RESUMO
Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfr and only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules.
RESUMO
Imbalance in trace metal metabolism may lead to metal interactions that may be of patho-physiological importance. Knowledge of the relation between trace metals in normal tissues is needed to assess abnormal deviations associated with disease. In this study correlations between Cu, Co, Cr, Fe, Mn, Ni, Se, Zn, Al, Ba, Cd, Pb and Sr within the same and between 6 different, normal autopsy tissues were determined using Spearman rank correlation analysis based on analytical data obtained by inductively coupled plasma atomic emission spectrometry (ICP-AES). Fe-Co were correlated in most tissues. Cu-Mn, Zn-Cu, Zn-Mn and Zn-Cd were highly correlated in the kidney medulla. Ni-Ni, Sr-Sr and Cd-Cd were correlated between several tissues, while Fe-Fe, Zn-Zn and Cu-Cu were correlated between kidney cortex and medulla. Mn-Mn was highly correlated between the liver and brain front lobe, cerebellum and heart. High correlations were found for Ni-Co and for Se-Mn between the kidney cortex and brain front lobe and pancreas respectively. Inverse correlations were found for Se-Cd between kidney cortex and cerebellum, for Se-Cd and Cd-Zn between kidney medulla and heart, for Co-Sr and Fe-Sr between the liver and kidney cortex and heart respectively, and for Sr-Mn between kidney medulla and pancreas. A large number of trace elements are statistically correlated within and between different, normal tissues. Knowledge of these correlations may contribute to increase the understanding of kinetic interactions of trace metals in the body and the role of such interactions in normal and disturbed trace metal metabolism.
