Outcomes of drug exposition during pregnancy: Analysis from a teratology information service.

OBJECTIVE: We aimed to characterize drug exposures during pregnancy where the outcome was known that had benefited from counselling through our Teratology Information Service (TIS) between 1994-2016. STUDY DESIGN: This observational study analysed data collected through the drug exposures during pregnancy counselling. Data was analysed descriptively. RESULTS: Data from a total of 1'374 pregnant women were collected. Mean age was of 32 years. These women were exposed to more than ten drugs in 1.4 % (N = 19) of cases, with a mean drug intake of two. Analysis of the drugs altogether (N = 3'129) showed that FDA Pregnancy Category C drugs represented 42.9 % (N = 1'342) of drugs and ATC code N (nervous system) represented 36.4 % (N = 1'138). The onset of drug exposure was during the first trimester of pregnancy in 95.1 % (N = 2'982) of patients. Regarding outcomes, the rate of induced abortion was 10.8 % (N = 151), of pregnancy complications was 11.2 % (N = 157) and of malformations was 4.5 % (N = 49). CONCLUSION: Pregnant women counselled by our TIS take a mean of two drugs, ranging from one to 17. Drugs are from FDA Pregnancy Category C and ATC N drugs in most cases, 42.9 % and 36.4 % respectively. The rate of malformation of our cohort was of 4.5 %, close to the estimated spontaneous rate of malformation. This data gives a reassuring aspect of drug exposure in pregnancy but takes into account the outcome at birth only.

Markov counting models for correlated binary responses.

We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology.

A pilot study of paternal drug exposure: the Motherisk experience.

BACKGROUND: There is a dearth of information on paternal drug exposure at the time of conception. The Motherisk Program, established in 1985, is a teratology information and clinical consultation service on drug safety during pregnancy and lactation, as well as paternal exposure (PEx). Here, we reviewed for the first time our experience with PEx. METHODS: This was an observational retrospective cohort study using a prospectively collected database. Telephone counselling records from January 2002 to December 2007, inclusive, were screened to identify cases concerning PEx. RESULTS: Of a total of 188,188 counselling requests over these 6 years, 301 (0.16%) pertained to PEx. Counselling was most frequently sought on methotrexate, finasteride, prednisone and azathioprine. For many drugs, limited or no information was available on PEx. CONCLUSIONS: Paternal exposure represents a small fraction of counselling requests to Motherisk. Our findings suggest that there is an ongoing need for information on paternal drug exposure.

A mixed model framework for teratology studies.

A mixed model framework is presented to model the characteristic multivariate binary anomaly data as provided in some teratology studies. The key features of the model are the incorporation of covariate effects, a flexible random effects distribution by means of a finite mixture, and the application of copula functions to better account for the relation structure of the anomalies. The framework is motivated by data of the Boston Anticonvulsant Teratogenesis study and offers an integrated approach to investigate substantive questions, concerning general and anomaly-specific exposure effects of covariates, interrelations between anomalies, and objective diagnostic measurement.

Prenatal drug exposure and teratological risk: one-year experience of an Italian Teratology Information Service.

BACKGROUND: Concern about exposure to drugs, radiation, or infection during pregnancy occur often because pregnancy is not always planned. A teratology information service offers rapid scientific counseling to all those worried about prenatal exposure. The aim of this study is to present data on the most common pharmaceutical products responsible for teratogenic risk in the one-year experience of a teratology information service in Italy. MATERIAL/METHODS: The survey was conducted among 8664 callers who contacted our Teratology Information Service in Rome between January and December 2006. Data on maternal age, gravidity, parity, maternal health status, and details of exposure (dose and timing) were collected and stored in a specific data base. Scientific counseling on prenatal exposure was given to the caller by a specialized service operator, specifying the type of risk and suggesting appropriate tests for prenatal diagnosis. RESULTS: Most of the people called regarding drug exposure; increased risk was present in only 5% of the pregnant women calling during pregnancy. Selective serotonin reuptake inhibitors (SSRIs) are the first category that are actually considered of increased risk to the fetus. The second category is represented by antiepileptic drugs. CONCLUSIONS: This experience confirms previous data that there is a high teratological risk perception among both women and physicians. The drugs estimated to present increased risk are medications used for chronic neurological diseases, mainly mood disorders and epilepsy. Preconceptional counseling for these women could be an effective strategy to prevent such exposure and to improve maternal and fetal outcome.

On models for binomial data with random numbers of trials.

A binomial outcome is a count s of the number of successes out of the total number of independent trials n=s+f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability pi of success that cannot be directly incorporated by the logistic regression model. Observations where n= 0 are excluded from the binomial analysis yet may be important to understanding how pi is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study.

Marginalized binary mixed-effects models with covariate-dependent random effects and likelihood inference.

Marginal models and conditional mixed-effects models are commonly used for clustered binary data. However, regression parameters and predictions in nonlinear mixed-effects models usually do not have a direct marginal interpretation, because the conditional functional form does not carry over to the margin. Because both marginal and conditional inferences are of interest, a unified approach is attractive. To this end, we investigate a parameterization of generalized linear mixed models with a structured random-intercept distribution that matches the conditional and marginal shapes. We model the marginal mean of response distribution and select the distribution of the random intercept to produce the match and also to model covariate-dependent random effects. We discuss the relation between this approach and some existing models and compare the approaches on two datasets.

Bayesian semiparametric analysis of developmental toxicology data.

Modeling of developmental toxicity studies often requires simple parametric analyses of the dose-response relationship between exposure and probability of a birth defect but poses challenges because of nonstandard distributions of birth defects for a fixed level of exposure. This article is motivated by two such experiments in which the distribution of the outcome variable is challenging to both the standard logistic model with binomial response and its parametric multistage elaborations. We approach our analysis using a Bayesian semiparametric model that we tailored specifically to developmental toxicology studies. It combines parametric dose-response relationships with a flexible nonparametric specification of the distribution of the response, obtained via a product of Dirichlet process mixtures approach (PDPM). Our formulation achieves three goals: (1) the distribution of the response is modeled in a general way, (2) the degree to which the distribution of the response adapts nonparametrically to the observations is driven by the data, and (3) the marginal posterior distribution of the parameters of interest is available in closed form. The logistic regression model, as well as many of its extensions such as the beta-binomial model and finite mixture models, are special cases. In the context of the two motivating examples and a simulated example, we provide model comparisons, illustrate overdispersion diagnostics that can assist model specification, show how to derive posterior distributions of the effective dose parameters and predictive distributions of response, and discuss the sensitivity of the results to the choice of the prior distribution.

Regression models and risk estimation for mixed discrete and continuous outcomes in developmental toxicology.

Multivariate dose-response models have recently been proposed for developmental toxicity data to simultaneously model malformation incidence (a binary outcome), and reductions in fetal weight (a continuous outcome). In this and other applications, the binary outcome often represents a dichotomization of another outcome or a composite of outcomes, which facilitates analysis. For example, in Segment II developmental toxicology studies, multiple malformation types (i.e., external, visceral, skeletal) are evaluated on each fetus; malformation status may also be ordinally measured (e.g., normal, signs of variation, full malformation). A model is proposed is for fetal weight and multiple malformation variables measured on an ordinal scale, where the correlations between the outcomes and between the offspring within a litter are taken into account. Fully specifying the joint distribution of outcomes within a litter is avoided by specifying only the distribution of the multivariate outcome for each fetus and using generalized estimating equation methodology to account for correlations due to litter clustering. The correlations between the outcomes are required to characterize joint risk to the fetus, and are therefore a focus of inference. Dose-response models and their application to quantitative risk assessment are illustrated using data from a recent developmental toxicology experiment of ethylene oxide in mice.

The teratogenicity of antirheumatic drugs--what is the evidence?

This review addresses on various methods used in the detection of human teratogenic effects of drug use in early pregnancy. Data are presented from the new Swedish ongoing recording of drug use in early pregnancy. These data do not indicate a teratogenic effect of the main antirheumatic drugs used in Sweden.

Use of computer simulation to evaluate a putative cluster of genetic or teratologic outcomes: adjustment for "multiple hypotheses" and application to a reported excess of Down's syndrome.

The identification of an apparent excess of a genetic outcome in a particular area and/or a particular time often provokes considerable public alarm about the presence of an environmental mutagen. It is often difficult to determine in any particular case whether the observation, whatever its nominal statistical significance, is due to chance concatenation of events or to an environmental factor. Statistical evaluation is made more difficult by the profuse number of possible hypotheses that could have triggered concern about an excess. This renders it difficult to calculate the actual probability of the observation (or one more extreme). By attempting to identify similar types of outcomes that could have provoked an apparent excess and then undertaking computer simulations assuming random deviations from a constant rate, one may attempt to adjust for the problem of multiple hypotheses. We apply this approach to a reported excess of Down's syndrome in Norway in 1985-1986 in younger mothers, and conclude that there is a high probability that it arose by chance.

Clustered binary logistic regression in teratology data using a finite mixture distribution.

The beta-binomial distribution introduced by Skellam has been applied in many teratology problems for modelling the litter effect. Recently, Morel and Nagaraj proposed a new distribution for modelling cluster multinomial data when the clustering is believed to be caused by clumped sampling. It turns out that the distribution is a mixture of two binomial distributions and accommodates the estimation of an additional parameter to account for intra-litter effect. The new distribution arises from a cluster mechanism in which some individuals within a cluster exhibit the same behaviour while the remaining individuals from the cluster react independently of each other. Such a mechanism is a natural model in teratology problems, where typically a genetic trait is passed with a certain probability to the foetuses of the same litter. In this article, we use the new distribution to model binary responses with logistic regression. We analyse data from a teratology experiment to demonstrate that the new model provides a useful addition to current methodology. The experiment investigates the synergistic effect of the anticonvulsant phenytoin and trichloropopene oxide on the prenatal development of inbred mice. In a simulation study we investigate the type I error rate and the power of the maximum likelihood ratio test when the data follow a finite mixture distribution.