RESUMO
OBJECTIVES: To develop and validate clinical risk prediction tools for neonatal abstinence syndrome (NAS). STUDY DESIGN: We developed prediction models for NAS based on a set of 30 demographic and antenatal exposure covariates collected during pregnancy. Data (outpatient prescription, vital, and administrative records), were obtained from enrollees in the Tennessee Medicaid Program from 2009 to 2014. Models were created using logistic regression and backward selection based on improvement in the Akaike information criterion, and internally validated using bootstrap cross-validation. RESULTS: A total of 218 020 maternal and infant dyads met inclusion criteria, of whom 3208 infants were diagnosed with NAS. The general population model included age, hepatitis C virus infection, days of opioid used by type, number of cigarettes used daily, and the following medications used in the last 30 day of pregnancy: bupropion, antinausea medicines, benzodiazepines, antipsychotics, and gabapentin. Infant characteristics included birthweight, small for gestational age, and infant sex. A high-risk model used a smaller number of predictive variables. Both models discriminated well with an area under the curve of 0.89 and were well-calibrated for low-risk infants. CONCLUSIONS: We developed 2 predictive models for NAS based on demographics and antenatal exposure during the last 30 days of pregnancy that were able to risk stratify infants at risk of developing the syndrome.
Assuntos
Síndrome de Abstinência Neonatal/diagnóstico , Medição de Risco/métodos , Adulto , Analgésicos/administração & dosagem , Analgésicos/efeitos adversos , Antieméticos/administração & dosagem , Antieméticos/efeitos adversos , Antipsicóticos/administração & dosagem , Antipsicóticos/efeitos adversos , Benzodiazepinas/administração & dosagem , Benzodiazepinas/efeitos adversos , Bupropiona/administração & dosagem , Bupropiona/efeitos adversos , Feminino , Gabapentina/administração & dosagem , Gabapentina/efeitos adversos , Hepatite C/epidemiologia , Humanos , Recém-Nascido de Baixo Peso , Recém-Nascido , Recém-Nascido Pequeno para a Idade Gestacional , Masculino , Idade Materna , Exposição Materna/efeitos adversos , Troca Materno-Fetal , Tratamento de Substituição de Opiáceos , Transtornos Relacionados ao Uso de Opioides/tratamento farmacológico , Gravidez , Estudos Retrospectivos , Distribuição por Sexo , Fumar/epidemiologia , Agentes de Cessação do Hábito de Fumar/administração & dosagem , Agentes de Cessação do Hábito de Fumar/efeitos adversos , Adulto JovemRESUMO
Most randomized trials are designed and analyzed using frequentist statistical approaches such as null hypothesis testing and P values. Conceptually, P values are cumbersome to understand, as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called "priors"). These priors are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis.