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Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for
Julia Böhnke1, Nicole Rübsamen2, Marcel Mast3
1Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany boehnkej@uni-muenster.de.
Insights
This study evaluates data-driven prediction models for early detection of Systemic Inflammatory Response Syndrome (SIRS) and sepsis in critically ill children. These models aim to improve timely diagnosis and treatment in pediatric intensive care units (PICUs).
Area of Science:
- Pediatric Critical Care Medicine
- Clinical Decision Support Systems
- Diagnostic Accuracy Studies
Background:
- Systemic inflammatory response syndrome (SIRS), sepsis, and organ dysfunction are critical conditions in pediatric intensive care units (PICUs).
- Timely diagnosis is challenging due to time pressure, resource limitations, and complex age-dependent criteria.
- Data-driven prediction models integrated into clinical decision support systems (CDSS) offer potential for early disease recognition.
Purpose of the Study:
- To estimate the sensitivity and specificity of existing prediction models for detecting SIRS, sepsis, and organ dysfunction in critically ill children.
- To assess the models' accuracy up to 12 hours prior to a reference standard diagnosis.
Main Methods:
- A prospective, monocentric diagnostic test accuracy study was conducted at Hannover Medical School.
- Eligible patients (0-17 years) staying ≥12 hours in the PICU were assessed using predictive and knowledge-based CDSS models.
- Sensitivity and specificity were estimated using a clustered nonparametric approach, with subgroup analyses planned.
Main Results:
- The study aims to provide crucial data on the diagnostic performance of predictive models.
- Results will quantify the accuracy of these models in identifying critical conditions early.
- Subgroup analyses will explore performance variations across different age groups and sexes.
Conclusions:
- Early recognition of SIRS and sepsis in critically ill children is vital for improving outcomes.
- Predictive models integrated into CDSS show promise for enhancing diagnostic capabilities in PICUs.
- This study will provide evidence to support the clinical implementation of these decision support tools.
Introduction:
Systemic inflammatory response syndrome (SIRS), sepsis and associated organ dysfunctions are life-threating conditions occurring at paediatric intensive care units (PICUs). Early recognition and treatment within the first hours of onset are critical. However, time pressure, lack of personnel resources, and the need for complex age-dependent diagnoses impede an accurate and timely diagnosis by PICU physicians. Data-driven prediction models integrated in clinical decision support systems (CDSS) could facilitate early recognition of disease onset.
Objectives:
To estimate the sensitivity and specificity of previously developed prediction models (index tests) for the detection of SIRS, sepsis and associated organ dysfunctions in critically ill children up to 12 hours before reference standard diagnosis is possible.
Methods And Analysis:
We conduct a monocentre, prospective diagnostic test accuracy study. Clinicians in the PICU of the tertiary care centre Hannover Medical School, Germany, continuously screen and recruit patients until the adaptive sample size (originally intended sample size of 500 patients) is enrolled. Eligible are children (0-17 years, all sexes) who stay in the PICU for ≥12 hours and for whom an informed consent is given. All eligible patients are independently assessed for SIRS, sepsis and organ dysfunctions using corresponding predictive and knowledge-based CDSS models. The knowledge-based CDSS models serve as imperfect reference standards. The assessments are used to estimate the sensitivities and specificities of each predictive model using a clustered nonparametric approach (main analysis). Subgroup analyses ('age groups', 'sex' and 'age groups by sex') are predefined.
Ethics And Dissemination:
This study obtained ethics approval from the Hannover Medical School Ethics Committee (No. 10188_BO_SK_2022). Results will be disseminated as peer-reviewed publications, at scientific conferences, and to patients in an appropriate dissemination approach.
Trial Registration Number:
This study was registered with the German Clinical Trial Register (DRKS00029071) on 2022-05-23.
Protocol Version:
10188_BO_SK_2022_V.2.0-20220330_4_Studienprotokoll.
