personalized treatment
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2022 ◽  
Author(s):  
Fionneke Bos ◽  
Lino von Klipstein ◽  
Ando C. Emerencia ◽  
Erwin Veermans ◽  
Tom Verhage ◽  
...  

Background: Smartphone self-monitoring through ecological momentary assessment (EMA) provides insights into the daily lives of people in psychiatric treatment and has the potential to improve their care. Currently, no clinical tools are available that help clients and clinicians with creating personalized EMA diaries and interpreting the gathered data. Integration of EMA in treatment is therefore difficult.Objective: To develop a web-based application for personalized EMA in routine psychiatric care, in close collaboration with all stakeholders (i.e., clients, clinicians, researchers, and software developers). Methods: We engaged 52 clients with mood, anxiety, and/or psychotic disorders and 45 clinicians (psychiatrists, psychologists, and psychiatric nurses) in interviews, focus groups, and usability sessions. We used human-centered design principles to determine important requirements for the web-app and designed high-fidelity prototypes that were continuously reevaluated and adapted. Results: The iterative development process resulted in PETRA (PErsonalized Treatment by Real-time Assessment), which is a scientifically grounded web-app for the integration of personalized EMA in clinical care. PETRA includes a decision aid to support clients and clinicians with constructing personalized EMA diaries, an EMA diary item repository, a text-message-based diary delivery system, and a feedback module for visualizing the gathered EMA data. PETRA is integrated in electronic health record (EHR) systems to ensure ease-of-use and sustainability, and adheres to privacy regulations.Conclusions: PETRA was built to fulfill the needs of clients and clinicians for a user-friendly and personalized EMA tool embedded in routine psychiatric care. PETRA is unique in this co-development process, its extensive yet user-friendly personalization options, its integration in EHR systems, its transdiagnostic focus, and its strong scientific foundation in the design of EMA diaries and feedback. The clinical effectiveness of integrating personalized diaries via PETRA into care awaits further research. As such, PETRA paves the way for a systematic investigation into the utility of personalized EMA for routine mental health care.


2022 ◽  
pp. 0272989X2110728
Author(s):  
Anna Heath ◽  
Petros Pechlivanoglou

Background Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.


2022 ◽  
pp. 1-21
Author(s):  
Mohammad Nami ◽  
Robert Thatcher ◽  
Nasser Kashou ◽  
Dahabada Lopes ◽  
Maria Lobo ◽  
...  

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.


Author(s):  
K. B. Kulasekera ◽  
Sudaraka Tholkage ◽  
Maiying Kong

In Vivo ◽  
2021 ◽  
Vol 36 (1) ◽  
pp. 294-305
Author(s):  
MIKLOS POZSGAI ◽  
ISTVAN SZABO ◽  
NORA NUSSER ◽  
REKA VARNAI ◽  
CSILLA SIPEKY

Author(s):  
Andrea Giannini ◽  
Ottavia D'Oria ◽  
Benito Chiofalo ◽  
Valentina Bruno ◽  
Ermelinda Baiocco ◽  
...  

2021 ◽  
Vol 76 (6) ◽  
pp. 604-611
Author(s):  
Irina N. Kondrakhina ◽  
Alexander M. Zatevalov ◽  
Eugenia R. Gatiatulina ◽  
Alexandr A. Nikonorov ◽  
Dmitry G. Deryabin ◽  
...  

Background. Androgenic alopecia (AGA) is the most common form of pathological hair loss with multiple micronutrient disorders involvedin its occurrence and development. Aimto evaluatethe effectiveness of personalized treatment of micronutrient deficiencies in patients with early stages of AGA and conservative therapy using a vasodilator drug minoxidil based on evidence-based medicine. Methods. A total 48 patients with stages IIV of AGA (according to the NorwoodHamilton scale) were recruited to experimental prospective clinical study evaluating the effectiveness of pharmaceutical forms of trace elements and vitamins. The primary diagnosis of micronutrient deficiency was carried out by comparing laboratory parameters of patients with AGA and 25 healthy volunteers. After that, conservative treatment with 5% topical minoxidilin AGA patients was enriched with 2-month personalized systemic supplementation of pharmaceutical forms of trace elements and vitamins. At the end of the study, the correspondence between changes in trace elements and vitamins content in the plasma and the trichogram parameters before and after conservative therapy was assessed. Results. The majority (96%) of the examined patients with AGA were characterized by mono- or polynutrient deficiencies. Personalized correction made it possible to restore the content of Se, Mg, Fe and vitamin E to the baseline levels and to achieve a significant increase in Zn, vitamin D and folic acid plasma content. The relationship between changes in the level of micronutrients and trichogram parameters was recorded only for Se (decrease in anagen hairs: r = 0.43; p = 0.037; decrease in hair density: r = 0.45; p = 0.028) and folic acid (an increase in anagen hairs: r = 0.41; p = 0.024); the positive effect of vitamin E on hair density was also detected. Conclusion. The results of the study allow to recommend a personalized treatment of folic acid and vitamin E deficiencies, with possible refusal to use the Se-containing drugs in conservative therapy of patients with the early stages of AGA.


2021 ◽  
Author(s):  
Stefano Olgiati ◽  
Nima Heidari ◽  
Davide Meloni ◽  
Federico Pirovano ◽  
Ali Noorani ◽  
...  

Background Quantum computing (QC) and quantum machine learning (QML) are promising experimental technologies which can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. In this paper we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methods Following patients consent and Research Ethics Committee approval, we collected clinico-demographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥ 3, OKS ≤ 27, Age ≥ 64 and idiopathic aetiology of arthritis) treated over a 2 year period with a single injection of microfragmented fat. Gender classes were balanced (76 M, 94 F) to mitigate gender bias. A patient with an improvement ≥ 7 OKS has been considered a Responder. We trained our QNN Classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 R, 40 NR) in pain and function at 1 year. Outliers were hidden from the training dataset but not from the validation set. Results We tested our QNN Classifier on a randomly selected test subset of 57 patients (34 R, 23 NR) including outliers. The No Information Rate was equal to 0.59. Our application correctly classified 28 Responders out of 34 and 6 non-Responders out of 23 (Sensitivity = 0.82, Specificity = 0.26, F1 Statistic= 0.71). The Positive (LR+) and Negative (LR-) Likelihood Ratios were respectively 1.11 and 0.68. The Diagnostic Odds Ratio (DOR) was equal to 2. Conclusions Preliminary results on a small validation dataset show that quantum machine learning applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. Our results need further research validation with larger, real-world unstructured datasets, and clinical validation with an AI Clinical Trial to test model efficacy, safety, clinical significance and relevance at a public health level.


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