The Early Prediction of Sepsis in ICU

NCT ID: NCT05088850

Last Updated: 2021-10-22

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-05-30

Study Completion Date

2023-12-31

Brief Summary

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Sepsis is a vital issue in critical care medicine, and early detection and intervention are key to survival. We aimed to establish an early warning system for sepsis based on a data integration platform that can be implemented in the ICU.

Detailed Description

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Conditions

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Prediction of Sepsis in Critical Patients

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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non-sepsis

no intervention

Intervention Type OTHER

no intervention

sepsis

no intervention

Intervention Type OTHER

no intervention

Interventions

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no intervention

no intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

Patients who met all of the following criteria were included in the case group:

1. At least 14 years old.
2. Sepsis onset at least 5 hours after ICU admission.
3. Sepsis onset is the first instance since admission to the hospital.

Exclusion Criteria

Patients meeting all of the following criteria were included in the control group:

1. At least 14 years old.
2. Patients staying in ICU for at least 5 hours and have not had sepsis in this time.
3. Patients without ICD-9 codes for sepsis (785·52, 995·91, 995·92).
4. SOFA score change is not more than 1 point in an arbitrary continuous 72 hours in the ICU stay.
Minimum Eligible Age

14 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Ruijin Hospital

OTHER

Sponsor Role lead

Responsible Party

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lei li

chief physician

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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ICU, Ruijin Hospital

Shanghai, Shanghai Municipality, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Ranran Li, PhD

Role: CONTACT

18317059746

Facility Contacts

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Ranran Li, PhD

Role: primary

18317059746

References

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Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Med Inform Decis Mak. 2022 Dec 29;22(1):343. doi: 10.1186/s12911-022-02090-3.

Reference Type DERIVED
PMID: 36581881 (View on PubMed)

Other Identifiers

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20211014LILEI

Identifier Type: -

Identifier Source: org_study_id