Simple Computer-Assisted Diagnosis of Acute Myocardial Infarction in Patients with Acute Thoracic Pain

Abstract:

In order to minimize the initial diagnostic uncertainty in patients suspected of having acute myocardial infarction, we prospectively extracted predictive variables from previous history, ECG, and clinical chemical parameters of 87 patients, who were admitted for acute thoracic pain. The variables thus extracted were: Thoracic pain in previous history, duration of pain, white blood cell count, blood glucose, creatine-kinase, and S-T elevation in the ECG. These parameters were used for formulating a mathematical model based upon univariate and multivariate statistical methods. The sensitivity of the model in the study population was 95% and the specificity 77%. Correct classification was achieved in 89% of cases.

In a second phase, the prognostic index was prospectively evaluated in a second set of 122 consecutive patients. In this test population, the sensitivity was 89% and the specificity 86%. 87% of patients were classified correctly.

Key-words

Computer-Assisted Diagnosis - Myocardial Infarction

• REFERENCES

• 1 Lichtlen PR. Unstabile Angina pectoris und Prä-Infarktsyndrom. Internist 1983; 24: 372-82.
• 2 Goldman L, Cook EF, Brand DA. et al. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med 1988; 318: 797-803.
• 3 Goldman L, Weinberg M, Weisberg M. et al. A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. N Engl J Med 1982; 307: 588-96.
• 4 Lubsen J, Pool J, van der Does E. A practical device for the application of a diagnostic or prognostic function. Methods Inform Med 1978; 17: 127-9.
• 5 Moussa MAA, Bhatnagar SK, Al-Yusuf AR. Discriminant methods as aids to the diagnosis of acute coronary heart disease in the emergency room. Methods Inform Med 1986; 25: 43-6.
• 6 Mulley AG, Thibault GE, Hughes RA. et al. The course of patients with suspected myocardial infarction: the identification of low-risk patients for early transfer from intensive care. N Engl J Med 1980; 302: 943-8.
• 7 Pipberger HV, Klingeman JD, Cosma J. Computer evaluation of statistical properties of clinical information in the differential diagnosis of chest pain. Methods Inform Med 1968; 07: 79-92.
• 8 Pozen MW, D’Agostino RB, Mitchell JB. et al. The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. Ann Intern Med 1980; 92: 238-42.
• 9 Pozen MW, D’Agostino RB, Selker HP, Sytkowski PA, Hood WB. A predictive instrument to improve coronary-care unit admission practices in acute ischemic heart disease. N Engl J Med 1984; 310: 1273-8.
• 10 Rude RE, Poole K, Muller JE. MILIS study group. et al. the Electrocardiographic and clinical criteria for recognition of acute myocardial infarction based on analysis of 3,697 patients. Am J Cardiol 1983; 52: 936-42.
• 11 Rose GA, Blackburn H, Gillum RF, Prineas RJ. Cardiovascular Survey Methods. 2nd ed. Geneva: World Health Organisation Monograph Series; 1982
• 12 Blackburn H, Keys A, Simonsen E, Rautaharju P, Punsar S. The electrocardiogram in population studies. A classification system. Circulation 1960; 21: 1160-75.
• 13 Banauch D, Brümmer W, Ebeling W. et al. Eine Glucose-Dehydrogenase für die Glu-cose-Bestimmung in Körperflüssigkeiten. Z Klin Chem Klin Biochem 1975; 13: 101-7.
• 14 Stein W. Laboratory Diagnosis of Acute Myocardial Infarction. Darmstadt: GIT-Verlag; 1988
• 15 Ferrer MI. Nomenclature and Criteria of Diagnosis of Diseases of the Heart and Great Vessels. The Criteria Committee of the New York Heart Association. 8th ed. Boston: Little Brown and Co; 1979
• 16 SAS Institute Inc. Statistical Analysis System. Cary NC: SAS Institute Inc; 1987
• 17 Sachs L. Angewandte Statistik. Berlin: Springer; 1978
• 18 Diamond G, Silverberg R, Charuzi Y, Vas R, Forrester J. A format for the presymp-tomatic detection of coronary artery disease. In: Clinical Strategies in Ischemic Heart Disease. Corday E, Swan HJC. eds. Baltimore: Williams and Wilkins; 1979
• 19 Galen RS, Gambino SR. Norm und Norm-abweichung klinischer Daten. Stuttgart: Fischer; 1979
• 20 Rice RL. Symptom pattern of the hyperventilation syndrome. Am J Med 1950; 08: 691-700.
• 21 Breslow WE, Day NE. Statistical Methods in Cancer Research. Lyon: International Agency for Research on Cancer; 1980
• 22 Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med 1979; 300: 1350-8.
• 23 Diamond GA, Staniloff HM, Forrester JS, Pollock BH, Swan HJC. Computer-assisted diagnosis in the noninvasive evaluation of patients with suspected coronary artery disease. J Am Coll Cardiol 1983; 01: 444-55.
• 24 Greenberg PS, Ellestad MH, Clover RC. Comparison of the multivariate analysis and CADENZA systems for determination of the probability of coronary artery disease. Am J Cardiol 1984; 53: 493-6.
• 25 Grimm RH, Neaton JD, Ludwig W. for the multiple risk factor intervention trial research group. Prognostic importance of the white blood cell count for coronary, cancer and all-cause mortality. JAMA 1985; 254: 1932-7.
• 26 Joswig BC, Glover MU, Nelson DP, Handler JB, Henderson J. Analysis of historical variables, risk factors and the resting electrocardiogram as an aid in the clinical diagnosis of recurrent chest pain. Comput Biol Med 1985; 15: 71-80.
• 27 Kostis JB, Turkevich D, Sharp J. Association between leucocyte count and the presence and extent of coronary atherosclerosis as determined by coronary arteriography. Am J Cardiol 1984; 53: 997-9.
• 28 Melin JA, Wijns W, Vanbutsele BS. et al. Alternative diagnostic strategies for coronary artery disease in women: demonstration of the usefulness and efficiency of probability analysis. Circulation 1985; 71: 535-42.
• 29 Patterson RE, Eng C, Horowitz SF. Practical diagnosis of coronary artery disease: a Bayes theorem nomogram to correlate clinical data with noninvasive exercise tests. Am J Cardiol 1984; 53: 252-6.
• 30 Pryor DB, Harrell FE, Lee KL, Califf RM, Rosati RA. Estimating the likelihood of significant coronary artery disease. Am J Med 1983; 75: 771-80.
• 31 Reardon MF, Nestel PJ, Craig IH, Harper RW. Lipoprotein predictors of the severity of coronary artery disease in men and women. Circulation 1985; 71: 881-8.
• 32 Rubenstein C, Romhilt D, Segal P. et al. Dyslipoproteinemias and manifestations of coronary heart disease. The lipid research clinics program prevalence study. Circulation 1986; 73 (Suppl I): 91-9.
• 33 Madsen EB, Hougaard P, Gilpin E, Pedersen A. The length of hospitalisation after acute myocardial infarction determined by risk calculation. Circulation 1983; 68: 9-16.
• 34 Willems JL, Pardaens J, DeGeest H. Early risk stratification using clinical findings in patients with acute myocardial infarction. Eur Heart J 1984; 05: 130-9.
• 35 Tierney WM, Roth BJ, Psaty B. et al. Predictors of myocardial infarction in emergency room patients. Crit Care Med 1985; 13: 526-31.