Remote Monitoring and Detecting of Tardive Dyskinesia for Improving Patient OutcomesTardive dyskinesia (TDD) is a common debilitating side effect of antipsychotic use. Characterized most notably byinvoluntary 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 acutelyaware of the impairment and disability experienced by patients who develop TDD. Early detection of TDD iscritical so that appropriate interventions can be instituted. What interventions are implemented is intimatelytied to knowing the patient's medication adherence. It is difficult for the most qualified diagnosticians todevote the 20-25 minutes of in-person time at the 4 to 6 times per year frequency necessary to provide everypatient the 1) "active monitoring," 2) discussion of results, 3) changes to medication and instructions expectedwith the urgent demands on every mental health professional today. This is increasingly challenging with theincrease 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 movementsare permanent. Currently, there are 200,000 individuals taking anti-TDD medications costing $60K and $105Kannually and this is increasing rapidly each year. A method for automatic TDD detection and accurate adherencewould enable timely intervention and avoid patient stigma, lower quality of life, and expensive ongoing treatmentfor permanent TDD.Antipsychotic prescriptions exceeded 50 million in 2020 and the reported prevalence of TDD is between 13% and24%. 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 patientsrequire observation of patients at infrequent in-person visits or self-reporting by vigilant but undertrained patientsand their families. Therefore, strong market potential exists for an automated remote adherence monitoring andTDD 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 technologiesthat 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 ofthe Phase I data, we propose here to extend collection and analysis of an additional 300 video recorded AIMSand 5-minute video interviews with individuals taking anti-psychotic medications. Half of the interviews will be withindividuals living with diagnosed TDD and the other without a diagnosis of TDD. The participants in the study willbe recruited to ensure an equal distribution of females and males as well as an ethnically and raciallyrepresentative sample.The proposed data gathering strategy will provide the source material necessary to finalize and deploy a powerfulsupervised machine learning derived video and audio analysis tool to detect TDD. The detection tool will becreated using 80% of the collected video data as a training set and validated on the remaining 20% reserved asthe control set. Based on industry experience with other supervised machine learning training sets and theamount of data to be collected, we set a goal of a 90% success rate in identifying TDD positive and TDD negativeparticipants in the control set.Once the detection tool is complete the project will conclude by incorporating access to the tool into an existingsmartphone app, iRxReminder, that is used for data gathering and monitoring of medication adherence, the othercritical component required for clinical intervention. The iRxReminder platform links patients directly toresearchers and their electronic records. The modified app will be tested in the laboratory to ensure the interfacecan 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 chronicmental illnesses. With feasibility established in Phase I, we propose a six-month long clinical trial whereparticipants will 1) be monitored for early detection of TDD (and confirmation of not having TDD, thus avoidingunnecessary diagnostician time) along with 2) goals for high adherence, 3) improved control of symptoms andside effects, and 4) more aggressive and frequent treatment responses by the healthcare team. Statistical tests ofthe 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 algorithmto detect TDD compared to a human assessment at the end of 6-months of monitoring will be a final field test ofthe technology.
Public Health Relevance Statement: A key study reported that TDD rates among newly treated elderly ranged from 7.2% for those taking Risperidone to 11.1% for those taking olanzapine after 2 years of treatment. Earlier meta-analyses estimate TDD frequency in women to be 26.6% and in men 21.6%. Ethnically Chinese and Malaysian mental health patients were studied in Singapore and patients taking anti-psychotic medications were found to have TDD in 40% and 29% of cases respectively. Regardless of prevalence, TDD is a threat to patient adherence and quality of life. TDD only remits in a minority of cases and can be permanent. With 50 million prescriptions for anti-psychotics written annually, more than 44 million persons living with a chronic mental illness are at risk of developing TDD. There are likely over 1.65 million patients living with TDD and the number of patients with TDD is expected to grow with the aging population and increasing off-label use of antipsychotics. This trend is likely accelerated with the increased isolation of existing patients and the increased need for psychiatric treatment due to the pandemic.
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