scholarly journals Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e030710 ◽  
Author(s):  
Nicole L Guthrie ◽  
Jason Carpenter ◽  
Katherine L Edwards ◽  
Kevin J Appelbaum ◽  
Sourav Dey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Treatment Response ◽  
Response To Treatment ◽  
Behavioural Intervention ◽  
Operating Characteristics ◽  
Explanatory Variables ◽  
Er Model ◽  
Digital Biomarkers ◽  
Predict Treatment Response

ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioural intervention.DesignMachine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP).SettingData generated through ad libitum use of a digital therapeutic in the USA.ParticipantsDeidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic.ResultsThe SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model.ConclusionsMachine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention.

Low-dose ACTH testing does not predict treatment response to corticosteroids in community-acquired pneumonia

2015 ◽  
Author(s):  
Claudine A Blum ◽  
Philipp Schuetz ◽  
Nicole Nigro ◽  
Bettina Winzeler ◽  
Birsen Arici ◽  
...  
Keyword(s):  
Treatment Response ◽  
Low Dose ◽  
Community Acquired Pneumonia ◽  
Predict Treatment Response

scholarly journals Biomarkers predictive of treatment response in psoriasis and psoriatic arthritis: a systematic review

2021 ◽  
Vol 13 ◽  
pp. 1759720X2110140
Author(s):  
Conor Magee ◽  
Hannah Jethwa ◽  
Oliver M. FitzGerald ◽  
Deepak R. Jadon
Keyword(s):  
Systematic Review ◽  
Psoriatic Arthritis ◽  
Treatment Response ◽  
Unmet Need ◽  
Response To Treatment ◽  
Cochrane Library ◽  
Original Research ◽  
Eligibility Criteria ◽  
Psoriatic Disease ◽  
Subsequent Response

Aims: The ability to predict response to treatment remains a key unmet need in psoriatic disease. We conducted a systematic review of studies relating to biomarkers associated with response to treatment in either psoriasis vulgaris (PsV) or psoriatic arthritis (PsA). Methods: A search was conducted in PubMed, Embase and the Cochrane library from their inception to 2 September 2020, and conference proceedings from four major rheumatology conferences. Original research articles studying pre-treatment biomarker levels associated with subsequent response to pharmacologic treatment in either PsV or PsA were included. Results: A total of 765 articles were retrieved and after review, 44 articles (22 relating to PsV and 22 to PsA) met the systematic review’s eligibility criteria. One study examined the response to methotrexate, one the response to tofacitinib and all the other studies to biologic disease-modifying antirheumatic drugs (DMARDs). Whilst several studies examined the HLA-C*06 allele in PsV, the results were conflicting. Interleukin (IL)-12 serum levels and polymorphisms in the IL-12B gene show promise as biomarkers of treatment response in PsV. Most, but not all, studies found that higher baseline levels of C-reactive protein (CRP) were associated with a better clinical response to treatment in patients with PsA. Conclusion: Several studies have identified biomarkers associated with subsequent response to treatment in psoriatic disease. However, due to the different types of biomarkers, treatments and outcome measures used, firm conclusions cannot be drawn. Further validation is needed before any of these biomarkers translate to clinical practice.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals Discovering Non-Invasive Biomarkers Predictive of Opioid Use Disorder Treatment Response

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 173-173
Author(s):  
Amir Levine ◽  
Kelly Clemenza ◽  
Shira Weiss ◽  
Adam Bisaga ◽  
Erez Eitan ◽  
...  
Keyword(s):  
Treatment Response ◽  
Mononuclear Cells ◽  
Specific Treatment ◽  
The United States ◽  
Opioid Use Disorder ◽  
Response To Treatment ◽  
Post Translational Modification ◽  
Opioid Use ◽  
Peripheral Blood Mononuclear ◽  
Long Term Treatment

AbstractBackgroundOpioid use disorder (OUD) continues to be the driving force behind drug overdoses in the United States, killing nearly 47,000 people in 2018 alone. The increasing presence of deadlier fentanyl analogues in the heroin drug supply are putting users at a greater risk for overdose than ever before. Admissions to treatment programs for OUD have also nearly doubled since 2006, yet relapse rates remain high. In response to these alarming statistics, developing approaches to reduce overdose deaths has become an area of high priority. As it is not yet known which patients are most likely to benefit from a specific treatment, there is a dire need to utilize new molecular tools to guide precision medicine approaches and improve treatment outcomes. Here we describe a proof-of-concept study evaluating plasma-derived extracellular vesicle (EV) signatures and how they differ in patients who responded to two pharmacologically contrasting treatments for OUD: the μOR agonist methadone, and the μOR antagonist naltrexone.MethodsWe obtained blood samples from patients with OUD who remained abstinent from illicit opioids for at least 3 months during treatment with methadone (n=5) and naltrexone (n=5), as well as matched healthy controls (n=5). EVs were isolated from plasma and histones were isolated from peripheral blood mononuclear cells (PBMCs). EVs were then analyzed for lipid and histone post-translational modification (PTM) content using liquid chromatography-mass spectrometry. EV miRNA cargo was determined by RNA sequencing.ResultsWe found one lipid class and six miRNAs that differed significantly between the naltrexone group and the methadone and control groups. We also found that histone H3acK9acK14 was increasingly acetylated in PMBCs from both the methadone and naltrexone groups compared to controls.DiscussionNaltrexone, which is used in treatment of OUD and other substance use disorders as well as disorders of impulse control, was found to have multiple potential corresponding molecular signatures that can be identified after long-term treatment. It remains to be seen if these markers can also be a good predictor for treatment response. In addition, significant gender differences in EV content are found between men and women with OUD, which supports the importance of examining changes in response to treatment in a gender informed way.

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