</cop><pub>Elsevier Inc</pub><pmid>33838242</pmid><doi>10.1016/j.avsg.2021.02.045</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-4533-6320</orcidid></addata></record> |
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subjects | Aged Aged, 80 and over Aortic Aneurysm - diagnosis Aortic Aneurysm - mortality Aortic Aneurysm - surgery Australia Blood Vessel Prosthesis Implantation - adverse effects Blood Vessel Prosthesis Implantation - mortality Clinical Decision-Making Decision Making, Shared Decision Support Techniques Endovascular Procedures - adverse effects Endovascular Procedures - mortality Female Humans Male Middle Aged Patient Participation Postoperative Cognitive Complications - etiology Predictive Value of Tests Prospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome |
title | Update of a Model to Predict Outcomes after Endovascular Aneurysm Repair |
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