A logistic regression model may be used to provide predictions of outcome for individual patients at another centre than where the model was developed. When empirical data are available from this centre, the validity of predictions can be assessed by comparing observed outcomes and predicted probabilities. Subsequently, the model may be updated to improve predictions for future patients. As an example, we analysed 30-day mortality after acute myocardial infarction in a large data set (GUSTO-I, n = 40 830). We validated and updated a previously published model from another study (TIMI-II, n = 3339) in validation samples ranging from small (200 patients, 14 deaths) to large (10,000 patients, 700 deaths). Updated models were tested on independent patients. Updating methods included re-calibration (re-estimation of the intercept or slope of the linear predictor) and more structural model revisions (re-estimation of some or all regression coefficients, model extension with more predictors). We applied heuristic shrinkage approaches in the model revision methods, such that regression coefficients were shrunken towards their re-calibrated values. Parsimonious updating methods were found preferable to more extensive model revisions, which should only be attempted with relatively large validation samples in combination with shrinkage.

Purpose: There is ongoing debate about whether testing low-risk genes at multiple loci will be useful in clinical care and public health. We investigated the usefulness of multiple genetic testing using simulated data. Methods:Usefulness was evaluated by the area under the receiver-operating characteristic curve (AUC), which indicates the accuracy of genetic profiling in discriminating between future patients and nonpatients. The AUC was investigated in relation to the number of genes assumed to be involved, the risk allele frequency, the odds ratio of the risk genotypes, and to the proportion of variance explained by genetic factors as an approximation of the heritability of the disease. Results: We demonstrated that a high (AUC Ͼ 0.80) to excellent discriminative accuracy (AUC Ͼ 0.95) can be obtained by simultaneously testing multiple susceptibility genes. A higher discriminative accuracy is obtained when genetic factors play a larger role in the disease, as indicated by the proportion of explained variance.The maximum discriminative accuracy of future genetic profiling can be estimated at present from the heritability and prevalence of disease. Conclusions: Genetic profiling may have the potential to identify individuals at higher risk of disease depending on the prevalence and heritability of the disease. Genet Med 2006:8(7):395-400.

We analyzed six spirometric data sets collected in the Netherlands, Austria, the United Kingdom, Spain, and Italy. The objectives were to establish whether (1) it was possible to describe spirometric indices from childhood to adulthood, taking into account the adolescent growth spurt, and (2) there are systematic differences in ventilatory function between children and adolescents in different parts of Western Europe. The study comprised 2,269 girls and 3,592 boys, aged 6-21 years. The range in standing height was 110-185 in girls, 110-205 in boys. The model applicable to all data sets was ln FVC or ln FEV1 = a + (b + c x A) x H, where H = standing height and A = age; this model prevents the phase shift between the adolescent growth spurt in length and lung volume from leading to an age-dependent bias in predicted values. There was surprising agreement between most of the data sets; systematic differences are probably due to technical factors arising from ATPS-BTPS corrections and from defining the end of breath with pneumotachometer systems. Taking those into account, prediction equations for FVC, FEV1, and FEV1%FVC were developed with "lower limits of normal" which should be applicable to children and adolescents of European descent. It is proposed that the approach of analyzing available data sets should also be applied to other ventilatory indices, data collected in adults and elderly subjects, or in other ethnic groups, and that an international data base be set up to that end.

This study sought to determine the contribution of neighborhood socioeconomic status to all-cause mortality and to explore its correlates. As part of the longitudinal "Gezondheid en LevensOmstandigheden Bevolking en omstreken" (GLOBE) study in the Netherlands, 8,506 randomly selected men and women aged 15-74 years from 86 neighborhoods in the city of Eindhoven reported on their socioeconomic status in the 1991 baseline survey. During the 6-year follow-up, 487 persons died. Neighborhood socioeconomic status was derived from individual reports on socioeconomic status. Its effect on mortality was stringently controlled for four individual-level socioeconomic indicators. Persons living in a neighborhood with a high percentage of unemployed/disabled or poor persons had a higher mortality risk than did those living in a neighborhood with a low percentage of unemployed/disabled or poor persons. This was independent of individual socioeconomic characteristics, including individual unemployment/disability or reports of severe financial problems. Educational and occupational neighborhood indicators were similarly, but less strongly, related to mortality. The prevalence of poor housing conditions, social disintegration, and unhealthy psychologic profiles and behaviors was higher in neighborhoods with a low socioeconomic status. Contextual effects of socioeconomic status may thus be due to one or more of these specific circumstances. The findings indicate potential public health benefits of modifying socioeconomic characteristics of areas.

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