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 Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms “Artificial intelligence,” “Machine learning,” and “regulation” from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl. Keywords: Acute kidney injury; Anaemia; Chronic kidney disease; Computational models; Data integratio

Supplemental Material - Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey

Supplemental Material for Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey by Annabelle Painter, Jackie van Dael, Ana Luisa Neves, Patrik Bachtiger, Niki O’Brien, Clarissa Gardner, Jennifer Quint, Alexander Adamson, Nicholas Peters, Ara Darzi, Saira Ghafur in Health Informatics Journal</p

Supplemental Material - Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey

Supplemental Material for Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey by Annabelle Painter, Jackie van Dael, Ana Luisa Neves, Patrik Bachtiger, Niki O’Brien, Clarissa Gardner, Jennifer Quint, Alexander Adamson, Nicholas Peters, Ara Darzi, Saira Ghafur in Health Informatics Journal</p

Cell death markers in patients with cirrhosis and acute decompensation

The aims of this study were to determine the role of cell death in patients with cirrhosis and acute decompensation (AD) and acute on chronic liver failure (ACLF) using plasma‐based biomarkers. The patients studied were part of the CANONIC (CLIF Acute‐on‐Chronic Liver Failure in Cirrhosis) study (N = 337; AD, 258; ACLF, 79); additional cohorts included healthy volunteers, stable patients with cirrhosis, and a group of 16 AD patients for histological studies. Caspase‐cleaved keratin 18 (cK18) and keratin 18 (K18), which reflect apoptotic and total cell death, respectively, and cK18:K18 ratio (apoptotic index) were measured in plasma by enzyme‐linked immunosorbent assay. The concentrations of cK18 and K18 increased and the cK18:K18 ratio decreased with increasing severity of AD and ACLF (P < 0.001, respectively). Alcohol etiology, no previous decompensation, and alcohol abuse were associated with increased cell death markers whereas underlying infection was not. Close correlation was observed between the cell death markers and, markers of systemic inflammation, hepatic failure, alanine aminotransferase, and bilirubin, but not with markers of extrahepatic organ injury. Terminal deoxynucleotidyl transferase dUTP nick‐end labeling staining confirmed evidence of greater hepatic cell death in patients with ACLF as opposed to AD. Inclusion of cK18 and K18 improved the performance of the CLIF‐C AD score in prediction of progression from AD to ACLF (P < 0.05). Conclusion: Cell death, likely hepatic, is an important feature of AD and ACLF and its magnitude correlates with clinical severity. Nonapoptotic forms of cell death predominate with increasing severity of AD and ACLF. The data suggests that ACLF is a heterogeneous entity and shows that the importance of cell death in its pathophysiology is dependent on predisposing factors, precipitating illness, response to injury, and type of organ failure

Cell death markers in patients with cirrhosis and acute decompensation

The aims of this study were to determine the role of cell death in patients with cirrhosis and acute decompensation (AD) and acute on chronic liver failure (ACLF) using plasma‐based biomarkers. The patients studied were part of the CANONIC (CLIF Acute‐on‐Chronic Liver Failure in Cirrhosis) study (N = 337; AD, 258; ACLF, 79); additional cohorts included healthy volunteers, stable patients with cirrhosis, and a group of 16 AD patients for histological studies. Caspase‐cleaved keratin 18 (cK18) and keratin 18 (K18), which reflect apoptotic and total cell death, respectively, and cK18:K18 ratio (apoptotic index) were measured in plasma by enzyme‐linked immunosorbent assay. The concentrations of cK18 and K18 increased and the cK18:K18 ratio decreased with increasing severity of AD and ACLF (P < 0.001, respectively). Alcohol etiology, no previous decompensation, and alcohol abuse were associated with increased cell death markers whereas underlying infection was not. Close correlation was observed between the cell death markers and, markers of systemic inflammation, hepatic failure, alanine aminotransferase, and bilirubin, but not with markers of extrahepatic organ injury. Terminal deoxynucleotidyl transferase dUTP nick‐end labeling staining confirmed evidence of greater hepatic cell death in patients with ACLF as opposed to AD. Inclusion of cK18 and K18 improved the performance of the CLIF‐C AD score in prediction of progression from AD to ACLF (P < 0.05). Conclusion: Cell death, likely hepatic, is an important feature of AD and ACLF and its magnitude correlates with clinical severity. Nonapoptotic forms of cell death predominate with increasing severity of AD and ACLF. The data suggests that ACLF is a heterogeneous entity and shows that the importance of cell death in its pathophysiology is dependent on predisposing factors, precipitating illness, response to injury, and type of organ failure