Taking ACTION: A Prognostic Tool for Pediatric Ventricular Assist Device Mortality

Boucek, K; Alzubi, Anaam; Zafar, Farhan; O’Connor, Matthew J.; Mehegan, Mary; Mokshagundam, Deepa; Davies, Ryan R.; Adachi, Iki; Lorts, Angela; Rosenthal, David N.

doi:10.1097/mat.0000000000001899

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Bioelectrical, biophysical, and biochemical signals can be employed in various contexts, including sports development, medical analysis and prevention, as well as rehabilitation [4]. Sensors play a crucial role in risk prevention, including health, road, natural, and architectural risks [5]. It is important to design, manufacture, and implement sensors in various contexts to mitigate these risks.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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Artificial intelligence in cardiac surgery: A systematic review

Sulague,

Beloy,

Medina

et al. 2024

World j. surg.

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BackgroundArtificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery.MethodsA systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized.ResultsA total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (n = 48), followed by support vector machine (n = 33), logistic regression (n = 32), and eXtreme Gradient Boosting (n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes.ConclusionAI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision‐making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.

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Mechanical Ventilation and Outcomes of Children Who Undergo Ventricular Assist Device Placement: 2014–2020 Linked Analysis From the Advanced Cardiac Therapies Improving Outcomes Network and Pediatric Cardiac Critical Care Consortium Registries

Perry,

Rosenthal,

Lorts

et al. 2024

Pediatric Critical Care Medicine

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Objectives: Placement of a ventricular assist device (VAD) improves outcomes in children with advanced heart failure, but adverse events remain important consequences. Preoperative mechanical ventilation (MV) increases mortality, but it is unknown what impact prolonged postoperative MV has. Design: Advanced Cardiac Therapies Improving Outcomes Network (ACTION) and Pediatric Cardiac Critical Care Consortium (PC4) registries were used to identify and link children with initial VAD placement admitted to the cardiac ICU (CICU) from August 2014 to July 2020. Demographics, cardiac diagnosis, preoperative and postoperative CICU courses, and outcomes were compiled. Univariable and multivariable statistics assessed association of patient factors with prolonged postoperative MV. Multivariable logistic regression sought independent associations with outcomes. Setting: Thirty-five pediatric CICUs across the United States and Canada. Patients: Children on VADs included in both registries. Interventions: None. Measurements and Main Results: Two hundred forty-eight ACTION subjects were linked to a matching patient in PC4. Median (interquartile) age 7.7 years (1.5–15.5 yr), weight 21.3 kg (9.1–58 kg), and 56% male. Primary diagnosis was congenital heart disease (CHD) in 35%. Pre-VAD explanatory variables independently associated with prolonged postoperative MV included: age (incidence rate ratio [IRR], 0.95; 95% CI, 0.93–0.96; p < 0.01); preoperative MV within 48 hours (IRR, 2.76; 95% CI, 1.59–4.79; p < 0.01), 2–7 days (IRR, 1.82; 95% CI, 1.15–2.89; p = 0.011), and greater than 7 days before VAD implant (IRR, 2.35; 95% CI, 1.62–3.4; p < 0.01); and CHD (IRR, 1.96; 95% CI, 1.48–2.59; p < 0.01). Each additional day of postoperative MV was associated with greater odds of mortality (odds ratio [OR], 1.09 per day; p < 0.01) in the full cohort. We identified an associated greater odds of mortality in the 102 patients with intracorporeal devices (OR, 1.24; 95% CI, 1.04–1.48; p = 0.014), but not paracorporeal devices (77 patients; OR, 1.04; 95% CI, 0.99–1.09; p = 0.115). Conclusions: Prolonged MV after VAD placement is associated with greater odds of mortality in intracorporeal devices, which may indicate inadequacy of cardiopulmonary support in this group. This linkage provides a platform for future analyses in this population.

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Taking ACTION: A Prognostic Tool for Pediatric Ventricular Assist Device Mortality

Cited by 3 publications

References 21 publications

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Artificial intelligence in cardiac surgery: A systematic review

Mechanical Ventilation and Outcomes of Children Who Undergo Ventricular Assist Device Placement: 2014–2020 Linked Analysis From the Advanced Cardiac Therapies Improving Outcomes Network and Pediatric Cardiac Critical Care Consortium Registries

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