Can pragmatic research, real-world data and digital technologies aid the development of psychedelic medicine?

Favourable regulatory assessments, liberal policy changes, new research centres and substantial commercial investment signal that psychedelic therapy is making a major comeback. Positive findings from modern trials are catalysing developments, but it is questionable whether current confirmatory trials are sufficient for advancing our understanding of safety and best practice. Here we suggest supplementing traditional confirmatory trials with pragmatic trials, real-world data initiatives and digital health solutions to better support the discovery of optimal and personalised treatment protocols and parameters. These recommendations are intended to help support the development of safe, effective and cost-efficient psychedelic therapy, which, given its history, is vulnerable to excesses of hype and regulation.

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Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

Frontiers in Computer Science ◽

10.3389/fcomp.2021.768266 ◽

2021 ◽

Vol 3 ◽

Author(s):

A. Damiani ◽

C. Masciocchi ◽

J. Lenkowicz ◽

N. D. Capocchiano ◽

L. Boldrini ◽

...

Keyword(s):

Artificial Intelligence ◽

Data Warehouse ◽

Real World ◽

Digital Health ◽

Hospital Data ◽

Real World Data ◽

World Data ◽

Real World Evidence ◽

Artificial Intelligence Laboratory ◽

New Research

The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

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The burden of incontinence in a real-world data environment—insights from a digital patient companion

International Urogynecology Journal ◽

10.1007/s00192-021-04683-4 ◽

2021 ◽

Author(s):

Alexandra von Au ◽

Stephanie Wallwiener ◽

Lina Maria Matthies ◽

Benjamin Friedrich ◽

Sabine Keim ◽

...

Keyword(s):

Pelvic Floor ◽

Real World ◽

Digital Health ◽

Therapy Monitoring ◽

Great Influence ◽

Scientific Data ◽

Real World Data ◽

World Data ◽

Number Of Children ◽

First Choice

Abstract Introduction and hypothesis Urinary incontinence (UI) has a potentially devastating effect on women’s quality of life (QoL). Conservative treatment by means of pelvic floor muscle training is the first-choice treatment modality. Nowadays, this can be supported by digital apps like pelvina©—a digital health companion pelvic floor course. Methods Using pelvina©, UI symptoms and QoL are regularly examined through the questionnaires QUID and SF-6D. Subsequently, we analyzed the incidence and degree of UI and its impact on QoL in 293 users in a real-world environment. Results The 293 patients included in this study had a median age of 36 years and a median of two children. Patients were slightly to moderately affected by UI with a QUID of 6 (2–11, maximum 24). Age and number of children were independently associated with the incidence of UI with an adjusted odds ratio (aOR) of 1.06 (95% CI 1.01–1.12) and aOR of 1.86 (95% CI 1.12–3.08). The severity of UI strongly correlated with impairment of QoL (ρ = 0.866, P < 0.001). Conclusions The use of real-world data generated by digital health solutions offers the opportunity to gain insight into the reality of patients’ lives. In this article, we corroborate the known associations between number of children and UI as well as the great influence UI has on QoL. This study shows that, in the future, the use of digital apps can make an important contribution to scientific data acquisition and, for example, therapy monitoring.

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The burden of incontinence in a real-world data environment – insights from a digital patient companion (Preprint)

10.2196/preprints.14970 ◽

2019 ◽

Author(s):

Alexandra von Au ◽

Stephanie Wallwiener ◽

Lina Maria Matthies ◽

Benjamin Friedrich ◽

Sabine Keim ◽

...

Keyword(s):

Urinary Incontinence ◽

Pelvic Floor ◽

Real World ◽

Pelvic Floor Muscle ◽

Digital Health ◽

Scientific Data ◽

Real World Data ◽

World Data ◽

First Choice ◽

The Impact

BACKGROUND Urinary Incontinence (UI) can have a potentially devastating effect on women’s quality of life (QoL) in the physical, social, sexual, and psychological spheres. During pregnancy and after delivery, the strength of the pelvic floor muscle may decrease, resulting in a high rate of UI. Conservative treatment by means of pelvic floor muscle training is the first-choice treatment modality. Nowadays, this can be supported by digital apps. Those apps have the advantage of giving insights into real-world data on UI. OBJECTIVE The aim of the present study was to analyze the impact of UI on QoL using the app pelvina. METHODS We analyzed data from pelvina - a digital health companion pelvic floor course. This course regularly examines incontinence symptoms through “The Questionnaire for Urinary Incontinence Diagnosis” (QUID) and QoL through SF-6D. Subsequently, we analyzed the incidence and degree of incontinence in a real-world environment and determined the influence of different demographic factors. In addition, the impact of UI on the QoL was evaluated in more detail. RESULTS In all, 293 patients with a median age of 36 years and a median of 2 deliveries could be included in this study. Patients were slightly to moderately affected by UI with a QUID of 6 (2 - 11, max: 24). Age and parity were independently associated with the incidence of UI with an adjusted odds ratio (aOR) of 1.06 (95% CI 1.01 – 1.12) and aOR of 1.86 (95% CI 1.12 – 3.08), respectively. The severity of incontinence symptoms strongly correlated with impairment in QoL ( = 0.489, P < 0.001). CONCLUSIONS The use of real-world data, as generated by digital health solutions such as pelvina, gives us, for the first time, the opportunity to gain insight into the reality of patients' lives outside of classical clinical studies. In this paper we can corroborate the known associations between parity and UI known from the literature and the great influence UI has on QoL on a daily basis. This study shows that, in the future, the use of digital apps can make an important contribution to scientific data acquisition and, for example, therapy monitoring.

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A Framework for Extension Studies Using Real-World Data to Examine Long-Term Safety and Effectiveness

Therapeutic Innovation & Regulatory Science ◽

10.1007/s43441-021-00322-8 ◽

2021 ◽

Author(s):

Mehmet Burcu ◽

Cyntia B. Manzano-Salgado ◽

Anne M. Butler ◽

Jennifer B. Christian

Keyword(s):

Clinical Trial ◽

Real World ◽

Population Level ◽

Real World Data ◽

World Data ◽

Cost Efficient ◽

Methodological Considerations ◽

Post Trial ◽

Design Options

AbstractUnderstanding the long-term benefits and risks of treatments, devices, and vaccines is critically important for individual- and population-level healthcare decision-making. Extension studies, or ‘roll-over studies,’ are studies that allow for patients participating in a parent clinical trial to ‘roll-over’ into a subsequent related study to continue to observe and measure long-term safety, tolerability, and/or effectiveness. These designs are not new and are often used as an approach to satisfy regulatory post-approval safety requirements. However, designs using traditional clinical trial infrastructure can be expensive and burdensome to conduct, particularly, when following patients for many years post trial completion. Given the increasing availability and access of real-world data (RWD) sources, direct-to-patient technologies, and novel real-world study designs, there are more cost-efficient approaches to conducting extension studies while assessing important long-term outcomes. Here, we describe various fit-for-purpose design options for extension studies, discuss related methodological considerations, and provide scientific and operational guidance on practices when planning to conduct an extension study using RWD. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

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Real-world use and acceptance of rituximab biosimilars in non-Hodgkin lymphoma in an oncologist network in Germany

Future Oncology ◽

10.2217/fon-2020-0180 ◽

2020 ◽

Vol 16 (15) ◽

pp. 1001-1012

Author(s):

Burkhard Otremba ◽

Jens Borchardt ◽

Andra Kuske ◽

Maike Hollnagel-Schmitz ◽

Florian O Losch

Keyword(s):

Chronic Lymphocytic Leukemia ◽

Hodgkin Lymphoma ◽

Real World ◽

Lymphocytic Leukemia ◽

Real World Data ◽

Treatment Protocols ◽

World Data ◽

Multiple Treatment ◽

Non Hodgkin Lymphoma ◽

Not Otherwise Specified

Aim: Present real-world data for rituximab (biosimilar and reference)-containing regimens in extrapolated indications in non-Hodgkin lymphoma (NHL)/chronic lymphocytic leukemia (CLL). Patients & methods: Data collected from office-based oncologic practices in Germany (July 2017–June 2019). Results: Of 1741 patients, 1241 had NHL; 500 had CLL. Of 7595 therapy cycles, 28.3% used reference rituximab; 55.2% used rituximab biosimilars; 2.0% used subcutaneous rituximab; 14.5% used rituximab, not otherwise specified. Rituximab biosimilars were used across all indications; 57.3% of cycles were administered in extrapolated indications. Over 24 months, the proportion of rituximab prescriptions that were for biosimilars increased from 12.0 to 83.0%. Conclusion: Our real-world data in NHL and CLL depicts increasing use of rituximab biosimilars across multiple treatment protocols, including extrapolated indications.

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Does order matter? Real-world data on outcomes of successive regimens involving anti-PDx-1 in advanced and metastatic non-small cell lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e21159 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e21159-e21159

Author(s):

David Hadley ◽

Shady Gendy

Keyword(s):

Real World ◽

Proportional Hazards ◽

The United States ◽

Driver Mutations ◽

Cancer Center ◽

Real World Data ◽

Treatment Protocols ◽

World Data ◽

Nsclc Patients ◽

Platinum Doublet

e21159 Background: In 2020, 65% of newly diagnosed advanced (adv)or metastatic (met) NSCLC patients in US started first line (1L) systemic therapy on anti-PDx-1 regimen, 53% of second line (2L). Between broader approvals of anti-PDx-1, increasing use in different therapy lines, and in different regimen combinations, selecting initial and subsequent regimens can be challenging, especially with limited research on outcomes based on order. This analysis uses real-world data to summarize treatment decisions made in a network of oncology centers and relates them to time to second disease progression (PFS2) and overall survival (OS). PRA US medical & prescription claims – 2020 (Jan-Aug). Methods: Deidentified data on adv/met NSCLC patients were selected from Inteliquet’s Cancer Center Research Consortium partners, which comprises academic & community oncology practices as well as integrated delivery networks across the United States. Analysis was limited to patients who started 1L systemic treatment in 2017 & 2018 (index event), progressed and started 2L. Either 1L / 2L / both must have been anti-PDx-1 based regimen. Patients with known actionable driver mutations were excluded. Data from the following 24 months was used to identify regimens and time to progression. PFS2 and OS across the combined 1L and 2L treatment protocols were assessed by proportional hazards regression. The analysis was adjusted for age at diagnosis, gender, PS and treating organization. Results: 132 patients met the study criteria: 53% were female, the median age range at diagnosis was 60-69 years, and 73% were diagnosed with stage IV, and 76% had PS 0-2 at diagnosis. After 24 months, 86% were alive, all had one progression and 44% had a 2nd progression. The most frequently observed treatment patterns are summarized in the table. Conclusions: Same regimens in different order showed different outcomes. There is a significant benefit for both OS & PFS2 by starting with a platinum doublet followed by IO PT, versus the same start followed by IO MT. There is a significant disadvantage in OS by starting with IO MT followed by platinum doublet, versus the reversed order. 1L IO PT versus baseline showed non-significant improvement in PFS2, but not OS.[Table: see text]

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Estimates of Causality with Medical Image in Oncology

ANALES RANM ◽

10.32440/ar.2021.138.01.rev02 ◽

2021 ◽

Vol 138 (138(01)) ◽

pp. 16-23

Author(s):

Luis Martí-Bonmatí

Keyword(s):

Causal Inference ◽

Real World ◽

Observational Studies ◽

Clinical History ◽

Response To Treatment ◽

Control Analysis ◽

Real World Data ◽

World Data ◽

New Research ◽

Case Control Analysis

This work defines a research on data strategy focused on medical imaging and derived image biomarkers to critically assess the concept of causal inference and uncertainties. Computational observational studies will be valued to generate casual inference from real world data. Our main goal is to propose a scientific methodology that allows to estimate causalities from observational studies through quality control of large databases, definition of plausible hypotheses, using computational estimated models and artificial intelligence tools. The computational approach of radiology to precision medicine by using epidemiological strategies is based on causal inference studies relies on real-world data observational, longitudinal, case-control analysis designed (being case the presence, and control the absence of the event to be estimated). In this new research setting, we consider disease in classical epidemiology as phenotyping, response to treatment and final prognosis; and exposure equals to the presence of a radiomic, dynamic image biomarker or AI modeling solution. Research with data on which causality is to be inferred must control for recruitment of closed cases, in which the researcher does not intervene in the patient’s clinical history but works on databases, collecting data to be secondary used in generating consistent causalities.

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