Evaluation of 6 years of eHealth data in the alcohol use disorder field indicates improved efficacy of care.

Background: Predictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder. Methods: Since the deployment of Previct Alcohol, structured data has been archived in a data warehouse, currently comprising 505,641 patient days. The frequencies of relapse and caregiver-patient messaging over time was studied. The effects of both introducing an AI-driven relapse prediction tool and the COVID-19 pandemic were analyzed. Results: The relapse frequency per patient day among Previct Alcohol users was 0.28 in 2016, 0.22 in 2020 and 0.25 in 2022 with no drastic change during COVID-19. When a relapse was predicted, the actual occurrence of relapse in the days immediately after was found to be above average. Additionally, there was a noticeable increase in caregiver interactions following these predictions. When caregivers were not informed of these predictions, the risk of relapse was found to be higher compared to when the prediction tool was actively being used. The prediction tool decreased the relapse risk by 9% for relapses that were of short duration and by 18% for relapses that lasted more than 3 days. Conclusions: The eHealth system Previct Alcohol allows for high resolution measurements, enabling precise identifications of relapse patterns and follow up on individual and population-based alcohol use disorder treatment. eHealth relapse prediction aids the caregiver to act timely, which reduces, delays, and shortens relapses.

Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring.

PURPOSE: eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners. METHODS: Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared. RESULTS: Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale. CONCLUSION: By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.

The Clinical Course of Alcohol Use Disorder Depicted by Digital Biomarkers.

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.

Breathalyser-Based eHealth Data Suggest That Self-Reporting of Abstinence Is a Poor Outcome Measure for Alcohol Use Disorder Clinical Trials.

AIMS: To evaluate the efficacy and monitoring capabilities of a breathalyser-based eHealth system for patients with alcohol use disorder (AUD) and to investigate the quality and validity of timeline follow-back (TLFB) as outcome measure in clinical trials and treatment. METHODS: Patients (n = 115) were recruited to clinical trials from a 12-step aftercare programme (12S-ABS) and from hospital care with abstinence (HC-ABS) or controlled drinking (HC-CDR) as goal and randomly divided into an eHealth and a control group. The effect of the eHealth system was analysed with TLFB-derived primary outcomes-change in number of abstinent days (AbsDay) and heavy drinking days (HDDs) compared to baseline-and phosphatidyl ethanol (PEth) measurements. Validity and quality of TLFB were evaluated by comparison with breath alcohol content (BrAC) and eHealth digital biomarkers (DBs): Addiction Monitoring Index (AMI) and Maximum Time Between Tests (MTBT). TLFB reports were compared to eHealth data regarding reported abstinence. RESULTS: The primary outcome (TLFB) showed no significant difference between eHealth and control groups, but PEth did show a significant difference especially at months 2 and 3. Self-reported daily abstinence suffered from severe quality issues: of the 28-day TLFB reports showing full abstinence eHealth data falsified 34% (BrAC measurements), 39% (MTBT), 54% (AMI) and 68% (BrAC/MTBT/AMI). 12S-ABS and HC-ABS patients showed severe under-reporting. CONCLUSIONS: No effect of the eHealth system was measured with TLFB, but a small positive effect was measured with PEth. The eHealth system revealed severe quality problems with TLFB, especially regarding abstinence-should measurement-based eHealth data replace TLFB as outcome measure for AUD?

Maximum Time Between Tests: A Digital Biomarker to Detect Therapy Compliance and Assess Schedule Quality in Measurement-Based eHealth Systems for Alcohol Use Disorder.

AIM: To evaluate, in a breathalyzer-based eHealth system, whether the time-based digital biomarker 'maximum time between tests' (MTBT) brings valuable information on alcohol consumption patterns as confirmed by correlation with blood phosphatidyl ethanol (PEth), serum carbohydrate deficient transferrin (CDT) and timeline follow-back data. METHOD: Data on 54 patients in follow-up for treatment of alcohol use disorder were analysed. RESULTS: The model of weekly averages of 24-log transformed MTBT adequately described timeline follow-back data (P  <  0.0001, R =  0.27-0.38, n  =  650). Significant correlations were noted between MTBT and PEth (P  <  0.0001, R  =  0.41, n  =  148) and between MTBT and CDT (P  <  0.0079, R  =  0.22, n  =  120). CONCLUSIONS: The time-based digital biomarker 'maximum time between tests' described here has the potential to become a generally useful metric for all scheduled measurement-based eHealth systems to monitor test behaviour and compliance, factors important for 'dosing' of eHealth systems and for early prediction and interventions of lapse/relapse.

Real-time Monitoring using a breathalyzer-based eHealth system can identify lapse/relapse patterns in alcohol use disorder Patients.

AIM: We introduce a new remote real-time breathalyzer-based method for monitoring and early identification of lapse/relapse patterns for alcohol use disorder (AUD) patients using a composite measure of sobriety, the Addiction Monitoring Index (AMI). METHODS: We constructed AMI from (a) obtained test results and (b) the pattern of ignored tests using data from the first 30 patients starting in the treatment arms of two on-going clinical trials. The patients performed 2-4 scheduled breath alcohol content (BrAC)-tests per day presented as blood alcohol content (BAC) data. In total, 10,973 tests were scheduled, 7743 were performed and 3230 were ignored during 3982 patient days. RESULTS: AMI-time profiles could be used to monitor the daily trends of alcohol consumption and detect early signs of lapse and relapses. The pattern of ignored tests correlates with the onset of drinking. AMI correlated with phosphatidyl ethanol (n = 61, F-ratio = 34.6, P < 0.0001, R = -0.61). The recognition of secret drinking could further be improved using a low alcohol detection threshold (BrAC = 0.025 mg/l, BACSwe = 0.05‰ or US = 0.0053g/dl), in addition to the legal Swedish traffic limit (BrAC = 0.1 mg/l, BACSwe = 0.2‰ or US = 0.021 g/dl). Nine out of 10 patients who dropped out from the study showed early risk signs as reflected in the level and pattern in AMI before the actual dropout. CONCLUSIONS: AMI-time profiles from an eHealth system are useful for monitoring the recovery process and for early identification of lapse/relapse patterns. High-resolution monitoring of sobriety enables new measurement-based treatment methods for proactive personalized long-term relapse prevention and treatment of AUD patients. CLINICAL TRIAL REGISTRATION: The data used for construction of AMI was from two clinical trials approved by the Regional Ethics Committee of Uppsala, Sweden and performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participating subjects. The study was registered at ClinicalTrials.gov (NCT03195894).

Identification of novel downstream molecules of tissue factor activation by comparative proteomic analysis.

Tissue factor (TF) is both an initiator of blood coagulation and a signaling receptor. Using a proteomic approach, we investigated the role of TF in cell signaling when stimulated by its ligand, activated factor VII (FVIIa). From a 2-D difference gel electrophoresis (DIGE) study we found forty one spots that were differentially regulated over time in FVIIa stimulated cells or in comparison to nonstimulated cells. Mass spectrometry identifies 23 out of these as 13 different proteins. One of them, elongation factor 2 (EF-2), was investigated in greater detail by Western blot, a protein synthesis assay and cell cycle analysis. When tissue factor was stimulated by FVIIa, the phosphorylation of EF-2 increased which inactivates this protein. Analyzing the effect using site inactivated FVIIa (FVIIai), as well as the protease activated receptor 2 (PAR-2) agonist SLIGKV, indicated that the inactivation was not PAR-2 dependent. A panel of tissue factor mutants was analyzed further to try to pinpoint what part of the cytoplasmic domain that is needed for this effect. Performing a protein synthesis assay in two different cell lines we could confirm that protein synthesis decreased upon stimulation by FVIIa. Cell cycle analysis showed that FVIIa also promotes a higher degree of cell proliferation.

Selective labelling of cell-surface proteins using CyDye DIGE Fluor minimal dyes.

Surface proteins are central to the cell's ability to react to its environment and to interact with neighboring cells. They are known to be inducers of almost all intracellular signaling. Moreover, they play an important role in environmental adaptation and drug treatment, and are often involved in disease pathogenesis and pathology (1). Protein-protein interactions are intrinsic to signaling pathways, and to gain more insight in these complex biological processes, sensitive and reliable methods are needed for studying cell surface proteins. Two-dimensional (2-D) electrophoresis is used extensively for detection of biomarkers and other targets in complex protein samples to study differential changes. Cell surface proteins, partly due to their low abundance (1 2% of cellular proteins), are difficult to detect in a 2-D gel without fractionation or some other type of enrichment. They are also often poorly represented in 2-D gels due to their hydrophobic nature and high molecular weight (2). In this study, we present a new protocol for intact cells using CyDye DIGE Fluor minimal dyes for specific labeling and detection of this important group of proteins. The results showed specific labeling of a large number of cell surface proteins with minimal labeling of intracellular proteins. This protocol is rapid, simple to use, and all three CyDye DIGE Fluor minimal dyes (Cy 2, Cy 3 and Cy 5) can be used to label cell-surface proteins. These features allow for multiplexing using the 2-D Fluorescence Difference Gel Electrophoresis (2-D DIGE) with Ettan DIGE technology and analysis of protein expression changes using DeCyder 2-D Differential Analysis Software. The level of cell-surface proteins was followed during serum starvation of CHO cells for various lengths of time (see Table 1). Small changes in abundance were detected with high accuracy, and results are supported by defined statistical methods.