Vital Signs Measurement Can Predict Cardiac Arrest

Researchers at the University of Chicago (IL, USA) performed a nested case-control study over the course of two years, matching 83 cardiac arrest patients to 332 patients controls divided into four randomly selected controls, who were patients in the same unit at the same time. Among the control patients, approximately 75% were medical admissions, while the rest were surgical. The researchers attempted to validate the Modified Early Warning Score (MEWS) composite index.

The results showed that the case patients were significantly older, were hospitalized longer prior to suffering cardiac arrest, and were more likely to have had a prior rapid response call or intensive care unit (ICU) admission. On admission, the case patients and the controls had similar mean vital signs and MEWS, with two significant exceptions: case patients had lower diastolic blood pressure and higher respiratory rates.

The results showed that mean MEWS was significantly higher in patients who would suffer a cardiac arrest within 48 hours than it was for the study's controls, and the difference increased leading up to the event. Individual vital signs, including maximum respiratory rate, maximum heart rate, maximum pulse pressure, and minimum diastolic blood pressure were also found to be statistically significant predictors of cardiac arrest. MEWS variables that were not statistically significant included temperature, minimum heart rate, and systolic blood pressure. The study was presented at the American Thoracic Society (ATS) international conference, held during May 2011 in Denver (CO, USA).

"Because current activation systems don't utilize the best vital sign predictors, they often suffer from poor sensitivity and high false-positive rates. This limits the effectiveness of the rapid response team, and the high false-positive rates can lead to 'alarm fatigue' and the wasting of hospital resources,” said lead author and study presenter Matthew Churpek, MD, MPH. "The creation of an evidence-based cardiac arrest prediction tool would decrease the false-positive rate and lead to a response system with a better chance of improving patient outcomes.”

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