Evaluation of CDSS in Detection of SIRS and Sepsis in Pediatric Patients

NCT ID: NCT03661450

Last Updated: 2019-08-14

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

COMPLETED

Total Enrollment

177 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-08-01

Study Completion Date

2019-03-31

Brief Summary

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This trial aims to evaluate the accuracy of a Clinical Decision-Support System to support early recognition of SIRS in paediatric intensive care patients. This assessment will be rated by the primary goals, the sensitivity and specificity of the system. Two experienced paediatric intensivists, who are blinded for the CDSS results, will analyse the electronic patient file (EPF) for SIRS criteria and thus establish our Goldstandard. All SIRS events recognized by the CDSS during the patient's stay are taken into account and will be compared with the established Goldstandard.

The secondary goal of this trial is to evaluate the CDSS-results with the assessment of SIRS by paediatric doctors during their routine work on the PICU.

Detailed Description

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Clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not wellestablished across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS. The investigators already successfully transferred their concept into a prototype and evaluated the practicability on clinical data sets and in close cooperation between the clinicians and the technical experts. To the author's knowledge, currently, there are no openEHR based CDSS approaches which have been implemented and evaluated with such complex and important clinical contexts. Hence, the first clinically evaluated CDSS based on openEHR was successfully designed. When enhancing the described approach and implementing a live system, it might support clinicians to identify the patient's course of disease at an early stage, which can lead to better outcome for the patient. Furthermore, the system can serve as a basis for integrating (cross-institutional) machine learning components that could facilitate dealing with other high-complex decision problems or revealing yet unknown disease patterns.

Conditions

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SIRS Sepsis Pediatric SIRS

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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PICU-Patients

Pediatric patients admitted after 01.08.2018

Clinical Decision-Support System

Intervention Type DIAGNOSTIC_TEST

Patient Data is evaluated by a Clinical Decision-Support System searching for age-adapted pediatric SIRS-criteria, aiming for a high sensitivity and specificity

Interventions

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Clinical Decision-Support System

Patient Data is evaluated by a Clinical Decision-Support System searching for age-adapted pediatric SIRS-criteria, aiming for a high sensitivity and specificity

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* all pediatric patients admitted to our PICU

Exclusion Criteria

* patients that are supposed to stay for less than 12 hours on our PICU
Maximum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Technische Universitaet Braunschweig

OTHER

Sponsor Role collaborator

Helmholtz Centre for Infection Research

OTHER

Sponsor Role collaborator

Hannover Medical School

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Michael Marschollek, PhD

Role: STUDY_CHAIR

Hannover Medical School

Locations

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Hannover Medical School

Hanover, Lower Saxony, Germany

Site Status

Countries

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Germany

References

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Wulff A, Montag S, Rubsamen N, Dziuba F, Marschollek M, Beerbaum P, Karch A, Jack T. Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children. BMC Med Inform Decis Mak. 2021 Feb 18;21(1):62. doi: 10.1186/s12911-021-01428-7.

Reference Type DERIVED
PMID: 33602206 (View on PubMed)

Wulff A, Montag S, Steiner B, Marschollek M, Beerbaum P, Karch A, Jack T. CADDIE2-evaluation of a clinical decision-support system for early detection of systemic inflammatory response syndrome in paediatric intensive care: study protocol for a diagnostic study. BMJ Open. 2019 Jun 19;9(6):e028953. doi: 10.1136/bmjopen-2019-028953.

Reference Type DERIVED
PMID: 31221891 (View on PubMed)

Other Identifiers

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7804_BO_S_2018

Identifier Type: -

Identifier Source: org_study_id

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