Item difficulty modeling studies involve (a) hypothesizing item features, or item response demands, that are likely to predict item difficulty with some degree of accuracy; and (b) entering the features as independent variables into a regression equation or other statistical model to predict difficulty. In this review, we report findings from 13 empirical item difficulty modeling studies of reading comprehension tests. We define reading comprehension item response demands as reading passage variables (e.g., length, complexity), passage-by-item variables (e.g., degree of correspondence between item and text, type of information requested), and item stem and response option variables. We report on response demand variables that are related to item difficulty and illustrate how they can be used to manage item difficulty in construct-relevant ways so that empirical item difficulties are within a targeted range (e.g., located within the Proficient or other proficiency level range on a test's IRT scale, where intended).