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Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease 1 , screening for cardiotoxicity 2 and decisions regarding the clinical management of patients with a critical illness 3 . However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training 4 , 5 . Here, to overcome this challenge, we present a video-based deep learning algorithm—EchoNet-Dynamic—that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos. A video-based deep learning algorithm—EchoNet-Dynamic—accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end systolic and diastolic volumes ( R 2  = 0.74 and R 2  = 0.70), and ejection fraction ( R 2  = 0.50), as well as predicted systemic phenotypes of age ( R 2  = 0.46), sex (AUC = 0.88), weight ( R 2  = 0.56), and height ( R 2  = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlights hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, provide preliminary interpretation in areas with insufficient qualified cardiologists, and predict phenotypes challenging for human evaluation.

Telehealth interventions are effective in hypertension management. However, the cost-effectiveness of using them for managing patients with hypertension remains inconclusive. Further research is required to understand the effectiveness and cost-effectiveness in the real-world setting. The Primary Technology-Enhanced Care for Hypertension (PTEC-HT) scaling program, a telehealth intervention for hypertension management, is currently being scaled nationwide in Singapore. The program comprises remote blood pressure (BP) monitoring at home, health care team support through teleconsultations, and in-app support with a digital chatbot. This study aimed to evaluate the program's effectiveness and cost-effectiveness. For patients under the PTEC-HT scaling program, BP readings over 6 months and 12 months, age, and gender were collected within the program. Health care use, health care cost, and patient ethnicity were extracted from the National Healthcare Group Polyclinics. For patients in the usual care group, demographic information, clinical data, health care use, and health care costs were extracted from the national claims records. Comparing the PTEC-HT scaling program with usual care, a trial-based economic evaluation using patient-level data was conducted to examine the effectiveness and cost-effectiveness over time horizons of 6 months and 12 months. The health care system's perspective was adopted. Regression analysis and exact matching were used to control for the differences between the PTEC-HT group and the usual care group. For the 6-month analysis, 427 patients were included in the PTEC-HT group, and 64,679 patients were included in the usual care group. For the 12-month analysis, 338 patients were included in the PTEC-HT group, and 7324 patients were included in the usual care group. Using exact matching plus regression, in the 6-month analysis, the probability of having controlled BP was 13.5% (95% CI 6.3%-20.7%) higher for the PTEC-HT group compared to the usual care group. In the 12-month analysis, the probability of having controlled BP was 16% (95% CI 10.7%-21.3%) higher for the PTEC-HT group. Without considering the cost of the BP machine and program maintenance cost, the direct medical cost was S $57.7 (95% CI 54.4-61.0; a currency exchange rate of S $1=US $0.74 was applicable;) lower per patient for the PTEC-HT group in the 6-month analysis and S $170.9 (95% CI 151.9-189.9) lower per patient for the PTEC-HT group in the 12-month analysis. With the cost of the BP machine and program maintenance considered, compared to usual care, the PTEC-HT program reached breakeven at around the sixth month and saved S $52.6 (95% CI 33.6-71.6) per patient at the 12th month. Implemented in a real-world setting in Singapore, our study showed that the PTEC-HT scaling program is more effective in controlling BP status with lower cost compared to the usual care over 12 months.

Autoimmune diseases can afflict every organ system, including blood vessels that are critically important for host survival. The most frequent autoimmune vasculitis is giant cell arteritis (GCA), which causes aggressive wall inflammation in medium and large arteries and results in vaso-occlusive wall remodeling. GCA shares with other autoimmune diseases that it occurs in genetically predisposed individuals, that females are at higher risk, and that environmental triggers are suspected to beget the loss of immunological tolerance. GCA has features that distinguish it from other autoimmune diseases and predict the need for tailored diagnostic and therapeutic approaches. At the core of GCA pathology are CD4 + T cells that gain access to the protected tissue niche of the vessel wall, differentiate into cytokine producers, attain tissue residency, and enforce macrophages differentiation into tissue-destructive effector cells. Several signaling pathways have been implicated in initiating and sustaining pathogenic CD4 + T cell function, including the NOTCH1-Jagged1 pathway, the CD28 co-stimulatory pathway, the PD-1/PD-L1 co-inhibitory pathway, and the JAK/STAT signaling pathway. Inadequacy of mechanisms that normally dampen immune responses, such as defective expression of the PD-L1 ligand and malfunction of immunosuppressive CD8 + T regulatory cells are a common theme in GCA immunopathology. Recent studies are providing a string of novel mechanisms that will permit more precise pathogenic modeling and therapeutic targeting in GCA and will fundamentally inform how abnormal immune responses in blood vessels lead to disease.

Autoimmune and autoinflammatory diseases of the medium and large arteries, including the aorta, cause life-threatening complications due to vessel wall destruction but also by wall remodeling, such as the formation of wall-penetrating microvessels and lumen-stenosing neointima. The two most frequent large vessel vasculitides, giant cell arteritis (GCA) and Takayasu arteritis (TAK), are HLA-associated diseases, strongly suggestive for a critical role of T cells and antigen recognition in disease pathogenesis. Recent studies have revealed a growing spectrum of effector functions through which T cells participate in the immunopathology of GCA and TAK; causing the disease-specific patterning of pathology and clinical outcome. Core pathogenic features of disease-relevant T cells rely on the interaction with endothelial cells, dendritic cells and macrophages and lead to vessel wall invasion, formation of tissue-damaging granulomatous infiltrates and induction of the name-giving multinucleated giant cells. Besides antigen, pathogenic T cells encounter danger signals in their immediate microenvironment that they translate into disease-relevant effector functions. Decisive signaling pathways, such as the AKT pathway, the NOTCH pathway, and the JAK/STAT pathway modify antigen-induced T cell activation and emerge as promising therapeutic targets to halt disease progression and, eventually, reset the immune system to reestablish the immune privilege of the arterial wall.

To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened. Studies including humans (real or simulated) exposed to a drug. In total, 72 original articles and 5 reviews were identified Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models. The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology. Systematic review registration number in PROSPERO: CRD42019136552.

The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the “true” exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21–4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.

Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques. Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated. Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods. Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.

Background Type 2 Diabetes Mellitus (T2DM) related healthcare expenditure is expected to rise drastically as the incidence of diabetes associated comorbidities increases. It is vital to maintain an optimum glycaemia for patients with diabetes to reduce the risk of diabetes complications. Given the strong predictive value for diabetes complications, HbA1c remains the gold standard to monitor glycaemic control in contemporary clinical practice. The Primary Technology Enhanced Care (PTEC) Home HbA1c Testing (HAT) Programme is a telehealth programme that is intended to empower low-risk patients to test their HbA1c independently at home, supported with tele-monitoring and review through teleconsultation, saving them up to three clinic visits per year. Given the programme’s reliance on active patient involvement, understanding patient experiences within the programme to identify the enablers and barriers of using various PTEC HAT components is important for guiding iterative improvement and informing scale-up strategies. Methods A qualitative study approach was used to explore the in-depth perceptions of the patients who were enrolled into the pilot PTEC HAT programme. The non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was used to guide the development of topic guide and the analysis. The emergent results were categorised into the enablers and barriers. Results Coaching by healthcare team and access to supporting materials enabled the patients to complete the programme. The proactive reminder for home HbA1c testing by the in-app chatbot, the flexibility to perform the test round the clock, instant result review and the convenience of teleconsultation following home HbA1c test saved time and reduced clinic visits. Patient characteristic which enabled successful participation included a reasonable level of digital literacy, prior experience with health monitoring, absence of needle-related distress and strong intrinsic motivation. HbA1c reagent storage, syncing results via Bluetooth device, the prolonged onboarding process and the time gap between onboarding and first home-based testing were found to be challenging. Conclusion PTEC HAT programme was seen as a good alternative to routine clinic care for T2DM. Refinement in the on-boarding process and better support between onboarding and home-based independent testing could improve patient experience and satisfaction.