Study design: Prospective, randomized, controlled, multicenter trial.
Setting: Five EDs in the U.S.; three included observation units.
Synopsis: The study enrolled 1,392 patients in a 2:1 ratio, with two-thirds undergoing CCTA and the other third treated with usual care (those admitted for possible ACS with a TIMI score of 0 to 2). Of 908 patients assigned to CCTA, 640 had a negative test, and none of them died or had a myocardial infarction within 30 days. Patients in the CCTA group were more likely than the group undergoing usual care to be discharged from the ED (49.6% vs. 22.7%), have a shorter length of stay (18 hours vs. 24.8 hours, P<.0001), and receive a coronary disease diagnosis.
Bottom line: In a randomized controlled trial, the use of CCTA to evaluate low- to intermediate-risk emergency department patients with possible ACS appears to be safe and effective, and leads to more discharges from the ED.
Citation: Litt HI, Gatsonis C, Snyder B, et al. CT angiography for safe discharge of patients with possible acute coronary syndromes. N Engl J Med. 2012;366:1393-1403.
Impending Physiological Deterioration Can Be Predicted Using Data from a Comprehensive EHR
Clinical question: Can impending physiological deterioration be predicted in medical-surgical ward patients using data from a comprehensive electronic health record (EHR)?
Background: Unplanned ICU transfer is associated with increased mortality and morbidity. Previous studies have examined physiological variables and track-and-trigger systems that might help in the identification of ward patients who are at risk for deterioration and transfer to a higher level of care. More hospitals across the U.S. are using EHRs, and eventually all hospitals are expected to be using one. However, it is unclear if an EHR-based model can accurately predict patients’ clinical deterioration.
Study design: Retrospective case-control study; the unit of analysis was the “patient shift,” each defined by a particular 12-hour period and patient. A complex model to predict patient deterioration and transfer to the ICU was developed and validated using EHR data that was available prior to the deterioration shift.
Setting: Fourteen hospitals of the Northern California Kaiser Permanente Medical Care Program (an integrated healthcare delivery system) that have used an operational EHR for at least three months.
Synopsis: The study identified 4,036 patient-shifts during which a transfer to the ICU occurred, and compared these with 39,782 patient-shifts during which no transfer occurred. Variables in the model included patient demographic data, diagnoses, comorbidities, lab results, and vital signs. The EHR-based predictive model performed well at predicting clinical deterioration and transfer to the ICU (derivation c-statistic 0.84, validation c-statistic 0.77). The model performed best among patients with gastrointestinal diseases (c-statistic 0.84) and worst in patients with acute myocardial infarction (c-statistic 0.57).
Bottom line: Impending physiologic deterioration can be predicted using EHR-based models.
Citation: Escobar GJ, Laguardia JC, Turk BJ, et al. Early detection of impending physiologic deterioration among patients who are not in intensive care: Development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012:doi:10.1002/jhm.1929 [Epub ahead of print].
Rapid Influenza Diagnostic Tests Have Low Sensitivity and High Specificity
Clinical question: How accurate are rapid influenza diagnostic tests, and can they be used to guide further management?
Background: Three million to 5 million people a year develop influenza, which has a high mortality rate and potential global implications. The gold-standard diagnostic tests, PCR and viral culture, have a long turnaround time and are expensive. There have been limited systematic reviews addressing the accuracy of various rapid influenza diagnostic tests (RIDTs), especially in adults.