In-hospital cardiac arrest (IHCA) is a significant public health concern, afflicting an estimated 370,000- 750,000 patients annually, with survival rates generally below 20%. Over half of these patients are known to display signs of clinical deterioration in the hours leading up to the arrest. Rapid Response Systems (RRSs), designed to respond to patients in the early stages of clinical deterioration, have been surprisingly underwhelming with regards to preventing IHCA and death, leading some policy makers and researchers to suggest failures to identify the signs of early clinical deterioration or to call for help as possible etiologies. One possible solution to this problem is the development of a risk prediction tool that could be used to accurately stratify patients based on their likelihood of impending IHCA or ICU transfer, allowing interventions to be targeted at high risk patients. Several physiology-based scoring systems, which assign point values to abnormal vital signs, have been proposed but their mediocre predictive ability and cumbersome nature have limited their adoption. We have developed a simple, single question, quantitative scale of clinical judgment regarding patient stability that predicts IHCA or ICU transfer within the next 24 hours. We propose to validate that tool in a larger sample of patients and compare it to two physiology-based prediction algorithms, in an attempt to find the most sensitive and specific predictor of impending clinical deterioration. We will then use the best of the three, or a combined measure if better, in order to identify high-risk non-ICU inpatients and target them for a RRS intervention that bypasses the need to identify deteriorating patients and call for help, thereby allowing a targeted assessment of the RRS in high risk patients.

Public Health Relevance

Some cardiac arrests in the hospital may be preventable if the clinical warning signs can be identified and acted upon quickly. Since it is not practical to monitor every hospitalized patient at all times, strategies to determine which patients are at high risk would allow additional resources to be targeted specifically at those patients.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Mentored Patient-Oriented Research Career Development Award (K23)
Project #
5K23HL097157-04
Application #
8290431
Study Section
Special Emphasis Panel (ZHL1-CSR-R (M1))
Program Officer
Scott, Jane
Project Start
2009-09-01
Project End
2014-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
4
Fiscal Year
2012
Total Cost
$129,600
Indirect Cost
$9,600
Name
University of Chicago
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
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Churpek, Matthew M; Yuen, Trevor C; Park, Seo Young et al. (2014) Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*. Crit Care Med 42:841-8
Nordseth, Trond; Edelson, Dana Peres; Bergum, Daniel et al. (2014) Optimal loop duration during the provision of in-hospital advanced life support (ALS) to patients with an initial non-shockable rhythm. Resuscitation 85:75-81
Churpek, Matthew M; Yuen, Trevor C; Edelson, Dana P (2013) Predicting clinical deterioration in the hospital: the impact of outcome selection. Resuscitation 84:564-8
Yoder, Jordan C; Yuen, Trevor C; Churpek, Matthew M et al. (2013) A prospective study of nighttime vital sign monitoring frequency and risk of clinical deterioration. JAMA Intern Med 173:1554-5
Phillips, Andrew W; Yuen, Trevor C; Retzer, Elizabeth et al. (2013) Supplementing cross-cover communication with the patient acuity rating. J Gen Intern Med 28:406-11
Churpek, Matthew M; Yuen, Trevor C; Edelson, Dana P (2013) Risk stratification of hospitalized patients on the wards. Chest 143:1758-65

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