AI Based Multi-modal Parameter of Peripheral Blood Cells (MMPBC) Predicts Survival Risk in Critically Ill Children

AI Based Multi-modal Parameter of Peripheral Blood Cells (MMPBC) Predicts Survival Risk in Critically Ill Children: a Multicenter, Retrospective Cohort Study

This study aims to investigate whether an AI prediction model based on blood cell multi-modal data can achieve early warning of survival risk in critically ill children through a large-scale multi-center cohort of critically ill children.

Study Overview

• Status: Recruiting
• Conditions: Children

Detailed Description

According to the definition of the United Nations Children's Fund (UNICEF), children are individuals between the ages of 0 and 18. Critically ill children are those who are admitted to the PICU or NICU and suffer from severe illnesses that require special treatment. These illnesses may endanger the child's life. Studies have reported that the international PICU mortality rate in developed countries is 2% to 3%; in recent years, the in-hospital mortality rate of PICU in China is 4.7% to 6.8%. The assessment of the survival risk of critically ill children has always been a focus of attention. Traditional assessment methods include physiological indicators, scoring tools, severity of illness, and diagnosis time, which can help doctors make decisions to a certain extent, but their predictive ability is limited and difficult to comprehensively reflect the child's physiological status and disease progression.

With the development of technology and social progress, blood cell analysis is evolving towards a highly integrated platform of multiple cell analysis technologies that provide more accurate results, more comprehensive parameters, and faster detection. Cell analysis applications are increasingly focused on the identification and alarm capabilities of abnormal samples, including reticulocytes, nucleated red blood cells, and immature granulocytes. In 2009, Mindray Group, in collaboration with the National Key Laboratory of Fine Chemicals, developed a new nucleic acid-targeted fluorescent dye that meets the requirements of blood cell analysis (the patented fluorescent dye won the National Science and Technology Progress Second Prize). This breakthrough technology overcame international intellectual property barriers and developed the first high-end blood cell analyzer, the BC-6800, with functions to detect nucleated red blood cells and reticulocytes. The device has been successfully promoted to over 90% of tertiary hospitals in China. While detecting routine blood cell ratios, this blood cell analyzer actually generates a large amount of multi-modal data on cell distribution characteristics, including cell distribution width and abnormal cell ratios. However, so far, these multi-modal data have not been fully utilized in clinical practice.

Preliminary exploration of multi-modal cell data has demonstrated its enormous value in predicting, diagnosing, and prognosing infectious diseases in small populations. This study aims to retrospectively collect clinical data and blood cell multi-modal data from NICU and PICU hospitalized children in multiple centers across China, to establish a national multi-center blood cell multi-modal database with no less than 100,000 people, and to use artificial intelligence technology to achieve accurate, repeatable, and unbiased identification and classification based on differences in cell morphology and structural distribution. A high-performance prediction model will be constructed in the discovery cohort to predict the survival risk of critically ill children; the performance of the model will be validated in the validation cohort to evaluate its applicability in the Chinese population of critically ill children. This study will provide solid evidence for evidence-based medicine based on multi-center cohort studies and offer potential new inspection technologies for predicting the survival risk of critically ill children, providing auxiliary decision support for clinicians, improving the survival rate of critically ill children, and promoting the development of precision medicine.

• Study Type: Observational
• Enrollment (Estimated): 3

Contacts and Locations

• Study Contact: Name: Ruowen He; Phone Number: 13434240706; Email: 1577576652@qq.com
• Study Contact Backup: Name: Jielin Huang; Phone Number: 13686562550; Email: HJielin2022@163.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Recruiting
      • Zhujiang Hospital of Southern Medical University
      • Contact: Hongwei Zhou, Professor; Phone Number: 18688489622; Email: hzhou@smu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The included population in this study consists of children who meet the following criteria: they were admitted to NICU and PICU between January 1, 2018, and March 31, 2023, and are under the age of 18 with no gender limitation. These children underwent blood routine tests using Mindray Medical's five-category blood cell analyzer, and the instrument or computer system retained relatively complete blood cell multi-modal data. Additionally, they have detailed clinical medical records related to this study. Excluded from the study are children with underlying blood, genetic, or chromosomal diseases, those who have received blood products in the past six months, and non-critically ill children who temporarily stayed in NICU or PICU due to bed availability issues in other wards.

Description

Inclusion Criteria:

1. Children who were admitted to NICU and PICU from January 1, 2018, to March 31, 2023.
2. Age <18 years, gender not limited.
3. Blood routine tests were performed using Mindray Medical's five-category blood cell analyzer (including BC6000, BC6000PLUS, BC6800PLUS, and BC7500 series), and the instrument or computer system retained relatively complete blood cell multi-modal data.
4. Detailed clinical medical records related to this study can be obtained.
5. Patients who were repeatedly admitted to NICU or PICU and had different conditions, causes, and outcomes each time were counted as new cases.

Exclusion Criteria:

1. Children with congenital immunodeficiency.
2. Children with blood diseases, including iron-deficiency anemia, macrocytic anemia, hereditary spherocytosis, glucose-6-phosphate dehydrogenase deficiency, thalassemia, autoimmune hemolytic anemia, aplastic anemia, immune thrombocytopenia, acute lymphoblastic leukemia, acute non-lymphoblastic leukemia, multiple myeloma, allergic purpura, myelodysplastic syndrome, etc.
3. Children with genetic metabolic diseases, including galactosemia, mucopolysaccharidosis, glycogen storage disease, phenylketonuria, albinism, alkaptonuria, hypoxanthine-guanine phosphoribosyltransferase deficiency, chromhidrosis, Goucher disease, Tay-Sachs disease, etc.
4. Children with chromosomal diseases, including Down syndrome, trisomy 18, etc.
5. Children who received blood products within six months, including transfused blood components, human immunoglobulin, etc.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Group 1; No Intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
death	diagnosis time based on medical records	through study completion, an average of 1 month
multiple organ dysfunction syndrome(MODS)	diagnosis time based on medical records	through study completion, an average of 1 month
sepsis	diagnosis time based on medical records	through study completion, an average of 1 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
disseminated intravascular coagulation(DIC)	diagnosis time based on medical records	through study completion, an average of 1 month
chronic lung disease or acute respiratory distress syndrome	diagnosis time based on medical records	through study completion, an average of 1 month
shock	diagnosis time based on medical records	through study completion, an average of 1 month
Length of stay in the pediatric intensive care unit(PICU) or neonatal intensive care unit(NICU) hospitalization duration	diagnosis time based on medical records	through study completion, an average of 1 month
brain injury or neurological complications	diagnosis time based on medical records	through study completion, an average of 1 month

Collaborators and Investigators

• Sponsor: Zhujiang Hospital

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2023
• Primary Completion (Estimated): September 30, 2023
• Study Completion (Estimated): September 30, 2023

Study Registration Dates

• First Submitted: August 11, 2023
• First Submitted That Met QC Criteria: September 12, 2023
• First Posted (Actual): September 13, 2023

Study Record Updates

• Last Update Posted (Actual): September 13, 2023
• Last Update Submitted That Met QC Criteria: September 12, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: artificial Intelligence; critically ill children; survival Risk
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Critical Illness
• Other Study ID Numbers: GPCRCLM0001

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No