Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure

Heart failure (HF) is an international public health priority and a focus of the NHS Long Term Plan. There is a particular need in primary care for screening and early detection of heart failure with reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic, and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable, accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and 80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was independent of operator, and usability of the tool extended to patients being able to self-examine. Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500 per patient). This underpins a health economic model for the deployment of AI-ECG across primary care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient self-administered AI-ECG screening programme. I propose approaches to mitigating any potential harms, towards preserving and promoting trust, patient engagement, and public health. Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health economic, and ethical considerations can directly serve patients and health systems by blueprinting best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction needed to realise the full potential of such technologies.Open Acces

Artificial neural network models utilize chamber-specific predictors of cardiac fibrosis in ovariectomized and aortic-banded Yucatan mini-swine

[EMBARGOED UNTIL 6/1/2023] Objective: Heart failure with preserved ejection fraction (HFpEF) is a disease associated with significant clinical pathophysiological heterogeneity in which maladaptive cardiac fibrosis, in both the right and left ventricles of the heart, plays a unique role in the manifestation of disease. Fibrotic remodeling quantified in this project occurs in a chamber-dependent manner on both sides of the heart. Extracellular matrix (ECM) remodeling is the core of this pathological process. The prevalence of HFpEF is greater in postmenopausal women with hypertension. Therefore, the goal of this study was to assess the role of female sex hormones on chamber-dependent differences i.e., left ventricle (LV) vs. right ventricle (RV), in ECM remodeling and regulation in a mini-swine model of pressure overload-induced heart failure (HF). To gain insight about the regulation of fibrosis in this model, biological inputs were measured in both the right and left ventricles and used as input variables in an artificial neural network model (ANN). This model will identify best predictors for experimental group status i.e., the combination of the loss of female sex hormone and/or pressure overload status, as an indication for the biological roles they play in the fibrotic remodeling process. Hypothesis: I hypothesized molecular markers involved in the bioregulation of the cardiac ECM can predict experimental group status in a chamber-specific manner. Methods: a) Animal model: An ovariectomy (OVX) model of surgically induced menopause was used to model the loss of female sex hormones. Separately, aortic banding (AB) was used to induce pressure-overload and mimic HFpEF. Animals that did not undergo ovariectomy were assigned to the intact (INT) groups and animals that did not undergo AB were assigned as control (CON). b) Data: 24 six month old female swine were categorized into 4 groups by ovariectomy and aortic-banded status: 1) Control, intact (CON-INT; n=6); 2) CON-OVX (n=5); 3) AB-INT (n=7) ;and 4) AB-OVX (n=6). c) Ninety-six biological measurements from both the LV and RV were considered including different mRNA, proteins, activity and/or abundance levels of various extracellular matrix components including structural proteins and regulatory pathways. d) Data preprocessing: Missing data were mean imputed and the min-max normalization method was used for all measures. One-way ANOVA models were used to identify mRNA or protein targets associated with group status for consideration in the ANN. Data were split into testing and training sets with one observation from each group (n=4 total) retained for later model testing i.e., 84 percent training and 16 percent testing e) Artificial neural network model: Measurements associated with group status were then used as input features in the ANN model. Multiple activation functions were considered. Different combinations of hidden layers and nodes within each layer were optimized. Cross-validation, confusion matrices, and F1 scores, percentage accuracy and balanced accuracy for each experimental group were used to describe the accuracy of the developed ANN model. Results: One-way ANOVA models indicated that in the LV, total collagen content, TIMP-1 mRNA, total JNK protein level, MMP-14 activity, MMP-2 activity and collagen I mRNA were associated with group status (p [less than] 0.1). In the RV, total collagen content and collagen I and III mRNA levels were associated with group status (p [less than] 0.1). These nine molecular markers were used to develop the ANN model. Cross-validation and confusion matrices indicate all nine targets formed a linear relationship predictive of group with an overall accuracy of 70.7 percent and F1 score of 0.81. Conclusion: Molecular mechanisms involved in the bioregulation of the ECM have analytical power to determine sex hormone and aortic-banding status in a pre-clinical model of pressure overload-induced HF. These findings indicate that nine biological measures could predict experimental group status in our pre-clinical swine model. Therefore, I identified these variables as potential biomarkers of fibrotic remodeling in a HFpEF phenotype with loss of female sex hormones and pressure overload. I also highlight the importance of these nine variables in the fibrotic remodeling process on both sides of the heart.Includes bibliographical references

Circulation

IntroductionHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (\u201cphenomapping\u201d) could identify phenotypically distinct HFpEF categories.Methods and ResultsWe prospectively studied 397 HFpEF patients and performed detailed clinical, laboratory, electrocardiographic, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering to define and characterize mutually exclusive groups comprising a novel classification of HFpEF. All phenomapping analyses were performed blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65\ub112 years, 62% were female, 39% were African-American, and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (e.g., pheno-group #3 had an increased risk of HF hospitalization [hazard ratio 4.2, 95% CI 2.0\u20139.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF pheno-group classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107).ConclusionsPhenomapping results in novel classification of HFpEF. Statistical learning algorithms, applied to dense phenotypic data, may allow for improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.K08 HL098361/HL/NHLBI NIH HHS/United StatesUL1 TR000150/TR/NCATS NIH HHS/United StatesR01 HL107557/HL/NHLBI NIH HHS/United StatesR01 HL107577/HL/NHLBI NIH HHS/United StatesK08 HL093861/HL/NHLBI NIH HHS/United StatesDP2HL123228/DP/NCCDPHP CDC HHS/United StatesDP2 HL123228/HL/NHLBI NIH HHS/United States2016-01-20T00:00:00Z25398313PMC4302027vault:141

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum:a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process.Summary:The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease. [Figure not available: see fulltext.]</p

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. Summary: The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease