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Am Heart J. 2001 Nov;142(5):799-805.

Predictors of length of stay after coronary stenting.

Author information

  • 1Department of Cardiology, Cleveland Clinic Foundation, Cleveland, Ohio, USA.

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 R(2) = 0.37). The addition of procedural variables added little to the model (adjusted R(2) = 0.39). Entering nonoutcome postprocedure variables significantly enhanced the predictive capacity of the model, explaining more than half the variability in postprocedure LOS (adjusted R(2) = 0.54). In the final model, addition of outcome variables increased its predictive capacity only slightly (adjusted R(2) = 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.

PMID:
11685165
[PubMed - indexed for MEDLINE]
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