Predictors of length of stay after coronary stenting

Background Postprocedure length of stay (LOS) remains an important determinant of medical costs after coronary stenting. Variables that predict LOS in this setting have not been well characterized. Methods We evaluated 359 consecutive patients who underwent coronary stenting with antiplatelet therap...

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Veröffentlicht in:The American heart journal 2001-11, Vol.142 (5), p.799-805
Hauptverfasser: Aronow, Herbert D., Peyser, Patricia A., Eagle, Kim A., Bates, Eric R., Werns, Steven W., Russman, Pamela L., Crum, Martha A., Harris, Kathi, Moscucci, Mauro
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container_end_page 805
container_issue 5
container_start_page 799
container_title The American heart journal
container_volume 142
creator Aronow, Herbert D.
Peyser, Patricia A.
Eagle, Kim A.
Bates, Eric R.
Werns, Steven W.
Russman, Pamela L.
Crum, Martha A.
Harris, Kathi
Moscucci, Mauro
description Background Postprocedure length of stay (LOS) remains an important determinant of medical costs after coronary stenting. Variables that predict LOS in this setting have not been well characterized. Methods We evaluated 359 consecutive patients who underwent coronary stenting with antiplatelet therapy. Sequential multiple linear regression (MLR) models were constructed with use of 4 types of variables to predict log-transformed LOS: preprocedure, intraprocedure, and postprocedure factors and adverse outcomes. Results Preprocedure factors alone explained more than one third of the variability in postprocedure LOS (adjusted R2 = 0.37). The addition of procedural variables added little to the model (adjusted R2 = 0.39). Entering nonoutcome postprocedure variables significantly enhanced the predictive capacity of the model, explaining more than half the variability in postprocedure LOS (adjusted R2 = 0.54). In the final model, addition of outcome variables increased its predictive capacity only slightly (adjusted R2 = 0.61). In this model, significant preprocedure factors included: myocardial infarction (MI) within 24 hours, MI within 1 to 30 days, women with peripheral vascular disease, intravenous heparin, and chronic atrial fibrillation. High-risk intervention was the only significant intraprocedure variable. Significant postprocedure factors included periprocedure ischemia; cerebrovascular accident or transient ischemic attack; treatment with intravenous heparin or nitroglycerin or intra-aortic balloon pump; and need for blood transfusion. Significant adverse outcomes included contrast nephropathy, gastrointestinal bleeding, arrhythmia, vascular complication, and repeat angiography. Conclusion This prediction model identifies a number of potentially reversible factors responsible for prolonging LOS and may enable the development of more accurate risk-adjusted methods with which to improve or compare care. (Am Heart J 2001;142:799-805.)
doi_str_mv 10.1067/mhj.2001.119371
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Variables that predict LOS in this setting have not been well characterized. Methods We evaluated 359 consecutive patients who underwent coronary stenting with antiplatelet therapy. Sequential multiple linear regression (MLR) models were constructed with use of 4 types of variables to predict log-transformed LOS: preprocedure, intraprocedure, and postprocedure factors and adverse outcomes. Results Preprocedure factors alone explained more than one third of the variability in postprocedure LOS (adjusted R2 = 0.37). The addition of procedural variables added little to the model (adjusted R2 = 0.39). Entering nonoutcome postprocedure variables significantly enhanced the predictive capacity of the model, explaining more than half the variability in postprocedure LOS (adjusted R2 = 0.54). In the final model, addition of outcome variables increased its predictive capacity only slightly (adjusted R2 = 0.61). In this model, significant preprocedure factors included: myocardial infarction (MI) within 24 hours, MI within 1 to 30 days, women with peripheral vascular disease, intravenous heparin, and chronic atrial fibrillation. High-risk intervention was the only significant intraprocedure variable. Significant postprocedure factors included periprocedure ischemia; cerebrovascular accident or transient ischemic attack; treatment with intravenous heparin or nitroglycerin or intra-aortic balloon pump; and need for blood transfusion. Significant adverse outcomes included contrast nephropathy, gastrointestinal bleeding, arrhythmia, vascular complication, and repeat angiography. Conclusion This prediction model identifies a number of potentially reversible factors responsible for prolonging LOS and may enable the development of more accurate risk-adjusted methods with which to improve or compare care. 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Variables that predict LOS in this setting have not been well characterized. Methods We evaluated 359 consecutive patients who underwent coronary stenting with antiplatelet therapy. Sequential multiple linear regression (MLR) models were constructed with use of 4 types of variables to predict log-transformed LOS: preprocedure, intraprocedure, and postprocedure factors and adverse outcomes. Results Preprocedure factors alone explained more than one third of the variability in postprocedure LOS (adjusted R2 = 0.37). The addition of procedural variables added little to the model (adjusted R2 = 0.39). Entering nonoutcome postprocedure variables significantly enhanced the predictive capacity of the model, explaining more than half the variability in postprocedure LOS (adjusted R2 = 0.54). In the final model, addition of outcome variables increased its predictive capacity only slightly (adjusted R2 = 0.61). In this model, significant preprocedure factors included: myocardial infarction (MI) within 24 hours, MI within 1 to 30 days, women with peripheral vascular disease, intravenous heparin, and chronic atrial fibrillation. High-risk intervention was the only significant intraprocedure variable. Significant postprocedure factors included periprocedure ischemia; cerebrovascular accident or transient ischemic attack; treatment with intravenous heparin or nitroglycerin or intra-aortic balloon pump; and need for blood transfusion. Significant adverse outcomes included contrast nephropathy, gastrointestinal bleeding, arrhythmia, vascular complication, and repeat angiography. Conclusion This prediction model identifies a number of potentially reversible factors responsible for prolonging LOS and may enable the development of more accurate risk-adjusted methods with which to improve or compare care. (Am Heart J 2001;142:799-805.)</description><subject>Biological and medical sciences</subject><subject>Coronary Disease - economics</subject><subject>Coronary Disease - surgery</subject><subject>Diseases of the cardiovascular system</subject><subject>Health Care Costs</subject><subject>Hospital Costs</subject><subject>Humans</subject><subject>Length of Stay - economics</subject><subject>Length of Stay - statistics &amp; numerical data</subject><subject>Medical sciences</subject><subject>Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. 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Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)</topic><topic>Stents - economics</topic><topic>Stents - utilization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aronow, Herbert D.</creatorcontrib><creatorcontrib>Peyser, Patricia A.</creatorcontrib><creatorcontrib>Eagle, Kim A.</creatorcontrib><creatorcontrib>Bates, Eric R.</creatorcontrib><creatorcontrib>Werns, Steven W.</creatorcontrib><creatorcontrib>Russman, Pamela L.</creatorcontrib><creatorcontrib>Crum, Martha A.</creatorcontrib><creatorcontrib>Harris, Kathi</creatorcontrib><creatorcontrib>Moscucci, Mauro</creatorcontrib><collection>Pascal-Francis</collection><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>The American heart journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aronow, Herbert D.</au><au>Peyser, Patricia A.</au><au>Eagle, Kim A.</au><au>Bates, Eric R.</au><au>Werns, Steven W.</au><au>Russman, Pamela L.</au><au>Crum, Martha A.</au><au>Harris, Kathi</au><au>Moscucci, Mauro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictors of length of stay after coronary stenting</atitle><jtitle>The American heart journal</jtitle><addtitle>Am Heart J</addtitle><date>2001-11-01</date><risdate>2001</risdate><volume>142</volume><issue>5</issue><spage>799</spage><epage>805</epage><pages>799-805</pages><issn>0002-8703</issn><eissn>1097-6744</eissn><coden>AHJOA2</coden><abstract>Background Postprocedure length of stay (LOS) remains an important determinant of medical costs after coronary stenting. Variables that predict LOS in this setting have not been well characterized. Methods We evaluated 359 consecutive patients who underwent coronary stenting with antiplatelet therapy. Sequential multiple linear regression (MLR) models were constructed with use of 4 types of variables to predict log-transformed LOS: preprocedure, intraprocedure, and postprocedure factors and adverse outcomes. Results Preprocedure factors alone explained more than one third of the variability in postprocedure LOS (adjusted R2 = 0.37). The addition of procedural variables added little to the model (adjusted R2 = 0.39). Entering nonoutcome postprocedure variables significantly enhanced the predictive capacity of the model, explaining more than half the variability in postprocedure LOS (adjusted R2 = 0.54). In the final model, addition of outcome variables increased its predictive capacity only slightly (adjusted R2 = 0.61). In this model, significant preprocedure factors included: myocardial infarction (MI) within 24 hours, MI within 1 to 30 days, women with peripheral vascular disease, intravenous heparin, and chronic atrial fibrillation. High-risk intervention was the only significant intraprocedure variable. Significant postprocedure factors included periprocedure ischemia; cerebrovascular accident or transient ischemic attack; treatment with intravenous heparin or nitroglycerin or intra-aortic balloon pump; and need for blood transfusion. Significant adverse outcomes included contrast nephropathy, gastrointestinal bleeding, arrhythmia, vascular complication, and repeat angiography. Conclusion This prediction model identifies a number of potentially reversible factors responsible for prolonging LOS and may enable the development of more accurate risk-adjusted methods with which to improve or compare care. 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source MEDLINE; Access via ScienceDirect (Elsevier)
subjects Biological and medical sciences
Coronary Disease - economics
Coronary Disease - surgery
Diseases of the cardiovascular system
Health Care Costs
Hospital Costs
Humans
Length of Stay - economics
Length of Stay - statistics & numerical data
Medical sciences
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Stents - economics
Stents - utilization
title Predictors of length of stay after coronary stenting
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