PMH67 USING REAL WORLD DATA TO PROFILE CURRENT STANDARD OF CARE FOR SCHIZOPHRENIA PATIENTS AT A MENTAL HEALTH HOSPITAL IN RWANDA, AFRICA

BACKGROUND A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. OBJECTIVE This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. METHODS In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. RESULTS The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. CONCLUSIONS The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.

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Deep learning from “passive feeding” to “selective eating” of real-world data

npj Digital Medicine ◽

10.1038/s41746-020-00350-y ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Zhongwen Li ◽

Chong Guo ◽

Danyao Nie ◽

Duoru Lin ◽

Yi Zhu ◽

...

Keyword(s):

Deep Learning ◽

Real World ◽

Diagnostic System ◽

Standard Of Care ◽

Poor Quality ◽

Diagnostic Systems ◽

Real World Data ◽

Clinical Images ◽

World Data ◽

Selective Eating

Abstract Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems.

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Diagnostic promiscuity: the use of real-world data to study multimorbidity in mental health

The British Journal of Psychiatry ◽

10.1192/bjp.2020.257 ◽

2021 ◽

pp. 1-3

Author(s):

Jorge Arias de la Torre ◽

Amy Ronaldson ◽

Jose M. Valderas ◽

Gemma Vilagut ◽

Antoni Serrano-Blanco ◽

...

Keyword(s):

Mental Health ◽

Mental Disorder ◽

Real World ◽

Knowledge Gaps ◽

Real World Data ◽

World Data ◽

Health Related

Mental health-related multimorbidity can be considered as multimorbidity in the presence of a mental disorder. Some knowledge gaps on the study of mental health-related multimorbidity were identified. These knowledge gaps could be potentially addressed with real-world data.

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Mapping mental health priorities in London with real-world data

The Lancet Psychiatry ◽

10.1016/s2215-0366(17)30362-0 ◽

2017 ◽

Vol 4 (10) ◽

pp. e24 ◽

Cited By ~ 3

Author(s):

Antonis A Kousoulis ◽

Isabella Goldie

Keyword(s):

Mental Health ◽

Real World ◽

Health Priorities ◽

Real World Data ◽

World Data

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Real-World Data on Frontline (FL) Treatments in Multiple Myeloma (MM) Patients across EU5 Countries

Blood ◽

10.1182/blood-2018-99-113561 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 5858-5858

Author(s):

Joana Anjo ◽

Alex Rider ◽

Abigail Bailey ◽

Maren Gaudig

Keyword(s):

Data Collection ◽

Real World ◽

Stem Cell Transplant ◽

Standard Of Care ◽

Cell Transplant ◽

Summary Statistics ◽

Real World Data ◽

World Data ◽

Induction Regimen ◽

Number Of Cycles

Abstract Objective This analysis was conducted to understand the clinical practice on FL treatment in patients with MM across EU5 countries. Methods Real world data were collected through Adelphi's Disease-Specific ProgrammeTM - a point in time survey administered to physicians (n=241) in EU5 countries between Nov 2017 - Feb 2018. Stem Cell Transplant eligibility and treatments, including number of cycles and dosage, were collected from patient record forms (n=1952). Summary statistics were reported and analysed descriptively. Results Data on FL treatment was collected for 1952 patients; 988 (51%) were still on FL treatment at the time of data collection. Bortezomib-based regimens were used in more than 70% of patients - in both transplanted/transplant eligible (TE, n=572) and non-transplanted/transplant ineligible patients (TIE, n= 1380). In TIE patients, bortezomib, melphalan and dexamethasone (VMP) was the most commonly used regimen, covering almost one third of the patients (31%), followed by bortezomib, either in combination with cyclophosphamide and dexamethasone (VCD, 10%) or thalidomide and dexamethasone (VTD, 10%). The other two FL regimens currently approved in Europe - thalidomide, melphalan and dexamethasone (MPT) and lenalidomide and dexamethasone (Rd) have patient shares of 9% each. When analyzing the TIE patients undergoing FL treatment at time of data collection (n=606), VMP remained the most used regimen (29%) and Rd the second (15%), with VTD and MPT being used in 8% of patients. In TE patients, VTD was the most commonly used induction regimen, being used in 50% of patients, followed by VCD (21%). The numbers remained the same when analyzing the TE patients in FL treatment at time of data collection (n=382), with 54% and 22% using VTD and VCD, respectively. Conclusions Collectively these results indicate that bortezomib-based regimens remain the standard of care in FL treatment of MM in EU5, in both transplant and non-transplant settings. Disclosures Anjo: Janssen: Employment. Rider:Adelphi Real World: Employment. Bailey:Adelphi Real World: Employment. Gaudig:Janssen: Employment.

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Using real world data to evaluate overall survival in PD1-treated vs. standard of care metastatic melanoma patients.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.15_suppl.e21606 ◽

2018 ◽

Vol 36 (15_suppl) ◽

pp. e21606-e21606 ◽

Cited By ~ 1

Author(s):

Purvi Dev-Vartak ◽

Xinyan Yu ◽

Fa-Qiang Liu ◽

Paul Cariola ◽

Jeffrey Hodge ◽

...

Keyword(s):

Overall Survival ◽

Metastatic Melanoma ◽

Real World ◽

Standard Of Care ◽

Real World Data ◽

World Data ◽

Melanoma Patients

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Using real-world cohorts to assess the generalizability and relevance of randomized clinical trials (RCTs).

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.6540 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 6540-6540 ◽

Cited By ~ 1

Author(s):

Caroline Savage Bennette ◽

Nathan Coleman Nussbaum ◽

Melissa D. Curtis ◽

Neal J. Meropol

Keyword(s):

Clinical Trials ◽

Real World ◽

Randomized Clinical Trials ◽

Treatment Options ◽

Metastatic Breast ◽

Standard Of Care ◽

Real World Data ◽

Eligibility Criteria ◽

Relevance Score ◽

World Data

6540 Background: RCTs are the gold standard for understanding the efficacy of new treatments, however, patients (pts) in RCTs often differ from those treated in the real-world. Further, selecting a standard of care (SOC) arm is challenging as treatment options may evolve during the course of a RCT. Our objective was to assess the generalizability and relevance of RCTs supporting recent FDA approvals of anticancer therapies. Methods: RCTs were identified that supported FDA approvals of anticancer therapies (1/1/2016 - 4/30/2018). Relevant pts were selected from the Flatiron Health longitudinal, EHR-derived database, where available. Two metrics were calculated: 1) a trial’s pt generalizability score (% of real-world pts receiving treatment consistent with the control arm therapy for the relevant indication who actually met the trial's eligibility criteria) and 2) a trial’s SOC relevance score (% of real-world pts with the relevant indication and meeting the trial's eligibility criteria who actually received treatment consistent with the control arm therapy). All analyses excluded real-world pts treated after the relevant trial’s enrollment ended. Results: 14 RCTs across 5 cancer types (metastatic breast, advanced non-small cell lung cancer, metastatic renal cell carcinoma, multiple myeloma, and advanced urothelial) were included. There was wide variation in the SOC relevance and pt generalizability scores. The median pt generalizability score was 63% (range 35% - 88%), indicating that most real-world pts would have met the RCT eligibility criteria. The median SOC relevance score was 37% (range 15% - 74%), indicating that most RCT control arms did not reflect the way trial-eligible real-world pts in the US were actually treated. Conclusions: There is great variability across recent RCTs in terms of pt generalizability and relevance of SOC arms. Real-world data can be used to inform selection of control arms, predict impact of inclusion/exclusion criteria, and also assess the generalizability of the results of completed trials. Incorporating real-world data in planning and interpretation of prospective clinical trials could improve accrual and enhance relevance of RCT outcomes.

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