Key result
Computer-based decision support identifies CCU admission need with ~96% sensitivity alongside standard clinical evaluation.
Why the study?
A computer-based decision support system was developed to improve immediate differentiation of acute chest pain patients needing urgent CCU transfer and to identify acute MI cases.
Does a computer-based decision support system accurately identify patients with acute chest pain in need of CCU transfer?
Observational (n=1,252)
Does a computer-based decision support system accurately identify patients with acute chest pain in need of CCU transfer?
A computer-based decision support system using case history data showed high sensitivity for identifying acute chest pain patients requiring CCU admission.
High-sensitivity DSP may aid safe CCU triage; leaves open randomized validation of outcomes and modern integration.
A recently designed computer based decision support system (DSP), almost exclusively based on case history data, was developed to facilitate immediate differentiation between patients with and without urgent need for coronary care unit (CCU) transferral from the emergency room, and additionally to distinguish between patients with and without acute myocardial infarction (MI). One-year's prospective testing in a consecutive series of 1252 patients with acute chest pain revealed that the DSP, used in addition to ECG and clinical examination, demonstrated a sensitivity of 96% in the detection of patients in need of CCU observation (MI-sensitivity of 98%), and a specificity of 56% in excluding patients who were not in need of CCU observation. The proportion of referrals to the CCU judged to be unnecessary was only 17% of the total number of patients seen in the emergency room.
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Jonsbu et al. (1993) conducted an observational in acute chest pain (n=1,252). computer based decision support system (DSP) was evaluated on detection of patients in need of CCU observation. A computer-based decision support system used alongside ECG and clinical examination demonstrated 96% sensitivity and 56% specificity for detecting patients needing coronary care unit observation.
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