personalized risk
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2022 ◽  
Vol 8 ◽  
Author(s):  
Katie J. Lee ◽  
Brigid Betz-Stablein ◽  
Mitchell S. Stark ◽  
Monika Janda ◽  
Aideen M. McInerney-Leo ◽  
...  

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.


2021 ◽  
Author(s):  
Sepehr Golriz Khatami ◽  
Maria Francesca Russo ◽  
Daniel Domingo-Fernandez ◽  
Andrea Zaliani ◽  
Sarah Mubeen ◽  
...  

The COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize and store studies, datasets and metadata in the area of COVID-19 in a comprehensive repository. We are convinced that our work will facilitate future research and development of personalized predictive risk models of COVID-19.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ji Soo Kim ◽  
Ami A. Shah ◽  
Laura K. Hummers ◽  
Scott L. Zeger

Abstract Background Scleroderma is a serious chronic autoimmune disease in which a patient’s disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients. Methods We use a Bayesian mixed model approach to simultaneously characterize each individual’s future trajectories for several biomarkers. We estimate this model using a large population of patients from the Johns Hopkins Scleroderma Center Research Registry. The joint probabilities of critical lung and heart events are then calculated as a byproduct of the mixed model. Results The performance of this approach is substantially better than standard, more common alternatives. In order to predict an individual’s risks in a clinical setting, we also develop a cross-validated, sequential prediction (CVSP) algorithm. As additional data are observed during a patient’s visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH. The updated prediction distributions with little additional computing, for example within an electronic health record (EHR). Conclusions This method that generates real-time personalized risk estimates has been implemented within the electronic health record system for clinical testing. To our knowledge, this work represents the first approach to compute personalized risk estimates for multiple scleroderma complications.


2021 ◽  
pp. 0272989X2110492
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty ◽  
Lurdes Y. T. Inoue ◽  
David L. Veenstra ◽  
Charles J. Wolock ◽  
...  

Background Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients’ earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. Methods We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient’s longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. Results When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an “always test” strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. Conclusions A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. Highlights In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk. We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)–based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs. A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.


2021 ◽  
pp. 78-87
Author(s):  
O. V. Smirnova ◽  
O. L. Moskalenko ◽  
E. V. Kasparov ◽  
I. E. Kasparova,

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disease in highly developed countries. The risk of developing NAFLD and associated complications varies greatly among people of different nationalities and is determined by environmental and genetic factors. Genome-wide studies have revealed strong and reproducible associations between gene variations such as PNPLA3, TM6SF2, MBOAT7, GCKR, HSD17B1, and NAFLD. In this article, we consider the influence of genes and environmental factors on the pathophysiological features of NAFLD. The use of a sufficient population sample with the analysis of SNP arrays and the use of sequencing methods (exome and genome as a whole) will lead to the discovery of additional genetic variants, will inevitably improve the understanding of the pathogenesis of NAFLD, and will allow the development of a technology for personalized risk in assessing the disease in a patient. The aim of our study was to study the genetic predictors of NAFLD based on literature data with the interpretation of the studies. There is now strong evidence that specific variants of genetic risk have a large effect on NAFLD, and their effect is comparable to that of major metabolic risk factors such as obesity and type 2 diabetes. The increased risk extends to the onset and progression of the entire spectrum of NAFLD manifestations, including overall mortality due to liver disease. Currently, individual genetic variants do not allow the creation of a personalized risk profile; therefore, the most expedient approach today is the development of polygenic risk assessments. The number of genetic loci associated with the prevalence and outcome of NAFLD remains limited. The use of a sufficient population sample with the analysis of SNP arrays and the use of sequencing methods (exome and genome as a whole) will lead to the discovery of additional genetic variants and will inevitably improve the understanding of the pathogenesis of NAFLD and will allow the development of a technology for personalized risk in the assessment of the disease.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Janice M. Ranson ◽  
Timothy Rittman ◽  
Shabina Hayat ◽  
Carol Brayne ◽  
Frank Jessen ◽  
...  

AbstractWe envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39–64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jure Knez ◽  
Monika Sobocan ◽  
Urska Belak ◽  
Rajko Kavalar ◽  
Mateja Zupin ◽  
...  

Abstract Background The aim of this study was to evaluate changes in prognostic risk profiles of women with endometrial cancer by comparing the clinical risk assessment with the integrated molecular risk assessment profiling. Patients and methods This prospective study recruited patients with biopsy proven endometrial cancer treated at the University Medical Centre Maribor between January 2020 to February 2021. Patient clinical data was assessed and categorized according to the currently valid European Society of Gynaecological Oncology, European SocieTy for Radiotherapy and Oncology, and European Society of Pathology (ESGO/ESTRO/ESP) guidelines on endometrial cancer. Molecular tumour characterization included determination of exonuclease domain of DNA polymerase-epsilon (POLE) mutational status by Sanger sequencing and imunohistochemical specimen evaluation on the presence of mismatch repair deficiencies (MMRd) and p53 abnormalities (p53abn). Results Fourty-five women were included in the study. Twenty-two tumours were of non-specific mutational profile (NSMP) (56.4%), 13 were classified as MMRd (33.3%), 3 were classified as p53abn (7.7%) and 1 was classified as POLE mutated (2.6%). Six tumours (15.4%) had multiple molecular classifiers, these were studied separately and were not included in the risk assessment. The clinical risk-assessment classified 21 women (53.8%) as low-risk, 5 women (12.8%) as intermediate risk, 2 women as high-intermediate risk (5.1%), 10 women (25.6%) as high risk and 1 patient as advanced metastatic (2.6%). The integrated molecular classification changed risk for 4 women (10.3%). Conclusions Integrated molecular risk improves personalized risk assessment in endometrial cancer and could potentially improve therapeutic precision. Further molecular stratification with biomarkers is especially needed in the NSMP group to improve personalized risk-assessment.


Author(s):  
Monia Renzi ◽  
Cristiana Guerranti ◽  
Eleonora Bertacchini ◽  
Stefano Maccanico ◽  
Eleonora Grazioli ◽  
...  

Food is usually the major source of human exposure to environmental contaminants like heavy metals and synthetic compounds. This study proposes a quick and simple approach to combine the estimate of the intake of certain pollutants with the diet, in combination with different nutritional plans (Mediterranean diet, weight loss and for athletes). The estimation of the intake of three heavy metals and two perfluoroalkyl substances was carried out by entering the type and quantity of the foods provided by each of the three selected dietary plans in the UltraBio® app. Recurring elements are high levels of Cd and Pb and very low levels of PFASs, for all the plans considered. The Mediterranean diet scheme was the one with the lowest intake of all contaminants, which, in any case, remains within the safety limits by a large margin. The high protein diet leads to exceeding the limits for two metals and critical values for the third. The advantages of this approach are mainly represented by the possibility of having a personalized risk assessment of the intake of important food contaminants for the prevention of exposures that, over time, could put health at risk.


Author(s):  
Mina Kelleni

Nucleic acid based - mRNA based and adenovirus vectored - vaccines, were first ever or first commercially ever approved for the public, respectively. However, these newly emergency approved types possess a potential risk to induce auto-immune diseases e.g., thrombocytopenia, myocarditis and immune induced thrombosis and thromboembolism that might be fatal and could reason for some of the post vaccination sudden death reports. Moreover, all SARS CoV-2 types of vaccines, depending on the spike protein immunogenicity, especially the conventional inactivated ones might increase the likelihood of COVID-19 severity upon re-infection through antibody dependent enhancement which might reason for the recently described abundance of hospital admissions within seven days of vaccination and might also reason for some of the serious adverse effects encountered with administration of convalescent plasma to COVID-19 patients as well as they might share in development of some lethal SARS CoV-2 variants. Importantly, we suggest that SARS CoV-2 mass vaccination campaigns were the worst ever decision made and that making these COVID-19 vaccines compulsory or administering them to children or pregnant participants might be considered as a crime against humanity to the extent that no prior companies- governmental agreements would ever secure impunity. Finally, a full informed personalized risk benefit ratio especially for some described high-risk groups must be secured while suggesting that the subunit vaccines are the least hazardous ones.


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