Dataset Information

Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.

ABSTRACT: Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual's quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

SUBMITTER: Yang NI

PROVIDER: S-EPMC8470162 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.

Yang Ning-I NI Yeh Chi-Hsiao CH Tsai Tsung-Hsien TH Chou Yi-Ju YJ Hsu Paul Wei-Che PW Li Chun-Hsien CH Chan Yun-Hsuan YH Kuo Li-Tang LT Mao Chun-Tai CT Shyu Yu-Chiau YC Hung Ming-Jui MJ Lai Chi-Chun CC Sytwu Huey-Kang HK Tsai Ting-Fen TF

Cells 20210915 9

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual's quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individ ...[more]

PMID: 34572079

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Project description:AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Project description:AimsRisk factors for asymptomatic echocardiographic abnormalities that predict symptomatic heart failure (HF) may provide insight into early mechanisms of HF pathogenesis. We examined risk factors associated with asymptomatic echocardiographic structural, systolic, and diastolic abnormalities, separately and in combination, and interactions between risk factors, in the prospective community-based SCReening Evaluation of the Evolution of New HF (SCREEN-HF) Study cohort of 3190 participants at increased risk of cardiovascular disease.Methods and resultsInclusion criteria were age ≥ 60 years with one or more of hypertension, diabetes, ischaemic heart disease, valvular heart disease, abnormal heart rhythm, cerebrovascular disease, or renal impairment. Exclusion criteria were known HF, ejection fraction < 50%, or >mild valve abnormality. Structural, systolic, and diastolic echocardiographic abnormalities were defined according to the Atherosclerosis Risk in Communities study criteria, and risk factors for asymptomatic structural, systolic, and diastolic abnormalities were identified using logistic regression analysis. In multivariable analysis, increased body mass index (BMI), non-steroidal anti-inflammatory drug therapy, and alcohol intake were risk factors for isolated structural abnormality, whereas male gender, increased heart rate, atrial fibrillation (AF), angiotensin-converting enzyme inhibitor therapy, and obstructive sleep apnoea were associated with a lower risk. Moreover, male gender, smoking, increased systolic blood pressure, and physical inactivity were risk factors for isolated systolic abnormality, whereas increased pulse pressure and antihypertensive therapy were associated with a lower risk. Furthermore, increased age, blood pressure, amino-terminal pro-B-type natriuretic peptide level, and warfarin therapy (associated with AF) were risk factors for isolated diastolic abnormality, whereas increased heart rate and triglyceride level (associated with BMI) were associated with a lower risk. The association of increased heart rate with lower risk of structural and diastolic abnormalities was independent of β-blocker therapy. Interactions between risk factors differed for structural, systolic, and diastolic abnormalities.ConclusionsThe different risk factors for asymptomatic structural, systolic, and diastolic abnormalities that predict symptomatic HF, and the interactions between risk factors, illustrate how these structural, systolic, and diastolic abnormalities represent unique trajectories that lead to symptomatic HF. Improved understanding of these trajectories may assist in the design of HF prevention strategies.

Project description:Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, “eXplainable AI” (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of two HF survival prediction models using a dataset that includes 299 patients who have experienced HF. The first model utilizes survival analysis, considering death events and time as target features, while the second model approaches the problem as a classification task to predict death. The model employs an optimization data workflow pipeline capable of selecting the best machine learning algorithm as well as the optimal collection of features. Moreover, different post hoc techniques have been used for the explainability analysis of the model. The main contribution of this paper is an explainability-driven approach to select the best HF survival prediction model balancing prediction performance and explainability. Therefore, the most balanced explainable prediction models are Survival Gradient Boosting model for the survival analysis and Random Forest for the classification approach with a c-index of 0.714 and balanced accuracy of 0.74 (std 0.03) respectively. The selection of features by the SCI-XAI in the two models is similar where “serum_creatinine”, “ejection_fraction”, and “sex” are selected in both approaches, with the addition of “diabetes” for the survival analysis model. Moreover, the application of post hoc XAI techniques also confirm common findings from both approaches by placing the “serum_creatinine” as the most relevant feature for the predicted outcome, followed by “ejection_fraction”. The explainable prediction models for HF survival presented in this paper would improve the further adoption of clinical prediction models by providing doctors with insights to better understand the reasoning behind usually “black-box” AI clinical solutions and make more reasonable and data-driven decisions.

Dataset Information

Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.

Publications

Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.

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