Value-added models and growth-based accountability aim to evaluate school's performance based on student growth in learning. The current focus is on linking the results from value-added models to the ones from growth-based accountability systems including Adequate Yearly Progress decisions mandated by No Child Left Behind. We present a new statistical approach that extends the current value-added modeling possibilities and focuses on using latent longitudinal growth curves to estimate the probabilities of students reaching proficiency. The aim is to utilize time-series measures of student achievement scores to estimate latent growth curves and use them as predictors of a dichotomous outcome, such as proficiency or passing a high-stakes exam, within a single multilevel longitudinal model. We illustrated this method through analyzing a three-year data set of longitudinal achievement scores and California High School Exit Exam scores from a large urban school district. This latent variable growth logistic model is useful for (1) early identification of students at risk of failing or of those who are most in need; (2) a validation or/and adequacy of student growth over years with relation to distal outcome criteria; (3) evaluation of a longitudinal intervention study.