P170 The validity and utility of the SLE-key® rule-out serological test when applied in a selected cohort of patients with SLE and other connective tissue diseases

Background/Aims We aimed to establish the validity of the SLE-key® rule-out test and analyse its utility in distinguishing systemic lupus erythematosus (SLE) from other autoimmune rheumatic connective tissue diseases. Methods We used data from the Lupus Extended Autoimmune Phenotype (LEAP) study, which included a representative cross-sectional sample of patients with a variety of rheumatic connective tissue diseases, including SLE, mixed connective tissue disease (MCTD), inflammatory myositis, systemic sclerosis, primary Sjögren’s syndrome and undifferentiated connective tissue disease (UCTD). The modified 1997 ACR criteria were used to classify patients with SLE. Banked serum samples were sent to Immune-Array’s CLIA- certified laboratory Veracis (Richmond, VA) for testing. Patients were assigned test scores between 0 and 1 where a score of 0 was considered a negative rule-out test (i.e. SLE cannot be excluded) whilst a score of 1 was assigned for a positive rule-out test (i.e. SLE excluded). Performance measures were used to assess the test’s validity and measures of association determined using linear regression and Spearman’s correlation. Results Our study included a total of 155 patients of whom 66 had SLE. The mean age in the SLE group was 44.2 years (SD 13.04). 146 patients (94.1%) were female. 84 (54.2%) patients from the entire cohort had ACR SLE scores of ≤ 3 whilst 71 (45.8%) had ACR SLE scores ≥ 4. The mean ACR SLE total score for the SLE patients was 4.85 (SD 1.67), ranging from 2 to 8, with mean disease duration of 12.9 years. The Sensitivity of the SLE-Key® Rule-Out test in diagnosing SLE from other connective tissue diseases was 54.5%, specificity was 44.9%, PPV 42.4% and NPV 57.1 %. 45% of the SLE patients had a positive rule-out test. SLE could not be ruled out in 73% of the MCTD patients whilst 51% of the UCTD patients had a positive Rule-Out test and >85% of the inflammatory myositis patients had a negative rule-out test. ROC analysis generated an AUC of 0.525 illustrating weak class separation capacity. Linear regression established a negative correlation between the SLE-key Rule-Out score and ACR SLE total scores. Spearman’s correlation was run to determine the relationship between ACR SLE total scores and SLE-key rule-out score and showed very weak negative correlation (rs = -0.0815, n = 155, p = 0.313). Conclusion Our findings demonstrate that when applied in clinical practice in a rheumatology CTD clinic setting, the SLE-key® rule-out test does not accurately distinguish SLE from other CTDs. The development of a robust test that could achieve this would be pivotal. It is however important to highlight that the test was designed to distinguish healthy subjects from SLE patients and not for the purpose of differentiating SLE from other connective tissue diseases. Disclosure S. Achieng: None. J.A. Reynolds: None. I.N. Bruce: Other; I.N.B is a National Institute for Health Research (NIHR) Senior Investigator and is funded by the NIHR Manchester Biomedical Research Centre. M. Bukhari: None.

The Interpreter, the Scientist, and the Analyst

Stanley Fish and Bernhard Schlink agree that there can be no rule for finding a correct legal interpretation. Each, however, offers a negative rule to recognize incorrect interpretations. Schlink asserts that incorrect interpretations can be eliminated through the scientific method of falsification. Fish claims that any interpretation not concerned with the author’s state of mind must be rejected. Unfortunately, Fish’s insistence on authorial intent could be read as downplaying the role of the interpreter. Although interpretation is objective in that it involves the examination of an object, it is not merely objective. Communication is collaboration; interpretation needs an interpreter. It is intersubjective. But interpretation cannot be relegated entirely to the intersubjective “symbolic” order where language and law is located. The symbolic can never be disentangled from the orders of the “imaginary” and the “real” that are its logical boundaries. Interpretation has a subjective aspect because it requires the creative act of the interpreter’s imagination. Schlink recognizes that a subjective moment of hypothesis formation is essential to interpretation but tries to distinguish it from a subsequent objective or intersubjective testing process. There is no rule that can disprove our legal interpretations. This is why judging is always a moral act.

THREE-WAY GOVERNMENT DECISION ANALYSIS WITH DECISION-THEORETIC ROUGH SETS

By considering the risks in policy making procedure, a three-way decision approach based on the decision-theoretic rough set model is adopted to risk government decision-making. A three-way decision is made based on a pair of thresholds on conditional probabilities. A positive rule makes a decision of executing, a negative rule makes a decision of non-executing, and a boundary rule makes a decision of deferment. The loss functions are used to calculate the required two thresholds to describe the decision risk with the Bayesian decision procedure. A case study of government petroleum risk investment demonstrates the proposed method.

Management Ethics

This chapter looks at some of the psychological underpinnings of rule breaking behavior in business -what drives rule breaking behavior and how can it be shaped. The authors contrast the positive and productive rule breaking, with the destructive and unethical rule breaking behavior seen in companies such as Enron. They consider some of the causes of deviant behavior using a social bonding framework and other potential predictors such as lack of self-awareness, lack of future commitment to the organization and lack of supervisory support. Narcissistic leaders are a special case that the authors examine because of their potential influence on either positive or negative rule breaking. Narcissistic leaders can have a positive impact on innovation, or they may elect to engage in unethical rule breaking. The chapter also explores practical prevention strategies and ends with an explanation of some of the major findings of positive psychology.

DIFFERENCE RELATION-BASED ROUGH SET AND NEGATIVE RULES IN INCOMPLETE INFORMATION SYSTEM

The purpose of this paper is to present a new rough set model for generating negative rules from the incomplete information system. A negative rule indicates that if an object does not satisfy the attribute-value pairs in the condition part, then we can exclude the decision part from such object. The proposed rough set model is constructed on the basis of a difference relation. Such difference relation is a binary relation without any constraints. Moreover, to simplify the negative rules generated from the difference relation-based rough approximations, the concepts of lower, upper approximate and rough reducts are also proposed. Some numerical examples are employed to substantiate the conceptual arguments.

Theoretical Considerations on the Health Checkup Accuracy of Combination Testing

Summary Objectives: To find basic theoretical evidence for an optimum combination of multi-phasic health checkup testing obtained by considering how the health checkup accuracy changes with the number of tests (n) and kinds of combination methods (A, B, C, D and E). Methods: To find how the health checkup accuracy changes with the number of tests and type of combination method, generalized formulas as functions of the number of tests, are obtained to calculate the over-all health checkup accuracy which is defined by sensitivity ( ), specificity ( ) and odds-ratio ( ), based on the two-by-two table. Five kinds of combination methods were considered: A) Sequential tests. B) Sequential tests after changing the order in A. C) Simultaneous tests using the Believe-the-Negative Rule. D) Simultaneous tests using the Believe-the-Positive Rule. E) Simultaneous tests using the Believe-all-Positive-all-Negative Rule. Results: It was provend that combination methods A, B and C are “equivalent” for health checkup accuracy. Therefore, the five methods could be summarized into three patterns. For A, B and C:andincreased but a decreased with increasing n. For D:increased butanddecreased with n. For E:,andincreased with n. Conclusion: Health checkup accuracy of combination testing is the best in case of E, although problems exist concerning how to judge the borderline subjects.