Application of the Improving Pediatric Sepsis Outcomes Definition for Pediatric Sepsis to Nationally Representative Emergency Department Data

Ramgopal, Sriram; Adler, Mark; Horvat, Christopher M.

doi:10.1097/pq9.0000000000000468

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…Granular data (i.e., displaying a high level of detail in the way data is structured), ideal for ML applications, is rarely available in pediatrics. Targeting a right balance between granularity and simplicity is a key factor in optimizing AI performance and ensuring significant outcomes from complex datasets [114]. Furthermore, in children data is not homogenously distributed resulting in inequality with some data sharply prevailing on others due to significative variations in features according to the patient's age.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Preprint

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The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Granular data (i.e., displaying a high level of detail in the way data are structured), ideal for ML applications, are rarely available in pediatrics. Targeting a right balance between granularity and simplicity is a key factor in optimizing AI performance and ensuring significant outcomes from complex datasets [ 135 ]. Furthermore, children data are not homogenously distributed resulting in inequality, with some data sharply prevailing on others due to significative variations in features according to the patient’s age.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Biomedicines

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The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.

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“…This issue seems particularly likely for type C subjects, in whom diagnoses of cancer and transplant were highest, and thus the threshold to give an empiric course of antibiotics was likely lower. We chose not to require an ICD code of sepsis because of the unreliable nature of billing codes (36,37). This uncertainty is unfortunately inherent to this type of EHR-based analysis.…”

Section: Discussionmentioning

confidence: 99%

Emergence of a Technology-Dependent Phenotype of Pediatric Sepsis in a Large Children’s Hospital

Aldewereld,

Horvat,

Carcillo

et al. 2023

Shock

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Objective To investigate whether pediatric sepsis phenotypes are stable in time. Methods Retrospective cohort study examining children with suspected sepsis admitted to a PICU at a large free-standing children’s hospital during two distinct periods: 2010-2014 (Early Cohort) and 2018-2020 (Late Cohort). K-means consensus clustering was used to derive types separately in the cohorts. Variables included ensured representation of all organ systems. Results 1,091 subjects were in the Early Cohort and 737 subjects in the Late Cohort. Clustering analysis yielded 4 phenotypes in the Early Cohort and 5 in the Late Cohort. Four types were in both: Type A (34% of Early Cohort, 25% of Late Cohort), mild sepsis, with minimal organ dysfunction and low mortality; Type B (25%, 22%), primary respiratory failure; Type C (25%, 18%), liver dysfunction, coagulopathy, and higher measures of systemic inflammation; Type D (16%, 17%), severe multiorgan dysfunction, with high degrees of cardiorespiratory support, renal dysfunction, and highest mortality. Type E was only detected in the Late Cohort (19%) and was notable for respiratory failure less severe than B or D, mild hypothermia, and high proportion of diagnoses and technologic dependence associated with medical complexity. Despite low mortality, this type had the longest PICU length of stay. Conclusions This single center study identified 4 pediatric sepsis phenotypes in an earlier epoch but 5 in a later epoch, with the new type having a large proportion of characteristics associated with medical complexity, particularly technology dependence. Personalized sepsis therapies need to account for this expanding patient population.

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Application of the Improving Pediatric Sepsis Outcomes Definition for Pediatric Sepsis to Nationally Representative Emergency Department Data

Abstract: Supplemental Digital Content is available in the text.

Cited by 5 publications

References 34 publications

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Emergence of a Technology-Dependent Phenotype of Pediatric Sepsis in a Large Children’s Hospital

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