Improving Measures to Identify Tics and Tic Disorders

     The purpose of this project is to analyze data from the existing questionnaires, the Motor or Vocal Inventory of Tics (MOVeIT) and the Description of Tics Symptoms (DoTS). Each questionnaire collects information related to tic frequency and severity, and we have access to data collected from more than 1000 children across multiple samples from diverse communities, including previous work in New York (University of Rochester Medical Center (UR)) and Florida (University of Florida College of Medicine in Jacksonville (UF)), and well as from an ongoing project in Florida (University of South Florida (USF)).

     Specifically, we will investigate which constellations of questions (i.e., items) across instruments provide the best results as defined by specific measures of accuracy, discussed below (e.g., positive prediction). It is of prime interest to pinpoint the item cluster that most accurately identifies those children who should be further examined to make a final diagnosis. Early and accurate diagnosis of a tic disorder sets affected children onto the right pathway for care for their tics/tic disorder directly, and for surveillance related to common co-occurring conditions that can also have a substantial impact on function and well-being. Because children will be further examined to make a final diagnosis, the screening tool must have very high negative predictive value (NPV); that is if the screening tool indicates no evidence of tic disorder, the actual probability of no tic disorder should be very high.
    
     This project will produce a tool that will combine information from extant data collection tools. The tool will then allow identification of a smaller subpopulation to be further evaluated to determine whether members of that subpopulation should be diagnosed with tic disorder. This will substantially decrease the time to diagnosis and increase the accuracy of which patients require evaluation.
Specifically, we will investigate which constellations of questions (i.e., items) across instruments provide the best results as defined by specific measures of accuracy, discussed below (e.g., positive prediction). It is of prime interest to pinpoint the item cluster that most accurately identifies those children who should be further examined to make a final diagnosis. Early and accurate diagnosis of a tic disorder sets affected children onto the right pathway for care for their tics/tic disorder directly, and for surveillance related to common co-occurring conditions that can also have a substantial impact on function and well-being. Because children will be further examined to make a final diagnosis, the screening tool must have very high negative predictive value (NPV); that is if the screening tool indicates no evidence of tic disorder, the actual probability of no tic disorder should be very high. This project will produce a tool that will combine information from extant data collection tools. The tool will then allow identification of a smaller subpopulation to be further evaluated to determine whether members of that subpopulation should be diagnosed with tic disorder. This will substantially decrease the time to diagnosis and increase the accuracy of which patients require evaluation.