To formally establish the risk of lupus anticoagulants and anticardiolipin antibodies for arterial and venous thrombosis, we ran a MEDLINE search of the literature from 1988 to 2000. Studies were selected for their case-control (11), prospective (9), cross-sectional (3), and ambispective (2) design. They provided or enabled us to calculate the odds ratio with 95% confidence interval (CI) of lupus anticoagulants and/or anticardiolipin antibodies for thrombosis in 4184 patients and 3151 controls. Studies were grouped according to the antibody investigated. Five studies compared lupus anticoagulants with anticardiolipin antibodies: the odds ratio with 95% CI of lupus anticoagulants for thrombosis was always significant. None of them found anticardiolipin antibodies were associated with thrombosis. Four studies analyzed only lupus anticoagulants: the odds ratio with 95% CI was always significant. The risk of lupus anticoagulants was independent of the site and type of thrombosis, the presence of systemic lupus erythematosus, and the coagulation tests employed to detect them. Sixteen studies served to assess 28 associations between anticardiolipin antibodies and thrombosis: the odds ratio with 95% CI was significant in 15 cases. Anticardiolipin titer correlated with the odds ratio of thrombosis. In conclusion, the detection of lupus anticoagulants and, possibly, of immunoglobulin G (IgG) anticardiolipin antibodies at medium or high titers helps to identify patients at risk for thrombosis. However, to take full advantage of the conclusions provided by the available evidence, there is an urgent need to harmonize investigational methods. IntroductionAntiphospholipid antibodies are a heterogeneous family of immunoglobulins that includes, among others, lupus anticoagulants and anticardiolipin antibodies. Lupus anticoagulants behave as acquired inhibitors of coagulation, prolonging phospholipid-dependent in vitro coagulation tests, 1 and anticardiolipin antibodies are measured by immunoassay, utilizing cardiolipin or other anionic phospholipids in solid phase. 2 Despite their name, antiphospholipid antibodies do not recognize phospholipids, but plasma proteins bound to suitable anionic (not necessarily phospholipid) surfaces. Among these, ␤ 2 -glycoprotein I 3,4 and prothrombin 5 are the most widely investigated antigenic targets. Most anticardiolipin antibodies need ␤ 2 -glycoprotein I to react with cardiolipin in immunoassays. 4 Specific subgroups of anti-␤ 2 -glycoprotein I 6 and antiprothrombin 7 antibodies are responsible for the lupus anticoagulant activity in phospholipid-dependent coagulation tests. Clinical interest in antiphospholipid antibodies is due to their relation with arterial and venous thrombosis in the antiphospholipid syndrome. Two forms of antiphospholipid syndrome have been described: a "primary" syndrome, 8 with no evidence of an underlying disease, and a "secondary" syndrome, mainly in the setting of systemic lupus erythematosus. 9 Thromboembolic events are reported in approximately one third of a...

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Methodologies to help benefit-risk assessments of medicines are diverse and each is associated with different limitations and strengths. There is not a 'one-size-fits-all' method, and a combination of methods may be needed for each benefit-risk assessment. The taxonomy introduced herein may guide choice of adequate methodologies. Finally, we recommend 13 of 49 methodologies for further appraisal for use in the real-life benefit-risk assessment of medicines.

Prognostic models applied in medicine must be validated on independent samples, before their use can be recommended. The assessment of calibration, i.e., the model's ability to provide reliable predictions, is crucial in external validation studies. Besides having several shortcomings, statistical techniques such as the computation of the standardized mortality ratio (SMR) and its confidence intervals, the Hosmer–Lemeshow statistics, and the Cox calibration test, are all non-informative with respect to calibration across risk classes. Accordingly, calibration plots reporting expected versus observed outcomes across risk subsets have been used for many years. Erroneously, the points in the plot (frequently representing deciles of risk) have been connected with lines, generating false calibration curves. Here we propose a methodology to create a confidence band for the calibration curve based on a function that relates expected to observed probabilities across classes of risk. The calibration belt allows the ranges of risk to be spotted where there is a significant deviation from the ideal calibration, and the direction of the deviation to be indicated. This method thus offers a more analytical view in the assessment of quality of care, compared to other approaches.