Detecting and Monitoring Tardive Dyskinesia to Improve Patient Outcomes

Tardive dyskinesiaTDis a common debilitating side effect of antipsychotic useCharacterized most notably by involuntary facial movements such as grimacinginvoluntary lipmouthand tongue movementsand eye blinkingTD is difficult to treat and potentially irreversiblePsychiatrists and other mental health professionals are acutely aware of the impairment and disability experienced by patients who develop TDEarly detection of TD is critical so that appropriate interventions can be institutedUnfortunatelydespite professionalsbest effortsit is often too late in the process and the involuntary movements are permanentAntipsychotic prescriptions exceededmillion inand the reported incidence of TD is betweenandRisk grows with advancing ageoff label usesand chronic exposure to antipsychoticsThereforeprevention and early detection are key to managing TDHowevercurrent methods for monitoring patients require observation of patients at infrequent in person visits or self reporting by vigilant patients and their familiesTherefore strong market potential exists for an automated detection systemThis Phase I project proposes to leverage existing telepsychiatry and video interview data gathering technologies available commercially to efficiently collect and analyze one hundred tenminute video interviews with individuals taking anti psychotic medicationsHalf of the interviews will be with individuals living with diagnosed TD and the other without a diagnosis of TDThe participants in the study will be recruited to ensure an equal distribution of females and males as well as an ethnically and racially representative sampleThe proposed data gathering strategy will provide the source material necessary to create a powerful supervised machine learning derived video and audio analysis tool to detect TDThe detection tool will be created usingof the collected video data as a training set and validated on the remainingreserved as the control setBased on industry experience with other supervised machine learning training sets and the amount of data to be collectedwe set a goal of asuccess rate in identifying TD positive and TD negative participants in the control setOnce the detection tool is complete the project will conclude by incorporating access to the tool into an existing smartphone appiRxReminderthat is used for data gathering and monitoring of medication adherenceThe iRxReminder system links patients directly to researchersand healthcare professionalselectronic recordsThe modified app will be tested in the laboratory to ensure the interface can be easily usedThe addendum request includes additional funding to participate in the I Corp trainingParticipants in the training will include CEO and PI PD Anthony Sterns PhDC level executive Fred Pollock serving as iRxReminder s CBOand Industry Expert Fred MaMD PhDwho serves as iRxReminder s regulatory consultantIn Phase II the iRxReminder system will be validated for use in supporting the self management and symptom monitoring of medication taking by individuals living with chronic mental illnessesOnce feasibility is establishedwe propose a year long RCT where participants will be monitored for early detection of TD along with goals for high adherenceimproved control of symptoms and side effectsand more aggressive and frequent treatment responses by the healthcare team A recent study reported that TD rates among newly treated elderly ranged fromfor those taking Risperidone tofor those taking olanzapine afteryears of treatmentEarlier meta analyses estimate TD frequency in women to beand in menEthnically Chinese and Malaysian mental health patients were studied in Singapore and patients taking anti psychotic medications were found to have TD inandof cases respectivelyRegardless of prevalenceTD is a threat to patient adherence and quality of lifeTD only remits in a minority of cases and can be permanentWithmillion prescriptions for anti psychotics written annuallymore thanmillion persons living with a chronic mental illness are at risk of developing TDThere are likely overmillion patients living with TD and the number of patients with TD is expected to grow with the aging population and increasing offlabel use of antipsychotics !