Score Big for Decreasing Mortality: ICD Risk Score Model

Citation data:

Journal of Patient-Centered Research and Reviews, Vol: 2, Issue: 4, Page: 205

Publication Year:
2015
Usage 85
Downloads 55
Abstract Views 30
Repository URL:
https://digitalrepository.aurorahealthcare.org/jpcrr/vol2/iss4/14
DOI:
10.17294/2330-0698.1224
Author(s):
Francaviglia, Linda; Petersen, Rachel; Stone, Maria; Mortada, M. Eyman
Publisher(s):
Aurora Health Care, Inc.
Tags:
implantable cardioverter-defibrillator; ICD; morbidity; mortality; risk score; Cardiology; Cardiovascular Diseases; Cardiovascular System
article description
Background: Aurora Health Care, a system of 14 acute care hospitals in eastern Wisconsin, has been a long-time participant in the American College of Cardiology’s National Cardiovascular Data Registries, submitting data to its ICD Registry™ since 2005. Our system’s implantable cardioverter-defibrillator (ICD) procedure volume averages 930 cases annually. During 2012 we experienced an increase in in-hospital mortality/morbidity for ICD cases.Purpose: A single-center study examining in-hospital mortality/morbidity post-ICD implant before and after changes in practice and patient selection.Methods: ICD implants and generator changes discharged from January 1, 2009, to December 31, 2012, were included in developing a risk model predicting in-hospital mortality/morbidity. The risk score was shared with physicians for clinical input. A point system was developed, including those factors with highest risk. Using the defined factors, a risk score > 14 was used to indicate those at highest risk for morbidity/mortality. The risk score model was fit on the development group (2009–2012), and then re-run for the intervention cohort from January 1, 2013, to June 30, 2014. Logistic regression was used in the risk model development and validation. Continuous variables were compared using Student’s t-test, and categorical variables were compared using chi-square test.Results: From 2009 to 2012, 3,417 ICD implants and generator changes were performed and included in risk model development. Of those, 200 (5.9%) patients were indicated as high risk with a score > 14. From January 2013 to June 2014, 1,057 implants and generator changes were performed, with 41 (3.4%) patients indicated as high risk with a score > 14. In the development phase, mean age was 67 years and 70% of patients were male. Post-model development, mean age was 66 years with 72% male. For patients indicated as high risk, in-hospital mortality/morbidity dropped from 20 (10%) to 2 (4.9%), though the decrease was not statistically significant (P = 0.39).Conclusion: Awareness of high-risk patients and changes in patient selection can lead to improvement in in-hospital mortality/morbidity among those high-risk patients. Although the improvement was not statistically significant, this was most likely due to low volumes and we will continue to monitor outcomes among these patients.