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The Clinical Course of Alcohol Use Disorder Depicted by Digital Biomarkers

Zetterström, Andreas (author)
Kontigo Care AB, Uppsala, Sweden
Hämäläinen, Markku D. (author)
Kontigo Care AB, Uppsala, Sweden
Winkvist, Maria (author)
Kontigo Care AB, Uppsala, Sweden
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Söderquist, Marcus (author)
Kontigo Care AB, Uppsala, Sweden
Öhagen, Patrik (author)
Uppsala universitet,Uppsala kliniska forskningscentrum (UCR)
Andersson, Karl, 1972- (author)
Uppsala universitet,Cancerprecisionsmedicin,Ridgeview Instruments AB, Vange, Sweden
Nyberg, Fred, 1945- (author)
Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Institutionen för läkemedelskemi
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 (creator_code:org_t)
2021-12-07
2021
English.
In: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 3
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Aims: This study introduces new digital biomarkers to be used as precise, objective tools to measure and describe the clinical course of patients with alcohol use disorder (AUD).Methods: An algorithm is outlined for the calculation of a new digital biomarker, the recovery and exacerbation index (REI), which describes the current trend in a patient's clinical course of AUD. A threshold applied to the REI identifies the starting point and the length of an exacerbation event (EE). The disease patterns and periodicity are described by the number, length, and distance between EEs. The algorithms were tested on data from patients from previous clinical trials (n = 51) and clinical practice (n = 1,717).Results: Our study indicates that the digital biomarker-based description of the clinical course of AUD might be superior to the traditional self-reported relapse/remission concept and conventional biomarkers due to higher data quality (alcohol measured) and time resolution. We found that EEs and the REI introduce distinct tools to identify qualitative and quantitative differences in drinking patterns (drinks per drinking day, phosphatidyl ethanol levels, weekday and holiday patterns) and effect of treatment time.Conclusions: This study indicates that the disease state-level, trend and periodicity-can be mathematically described and visualized with digital biomarkers, thereby improving knowledge about the clinical course of AUD and enabling clinical decision-making and adaptive care. The algorithms provide a basis for machine-learning-driven research that might also be applied for other disorders where daily data are available from digital health systems.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Beroendelära (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Substance Abuse (hsv//eng)

Keyword

digital biomarker
recovery and exacerbation index
addiction monitoring index
maximum time between tests
identification of a relapse

Publication and Content Type

ref (subject category)
art (subject category)

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