Artificial Intelligence Platform Demonstrates High Adherence in Patients Receiving Fixed-Dose Ledipasvir and Sofosbuvir: A Pilot Study

Litwin, Alain H.; Shafner, Laura; Norton, Brianna L.; Akiyama, Matthew J.; Agyemang, Linda; Guzman, Mercedes; Vera, Tatiana; Heo, Moonseong

doi:10.1093/ofid/ofaa290

Cited by 10 publications

(13 citation statements)

References 11 publications

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“…Mean cumulative adherence in the AIP studies were 91.3% 45 and 96.2%, 47 respectively. These are relatively higher than the adherence measured by blister packs in the 51 participants in the DOT arm of the PREVAIL study (86%) 33 and that of the SIT arm (75%).…”

Section: Discussionmentioning

confidence: 94%

“…The first was an open-label, non-randomized study of 17 PWID during self-administration of LDV/SOF for 8 ( n = 2) or 12 weeks ( n = 15). 45 All participants completed treatment and mean cumulative adherence by visual AIP was 91.3%. Fifteen of 17 participants (88.2%) achieved SVR12.…”

Section: Artificial Intelligence Platformsmentioning

confidence: 98%

“…Fifteen of 17 participants (88.2%) achieved SVR12. 45 The two participants who failed to achieve cure despite high adherence rates of 98% and 100%, had stage 3 fibrosis and cirrhosis, respectively. The former received only 8 weeks of LDV/SOF.…”

Section: Artificial Intelligence Platformsmentioning

confidence: 99%

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The use of technology-based adherence monitoring in the treatment of hepatitis C virus

Adje

Brooks

Castillo‐Mancilla

et al. 2022

Therapeutic Advances in Infection

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Direct-acting antivirals (DAAs) achieve high hepatitis C virus (HCV) cure rates and are forgiving to missed doses, but adherence–efficacy relationships have not been well defined. Traditional adherence measures (e.g. pill counts, self-report and pharmacy refills) over-estimate medication adherence. Newer technology-based tools have been used to provide more objective adherence data. Herein, electronic medication diaries (e-diaries), medication events monitoring system (MEMS®) caps, electronic blister packs, electronic pill boxes, video-based directly observed therapy (vDOT), artificial intelligence platforms (AIPs), and ingestible sensor systems are described, and compared based on existing studies using DAA. Percent adherence, predictors of adherence, and HCV cure rates utilizing these technologies are included. DAA adherence with e-diaries was 95–96%, MEMS® caps and ingestible biosensors were between 95% and 97%, blister pack weekly dosing ranged 73–98%, and daily dosing 73–94%, whereas electronic pill boxes ranged between 39% and 89%, vDOT was 98% and AIP 91–96%. Despite a wide range of adherence, high sustained virologic response (SVR) rates (86–100%) were observed across all studies utilizing these different technology-based tools. Current data support the forgiveness of DAA therapies to missed doses using tools that provide more quantitative adherence measures compared with self-report and provide insight on adherence–efficacy relationships for contemporary DAA.

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Section: Discussionmentioning

confidence: 94%

Section: Artificial Intelligence Platformsmentioning

confidence: 98%

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The use of technology-based adherence monitoring in the treatment of hepatitis C virus

Adje

Brooks

Castillo‐Mancilla

et al. 2022

Therapeutic Advances in Infection

View full text Add to dashboard Cite

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“…The vDOT could bridge this gap where more objective adherence monitoring approaches are desired in persons where adherence is of concern. In line with this, artificial intelligence-based mobile adherence monitoring platforms have also been piloted [ 33 ], including a study in 17 PWID that showed mean DAA adherence rates of 91% [ 34 ]. Collectively, several adherence monitoring tools have been used with success in PWUD, and our findings provide support for vDOT and WOT as additional tools that can be used in this population.…”

Section: Discussionmentioning

confidence: 99%

Adherence to Direct-Acting Antiviral Therapy in People Actively Using Drugs and Alcohol: The INCLUD Study

Brooks

Castillo‐Mancilla

Morrow

et al. 2020

Open Forum Infectious Diseases

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Background HCV treatment in persons who use drugs (PWUD) is often withheld due to adherence and reinfection concerns. Here, we report treatment outcomes, technology-based adherence data, and adherence predictors in PWUD and/or alcohol. Methods INCLUD was a prospective, open-label study of ledipasvir/sofosbuvir for 12 weeks in PWUD aged 18-70 years. Participants were randomized to wireless (WOT, Wisepill®) or video-based directly observed therapy (vDOT, Emocha®). Drug use was assessed every 2 weeks. Sustained virologic response (SVR) was examined by intention-to-treat and as-treated. Factors associated with missing ≥1 dose(s) between visits were examined using generalized linear models. Results Sixty participants received ≥1 ledipasvir/sofosbuvir dose (47 HIV/HCV, 13 HCV only; 78% male; 22% Black; 25% cirrhotic). Substance use occurred at 94% of person-visits: 60% marijuana, 56% alcohol, 37% methamphetamine, 22% opioids, 17% cocaine, and 20% injection drug use. SVR by intention-to-treat was 86.7% (52/60) and as-treated was 94.5% (52/55). Confirmed failures included 1 relapse, 1 reinfection, and 1 unknown (suspected reinfection). Median (IQR [range]) total adherence was 96% (85-100% [30-101%]) and between-visit adherence was 100% (86-100% [0-107%]). Odds [95% CI] of missing ≥1 dose between visits increased with HIV coinfection (2.94 [1.37, 6.32], p=0.006), Black race (4.09 [1.42, 11.74], p=0.009), methamphetamine use (2.51 [1.44, 4.37], p=0.0.001) and cocaine use (2.12 [1.08, 4.18], p=0.03), and decreased with marijuana use (0.34 [0.17, 0.70], p=0.003) and vDOT (0.43 [0.21, 0.87], p=0.02). Conclusions PWUD achieved high SVR rates with high, but variable, ledipasvir/sofosbuvir adherence using technology-based methods. These findings support efforts to expand HCV treatment in PWUD.

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“…Infographics or interactive visualizations using pictures and illustrations have been effective for complex information communication and have proven the age-old saying "a picture is worth a thousand words" [22][23][24][25]. Furthermore, AI-based smartphone devices have proven to be effective in clinical trials and chronic disease management to improve medication adherence [21,26]. Utilizing advanced machine learning algorithms, smartphone apps have the potential to improve medication information delivery to patients.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence–Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

Roosan¹,

Chok²,

Karim³

et al. 2020

JMIR Res Protoc

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Background Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients’ cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients. Objective Our objective is to improve the design of an innovative AI- and human factor–based interface that supports patients’ medication information comprehension that could potentially improve medication adherence. Methods This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks. Results Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022. Conclusions This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides. International Registered Report Identifier (IRRID) PRR1-10.2196/21659

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Artificial Intelligence Platform Demonstrates High Adherence in Patients Receiving Fixed-Dose Ledipasvir and Sofosbuvir: A Pilot Study

Cited by 10 publications

References 11 publications

The use of technology-based adherence monitoring in the treatment of hepatitis C virus

The use of technology-based adherence monitoring in the treatment of hepatitis C virus

Adherence to Direct-Acting Antiviral Therapy in People Actively Using Drugs and Alcohol: The INCLUD Study

Artificial Intelligence–Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

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