Near infrared spectroscopy is routinely used in the noninvasive monitoring of cerebral and somatic regional oxygen saturation (rSO) in pediatric patients following surgery for congenital heart disease. We sought to evaluate the association of a bedside rSO thought algorithm with clinical outcomes in a cohort of pediatric patients following cardiac surgery. This was a single-center retrospective cohort study of patients admitted following cardiac surgery over a 42-month period. The intervention was the implementation of an rSO thought algorithm, the primary goal of which was to supply bedside providers with a thought aide to help identify, and guide response to, changes in rSO in post-operative cardiac surgical patients. Surgical cases were stratified into two 18-month periods of observation, pre- and post-intervention allowing for a 6-month washout period during implementation of the thought algorithm. Clinical outcomes were compared between pre- and post-intervention periods. There were 434 surgical cases during the period of study. We observed a 27% relative risk reduction in our standardized mortality rate (0.61 to 0.48, p = 0.01) between the pre- and post-intervention periods. We did not observe differences in other post-operative clinical outcomes such as ventilator free days or post-operative ICU length of stay. Providing frontline clinical staff with education and tools, such as a bedside rSO thought algorithm, may aide in the earlier detection of imbalance between oxygen delivery and consumption and may contribute to improved patient outcomes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745270PMC
http://dx.doi.org/10.1007/s00246-022-03071-zDOI Listing

Publication Analysis

Top Keywords

thought algorithm
20
clinical outcomes
16
pediatric patients
12
cardiac surgery
12
rso thought
12
pre- post-intervention
12
regional oxygen
8
oxygen saturation
8
patients cardiac
8
bedside rso
8

Similar Publications

Individual differences in temporal order judgment.

Sci Rep

January 2025

Department of Psychology, Bar-Ilan University, 5290002, Ramat-Gan, Israel.

Large individual differences can be observed in studies reporting spectral TOJ. In the present study, we aimed to explore these individual differences and explain them by employing Warren and Ackroff (1976) framework of direct identification of components and their order (direct ICO) and holistic pattern recognition (HPR). In Experiment 1, results from 177 participants replicated the large variance in participants' performance and suggested three response patterns, validated using the K-Means clustering algorithm.

View Article and Find Full Text PDF

Artificial Intelligence-Powered Training Database for Clinical Thinking: App Development Study.

JMIR Form Res

January 2025

Centre for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.

Background: With the development of artificial intelligence (AI), medicine has entered the era of intelligent medicine, and various aspects, such as medical education and talent cultivation, are also being redefined. The cultivation of clinical thinking abilities poses a formidable challenge even for seasoned clinical educators, as offline training modalities often fall short in bridging the divide between current practice and the desired ideal. Consequently, there arises an imperative need for the expeditious development of a web-based database, tailored to empower physicians in their quest to learn and hone their clinical reasoning skills.

View Article and Find Full Text PDF

This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. This paper presents a new solution, the Volunteer Task Allocation Engine (VTAE), to address these challenges.

View Article and Find Full Text PDF

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

Int J Mol Sci

December 2024

Laboratory Adhesion and Inflammation (LAI), Inserm UMR 1067, Cnrs Umr 7333, Aix-Marseille Université UM 61, 13009 Marseille, France.

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated.

View Article and Find Full Text PDF

Background: Road traffic injury is the leading cause of death among young people globally, with motor vehicle collisions often resulting in severe injuries and entrapment. Traditional extrication techniques focus on limiting movement to prevent spinal cord injuries, but recent findings from the EXIT project challenge this approach. This paper presents updated recommendations from the Faculty of Pre-Hospital Care (FPHC) that reflect the latest evidence on extrication practices.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!