| Autor: |
Andre Karch, Antje Wulff, Michael Marschollek, Philipp Beerbaum, Thomas Jack, Julia Böhnke, Nicole Rübsamen, Marcel Mast, Henning Rathert, Louisa Bode, Sven Schamer, Pronaya Prosun Das, Lena Wiese, Christian Groszewski-Anders, Andreas Haller, Torsten Frank |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
BMJ Paediatrics Open, Vol 6, Iss 1 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2399-9772 |
| DOI: |
10.1136/bmjpo-2022-001618 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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