Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning

Zhou, Liye; Guo, Zhifei; Wang, Bijue; Wu, Yongqing; Li, Zhi; Yao, Hongmei; Fang, Ruiling; Huang, Yang; Cao, Hongyan; Cui, Yuehua

doi:10.3389/fgene.2021.652315

Cited by 13 publications

(27 citation statements)

References 53 publications

(34 reference statements)

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“…Machine and statistical-learning models applied to omics datasets are being increasingly explored to classify patient cohorts and predict pathologies and associated risks in a broad range of diseases, including HFpEF [ 49 ]. The data generated herein constitutes a valuable resource for additional analysis contributing to a better understanding of the disease’s underlying cellular mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Unveiling Human Proteome Signatures of Heart Failure with Preserved Ejection Fraction

et al. 2022

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Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent but still poorly understood clinical entity. Its current pathophysiological understanding supports a critical role of comorbidities and their chronic effect on cardiac function and structure. Importantly, despite the replication of some HFpEF phenotypic features, to this day, experimental models have failed to bring new effective therapies to the clinical setting. Thus, the direct investigation of HFpEF human myocardial samples may unveil key, and possibly human-specific, pathophysiological mechanisms. This study employed quantitative proteomic analysis by advanced mass spectrometry (SWATH–MS) to investigate signaling pathways and pathophysiological mechanisms in HFpEF. Protein-expression profiles were analyzed in human left ventricular myocardial samples of HFpEF patients and compared with a mixed control group. Functional analysis revealed several proteins that correlate with HFpEF, including those associated with mitochondrial dysfunction, oxidative stress, and inflammation. Despite the known disease heterogeneity, proteomic profiles could indicate a reduced mitochondrial oxidative phosphorylation and fatty-acid oxidation capacity in HFpEF patients with diabetes. The proteomic characterization described in this work provides new insights. Furthermore, it fosters further questions related to HFpEF cellular pathophysiology, paving the way for additional studies focused on developing novel therapies and diagnosis strategies for HFpEF patients.

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Section: Discussionmentioning

confidence: 99%

Unveiling Human Proteome Signatures of Heart Failure with Preserved Ejection Fraction

et al. 2022

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“…There are no precise biomarkers for it, and therapies are not specifically suitable. In addition, HFpEF is highly heterogeneous, making it difficult to reach a consensus on which predictors to use reliably [15]. The critical need for better stratification was underlined after the uncertain results of the PARAGON trial [2,3].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, this prediction model does not yet allow the net discrimination between the two HF subpopulations. One axis of progress rates is developing the present research to a larger cohort and integrating a set of genomic variables to help measure the robustness of predictions made and set a realistic benchmark for predictive early diagnostic [15,19]. Therefore, the reasonable idea is to generalize this methodology to validate our pilot study using a larger cohort or selecting different parameters.…”

Section: Discussionmentioning

confidence: 99%

“…This approach followed previous studies in various fields, including the cardiovascular domain, to prospectively evaluate predictive ML algorithms in a real-world setting [18][19][20]. In a study focusing on a cohort of 149 patients from the Framingham Heart Study, the comparison of five ML models with one conventional logistic regression model to predict HFpEF risk and to identify subgroups based on gene expression data showed that the kernel partial least squares with the genetic algorithm model exhibited the best performance in predicting patient risk of death [15]. This discriminatory capacity approach can potentially be applied to more complex problems, particularly recognition within the same HFpEF population for better stratification and more precise patient management [21].…”

Section: Discussionmentioning

confidence: 99%

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Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?

Lotierzo

Bruno²,

Finan-Marchi

et al. 2021

Medicina

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Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying “high-yield” populations for clinical trials.

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“…A comparative experiment was performed with the following models: eXtreme gradient boosting (XGBoost) model, 18 random forest (RF) model, 19 support vector machines (SVM) model, 20 logistic regression (LR) model, 21 and backpropagation neural network (BPNN) model. 22 In addition, each machine learning algorithm solve the problems in a slightly different way, and different algorithms may give different answers to the same problem, therefore, we adopted ensemble model by weighted voting (an ensemble of XGBoost, RF, SVM, and LR models), 23 and the highest probability among the four model predictions was defined as the final prediction result.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Prediction of Lower Extremity Deep Vein Thrombosis Risk After Knee/Hip Arthroplasty

Wang

Geng

et al. 2023

Clin Appl Thromb Hemost

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Deep vein thrombosis (DVT) is a common postoperative complication of knee/hip arthroplasty. There is a continued need for artificial intelligence-based methods of predicting lower extremity DVT risk after knee/hip arthroplasty. In this study, we performed a retrospective study to analyse the data from patients who underwent primary knee/hip arthroplasty between January 2017 and December 2021 with postoperative bilateral lower extremity venous ultrasonography. Patients’ features were extracted from electronic health records (EHRs) and assigned to the training (80%) and test (20%) datasets using six models: eXtreme gradient boosting, random forest, support vector machines, logistic regression, ensemble, and backpropagation neural network. The Caprini score was calculated according to the Caprini score measurement scale, and the corresponding optimal cut-off Caprini score was calculated according to the largest Youden index. In total, 6897 cases of knee/hip arthroplasty were included (average age, 65.5 ± 8.9 years; 1702 men), among which 1161 (16.8%) were positive and 5736 (83.2%) were negative for deep vein thrombosis. Among the six models, the ensemble model had the highest area under the curve [0.9206 (0.8956, 0.9364)], with a sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of 0.8027, 0.9059, 0.6100, 0.9573 and 0.7003, respectively. The corresponding optimal cut-off Caprini score was 10, with an area under the curve, sensitivity, specificity, positive predictive value, and negative predictive values of 0.5703, 0.8915, 0.2491, 0.1937, 0.9191, and 0.3183, respectively. In conclusion, machine learning models based on EHRs can help predict the risk of deep vein thrombosis after knee/hip arthroplasty.

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Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning

Cited by 13 publications

References 53 publications

Unveiling Human Proteome Signatures of Heart Failure with Preserved Ejection Fraction

Unveiling Human Proteome Signatures of Heart Failure with Preserved Ejection Fraction

Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?

Artificial Intelligence-Based Prediction of Lower Extremity Deep Vein Thrombosis Risk After Knee/Hip Arthroplasty

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