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.
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.
|Hamilton, David E; Press, Valerie G; Twu, Nicole M et al. (2016) Testing the functional assessment of mentation: A mobile application based assessment of mental status. J Hosp Med 11:463-6|
|Zadravecz, Frank J; Tien, Linda; Robertson-Dick, Brian J et al. (2015) Comparison of mental-status scales for predicting mortality on the general wards. J Hosp Med 10:658-63|
|Lyons, Patrick G; Zadravecz, Frank J; Edelson, Dana P et al. (2015) Obstructive sleep apnea and adverse outcomes in surgical and nonsurgical patients on the wards. J Hosp Med 10:592-8|
|Bhattacharjee, Poushali; Edelson, Dana P (2015) In reference to "Development, implementation, and impact of an automated early warning and response system for sepsis". J Hosp Med 10:340|
|Churpek, Matthew M; Yuen, Trevor C; Winslow, Christopher et al. (2015) Differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest. Crit Care Med 43:816-22|
|Churpek, Matthew M; Edelson, Dana P (2015) In search of the optimal rapid response system bundle. J Hosp Med 10:411|
|Razi, Rabia R; Churpek, Matthew M; Yuen, Trevor C et al. (2015) Racial disparities in outcomes following PEA and asystole in-hospital cardiac arrests. Resuscitation 87:69-74|
|Yoder, Jordan C; Arora, Vineet M; Edelson, Dana P (2014) Acutely ill patients will likely benefit from more monitoring, not less--reply. JAMA Intern Med 174:475-6|
|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|
|Peace, Jack M; Yuen, Trevor C; Borak, Meredith H et al. (2014) Tablet-based cardiac arrest documentation: a pilot study. Resuscitation 85:266-9|
Showing the most recent 10 out of 26 publications