The Goldman algorithm revisited: prospective evaluation of a computer-derived algorithm versus unaided physician
1Departments of Cardiology, Bridgeport Hospital, Bridgeport, Connecticut, USA.
Background:
It has been nearly a decade since Goldman's computer-driven algorithm to predict myocardial infarction was validated. Despite the potential to avoid admission of patients without acute myocardial infarction (AMI) to the coronary care unit (CCU), the routine use of computer-generated protocols has not been widely adopted.
Methods:
Two hundred consecutive patients admitted to a university-affiliated community hospital with the suspected diagnosis of AMI as determined by physicians without the aid of the Goldman protocol underwent a blinded prospective evaluation to assess the performance of the Goldman algorithm in predicting the presence of AMI. Over the same time period, the Goldman algorithm was applied by retrospective chart review in 762 patients with non-AMI admitting diagnoses. Prospective history, physical examination, and electrocardiographic data were obtained within 24 hours of admission to the CCU by a physician blinded to each patient's clinical course. Retrospective chart reviews were conducted for 762 patients with chest pain given with non-AMI diagnoses.
Results:
The diagnosis of AMI was confirmed in 68.5% (137/200) of patients with suspected AMI admitted to the CCU. In prospective parallel evaluations the Goldman algorithm predicted the presence of AMI in 167 (83.5%) of these 200 patients. All 137 confirmed patients with AMI were correctly identified by the Goldman algorithm. All major in-hospital complications occurred in the 137 patients who were diagnosed as having AMI. Of the 762 patients with chest pain with non-AMI diagnoses, only 27 (3.5%) sustained an AMI. The Goldman algorithm predicted the presence of AMI in 85% (23/27) of these patients. Adherence to the use of Goldman's algorithm in the triage of chest pain could have prevented 16.5% of CCU admissions for AMI.
Conclusions:
Routine adherence to the Goldman algorithm for the evaluation of patients with acute chest pain could have decreased the number of CCU admissions for suspected AMI by 16. 5%. Because major in-hospital complications occurred only in patients with AMI, this strategy would result in significant cost savings to our health care system without jeopardizing patient safety.
