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Molecular prediction for atherogenic risks across different cell types of leukocytes.

ABSTRACT: Diagnosing subclinical atherosclerosis is often difficult since patients are asymptomatic. In order to alleviate this limitation, we have developed a molecular prediction technique for predicting patients with atherogenic risks using multi-gene expression biomarkers on leukocytes.We first discovered 356 expression biomarkers which showed significant differential expression between genome-wide microarray data of monocytes from patients with familial hyperlipidemia and increased risk of atherosclerosis compared to normal controls. These biomarkers were further triaged with 56 biomarkers known to be directly related to atherogenic risks. We also applied a COXEN algorithm to identify concordantly expressed biomarkers between monocytes and each of three different cell types of leukocytes. We then developed a multi-gene predictor using all or three subsets of these 56 biomarkers on the monocyte patient data. These predictors were then applied to multiple independent patient sets from three cell types of leukocytes (macrophages, circulating T cells, or whole white blood cells) to predict patients with atherogenic risks.When the 56 predictor was applied to the three patient sets from different cell types of leukocytes, all significantly stratified patients with atherogenic risks from healthy people in these independent cohorts. Concordantly expressed biomarkers identified by the COXEN algorithm provided slightly better prediction results.These results demonstrated the potential of molecular prediction of atherogenic risks across different cell types of leukocytes.

SUBMITTER: Cheng F

PROVIDER: S-EPMC3271975 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Molecular prediction for atherogenic risks across different cell types of leukocytes.

Cheng Feng F Keeley Ellen C EC Lee Jae K JK

BMC medical genomics 20120113

<h4>Background</h4>Diagnosing subclinical atherosclerosis is often difficult since patients are asymptomatic. In order to alleviate this limitation, we have developed a molecular prediction technique for predicting patients with atherogenic risks using multi-gene expression biomarkers on leukocytes.<h4>Methods</h4>We first discovered 356 expression biomarkers which showed significant differential expression between genome-wide microarray data of monocytes from patients with familial hyperlipidem ...[more]

PMID: 22244445

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Project description:BackgroundMachine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. In this study, we provide detailed evaluations of clinical risk prediction models in live clinical workflows for three different use cases in three different hospitals.ObjectiveThe main objective of this study was to evaluate clinical risk prediction models in live clinical workflows and compare their performance in these setting with their performance when using retrospective data. We also aimed at generalizing the results by applying our investigation to three different use cases in three different hospitals.MethodsWe trained clinical risk prediction models for three use cases (ie, delirium, sepsis, and acute kidney injury) in three different hospitals with retrospective data. We used machine learning and, specifically, deep learning to train models that were based on the Transformer model. The models were trained using a calibration tool that is common for all hospitals and use cases. The models had a common design but were calibrated using each hospital's specific data. The models were deployed in these three hospitals and used in daily clinical practice. The predictions made by these models were logged and correlated with the diagnosis at discharge. We compared their performance with evaluations on retrospective data and conducted cross-hospital evaluations.ResultsThe performance of the prediction models with data from live clinical workflows was similar to the performance with retrospective data. The average value of the area under the receiver operating characteristic curve (AUROC) decreased slightly by 0.6 percentage points (from 94.8% to 94.2% at discharge). The cross-hospital evaluations exhibited severely reduced performance: the average AUROC decreased by 8 percentage points (from 94.2% to 86.3% at discharge), which indicates the importance of model calibration with data from the deployment hospital.ConclusionsCalibrating the prediction model with data from different deployment hospitals led to good performance in live settings. The performance degradation in the cross-hospital evaluation identified limitations in developing a generic model for different hospitals. Designing a generic process for model development to generate specialized prediction models for each hospital guarantees model performance in different hospitals.

Dataset Information

Molecular prediction for atherogenic risks across different cell types of leukocytes.

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Molecular prediction for atherogenic risks across different cell types of leukocytes.

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