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Tool accurately predicts neurological outcomes after in-hospital cardiac arrest
A bedside prediction tool permitted an accurate estimation of favorable neurological survival in patients after an in-hospital cardiac arrest, according to a study published in the Archives of Internal Medicine.
Lead author Paul S. Chan, MD, of Saint Luke’s Mid America Heart Institute, and the department of internal medicine, University of Missouri-Kansas City in Missouri, and colleagues, used the Get With the Guidelines–Resuscitation registry to identify 42,957 patients from 551 hospitals admitted between January 2000 and October 2009. All patients experienced an in-hospital cardiac arrest and were successfully resuscitated. The mean age of the patients was 66 years, 56% were men and 19% were black. Two-thirds were randomly selected as the derivation cohort. One-third served as the validation cohort.
Researchers then developed a prediction tool using multivariate logistic regression to determine favorable neurological survival in these patients. Favorable neurological status was determined by the absence of severe neurological deficits.
The derivation cohort and the validation cohort showed similar rates of favorable neurological survival (24.6% and 24.5%). The final model had 11 variables associated with favorable neurological survival: younger age; initial cardiac arrest rhythm of ventricular fibrillation or pulseless ventricular tachycardia with a defibrillation time of 2 minutes or less; baseline neurological status without disability; arrest location in a monitored unit; shorter duration of resuscitation; as well as absence of mechanical ventilation, renal insufficiency, hepatic insufficiency, sepsis, malignant disease and hypotension before the arrest. The prediction tool helped identify patients with favorable neurological survival rates. Patients in the top decile had a 70.7% probability of having favorable neurological survival compared with patients in the bottom decile (2.8% probability).
“By leveraging the size and scope of the [Get With the Guidelines]–Resuscitation registry, we have developed and validated a simple bedside prediction tool — the CASPRI score — that can be used to estimate the likelihood of survival to discharge with favorable neurological status for patients initially resuscitated from an in-hospital cardiac arrest. To our knowledge, this is the first risk score developed among successfully resuscitated patients who develop cardiac arrest in the hospital setting. Because this prediction tool was developed in over 40,000 patients from 551 hospitals and used clinical factors that can be readily assessed, we believe that it offers the potential to provide physicians reliable and valuable prognostic information for discussions with patients and their families after successful resuscitation,” Chan and colleagues said in the study.
Disclosures: Dr. Krumholz is a recipient of a research grant from Medtronic Inc. through Yale University. Dr. Chan was supported by a grant from the National Heart, Lung, and Blood Institute (NHLBI). Dr. Krumholz was supported by a grant from the NHLBI (Center for Cardiovascular Outcomes Research at Yale University).