Use of Machine Learning to Classify High Risk Variants of Uncertain Significance in Lamin A/C Cardiac Disease

Heart Rhythm ◽  
2021 ◽  
Author(s):  
Jeffrey S. Bennett ◽  
David M. Gordon ◽  
Uddalak Majumdar ◽  
Patrick J. Lawrence ◽  
Adrianna Matos Nieves ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cardiac Disease ◽  
Lamin A ◽  
Variants Of Uncertain Significance ◽  
Risk Variants ◽  
Uncertain Significance

scholarly journals Prevalence and Spectrum of Germline BRCA1 and BRCA2 Variants of Uncertain Significance in Breast/Ovarian Cancer: Mysterious Signals From the Genome

2021 ◽  
Vol 11 ◽  
Author(s):  
Daniele Fanale ◽  
Alessia Fiorino ◽  
Lorena Incorvaia ◽  
Alessandra Dimino ◽  
Clarissa Filorizzo ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Significance ◽  
Clinical Management ◽  
University Hospital ◽  
Sequencing Analysis ◽  
Brca1 And Brca2 ◽  
Variants Of Uncertain Significance ◽  
Pathogenic Variants ◽  
Risk Variants ◽  
Uncertain Significance

About 10–20% of breast/ovarian (BC/OC) cancer patients undergoing germline BRCA1/2 genetic testing have been shown to harbor Variants of Uncertain Significance (VUSs). Since little is known about the prevalence of germline BRCA1/2 VUS in Southern Italy, our study aimed at describing the spectrum of these variants detected in BC/OC patients in order to improve the identification of potentially high-risk BRCA variants helpful in patient clinical management. Eight hundred and seventy-four BC or OC patients, enrolled from October 2016 to December 2020 at the “Sicilian Regional Center for the Prevention, Diagnosis and Treatment of Rare and Heredo-Familial Tumors” of University Hospital Policlinico “P. Giaccone” of Palermo, were genetically tested for germline BRCA1/2 variants through Next-Generation Sequencing analysis. The mutational screening showed that 639 (73.1%) out of 874 patients were BRCA-w.t., whereas 67 (7.7%) were carriers of germline BRCA1/2 VUSs, and 168 (19.2%) harbored germline BRCA1/2 pathogenic/likely pathogenic variants. Our analysis revealed the presence of 59 different VUSs detected in 67 patients, 46 of which were affected by BC and 21 by OC. Twenty-one (35.6%) out of 59 variants were located on BRCA1 gene, whereas 38 (64.4%) on BRCA2. We detected six alterations in BRCA1 and two in BRCA2 with unclear interpretation of clinical significance. Familial anamnesis of a patient harboring the BRCA1-c.3367G>T suggests for this variant a potential of pathogenicity, therefore it should be carefully investigated. Understanding clinical significance of germline BRCA1/2 VUS could improve, in future, the identification of potentially high-risk variants useful for clinical management of BC or OC patients and family members.

scholarly journals Machine Learning Models for Accurate Prioritization of Variants of Uncertain Significance

2020 ◽  
Author(s):  
Daniel Mahecha ◽  
Haydemar Nuñez ◽  
Maria Lattig ◽  
Jorge Duitama
Keyword(s):  
Machine Learning ◽  
Genetic Diagnosis ◽  
Software Tool ◽  
Support Vector ◽  
Clinical Settings ◽  
Loss Of Function ◽  
Variants Of Uncertain Significance ◽  
Sequencing Technologies ◽  
Uncertain Significance ◽  
New Generation

The growing use of new generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of Variants of Uncertain Significance (VUS). In this manuscript we compare three machine learning methods to classify VUS as Pathogenic or No pathogenic, implementing a Random Forest (RF), a Support Vector Machine (SVM), and a Multilayer Perceptron (MLP). To train the models, we extracted 82,463 high quality variants from ClinVar, using 9 conservation scores, the loss of function tool and allele frequencies. For the RF and SVM models, hyperparameters were tuned using cross validation with a grid search. The three models were tested on a set of 5,537 variants that had been classified as VUS any time along the last three years but had been reclassified in august 2020. The three models yielded superior accuracy on this set compared to the benchmarked tools. The RF based model yielded the best performance across different variant types and was used to create VusPrize, an open source software tool for prioritization of variants of uncertain significance. We believe that our model can improve the process of genetic diagnosis on research and clinical settings.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals SARS-CoV-2 testing and sequencing for international arrivals reveals significant cross border transmission of high risk variants into the United Kingdom

2021 ◽  
pp. 101021
Author(s):  
Gareth H Williams ◽  
Alexander Llewelyn ◽  
Ruben Brandao ◽  
Kaiya Chowdhary ◽  
Keeda-Marie Hardisty ◽  
...  
Keyword(s):  
United Kingdom ◽  
High Risk ◽  
Cross Border ◽  
The United Kingdom ◽  
Risk Variants

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

scholarly journals Actions and Uncertainty: How Prenatally Diagnosed Variants of Uncertain Significance Become Actionable

2019 ◽  
Vol 49 ◽  
pp. S61-S71 ◽  
Author(s):  
Allison Werner-Lin ◽  
Judith L. M. Mccoyd ◽  
Barbara A. Bernhardt
Keyword(s):  
Variants Of Uncertain Significance ◽  
Uncertain Significance
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