IntroductionSystemic 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.ObjectivesTo 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 analysisWe 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 disseminationThis 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 numberThis study was registered with the German Clinical Trial Register (DRKS00029071) on 2022-05-23.Protocol version10188_BO_SK_2022_V.2.0–20220330_4_Studienprotokoll.

Background: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinicians’ ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics. Objectives: To enhance an existing, interoperable, and rule-based CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy. Methods: We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians’ diagnoses as reference. Results: We successfully enhanced an existing interoperable CDSS concept with the new task of detecting SIRS/sepsis-associated hematologic OD. We modeled openEHR templates, integrated and standardized routine data, developed a rule-based, interoperable model, and demonstrated its accuracy. The CDSS detected hematologic OD with a sensitivity of 0.821 (95% CI: 0.708-0.904) and a specificity of 0.970 (95% CI: 0.942-0.987). Conclusions: We could confirm our approach for designing an interoperable CDSS as reproducible and transferable to other critical diseases. Our findings are of direct practical relevance, as they present one of the first interoperable CDSS modules that detect pediatric SIRS/sepsis-associated hematologic OD.

In this study we evaluate how to estimate diagnostic test accuracy (DTA) correctly in the presence of longitudinal patient data (i.e., repeated test applications per patient). We used a nonparametric approach to estimate sensitivity and specificity of diagnostic tests for three use cases with different characteristics (i.e., episode length and intervals between episodes): 1) systemic inflammatory response syndrome, 2) depression, and 3) epilepsy. DTA was estimated on the levels ‘time’, ‘event’, and ‘patient-time’ for each diagnosis, representing different research questions. A comparison of DTA for these levels per and across use cases showed variations in the estimates, which resulted from the used level, the time unit (i.e., per minute/hour/day), the resulting number of observations per patient, and the diagnosis-specific characteristics. Researchers need to predefine their choices (i.e., estimation levels and time units) based on their individual research aims, including the estimand definitions, and give an appropriate rationale considering the diagnosis-specific characteristics of the target outcomes and the number of observations per patient to make sure that unbiased and clinically relevant measures are communicated. Nonetheless, researchers could report the DTA of the test using more than one estimation level and/or time unit if this still complies with the research aim.

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