Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis
We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data f...
Gespeichert in:
| Veröffentlicht in: | SpringerPlus 2016-03, Vol.5 (1), p.304-304, Article 304 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 304 |
|---|---|
| container_issue | 1 |
| container_start_page | 304 |
| container_title | SpringerPlus |
| container_volume | 5 |
| creator | Vanniyasingam, Thuva Rodseth, Reitze N. Lurati Buse, Giovanna A. Bolliger, Daniel Burkhart, Christoph S. Cuthbertson, Brian H. Gibson, Simon C. Mahla, Elisabeth Leibowitz, David W. Biccard, Bruce M. Thabane, Lehana |
| description | We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data from six cohorts reporting B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NTproBNP) were obtained. These biomarkers were dichotomized using the minimum p value method and compared with previously reported ROC curve-derived thresholds using logistic regression analysis. A final prediction model was developed, internally validated, and assessed for its sensitivity to clustering effects. Finally, a preoperative risk score system was proposed. Thresholds identified by the minimum p value method and ROC curve approach were 115.57 pg/ml (p |
| doi_str_mv | 10.1186/s40064-016-1936-8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4783313</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1780813071</sourcerecordid><originalsourceid>FETCH-LOGICAL-c536t-eaad25bcc8beb0ffd2613cdb1c2a882b93eb4240d3fb1a76172e002f08b9a2013</originalsourceid><addsrcrecordid>eNp1UtuK1TAULaI4wzgf4IsEfPGlmkunzfFBkIM3GBgRfQ67ye5pDm1Sk6bD-Ru_xb_wb0w543AUzEt22GutvTZZRfGU0ZeMyfpVrCitq5KyumQbUZfyQXHOc1UySdnDk_qsuIxxT_OpG1Y19HFxxhta10LI8-LX54DG6tm6HZl7JF7rFAI6ncuOjLD3gYBZMEQkGoKxoAku6OZIbu3cW0cE_fnDwCGueCALRJ0GCCSmsMNweE3AERwnG6yGgWg_ThBs9G6FrwNH6-yYRjJl6pDyG-fem8wy5MvNlmQ3CxKYpuBB9yTF1ah1xi7WpCw4wWyzG2JghpULJTgYDtHGJ8WjDoaIl3f3RfHt_buv24_l9c2HT9u316W-EvVcIoDhV63WssWWdp3hNRPatExzkJK3G4FtxStqRNcyaGrWcKSUd1S2G-CUiYvizVF3Su2IRmc3AQY1BTtCOCgPVv3dcbZXO7-oqpFCMJEFXtwJBP89YZzVaKPGYQCHPkXFGkklE7RZZz3_B7r3KeSFV1STreX_XgXZEaWDjzFgd2-GUbVmRx2zo3J21JodJTPn2ekW94w_SckAfgTE3HL5a09G_1f1N_L-1co</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1771721803</pqid></control><display><type>article</type><title>Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>PubMed Central</source><source>Springer Nature OA/Free Journals</source><source>Free Full-Text Journals in Chemistry</source><creator>Vanniyasingam, Thuva ; Rodseth, Reitze N. ; Lurati Buse, Giovanna A. ; Bolliger, Daniel ; Burkhart, Christoph S. ; Cuthbertson, Brian H. ; Gibson, Simon C. ; Mahla, Elisabeth ; Leibowitz, David W. ; Biccard, Bruce M. ; Thabane, Lehana</creator><creatorcontrib>Vanniyasingam, Thuva ; Rodseth, Reitze N. ; Lurati Buse, Giovanna A. ; Bolliger, Daniel ; Burkhart, Christoph S. ; Cuthbertson, Brian H. ; Gibson, Simon C. ; Mahla, Elisabeth ; Leibowitz, David W. ; Biccard, Bruce M. ; Thabane, Lehana</creatorcontrib><description>We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data from six cohorts reporting B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NTproBNP) were obtained. These biomarkers were dichotomized using the minimum p value method and compared with previously reported ROC curve-derived thresholds using logistic regression analysis. A final prediction model was developed, internally validated, and assessed for its sensitivity to clustering effects. Finally, a preoperative risk score system was proposed. Thresholds identified by the minimum p value method and ROC curve approach were 115.57 pg/ml (p < 0.001) and 116 pg/ml for BNP, and 241.7 pg/ml (p = 0.001) and 277.5 pg/ml for NTproBNP, respectively. The minimum p value thresholds were slightly stronger predictors based on our logistic regression analysis. The final model included a composite predictor of the minimum p value method’s BNP and NTproBNP thresholds [odds ratio (OR) = 8.5, p < 0.001], surgery type (OR = 2.5, p = 0.002), and diabetes (OR = 2.1, p = 0.015). Preoperative risks using the scoring system ranged from 2 to 49 %. The minimum p value method and ROC curve approach identify similar optimal thresholds. We propose to replace the revised cardiac risk index with our risk score system for individual-specific preoperative risk stratification after noncardiac nonvascular surgery.</description><identifier>ISSN: 2193-1801</identifier><identifier>EISSN: 2193-1801</identifier><identifier>DOI: 10.1186/s40064-016-1936-8</identifier><identifier>PMID: 27066338</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Humanities and Social Sciences ; Medicine ; multidisciplinary ; Science ; Science (multidisciplinary)</subject><ispartof>SpringerPlus, 2016-03, Vol.5 (1), p.304-304, Article 304</ispartof><rights>Vanniyasingam et al. 2016</rights><rights>The Author(s) 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c536t-eaad25bcc8beb0ffd2613cdb1c2a882b93eb4240d3fb1a76172e002f08b9a2013</citedby><cites>FETCH-LOGICAL-c536t-eaad25bcc8beb0ffd2613cdb1c2a882b93eb4240d3fb1a76172e002f08b9a2013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783313/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783313/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,886,27929,27930,41125,42194,51581,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27066338$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Vanniyasingam, Thuva</creatorcontrib><creatorcontrib>Rodseth, Reitze N.</creatorcontrib><creatorcontrib>Lurati Buse, Giovanna A.</creatorcontrib><creatorcontrib>Bolliger, Daniel</creatorcontrib><creatorcontrib>Burkhart, Christoph S.</creatorcontrib><creatorcontrib>Cuthbertson, Brian H.</creatorcontrib><creatorcontrib>Gibson, Simon C.</creatorcontrib><creatorcontrib>Mahla, Elisabeth</creatorcontrib><creatorcontrib>Leibowitz, David W.</creatorcontrib><creatorcontrib>Biccard, Bruce M.</creatorcontrib><creatorcontrib>Thabane, Lehana</creatorcontrib><title>Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis</title><title>SpringerPlus</title><addtitle>SpringerPlus</addtitle><addtitle>Springerplus</addtitle><description>We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data from six cohorts reporting B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NTproBNP) were obtained. These biomarkers were dichotomized using the minimum p value method and compared with previously reported ROC curve-derived thresholds using logistic regression analysis. A final prediction model was developed, internally validated, and assessed for its sensitivity to clustering effects. Finally, a preoperative risk score system was proposed. Thresholds identified by the minimum p value method and ROC curve approach were 115.57 pg/ml (p < 0.001) and 116 pg/ml for BNP, and 241.7 pg/ml (p = 0.001) and 277.5 pg/ml for NTproBNP, respectively. The minimum p value thresholds were slightly stronger predictors based on our logistic regression analysis. The final model included a composite predictor of the minimum p value method’s BNP and NTproBNP thresholds [odds ratio (OR) = 8.5, p < 0.001], surgery type (OR = 2.5, p = 0.002), and diabetes (OR = 2.1, p = 0.015). Preoperative risks using the scoring system ranged from 2 to 49 %. The minimum p value method and ROC curve approach identify similar optimal thresholds. We propose to replace the revised cardiac risk index with our risk score system for individual-specific preoperative risk stratification after noncardiac nonvascular surgery.</description><subject>Humanities and Social Sciences</subject><subject>Medicine</subject><subject>multidisciplinary</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><issn>2193-1801</issn><issn>2193-1801</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UtuK1TAULaI4wzgf4IsEfPGlmkunzfFBkIM3GBgRfQ67ye5pDm1Sk6bD-Ru_xb_wb0w543AUzEt22GutvTZZRfGU0ZeMyfpVrCitq5KyumQbUZfyQXHOc1UySdnDk_qsuIxxT_OpG1Y19HFxxhta10LI8-LX54DG6tm6HZl7JF7rFAI6ncuOjLD3gYBZMEQkGoKxoAku6OZIbu3cW0cE_fnDwCGueCALRJ0GCCSmsMNweE3AERwnG6yGgWg_ThBs9G6FrwNH6-yYRjJl6pDyG-fem8wy5MvNlmQ3CxKYpuBB9yTF1ah1xi7WpCw4wWyzG2JghpULJTgYDtHGJ8WjDoaIl3f3RfHt_buv24_l9c2HT9u316W-EvVcIoDhV63WssWWdp3hNRPatExzkJK3G4FtxStqRNcyaGrWcKSUd1S2G-CUiYvizVF3Su2IRmc3AQY1BTtCOCgPVv3dcbZXO7-oqpFCMJEFXtwJBP89YZzVaKPGYQCHPkXFGkklE7RZZz3_B7r3KeSFV1STreX_XgXZEaWDjzFgd2-GUbVmRx2zo3J21JodJTPn2ekW94w_SckAfgTE3HL5a09G_1f1N_L-1co</recordid><startdate>20160309</startdate><enddate>20160309</enddate><creator>Vanniyasingam, Thuva</creator><creator>Rodseth, Reitze N.</creator><creator>Lurati Buse, Giovanna A.</creator><creator>Bolliger, Daniel</creator><creator>Burkhart, Christoph S.</creator><creator>Cuthbertson, Brian H.</creator><creator>Gibson, Simon C.</creator><creator>Mahla, Elisabeth</creator><creator>Leibowitz, David W.</creator><creator>Biccard, Bruce M.</creator><creator>Thabane, Lehana</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X2</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KB.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160309</creationdate><title>Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis</title><author>Vanniyasingam, Thuva ; Rodseth, Reitze N. ; Lurati Buse, Giovanna A. ; Bolliger, Daniel ; Burkhart, Christoph S. ; Cuthbertson, Brian H. ; Gibson, Simon C. ; Mahla, Elisabeth ; Leibowitz, David W. ; Biccard, Bruce M. ; Thabane, Lehana</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c536t-eaad25bcc8beb0ffd2613cdb1c2a882b93eb4240d3fb1a76172e002f08b9a2013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Humanities and Social Sciences</topic><topic>Medicine</topic><topic>multidisciplinary</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vanniyasingam, Thuva</creatorcontrib><creatorcontrib>Rodseth, Reitze N.</creatorcontrib><creatorcontrib>Lurati Buse, Giovanna A.</creatorcontrib><creatorcontrib>Bolliger, Daniel</creatorcontrib><creatorcontrib>Burkhart, Christoph S.</creatorcontrib><creatorcontrib>Cuthbertson, Brian H.</creatorcontrib><creatorcontrib>Gibson, Simon C.</creatorcontrib><creatorcontrib>Mahla, Elisabeth</creatorcontrib><creatorcontrib>Leibowitz, David W.</creatorcontrib><creatorcontrib>Biccard, Bruce M.</creatorcontrib><creatorcontrib>Thabane, Lehana</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>SpringerPlus</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vanniyasingam, Thuva</au><au>Rodseth, Reitze N.</au><au>Lurati Buse, Giovanna A.</au><au>Bolliger, Daniel</au><au>Burkhart, Christoph S.</au><au>Cuthbertson, Brian H.</au><au>Gibson, Simon C.</au><au>Mahla, Elisabeth</au><au>Leibowitz, David W.</au><au>Biccard, Bruce M.</au><au>Thabane, Lehana</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis</atitle><jtitle>SpringerPlus</jtitle><stitle>SpringerPlus</stitle><addtitle>Springerplus</addtitle><date>2016-03-09</date><risdate>2016</risdate><volume>5</volume><issue>1</issue><spage>304</spage><epage>304</epage><pages>304-304</pages><artnum>304</artnum><issn>2193-1801</issn><eissn>2193-1801</eissn><abstract>We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data from six cohorts reporting B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NTproBNP) were obtained. These biomarkers were dichotomized using the minimum p value method and compared with previously reported ROC curve-derived thresholds using logistic regression analysis. A final prediction model was developed, internally validated, and assessed for its sensitivity to clustering effects. Finally, a preoperative risk score system was proposed. Thresholds identified by the minimum p value method and ROC curve approach were 115.57 pg/ml (p < 0.001) and 116 pg/ml for BNP, and 241.7 pg/ml (p = 0.001) and 277.5 pg/ml for NTproBNP, respectively. The minimum p value thresholds were slightly stronger predictors based on our logistic regression analysis. The final model included a composite predictor of the minimum p value method’s BNP and NTproBNP thresholds [odds ratio (OR) = 8.5, p < 0.001], surgery type (OR = 2.5, p = 0.002), and diabetes (OR = 2.1, p = 0.015). Preoperative risks using the scoring system ranged from 2 to 49 %. The minimum p value method and ROC curve approach identify similar optimal thresholds. We propose to replace the revised cardiac risk index with our risk score system for individual-specific preoperative risk stratification after noncardiac nonvascular surgery.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>27066338</pmid><doi>10.1186/s40064-016-1936-8</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2193-1801 |
| ispartof | SpringerPlus, 2016-03, Vol.5 (1), p.304-304, Article 304 |
| issn | 2193-1801 2193-1801 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4783313 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; PubMed Central; Springer Nature OA/Free Journals; Free Full-Text Journals in Chemistry |
| subjects | Humanities and Social Sciences Medicine multidisciplinary Science Science (multidisciplinary) |
| title | Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T10%3A09%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20the%20occurrence%20of%20major%20adverse%20cardiac%20events%20within%2030%C2%A0days%20of%20a%20vascular%20surgery:%20an%20empirical%20comparison%20of%20the%20minimum%20p%20value%20method%20and%20ROC%20curve%20approach%20using%20individual%20patient%20data%20meta-analysis&rft.jtitle=SpringerPlus&rft.au=Vanniyasingam,%20Thuva&rft.date=2016-03-09&rft.volume=5&rft.issue=1&rft.spage=304&rft.epage=304&rft.pages=304-304&rft.artnum=304&rft.issn=2193-1801&rft.eissn=2193-1801&rft_id=info:doi/10.1186/s40064-016-1936-8&rft_dat=%3Cproquest_pubme%3E1780813071%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1771721803&rft_id=info:pmid/27066338&rfr_iscdi=true |