BackgroundTraumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors.Methods and FindingsProspectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial.ConclusionsPrognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.

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.

Logistic regression analysis may well be used to develop a prognostic model for a dichotomous outcome. Especially when limited data are available, it is di$cult to determine an appropriate selection of covariables for inclusion in such models. Also, predictions may be improved by applying some sort of shrinkage in the estimation of regression coe$cients. In this study we compare the performance of several selection and shrinkage methods in small data sets of patients with acute myocardial infarction, where we aim to predict 30-day mortality. Selection methods included backward stepwise selection with signi"cance levels of 0.01, 0.05, 0.157 (the AIC criterion) or 0.50, and the use of qualitative external information on the sign of regression coe$cients in the model. Estimation methods included standard maximum likelihood, the use of a linear shrinkage factor, penalized maximum likelihood, the Lasso, or quantitative external information on univariable regression coe$cients. We found that stepwise selection with a low (for example, 0.05) led to a relatively poor model performance, when evaluated on independent data. Substantially better performance was obtained with full models with a limited number of important predictors, where regression coe$cients were reduced with any of the shrinkage methods. Incorporation of external information for selection and estimation improved the stability and quality of the prognostic models. We therefore recommend shrinkage methods in full models including prespeci"ed predictors and incorporation of external information, when prognostic models are constructed in small data sets.

Objective: The aim of this study was to assess which of the basal ovarian reserve markers provides the best reflection of the changes occurring in ovarian function over time (i.e., reproductive aging). Design: Prospective longitudinal study. Setting: Healthy volunteers in an academic research center. Patient(s): Eighty-one women with normal reproductive performance during the course of their lives were longitudinally assessed. In this select group of women, becoming chronologically older was considered as a proxy variable for becoming older from a reproductive point of view. Intervention(s):The women were assessed twice, with on average a 4-year interval (T 1 and T 2 ). The number of antral follicles on ultrasound (AFC) and blood levels of antimüllerian hormone (AMH), FSH, inhibin B, and E 2 were assessed. Main Outcome Measure(s): Longitudinal changes of the markers mentioned and the consistency of these parameters over time. Result(s):The mean ages at T 1 and T 2 were 39.6 and 43.6 years, respectively. Although AFC was strongly associated with age in a cross-sectional fashion, it did not change over time. The AMH, FSH, and inhibin B levels showed a significant change over time, in contrast to E 2 levels. The AMH and AFC were highly correlated with age both at T 1 and T 2 , whereas FSH and inhibin B predominantly changed in women more than 40 years of age. To assess the consistency of these parameters over time, we investigated whether a woman's individual level above or below the mean of her age group at T 1 remained above or below the mean of her age group at T 2 . Serum AMH concentrations showed the best consistency, with AFC as second best. The FSH and inhibin B showed only modest consistency, whereas E 2 showed no consistency at all. Conclusion(s):These results indicate that serum AMH represents the best endocrine marker to assess the age-related decline of reproductive capacity. (Fertil Steril 2005;83:979 -87.

SUMMARYLogistic regression analysis may well be used to develop a prognostic model for a dichotomous outcome. Especially when limited data are available, it is di$cult to determine an appropriate selection of covariables for inclusion in such models. Also, predictions may be improved by applying some sort of shrinkage in the estimation of regression coe$cients. In this study we compare the performance of several selection and shrinkage methods in small data sets of patients with acute myocardial infarction, where we aim to predict 30-day mortality. Selection methods included backward stepwise selection with signi"cance levels of 0.01, 0.05, 0.157 (the AIC criterion) or 0.50, and the use of qualitative external information on the sign of regression coe$cients in the model. Estimation methods included standard maximum likelihood, the use of a linear shrinkage factor, penalized maximum likelihood, the Lasso, or quantitative external information on univariable regression coe$cients. We found that stepwise selection with a low (for example, 0.05) led to a relatively poor model performance, when evaluated on independent data. Substantially better performance was obtained with full models with a limited number of important predictors, where regression coe$cients were reduced with any of the shrinkage methods. Incorporation of external information for selection and estimation improved the stability and quality of the prognostic models. We therefore recommend shrinkage methods in full models including prespeci"ed predictors and incorporation of external information, when prognostic models are constructed in small data sets.