Estimation of Prevalence and Incremental Costs of Systemic Lupus Erythematosus in a Middle-Income Country Using Machine Learning on Administrative Health Data

2021 ◽  
Vol 26 ◽  
pp. 98-104
Author(s):  
Santiago Castro-Villarreal ◽  
Adriana Beltran-Ostos ◽  
Carlos F. Valencia
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Health Data ◽  
Middle Income Country ◽  
Income Country ◽  
Systemic Lupus ◽  
Middle Income ◽  
Administrative Health Data

scholarly journals Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

2019 ◽  
Author(s):  
William A Figgett ◽  
Katherine Monaghan ◽  
Milica Ng ◽  
Monther Alhamdoosh ◽  
Eugene Maraskovsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Clinical Trials ◽  
Lupus Erythematosus ◽  
Whole Blood ◽  
Rna Seq ◽  
Systemic Lupus ◽  
Disease Heterogeneity ◽  
Healthy Donors

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

scholarly journals Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0174200 ◽  
Author(s):  
Fulvia Ceccarelli ◽  
Marco Sciandrone ◽  
Carlo Perricone ◽  
Giulio Galvan ◽  
Francesco Morelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Learning Models ◽  
Systemic Lupus ◽  
Machine Learning Models

scholarly journals Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling

2018 ◽  
Vol 26 (1) ◽  
pp. 61-65 ◽  
Author(s):  
Sara G Murray ◽  
Anand Avati ◽  
Gabriela Schmajuk ◽  
Jinoos Yazdany
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Complex Disease ◽  
Text Processing ◽  
Systemic Lupus ◽  
Silver Standard ◽  
Mesh Terms ◽  
Electronic Health ◽  
Negative Controls

Abstract Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging task that has historically relied on tedious clinician review and oversimplification of the disease. Here we adapt methods that allow for automated “noisy labeling” of positive and negative controls to create a “silver standard” for machine learning to automate identification of systemic lupus erythematosus (SLE). Our final model, which includes both structured data as well as text processing of clinical notes, outperformed all existing algorithms for SLE (AUC 0.97). In addition, we demonstrate how the probabilistic outputs of this model can be adapted to various clinical needs, selecting high thresholds when specificity is the priority and lower thresholds when a more inclusive patient population is desired. Deploying a similar methodology to other complex diseases has the potential to dramatically simplify the landscape of population identification in the EHR. MeSH terms Electronic Health Records, Machine Learning, Lupus Erythematosus, Phenotype, Algorithms

The development of quality indicators for systemic lupus erythematosus using electronic health data: A modified RAND appropriateness method

2019 ◽  
Vol 30 (3) ◽  
pp. 525-531 ◽  
Author(s):  
Nobuyuki Yajima ◽  
Yasushi Tsujimoto ◽  
Shingo Fukuma ◽  
Ken-ei Sada ◽  
Sayaka Shimizu ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Quality Indicators ◽  
Lupus Erythematosus ◽  
Health Data ◽  
Systemic Lupus ◽  
Electronic Health

Survival in adults and children with systemic lupus erythematosus: a systematic review and Bayesian meta-analysis of studies from 1950 to 2016

2017 ◽  
Vol 76 (12) ◽  
pp. 2009-2016 ◽  
Author(s):  
Maria G Tektonidou ◽  
Laura B Lewandowski ◽  
Jinxian Hu ◽  
Abhijit Dasgupta ◽  
Michael M Ward
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
High Income ◽  
Systemic Lupus ◽  
Middle Income ◽  
Middle Income Countries ◽  
Paediatric Patients ◽  
Low Middle Income Countries ◽  
Adults And Children ◽  
Over Time

ObjectiveTo determine trends in survival among adult and paediatric patients with systemic lupus erythematosus (SLE) from 1950 to the present.MethodsWe performed a systematic literature review to identify all published cohort studies on survival in patients with SLE. We used Bayesian methods to derive pooled survival estimates separately for adult and paediatric patients, as well as for studies from high-income countries and low/middle-income countries. We pooled contemporaneous studies to obtain trends in survival over time. We also examined trends in major causes of death.ResultsWe identified 125 studies of adult patients and 51 studies of paediatric patients. Among adults, survival improved gradually from the 1950s to the mid-1990s in both high-income and low/middle-income countries, after which survival plateaued. In 2008–2016, the 5-year, 10-year and 15-year pooled survival estimates in adults from high-income countries were 0.95, 0.89 and 0.82, and in low/middle-income countries were 0.92, 0.85 and 0.79, respectively. Among children, in 2008–2016, the 5-year and 10-year pooled survival estimates from high-income countries were 0.99 and 0.97, while in low/middle-income countries were 0.85 and 0.79, respectively. The proportion of deaths due to SLE decreased over time in studies of adults and among children from high-income countries.ConclusionsAfter a period of major improvement, survival in SLE has plateaued since the mid-1990s. In high-income countries, 5-year survival exceeds 0.95 in both adults and children. In low/middle-income countries, 5-year and 10-year survival was lower among children than adults.

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