Researchers have developed a simple bedside prediction tool – the CASPRI score – to help clinicians estimate which patients resuscitated from in-hospital cardiac arrest will survive to discharge with favorable neurologic status and which will not, according to a report published in the Archives of Internal Medicine.
"We believe that this tool is simple to use, addresses a critical unmet need for better prognostication after cardiac arrest, and has the potential to enhance communication with patients and families," said Dr. Paul S. Chan of Saint Luke’s Mid-America Heart Institute, Kansas City, Mo., and his associates.
They used data from the Get With the Guidelines Resuscitation registry (formerly known as the National Registry of Cardiopulmonary Resuscitation) to develop this prediction tool, noting that families and caregivers "are eager for more precise information about the likelihood of survival and neurologic outcome" when patients have survived in-hospital resuscitation.
The investigators assessed the cases of 42,957 patients in inpatient wards or intensive care units at 551 U.S. hospitals in 2000-2009 who were successfully resuscitated. The mean patient age was 66 years. Fifty-six percent of the cohort was male, 19% was African American.
The first step in developing their prediction tool, known as the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score, was to determine the value of 37 baseline characteristics in predicting the outcomes of the 28,629 subjects who composed the derivation cohort. These characteristics included patient demographics such as age and sex; the location (within the hospital) of the arrest; the initial cardiac arrest rhythm; the timing of the arrest (during regular work hours or off-hours); the patient’s neurologic status before the cardiac arrest; comorbidities; critical care interventions already in place at the time of the cardiac arrest, such as mechanical ventilation or vasopressor medications; and key cardiac variables such as the duration of resuscitation efforts and time to defibrillation.
From this analysis, the researchers identified the 11 variables with the greatest ability to predict neurologically intact survival. They used those variables to construct a predictive model, and a table for converting the results into a numerical risk score from 1 to 40 points, with higher a total indicating a lower likelihood of favorable neurologic survival.
It was notable that two variables found to have the greatest predictive ability were factors pertaining to the cardiac arrest itself: the initial cardiac arrest rhythm, and the duration of resuscitation until spontaneous circulation was restored. In contrast, many patient factors were not found to be predictive and were discarded from the final model, the investigators said (Arch. Intern. Med. 2012 May 28 [doi:10.1001/archinternmed.2012.2050]).
Dr. Chan and his colleagues then conducted a validation study to test the accuracy of the CASPRI score in the remaining 14,328 subjects. "Patients in the top decile (CASPRI score of less than 10) had a 70.7% mean probability of favorable neurologic survival, whereas patients in the bottom decile (CASPRI score of 28 or higher) had a 2.8% mean probability of favorable neurologic survival," they noted.
"Providing concrete probabilities for favorable neurologic survival after cardiac arrest is an important discussion that clinicians have with patients and their families to manage expectations. By converting our prediction model into a risk score, we have sought to create an infrastructure with which clinicians can identify [the] 10% of patients ... who have a greater than 70% probability of favorable neurologic survival to discharge, compared with another 10% who have less than a 3% chance of this outcome," they added.
This study was supported by the National Heart, Lung, and Blood Institute and the American Heart Association.