Browsing by Subject "precision medicine"

Now showing 1 - 8 of 8
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    A content analysis of the views of genetics professionals on race, ancestry, and genetics.
    (AJOB empirical bioethics, 2018-10) Nelson, Sarah C; Yu, Joon-Ho; Wagner, Jennifer K; Harrell, Tanya M; Royal, Charmaine D; Bamshad, Michael J
    Over the past decade, the proliferation of genetic studies on human health and disease has reinvigorated debates about the appropriate role of race and ancestry in research and clinical care. Here we report on the responses of genetics professionals to a survey about their views on race, genetics, and ancestry across the domains of science, medicine, and society. Through a qualitative content analysis of free-text comments from 515 survey respondents, we identified key themes pertaining to multiple meanings of race, the use of race as a proxy for genetic ancestry, and the relevance of race and ancestry to health. Our findings suggest that for many genetics professionals the questions of what race is and what race means remain both professionally and personally contentious. Looking ahead as genomics is translated into the practice of precision medicine and as learning health care systems offer continued improvements in care through integrated research, we argue for nuanced considerations of both race and genetic ancestry across research and care settings.
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    Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.
    (Tomography (Ann Arbor, Mich.), 2020-09) Shoghi, Kooresh I; Badea, Cristian T; Blocker, Stephanie J; Chenevert, Thomas L; Laforest, Richard; Lewis, Michael T; Luker, Gary D; Manning, H Charles; Marcus, Daniel S; Mowery, Yvonne M; Pickup, Stephen; Richmond, Ann; Ross, Brian D; Vilgelm, Anna E; Yankeelov, Thomas E; Zhou, Rong
    The National Institutes of Health's (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.
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    Machine Learning and Precision Medicine in Emergency Medicine: The Basics.
    (Cureus, 2021-09) Lee, Sangil; Lam, Samuel H; Hernandes Rocha, Thiago Augusto; Fleischman, Ross J; Staton, Catherine A; Taylor, Richard; Limkakeng, Alexander T
    As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.
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    Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.
    (Journal of personalized medicine, 2021-11) Pattharanitima, Pattharawin; Thongprayoon, Charat; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; Kaewput, Wisit; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; O'Corragain, Oisin A; Mao, Michael A; Garovic, Vesna D; Qureshi, Fawad; Dillon, John J; Cheungpasitporn, Wisit
    Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.
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    Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.
    (Journal of clinical medicine, 2021-10) Pattharanitima, Pattharawin; Thongprayoon, Charat; Kaewput, Wisit; Qureshi, Fawad; Qureshi, Fahad; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; O'Corragain, Oisin A; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; Guru, Pramod K; Mao, Michael A; Garovic, Vesna D; Dillon, John J; Cheungpasitporn, Wisit
    Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.
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    On enrichment strategies for biomarker stratified clinical trials
    (Journal of Biopharmaceutical Statistics, 2017-09-07) Wang, X; Zhou, J; Wang, T; George, SL
    In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker strati ed design (BSD), the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specifi c treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker strati ed design (EBSD) enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker strati ed design (AEBSD) enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in term of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker strati ed trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.
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    The new landscape of medication adherence improvement: where population health science meets precision medicine.
    (Patient preference and adherence, 2018-01) Zullig, Leah L; Blalock, Dan V; Dougherty, Samantha; Henderson, Rochelle; Ha, Carolyn C; Oakes, Megan M; Bosworth, Hayden B
    Despite the known health and economic benefits of medications, nonadherence remains a significant, yet entirely preventable public health burden. Over decades, there have been numerous research studies evaluating health interventions and policy efforts aimed at improving adherence, yet no universal or consistently high impact solutions have been identified. At present, new challenges and opportunities in policy and the movement toward value-based care should foster an environment that appreciates adherence as a mechanism to improve health outcomes and control costs (eg, fewer hospitalizations, reduced health care utilization). Our objective was to provide a commentary on recent changes in the landscape of research and health policy directed toward improving adherence and an actionable agenda to achieve system level savings and improved health by harnessing the benefits of medications. Specifically, we address the complementary perspectives of precision medicine and population health management; integrating data sources to develop innovative measurement of adherence and target adherence interventions; and behavioral economics to determine appropriate incentives.
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    Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.
    (Kidney international, 2024-01) Reznichenko, Anna; Nair, Viji; Eddy, Sean; Fermin, Damian; Tomilo, Mark; Slidel, Timothy; Ju, Wenjun; Henry, Ian; Badal, Shawn S; Wesley, Johnna D; Liles, John T; Moosmang, Sven; Williams, Julie M; Quinn, Carol Moreno; Bitzer, Markus; Hodgin, Jeffrey B; Barisoni, Laura; Karihaloo, Anil; Breyer, Matthew D; Duffin, Kevin L; Patel, Uptal D; Magnone, Maria Chiara; Bhat, Ratan; Kretzler, Matthias
    Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.