Objectives. To assess how ethanol in potential lethal serum concentrations affects features of the ECG that may be associated with cardiac arrhythmias. Design. We included 84 patients, who were hospitalised with assumed acute ethanol intoxication. In the emergency room resting ECG was recorded and blood was collected for serum osmolality measurement used as a proxy for ethanol level. Thirty-two also had ECG recorded at discharge. Twenty-seven hospitalised patients without known alcohol ingestion served as controls. ECG segment durations were compared with controls and related to intoxication level. Results. In subjects with moderately elevated to high serum osmolality, the P wave and QTc intervals were prolonged compared with sober subjects. P wave, PR, QRS and QTc intervals were longer when the subjects had high blood ethanol levels (at admission) than at discharge (p-values: 0.0001, 0.0002, 0.010 and B/0.0001 for P wave, PR, QRS and QTc intervals. n0/32). Conclusions. Ethanol at high to very high blood concentration causes several changes in the ECG that might be associated with increased risk of arrhythmias.

A computer system to be used in the emergency room has been developed for estimating the risk of acute coronary heart disease (ACHD). The system uses data on 38 case history and clinical variables collected consecutively over a year from 918 patients with acute chest pain. A statistical procedure based on Bayes' formula is used to estimate disease probabilities. A quadratic scoring rule was used for variable selection. The score increased markedly until 15-20 variables had been added, reached a maximum after inclusion of about 30 variables and then deteriorated slightly. Thus, the number of variables carrying additional information on the presence/absence of ACHD seems to be much larger than the number normally utilized by doctors and by other decision support systems. Reclassification into two groups, those with and without ACHD, gives a diagnostic accuracy of 89%. We conclude that analysing detailed case histories by computer is a promising decision support system for use in the emergency room as a supplement to ECG analysis.

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.

The value of thorough examination of the case history as a diagnostic tool on hospitalization of patients with suspected myocardial infarction was investigated in three independent prospective studies. Use of a limited number of pain-related elements (= 'criteria'), that had already been obtained in the emergency room, could improve the decision on whether or not to admit patients to the coronary-care unit. As an example, in one of the studies, use of such criteria would have reduced the number of 'unnecessary' coronary-care-unit admissions from 298 to 162, a 46% reduction (P less than 0.001). In the same patient sample, use of the criteria could have reduced the number of patients with definite acute myocardial infarction, admitted to the general wards, from 47 to 22, a 53% reduction (P less than 0.01). These favourable results were confirmed in the two independent, smaller-scale studies.

A simple algorithm, which improves the diagnostic performance in patients arriving with acute chest pain in the emergency room, has been developed. The algorithm is solely based on information immediately available to the physician and includes elements from ECG, clinical findings and case history. As postulated, a stepwise use of all these variables improved the diagnostic accuracy and reduced the false positive cardiac-care unit (CCU) referral rate in a prospective study of 1450 patients admitted with acute chest pain. Compared to previous hospital practice during a preceding control period, sensitivity in diagnosing patients with unstable ischaemic heart diseases increased from 86% to 94% (P < 0.01), and specificity increased from 44% to 56% (P < 0.001). Accordingly, accuracy increased from 67% to 81% (P < 0.001), and false positive CCU-admission rate decreased from 35% to 19%. The greatest improvement in physician's diagnostic decisions was observed among patients without clear-cut signs of acute ischaemic heart disease on admission.