Dataset Information

A machine learning approach for the prediction of pulmonary hypertension.

ABSTRACT: BACKGROUND:Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters. METHODS:In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme. RESULTS:ML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73-0.93), boosted classification trees (AUC 0.80; 95% CI 0.68-0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67-0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75-0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78-0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance. CONCLUSIONS:Using ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.

SUBMITTER: Leha A

PROVIDER: S-EPMC6814224 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A machine learning approach for the prediction of pulmonary hypertension.

Leha Andreas A Hellenkamp Kristian K Unsöld Bernhard B Mushemi-Blake Sitali S Shah Ajay M AM Hasenfuß Gerd G Seidler Tim T

PloS one 20191025 10

<h4>Background</h4>Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters.<h4>Methods</h4>In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PA ...[more]

PMID: 31652290

Similar Datasets

Project description:Purpose To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional patterns of systolic cardiac motion. Materials and Methods The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age ± standard deviation, 63 years ± 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. Results At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P < .001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P < .001). Conclusion A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension. Online supplemental material is available for this article.

Project description:Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.

Project description:Abstract Aims The detection of white-coat hypertension/white-coat uncontrolled hypertension (WCH/WUCH) with out-of-office blood pressure (BP) monitoring is time- and resource-consuming. We aim to develop a machine learning (ML)-derived prediction model based on the characteristics of patients from a single outpatient visit. Methods and results Data from two cohorts in Taiwan were used. Cohort one (970 patients) was used for development and internal validation, and cohort two (464 patients) was used for external validation. WCH/WUCH was defined as an office BP of ≥140/90 mmHg and daytime ambulatory BP of <135/85 mmHg in treatment-naïve or treated individuals. Logistic regression, random forest (RF), eXtreme Gradient Boosting, and artificial neural network models were trained using 26 patient parameters. We used SHapley Additive exPlanations values to provide explanations for the risk factors. All models achieved great area under the receiver operating characteristic curve (AUROC), specificity, and negative predictive value in both validations (AUROC = 0.754–0.891; specificity = 0.682–0.910; negative predictive value = 0.831–0.968). The RF model was the best performing (AUROC = 0.884; sensitivity = 0.619; specificity = 0.887; negative predictive value = 0.872; accuracy = 0.819). The five most influential features of the RF model were office diastolic BP, office systolic BP, current smoker, estimated glomerular filtration rate, and fasting glucose level. Conclusion Our prediction models achieved good performance, underlining the feasibility of applying ML models to outpatient populations for the diagnosis of WCH and WUCH. Further validation with other prospective data sets should be considered in the future. Graphical Abstract Graphical Abstract Two cohorts were used for model development after splitting and external validation respectively. With oversampled training set, four algorithms were developed through the process of feature ranking with SHapley Additive exPlantions values, feature selection, and threshold tuning under five-fold cross-validation. Models were then evaluated by variant metrics. Present study demonstrated the feasibility of applying machine learning models to outpatient population for the identify of white-coat hypertension and white-coat uncontrolled hypertension. ANN, artificial neural network; AUROC, area under the receiver operating characteristic curve; DBP, diastolic blood pressure; LR, logistic regression; NPV, negative predictive value; PPV, positive predictive value; RF, random forest; ROC, receiver operating characteristic; SBP, systolic blood pressure; Sen, sensitivity; SHAP, SHapley Additive exPlanations; Spe, specificity; WCH/WUCH, white-coat hypertension/white-coat uncontrolled hypertension; XGboost, eXtreme Gradient Boosting.

Project description:RationaleAccumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, although it remains unknown if distinct immune phenotypes exist.ObjectiveIdentify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles.Methods and resultsIn a prospective observational study of group 1 PAH patients evaluated at Stanford University (discovery cohort; n=281) and University of Sheffield (validation cohort; n=104) between 2008 and 2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters. Four PAH clusters with distinct proteomic immune profiles were identified in the discovery cohort. Cluster 2 (n=109) had low cytokine levels similar to controls. Other clusters had unique sets of upregulated proteins central to immune networks-cluster 1 (n=58; TRAIL [tumor necrosis factor-related apoptosis-inducing ligand], CCL5 [C-C motif chemokine ligand 5], CCL7, CCL4, MIF [macrophage migration inhibitory factor]), cluster 3 (n=77; IL [interleukin]-12, IL-17, IL-10, IL-7, VEGF [vascular endothelial growth factor]), and cluster 4 (n=37; IL-8, IL-4, PDGF-β [platelet-derived growth factor beta], IL-6, CCL11). Demographics, PAH clinical subtypes, comorbidities, and medications were similar across clusters. Noninvasive and hemodynamic surrogates of clinical risk identified cluster 1 as high-risk and cluster 3 as low-risk groups. Five-year transplant-free survival rates were unfavorable for cluster 1 (47.6%; 95% CI, 35.4%-64.1%) and favorable for cluster 3 (82.4%; 95% CI, 72.0%-94.3%; across-cluster P<0.001). Findings were replicated in the validation cohort, where machine learning classified 4 immune clusters with comparable proteomic, clinical, and prognostic features.ConclusionsBlood cytokine profiles distinguish PAH immune phenotypes with differing clinical risk that are independent of World Health Organization group 1 subtypes. These phenotypes could inform mechanistic studies of disease pathobiology and provide a framework to examine patient responses to emerging therapies targeting immunity.

Dataset Information

A machine learning approach for the prediction of pulmonary hypertension.

Publications

A machine learning approach for the prediction of pulmonary hypertension.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets