Clinical Sonar is Warranted in the Area of Big Data for Management of Acute Coronary Syndromes

Background: The diagnosis of Acute Coronary Syndromes (ACS) is challenging due to wide range of clinical presentation on one hand and the burden of patients presenting to the emergency department (ED) with chest discomfort. The "sea of data" is available in the several different stand alone computerized databases that can facilitate risk stratifying these patients. The extent of missing diagnosis in the ER is unknown.

Aim: To document the gap between previously existing diagnoses and those identified in the ED.

Methods: a retrospective study of 240 patients, which were discharged alive from a tertiary medical center with a diagnosis of NSTEMI. Patients presenting with sudden death, shock or secondary NSTEMI were excluded. Data was collected from electronic medical records (EMR) and ED files that were manually evaluated for documented diagnoses, risk factors and comorbidities. Missing ED diagnoses were compared to documented diagnoses in the EMR.

Results: the following diagnoses gaps were found: 40% of extra-cardial vascular disease (stroke, carotid atherosclerosis and peripheral vascular disease), 20% of previous coronary revascularization, 12% of congestive heart failure, 8% of ischemic heart disease. The rate of risk factors not recorded were: 50% of dyslipidemia, 35% of renal failure, 34% of hypertension and 26% of diabetes.

Comment: the triage of the huge effluxes of patients with chest discomfort to the ER may potentially benefit by using "signal system" acting like sonar in the sea of big data. A quick and efficient algorithm that will screen relevant databases looking for pre-specified diagnoses may potentially shorten the time of triage and help risk stratify patients with chest discomfort.