Clinical trial NCT03661450: Evaluation of CDSS in Detection of SIRS and Sepsis in Pediatric Patients
Official title: Evaluation of the Accuracy of a Clinical Decision-Support System (CDSS) to Support Detection of SIRS and Sepsis in Paediatric Intensive Care Patients Compared to Medical Specialists
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: SIRS
Sepsis
Pediatric SIRS
Keywords: Clinical Decision-Support System
Lead sponsor: Hannover Medical School
Sponsor class: OTHER
Collaborators: name: Technische Universitaet Braunschweig
name: Helmholtz Centre for Infection Research
First posted: 2018-09-07
Enrollment: 177
Sex: ALL
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Pediatric patients from newborn to young adults up to 18 years that are admitted to our PICU, there is a wide range from infectious causes, post-operative-care of pediatric surgery, cardiac surgery, organ-transplants etc, newborns connatal syndromes and organdysplasia.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Sensitivity and specificity of CDSS compared to Goldstandard
timeFrame: 8 months
Secondary outcomes:
measure: Sensitivity and specificity of CDSS on daily level
timeFrame: 8 months
measure: Comparison of CDSS with assessment of MDs taking care of the patients
timeFrame: 10 months
Other outcomes:
Locations:
city: Hannover
country: Germany
facility: Hannover Medical School
state: Lower Saxony
Study officials:
affiliation: Hannover Medical School
name: Michael Marschollek, PhD
role: STUDY_CHAIR
References:
citation: 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.
citation: 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.