Evidence map›Paper›PMID 32773373›Full record

ArticleJMIR research protocols2020

A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study.

Tony Olmert, Jason D Cooper, Sung Yeon Sarah Han, Giles Barton-Owen, Lynn Farrag, Emily Bell, Lauren V Friend, Sureyya Ozcan, Nitin Rustogi, Rhian L Preece and 4 more

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 20 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Tony OlmertDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0003-4641-6442
Jason D CooperDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-2459-5286
Sung Yeon Sarah HanDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-1459-2516
Giles Barton-OwenPsyomics Ltd, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-7552-1295
Lynn FarragPsyomics Ltd, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-5045-5308
Emily BellPsyomics Ltd, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0003-2037-3093
Lauren V FriendPsyomics Ltd, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0003-4340-7401
Sureyya OzcanDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-9371-3696
Nitin RustogiDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-4381-1271
Rhian L PreeceDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0001-8022-0182
Pawel EljaszDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-0592-3934
Jakub TomasikDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-2127-4487
Daniel CowellPsyomics Ltd, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0001-6061-3544
Sabine BahnDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0003-4690-6302
University of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMood disorders affect hundreds of millions of people worldwide, imposing a substantial medical and economic burden. Existing diagnostic methods for mood disorders often result in a delay until accurate diagnosis, exacerbating the challenges of these disorders. Advances in digital tools for psychiatry and understanding the biological basis of mood disorders offer the potential for novel diagnostic methods that facilitate early and accurate diagnosis of patients.

objectiveThe Delta Trial was launched to develop an algorithm-based diagnostic aid combining symptom data and proteomic biomarkers to reduce the misdiagnosis of bipolar disorder (BD) as a major depressive disorder (MDD) and achieve more accurate and earlier MDD diagnosis.

methodsParticipants for this ethically approved trial were recruited through the internet, mainly through Facebook advertising. Participants were then screened for eligibility, consented to participate, and completed an adaptive digital questionnaire that was designed and created for the trial on a purpose-built digital platform. A subset of these participants was selected to provide dried blood spot (DBS) samples and undertake a World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI). Inclusion and exclusion criteria were chosen to maximize the safety of a trial population that was both relevant to the trial objectives and generalizable. To provide statistical power and validation sets for the primary and secondary objectives, 840 participants were required to complete the digital questionnaire, submit DBS samples, and undertake a CIDI.

resultsThe Delta Trial is now complete. More than 3200 participants completed the digital questionnaire, 924 of whom also submitted DBS samples and a CIDI, whereas a total of 1780 participants completed a 6-month follow-up questionnaire and 1542 completed a 12-month follow-up questionnaire. The analysis of the trial data is now underway.

conclusionsIf a diagnostic aid is able to improve the diagnosis of BD and MDD, it may enable earlier treatment for patients with mood disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18453.

Indexed as

bipolar disorderearly diagnosismajor depressive disordersmood disordersproteomics

Identifiers

PMID32773373
PMCPMC7445599
OpenAlexW3048384775

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.