Remote Monitoring and Detecting of Tardive Dyskinesia for Improving Patient Outcomes

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Abstract - Remote Monitoring and Detecting of Tardive Dyskinesia for Improving Patient Outcomes Tardive dyskinesia (TDD) is a common debilitating side effect of antipsychotic use. Characterized most notably by involuntary facial movements such as grimacing, involuntary lip, mouth, and tongue movements, and eye blinking, TDD is difficult to treat and potentially irreversible. Psychiatrists and other mental health professionals are acutely aware of the impairment and disability experienced by patients who develop TDD. Early detection of TDD is critical so that appropriate interventions can be instituted. What interventions are implemented is intimately tied to knowing the patient’s medication adherence. It is difficult for the most qualified diagnosticians to devote the 20-25 minutes of in-person time at the 4 to 6 times per year frequency necessary to provide every patient the 1) “active monitoring,” 2) discussion of results, 3) changes to medication and instructions expected with the urgent demands on every mental health professional today. This is increasingly challenging with the increase in telemedicine and patient populations and decreasing human resources due to the pandemic. Unfortunately, despite professionals’ best efforts, it is often too late in the process and the involuntary movements are permanent. Currently, there are 200,000 individuals taking anti-TDD medications costing $60K and $105K annually and this is increasing rapidly each year. A method for automatic TDD detection and accurate adherence would enable timely intervention and avoid patient stigma, lower quality of life, and expensive ongoing treatment for permanent TDD. Antipsychotic prescriptions exceeded 50 million in 2020 and the reported prevalence of TDD is between 13% and 24%. Risk grows with advancing age, off-label uses, and chronic exposure to antipsychotics. Therefore, prevention and early detection are key to managing TDD. However, current methods for monitoring patients require observation of patients at infrequent in-person visits or self-reporting by vigilant but undertrained patients and their families. Therefore, strong market potential exists for an automated remote adherence monitoring and TDD detection system. Our go-to-market strategy is presented in the commercialization plan. This Phase II project proposes to leverage existing telepsychiatry and video interview data gathering technologies that in Phase I demonstrated up to 77% discrimination in categorizing individuals with TDD compared to a 3- person panel of trained clinical professionals evaluating the same video materials. Based on a power analysis of the Phase I data, we propose here to extend collection and analysis of an additional 300 video recorded AIMS and 5-minute video interviews with individuals taking anti-psychotic medications. Half of the interviews will be with individuals living with diagnosed TDD and the other without a diagnosis of TDD. The participants in the study will be recruited to ensure an equal distribution of females and males as well as an ethnically and racially representative sample. The proposed data gathering strategy will provide the source material necessary to finalize and deploy a powerful supervised machine learning derived video and audio analysis tool to detect TDD. The detection tool will be created using 80% of the collected video data as a training set and validated on the remaining 20% reserved as the control set. Based on industry experience with other supervised machine learning training sets and the amount of data to be collected, we set a goal of a 90% success rate in identifying TDD positive and TDD negative participants in the control set. Once the detection tool is complete the project will conclude by incorporating access to the tool into an existing smartphone app, iRxReminder, that is used for data gathering and monitoring of medication adherence, the other critical component required for clinical intervention. The iRxReminder platform links patients directly to researchers and their electronic records. The modified app will be tested in the laboratory to ensure the interface can be easily used. This Phase II project will then use the iRxReminder platform for use in supporting the self- management and TDD and other symptoms monitoring of medication taking by individuals living with chronic mental illnesses. With feasibility established in Phase I, we propose a six-month long clinical trial where participants will 1) be monitored for early detection of TDD (and confirmation of not having TDD, thus avoiding unnecessary diagnostician time) along with 2) goals for high adherence, 3) improved control of symptoms and side effects, and 4) more aggressive and frequent treatment responses by the healthcare team. Statistical tests of the ease-of-use by patients and the care team will be conducted. The impact on revenue, treatment trajectory (number of side effects detected and medication changes made) will be assessed. The success of the algorithm to detect TDD compared to a human assessment at the end of 6-months of monitoring will be a final field test of the technology.