Asia Pediatric Intensive Care Epidemiology and Outcomes Study
AI algorithms are trained on paediatric ICU admission data to predict mortality, length of ICU stay and resource utilisation, supporting risk stratification and benchmarking of critical-care performance.
- What this means:
- A curator has read the source and confirmed our reading of it — the classifications that let this record be compared with others — along with the AI product and the clinical use. That is a check of our work, not a judgement on the AI.
- What would change it:
- The pair enters the AIH factory, where clinical, technical and governance findings are worked section by section.
- Who established it:
- An agent re-read the source and confirmed OUR reading of it — the family, the clinical problem, the point of care, what the AI outputs. Those are our judgements, not the registry's, which is why they are the ones checked. It also confirmed the record resolves to its source. No person has read it. This is not an independent check.
- Submitted
- 2Curated
- 3Maturing
- 4Validated
- 5Scaled
Plain-language summary
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- Clinical problem
- Predict a future risk
- Point of care
- Monitoring
- Nature of AI output
- A risk score
- Clinical specialty
- Intensive care
- Care setting
- Intensive care unit
- Patient population
- Critically ill children admitted to paediatric intensive care units across Asian hospitals · Cohort: N=10000; all sexes.
- Intended use
- AI algorithms are trained on paediatric ICU admission data to predict mortality, length of ICU stay and resource utilisation, supporting risk stratification and benchmarking of critical-care performance.
Full study description
Critical illness is associated with high mortality and morbidity. The mortality rate in pediatric ICUs globally may vary from 2% in high resource settings to a 28% in low resource settings. This variable mortality outcome is due to several factors. There are differing patient populations (e.g. medical, surgical, oncology) who get admitted to the ICU and at different severity thresholds. Patients in the region are ethnically and genetically diverse. And lastly, ICU management itself is variable and dependent on expertise and resources. This study will adopt a multicentered prospective observational design. Data on patient demographics, clinical characteristics, ICU therapies, ICU quality indicators and outcomes will be recorded prospectively. Statistical analysis will include descriptive statistics, multivariable analysis, area under the receiver operating curve analysis and a variety of machine learning algorithms to achieve its aims. Existing PICU severity scores (including but not limited to the PIM3, PRISM3/4, PELOD2, PSS, pSOFA) will be evaluated and if necessary, new variables/scores developed which perform better in the regional setting. Though the main methodology is recruitment of all pediatric ICU admissions, a pre-determined random sampling protocol is allowed for sites with limited in resources for recruitment and data collection. This protocol may involve recruitment of all admissions for 1year (52 weeks), all admissions for 1 month per quarter over 1 year (16 weeks), all admissions for 1 week per month over 1 year (12 weeks), all admissions for 2 weeks per quarter over 1 year (8 weeks) or all admissions for 1 week for quarter over 1 year (4 weeks) - this will be declared at the start of the study by each participating site before recruitment begins. This strategy will allow us to include sites with and without sufficient resources to be represented in this study. As ethics approval will take time, each site may enter the study at differing time points and recruit for 1 year. Patients who have previously consented under the Singapore Pediatric Intensive Care Registry (SG-PedIC) under a similar single-center pilot study protocol which started from 2020 may be included. In the statistical analysis, random down sampling may be performed to avoid over-representation from high recruitment sites. A planned subgroup study will be conducted for patients with and without pediatric chronic complex conditions (PCCC). PCCC represent a significant and growing subset of patients admitted to PICU. Although PCCCs make up only 10% to 17% of pediatric hospital admissions, they account for more than 50% of PICU admissions and use more than 75% of PICU resources. These conditions often involve multi-system involvement and prolonged hospitalizations, leading to substantial healthcare resource utilization and posing considerable challenges for clinical management. Understanding the characteristics, outcomes, and risk factors associated with these patients is crucial for improving clinical care and resource allocation. In this subgroup study, we will characterise patients with PCCCs, determine outcomes and identify risk factors for poor outcomes.
- AI technique
- Classical machine learning, Statistical model
- Input data
- Structured EHR data, Vital signs
- Output type
- Risk score
- Autonomy level
- Informs a human (advisory)
- Model provenance
- Research model
- Country
- Singapore
- Deployment date
- 1 August 2024
- Sites
- 2
- Medical device
- No
- Data identifiability
- Not stated in the source
Similar deployments
AIStudy details
- Study type
- Observational
- Sample size
- 10000 participants
Contributors
- Deploying organisation
- KK Women's and Children's Hospital · Hospital / health system · Singapore
- Source record
- ClinicalTrials.gov · NCT06481644