Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention

Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. This study sought to develop a model for predicting i...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American College of Cardiology 2021-07, Vol.78 (3), p.216-229
Hauptverfasser: Castro-Dominguez, Yulanka S., Wang, Yongfei, Minges, Karl E., McNamara, Robert L., Spertus, John A., Dehmer, Gregory J., Messenger, John C., Lavin, Kimberly, Anderson, Cornelia, Blankinship, Kristina, Mercado, Nestor, Clary, Julie M., Osborne, Anwar D., Curtis, Jeptha P., Cavender, Matthew A.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 229
container_issue 3
container_start_page 216
container_title Journal of the American College of Cardiology
container_volume 78
creator Castro-Dominguez, Yulanka S.
Wang, Yongfei
Minges, Karl E.
McNamara, Robert L.
Spertus, John A.
Dehmer, Gregory J.
Messenger, John C.
Lavin, Kimberly
Anderson, Cornelia
Blankinship, Kristina
Mercado, Nestor
Clary, Julie M.
Osborne, Anwar D.
Curtis, Jeptha P.
Cavender, Matthew A.
description Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts. [Display omitted]
doi_str_mv 10.1016/j.jacc.2021.04.067
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2524361807</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0735109721049342</els_id><sourcerecordid>2524361807</sourcerecordid><originalsourceid>FETCH-LOGICAL-c466t-47acc9dad0ba3a79c25da7ecee28aef8edb9dbc6ce2732537e6c2328186400353</originalsourceid><addsrcrecordid>eNp9kE1PAjEURRujEUT_gAszSzcz9mPaziRuDFEhwchCNm6a0j5ICUyxLST8e0tQl67e5rybew9CtwRXBBPxsKpW2piKYkoqXFdYyDPUJ5w3JeOtPEd9LBkvCW5lD13FuMIYi4a0l6jHWMslZW0ffU4DWGeS65bFuCtHPm5d0uvizYd8XDoUriumOjnoUixmnYWw9Ed4CsHsku7A72Ix9MF3OhxyRIKwz6zz3TW6WOh1hJufO0Czl-eP4aicvL-Oh0-T0tRCpLKWeURrtcVzzbRsDeVWSzAAtNGwaMDOWzs3wgCVjHImQRjKaEMaUWPMOBug-1PuNvivHcSkNi4aWK9P5RTltGaCNNnGANETaoKPMcBCbYPb5OKKYHV0qlbq6FQdnSpcq-w0P9395O_mG7B_L78SM_B4AiCv3DsIKprsy2SxAUxS1rv_8r8BkyOJ_A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2524361807</pqid></control><display><type>article</type><title>Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Alma/SFX Local Collection</source><creator>Castro-Dominguez, Yulanka S. ; Wang, Yongfei ; Minges, Karl E. ; McNamara, Robert L. ; Spertus, John A. ; Dehmer, Gregory J. ; Messenger, John C. ; Lavin, Kimberly ; Anderson, Cornelia ; Blankinship, Kristina ; Mercado, Nestor ; Clary, Julie M. ; Osborne, Anwar D. ; Curtis, Jeptha P. ; Cavender, Matthew A.</creator><creatorcontrib>Castro-Dominguez, Yulanka S. ; Wang, Yongfei ; Minges, Karl E. ; McNamara, Robert L. ; Spertus, John A. ; Dehmer, Gregory J. ; Messenger, John C. ; Lavin, Kimberly ; Anderson, Cornelia ; Blankinship, Kristina ; Mercado, Nestor ; Clary, Julie M. ; Osborne, Anwar D. ; Curtis, Jeptha P. ; Cavender, Matthew A.</creatorcontrib><description>Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts. [Display omitted]</description><identifier>ISSN: 0735-1097</identifier><identifier>EISSN: 1558-3597</identifier><identifier>DOI: 10.1016/j.jacc.2021.04.067</identifier><identifier>PMID: 33957239</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Aged ; Coronary Artery Disease - mortality ; Coronary Artery Disease - surgery ; Female ; Follow-Up Studies ; hierarchical logistic regression model ; Hospital Mortality - trends ; Humans ; Male ; Percutaneous Coronary Intervention ; Preoperative Period ; Registries ; Reproducibility of Results ; Retrospective Studies ; Risk Assessment - methods ; Risk Factors ; risk-standardized mortality rates ; Survival Rate - trends ; Time Factors ; United States - epidemiology</subject><ispartof>Journal of the American College of Cardiology, 2021-07, Vol.78 (3), p.216-229</ispartof><rights>2021 American College of Cardiology Foundation</rights><rights>Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-47acc9dad0ba3a79c25da7ecee28aef8edb9dbc6ce2732537e6c2328186400353</citedby><cites>FETCH-LOGICAL-c466t-47acc9dad0ba3a79c25da7ecee28aef8edb9dbc6ce2732537e6c2328186400353</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0735109721049342$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33957239$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Castro-Dominguez, Yulanka S.</creatorcontrib><creatorcontrib>Wang, Yongfei</creatorcontrib><creatorcontrib>Minges, Karl E.</creatorcontrib><creatorcontrib>McNamara, Robert L.</creatorcontrib><creatorcontrib>Spertus, John A.</creatorcontrib><creatorcontrib>Dehmer, Gregory J.</creatorcontrib><creatorcontrib>Messenger, John C.</creatorcontrib><creatorcontrib>Lavin, Kimberly</creatorcontrib><creatorcontrib>Anderson, Cornelia</creatorcontrib><creatorcontrib>Blankinship, Kristina</creatorcontrib><creatorcontrib>Mercado, Nestor</creatorcontrib><creatorcontrib>Clary, Julie M.</creatorcontrib><creatorcontrib>Osborne, Anwar D.</creatorcontrib><creatorcontrib>Curtis, Jeptha P.</creatorcontrib><creatorcontrib>Cavender, Matthew A.</creatorcontrib><title>Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention</title><title>Journal of the American College of Cardiology</title><addtitle>J Am Coll Cardiol</addtitle><description>Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts. [Display omitted]</description><subject>Aged</subject><subject>Coronary Artery Disease - mortality</subject><subject>Coronary Artery Disease - surgery</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>hierarchical logistic regression model</subject><subject>Hospital Mortality - trends</subject><subject>Humans</subject><subject>Male</subject><subject>Percutaneous Coronary Intervention</subject><subject>Preoperative Period</subject><subject>Registries</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>risk-standardized mortality rates</subject><subject>Survival Rate - trends</subject><subject>Time Factors</subject><subject>United States - epidemiology</subject><issn>0735-1097</issn><issn>1558-3597</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1PAjEURRujEUT_gAszSzcz9mPaziRuDFEhwchCNm6a0j5ICUyxLST8e0tQl67e5rybew9CtwRXBBPxsKpW2piKYkoqXFdYyDPUJ5w3JeOtPEd9LBkvCW5lD13FuMIYi4a0l6jHWMslZW0ffU4DWGeS65bFuCtHPm5d0uvizYd8XDoUriumOjnoUixmnYWw9Ed4CsHsku7A72Ix9MF3OhxyRIKwz6zz3TW6WOh1hJufO0Czl-eP4aicvL-Oh0-T0tRCpLKWeURrtcVzzbRsDeVWSzAAtNGwaMDOWzs3wgCVjHImQRjKaEMaUWPMOBug-1PuNvivHcSkNi4aWK9P5RTltGaCNNnGANETaoKPMcBCbYPb5OKKYHV0qlbq6FQdnSpcq-w0P9395O_mG7B_L78SM_B4AiCv3DsIKprsy2SxAUxS1rv_8r8BkyOJ_A</recordid><startdate>20210720</startdate><enddate>20210720</enddate><creator>Castro-Dominguez, Yulanka S.</creator><creator>Wang, Yongfei</creator><creator>Minges, Karl E.</creator><creator>McNamara, Robert L.</creator><creator>Spertus, John A.</creator><creator>Dehmer, Gregory J.</creator><creator>Messenger, John C.</creator><creator>Lavin, Kimberly</creator><creator>Anderson, Cornelia</creator><creator>Blankinship, Kristina</creator><creator>Mercado, Nestor</creator><creator>Clary, Julie M.</creator><creator>Osborne, Anwar D.</creator><creator>Curtis, Jeptha P.</creator><creator>Cavender, Matthew 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></search><sort><creationdate>20210720</creationdate><title>Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention</title><author>Castro-Dominguez, Yulanka S. ; Wang, Yongfei ; Minges, Karl E. ; McNamara, Robert L. ; Spertus, John A. ; Dehmer, Gregory J. ; Messenger, John C. ; Lavin, Kimberly ; Anderson, Cornelia ; Blankinship, Kristina ; Mercado, Nestor ; Clary, Julie M. ; Osborne, Anwar D. ; Curtis, Jeptha P. ; Cavender, Matthew A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-47acc9dad0ba3a79c25da7ecee28aef8edb9dbc6ce2732537e6c2328186400353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Coronary Artery Disease - mortality</topic><topic>Coronary Artery Disease - surgery</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>hierarchical logistic regression model</topic><topic>Hospital Mortality - trends</topic><topic>Humans</topic><topic>Male</topic><topic>Percutaneous Coronary Intervention</topic><topic>Preoperative Period</topic><topic>Registries</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Risk Assessment - methods</topic><topic>Risk Factors</topic><topic>risk-standardized mortality rates</topic><topic>Survival Rate - trends</topic><topic>Time Factors</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Castro-Dominguez, Yulanka S.</creatorcontrib><creatorcontrib>Wang, Yongfei</creatorcontrib><creatorcontrib>Minges, Karl E.</creatorcontrib><creatorcontrib>McNamara, Robert L.</creatorcontrib><creatorcontrib>Spertus, John A.</creatorcontrib><creatorcontrib>Dehmer, Gregory J.</creatorcontrib><creatorcontrib>Messenger, John C.</creatorcontrib><creatorcontrib>Lavin, Kimberly</creatorcontrib><creatorcontrib>Anderson, Cornelia</creatorcontrib><creatorcontrib>Blankinship, Kristina</creatorcontrib><creatorcontrib>Mercado, Nestor</creatorcontrib><creatorcontrib>Clary, Julie M.</creatorcontrib><creatorcontrib>Osborne, Anwar D.</creatorcontrib><creatorcontrib>Curtis, Jeptha P.</creatorcontrib><creatorcontrib>Cavender, Matthew 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>Journal of the American College of Cardiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Castro-Dominguez, Yulanka S.</au><au>Wang, Yongfei</au><au>Minges, Karl E.</au><au>McNamara, Robert L.</au><au>Spertus, John A.</au><au>Dehmer, Gregory J.</au><au>Messenger, John C.</au><au>Lavin, Kimberly</au><au>Anderson, Cornelia</au><au>Blankinship, Kristina</au><au>Mercado, Nestor</au><au>Clary, Julie M.</au><au>Osborne, Anwar D.</au><au>Curtis, Jeptha P.</au><au>Cavender, Matthew A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention</atitle><jtitle>Journal of the American College of Cardiology</jtitle><addtitle>J Am Coll Cardiol</addtitle><date>2021-07-20</date><risdate>2021</risdate><volume>78</volume><issue>3</issue><spage>216</spage><epage>229</epage><pages>216-229</pages><issn>0735-1097</issn><eissn>1558-3597</eissn><abstract>Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts. [Display omitted]</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>33957239</pmid><doi>10.1016/j.jacc.2021.04.067</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0735-1097
ispartof Journal of the American College of Cardiology, 2021-07, Vol.78 (3), p.216-229
issn 0735-1097
1558-3597
language eng
recordid cdi_proquest_miscellaneous_2524361807
source MEDLINE; Elsevier ScienceDirect Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection
subjects Aged
Coronary Artery Disease - mortality
Coronary Artery Disease - surgery
Female
Follow-Up Studies
hierarchical logistic regression model
Hospital Mortality - trends
Humans
Male
Percutaneous Coronary Intervention
Preoperative Period
Registries
Reproducibility of Results
Retrospective Studies
Risk Assessment - methods
Risk Factors
risk-standardized mortality rates
Survival Rate - trends
Time Factors
United States - epidemiology
title Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T19%3A24%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20In-Hospital%20Mortality%20in%20Patients%20Undergoing%20Percutaneous%20Coronary%20Intervention&rft.jtitle=Journal%20of%20the%20American%20College%20of%20Cardiology&rft.au=Castro-Dominguez,%20Yulanka%20S.&rft.date=2021-07-20&rft.volume=78&rft.issue=3&rft.spage=216&rft.epage=229&rft.pages=216-229&rft.issn=0735-1097&rft.eissn=1558-3597&rft_id=info:doi/10.1016/j.jacc.2021.04.067&rft_dat=%3Cproquest_cross%3E2524361807%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2524361807&rft_id=info:pmid/33957239&rft_els_id=S0735109721049342&rfr_iscdi=true