{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/84161"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/84161","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Mortality Risk Among Patients Who Present to Hospitals with Out-Of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction","abstract":"In the emergent setting of an ST-elevation myocardial infarction (STEMI) presenting with an out-of-hospital cardiac arrest (OHCA), decisions for immediate coronary angiography are made when the likelihood of survival is highly variable and unknown. A simple prognostic tool that can identify patients with a very high mortality risk upon hospital presentation may inform decision-making regarding emergent procedures. Within the Cardiac Arrest Registry to Enhance Survival (CARES), I included adult patients with OHCA and STEMI who presented from January 2013 to December 2019. Using multivariable logistic regression, I developed a predictive model and risk score for in-hospital mortality. Of 13,444 hospitalized patients with OHCA and STEMI (median age 64 [IQR 55-74], 31.6% female, 56.6% white), 8141 (60.6%) died. Higher age, non-shockable cardiac arrest rhythm, not having sustained return of spontaneous circulation upon hospital arrival, and total resuscitation time on scene were most predictive of mortality (C-statistic, 0.86). An integer risk score (range: 0-7) derived from this model estimated that patients with STEMI and OHCA has an in-hospital mortality from 15% to nearly 100%, with the odds of in-hospital mortality more than doubling for each additional point (odds ratio, 2.64; 95% CI, 2.55–2.73; p<0.001; C-statistic, 0.85). STEMI patients with OHCA have highly variable mortality risk. I created a simple prediction model comprised of four prehospital characteristics to estimate this risk. Further work is needed to define how this model can support procedural decision-making and better risk-adjustment for mortality-based quality measures in this high-risk population.","abstract_html":"In the emergent setting of an ST-elevation myocardial infarction (STEMI) presenting with an out-of-hospital cardiac arrest (OHCA), decisions for immediate coronary angiography are made when the likelihood of survival is highly variable and unknown. A simple prognostic tool that can identify patients with a very high mortality risk upon hospital presentation may inform decision-making regarding emergent procedures. Within the Cardiac Arrest Registry to Enhance Survival (CARES), I included adult patients with OHCA and STEMI who presented from January 2013 to December 2019. Using multivariable logistic regression, I developed a predictive model and risk score for in-hospital mortality. Of 13,444 hospitalized patients with OHCA and STEMI (median age 64 [IQR 55-74], 31.6% female, 56.6% white), 8141 (60.6%) died. Higher age, non-shockable cardiac arrest rhythm, not having sustained return of spontaneous circulation upon hospital arrival, and total resuscitation time on scene were most predictive of mortality (C-statistic, 0.86). An integer risk score (range: 0-7) derived from this model estimated that patients with STEMI and OHCA has an in-hospital mortality from 15% to nearly 100%, with the odds of in-hospital mortality more than doubling for each additional point (odds ratio, 2.64; 95% CI, 2.55–2.73; p&lt;0.001; C-statistic, 0.85). STEMI patients with OHCA have highly variable mortality risk. I created a simple prediction model comprised of four prehospital characteristics to estimate this risk. Further work is needed to define how this model can support procedural decision-making and better risk-adjustment for mortality-based quality measures in this high-risk population.","abstract_has_math":false,"creators":["Tran, Andy T"],"institution":"University of Missouri--Kansas City","degree_name":"M.S. 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A simple prognostic tool that can identify patients with a very high mortality risk upon hospital presentation may inform decision-making regarding emergent procedures. Within the Cardiac Arrest Registry to Enhance Survival (CARES), I included adult patients with OHCA and STEMI who presented from January 2013 to December 2019. Using multivariable logistic regression, I developed a predictive model and risk score for in-hospital mortality. Of 13,444 hospitalized patients with OHCA and STEMI (median age 64 [IQR 55-74], 31.6% female, 56.6% white), 8141 (60.6%) died. Higher age, non-shockable cardiac arrest rhythm, not having sustained return of spontaneous circulation upon hospital arrival, and total resuscitation time on scene were most predictive of mortality (C-statistic, 0.86). An integer risk score (range: 0-7) derived from this model estimated that patients with STEMI and OHCA has an in-hospital mortality from 15% to nearly 100%, with the odds of in-hospital mortality more than doubling for each additional point (odds ratio, 2.64; 95% CI, 2.55–2.73; p<0.001; C-statistic, 0.85). STEMI patients with OHCA have highly variable mortality risk. I created a simple prediction model comprised of four prehospital characteristics to estimate this risk. Further work is needed to define how this model can support procedural decision-making and better risk-adjustment for mortality-based quality measures in this high-risk population."]},{"key":"dc:title","label":"Title","values":["Mortality Risk Among Patients Who Present to Hospitals with Out-Of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gaddis, Monica Louise, 1955-"],"dc:creator":["Tran, Andy T"],"dc:date.accessioned":["2021-06-02T16:11:19Z"],"dc:date.available":["2021-06-02T16:11:19Z"],"dc:date.issued":["2021"],"dc:description":["Title from PDF of title page viewed June 11, 2021","Thesis advisor:Monica Gaddis","Vita","Includes bibliographical references (pages 31-38)","Thesis (M.S.)--School of Medicine. University of Missouri--Kansas City, 2021"],"dc:description.abstract":["In the emergent setting of an ST-elevation myocardial infarction (STEMI) presenting with an out-of-hospital cardiac arrest (OHCA), decisions for immediate coronary angiography are made when the likelihood of survival is highly variable and unknown. A simple prognostic tool that can identify patients with a very high mortality risk upon hospital presentation may inform decision-making regarding emergent procedures. Within the Cardiac Arrest Registry to Enhance Survival (CARES), I included adult patients with OHCA and STEMI who presented from January 2013 to December 2019. Using multivariable logistic regression, I developed a predictive model and risk score for in-hospital mortality. Of 13,444 hospitalized patients with OHCA and STEMI (median age 64 [IQR 55-74], 31.6% female, 56.6% white), 8141 (60.6%) died. Higher age, non-shockable cardiac arrest rhythm, not having sustained return of spontaneous circulation upon hospital arrival, and total resuscitation time on scene were most predictive of mortality (C-statistic, 0.86). An integer risk score (range: 0-7) derived from this model estimated that patients with STEMI and OHCA has an in-hospital mortality from 15% to nearly 100%, with the odds of in-hospital mortality more than doubling for each additional point (odds ratio, 2.64; 95% CI, 2.55–2.73; p<0.001; C-statistic, 0.85). STEMI patients with OHCA have highly variable mortality risk. I created a simple prediction model comprised of four prehospital characteristics to estimate this risk. Further work is needed to define how this model can support procedural decision-making and better risk-adjustment for mortality-based quality measures in this high-risk population."],"dc:identifier.uri":["https://hdl.handle.net/10355/84161"],"dc:title":["Mortality Risk Among Patients Who Present to Hospitals with Out-Of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction"],"thesis:degree_discipline":["Bioinformatics (UMKC)"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S. (Master of Science)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:18:49Z"}