Early observations of S-myoglobin in the diagnosis of acute myocardial infarction. The influence of discrimination limit, analytical quality, patient's sex and prevalence of disease

Abstract

By means of a graphical method the influence of the analytical variation and the discrimination limit (DL) on the diagnostic power of the maximum serum myoglobin value observed from 4 to 12 h after onset of symptoms in 291 patients suspected for myocardial infarction (AMI) was examined. The prevalence of AMI was 0.45 and the male to female ratio 2:1. Serum myoglobin (S-myoglobin) was measured by a radioimmunoassay (RIA) with a coefficient of analytical variation (CVA) of 9%. For the distributions of the log values of maximum S-myoglobin for AMI patients and non-AMI patients straight lines were obtained on a probit scale. A statistically significant difference was found between the distributions for females and males without AMI, whereas no difference was found between females and males with AMI. The distributions of patients with and without AMI overlapped markedly giving a high number of misclassifications. The minimum fraction of misclassifications among all patients admitted occurred at a DL of 325 micrograms/l and was 0.16. When S-myoglobin is used for the purpose of early diagnosis of AMI the DL should be chosen so that the fraction of false negative patients is small. Consequently the fraction of false positive patients will be relatively high. At a DL of, for example, 175 micrograms/l, the false negative fraction was 0.06 of all patients with AMI (sensitivity 0.94), and the fraction of false positive patients was 0.35 (specificity 0.65).(ABSTRACT TRUNCATED AT 250 WORDS)

Biochemical Markers of Ischaemia for the Early Identification of Acute Myocardial Infarction without ST Segment Elevation

“…If the markers were used in a population with a higher a priori probability of AMI, PPV would increase per se [11, 12]. The majority of studies dealing with biochemical markers for early AMI diagnosis have reported a much higher AMI prevalence and PPV by also including patients with significant ST elevation [13, 14, 15].…”

A Comparison of Analytical Goals for Haemoglobin A1c Assays Derived Using Different Strategies

“…[13][14][15][16][17][18][19][20][21][22] Discussion has often been centred on whether one or another analytical goal is the most correct, or on which strategy is most appropriate. All goals, however, are based on some assumptions and are arbitrary in the sense of the degree of variation the analytical performance (and error) may introduce to variability of the true result.37…”

Performance criteria based on true and false classification and clinical outcomes. Influence of analytical performance on diagnostic outcome using a single clinical component

“…This is often forgotten by scientists who simply describe their distributions as Gaussian so it is necessary to transform these distributions to ln-Gaussian (e.g., by the formulas from Fokkema et al [4]). The method of simulating analytical errors in non-parametric bimodal models was introduced by Groth [5] and compared with a parametric model [6], which was further expanded in, e.g., Nørregaard-Hansen et al [7].…”

“…The concentration for which sensitivity = specificity is often chosen as the optimum cut-off, here 4.5. If fractions of false positive (FP) and false negative (FN) are used instead, it is easier to introduce the prevalence of disease [7,8], so the curves for FP will decrease and FN will increase for increasing concentrations and if the prevalence is, e.g., 20% (from 200 healthy and 50 diseased), then the fraction of FP will have a maximum of 0.8 and FN a maximum of 0.2 as calculated from the total sum of the two populations ( Figure 1A). In order to demonstrate the importance of prevalence for choosing the cut-off point and the effect on analytical quality specifications, the optimum cut-off from the sensitivity = specificity is chosen so the sum of FP and FN ( = the combined fraction of misclassifications) will show up as a minimum at a higher concentration than the first cut-off ( Figure 1B).…”