RESUMO
The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence-based deployment in congenital heart disease.
RESUMO
The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape.
RESUMO
Development of antibodies (Ab) that inhibit the procoagulant function of factor VIII (fVIII) seriously complicates the treatment of hemophilia A patients. It also causes acquired hemophilia, a rare yet serious autoimmune disease. The design of effective fVIII-specific tolerizing procedures will require lucidation of the role of the different CD4(+) T cell subsets that drive inhibitor synthesis. To examine the contribution of Th1 and Th2 cells in the anti-fVIII Ab response, we measured the concentration of Th1- and Th2-driven anti-fVIII IgG subclasses in 17 patients with severe hemophilia A and 18 patients with acquired hemophilia. We found that both congenital and acquired hemophilia patients had similar and comparable proportions of Th1- and Th2-induced anti-fVIII Ab, suggesting a more important role of Th1 cells in the immune response to fVIII than previously appreciated. The distribution of anti-fVIII IgG subclasses was stable for periods of up to six months. More intense anti-fVIII Ab responses and higher inhibitor titers correlated with a predominance of Th2-driven subclasses. In contrast, Th1-driven anti-fVIII Ab were predominant in patients who had low anti-fVIII Ab concentrations, even when this was the result of successful immune tolerance or immunosuppressive therapy, which had caused drastic reduction or disappearance of inhibitors. Thus, synthesis of Th2-driven inhibitors occurs when the anti-fVIII Ab response is intense, while Th1 cells may be involved in the long-term maintenance of anti-fVIII Ab synthesis.