ABSTRACT Background Sepsis is a life-threatening condition triggered by infection and associated with dysregulated immune response of the patient followed often by multiorgan dysfunction or failure. In the clinical evolution of sepsis towards organ dysfunction, early initiation of a suited therapy significantly increases patient outcomes and reduces mortality rates. Since electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Methods We developed an interoperable, time-sensitive CDSS that able to detect systemic inflammation and the different classifications of sepsis (bacterial/viral, suspected/proven, on admission/PICU acquired) in pediatric patients based on the analysis of routine clinical data. This application is provided as part of this publication as an open demonstrator (web application), and the usability and accuracy of the CDSS is shown by a retrospective creation of sepsis outcome labels for a routine data set of 4,655 pediatric patients. As a reference standard, the patients were manually assessed by blinded clinical experts. Results In comparison with the reference standard, the CDSS achieved sensitivity of 96.9% (95% CI: 80.9-99.6%) and specificity of 99.1% (95% CI: 95.1-99.8%). In the context of a sepsis outcome labeling for 4,655 patients, the CDSS detected 4,342 episodes of inflammation of which 1,723 were classified as sepsis. Conclusions We demonstrated that our routine-data based CDSS is able to perform a complex sepsis detection process with high diagnostic accuracy. Such CDSS with the ability to differentiate between SIRS, sepsis on admission, suspected and proven sepsis can prospectively support clinical management, monitoring and quality management.

BACKGROUND:Acute kidney injury (AKI) is common in children with congenital heart disease following open-heart surgery with cardiopulmonary bypass (CPB). Early AKI detection in critically ill children requires clinician expertise to compile various data from different sources within a stressful and time-sensitive environment. However, as electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Therefore, we developed a time-aware, rule-based clinical decision support system (CDSS) to detect, stage, and track temporal AKI progression in children. METHODS:We integrated retrospective clinical routine data from n = 290 randomly selected cases (n = 263 patients, aged 0-17 years) who underwent cardiac surgery with CPB into a dataset. We adapted Kidney Disease: Improving Global Outcome (KDIGO) criteria, including serum creatinine, urine output, and estimated glomerular filtration rate, and translated them into computable rules for the CDSS. As a reference standard, patients were manually assessed by blinded clinical experts. RESULTS:The AKI incidence, according to the reference standard, was n = 146 cases for stage 1, n = 58 for stage 2, and n = 20 for stage 3. The CDSS achieved sensitivities of 92.2 % (95 % CI: 86.8-95.5 %) for AKI stage 1, 88.1 % (95 % CI: 77.2-94.2 %) for stage 2, and 95 % (95 % CI: 70.5-99.3 %) for stage 3. The specificities were 97.0 % (95 % CI: 94.4-98.4 %), 98.5 % (95 % CI: 96.5-99.4 %), and 99.3 % (95 % CI: 97.3-99.8 %), respectively. CONCLUSIONS:We demonstrated that a CDSS is able to perform a complex AKI detection and staging process, including 11 criteria across three stages. For accurate automated AKI detection, standardized machine-readable data of high data quality are required. CDSS with high diagnostic accuracy, like presented, can support clinical management and be used for surveillance and quality management. The prototypical use for surveillance and further studies, such as the development of prediction models, should demonstrate the system's benefits in the future.

Background: Acute kidney injury (AKI) is a frequent complication in children with congenital heart disease following open-heart surgery. In intensive care units, detecting AKI requires expertise of clinicians who have to compile data from various sources within a critical and time-sensitive setting. However, as electronic health records provide data in a machine-readable format, this process could be executed through computerized systems to support physicians. Therefore, we developed a time-aware, rule-based clinical decision support system (CDSS) to detect, stage, and track the temporal progression of AKI in children.

Sepsis is a severe and expensive medical emergency that requires prompt identification in order to improve patient mortality. The objective of our research is to develop an attention-based bidirectional LSTM-CNN (AT-BiLSTM-CNN) hybrid architecture for the early prediction of sepsis using electronic health records (EHRs) obtained from intensive care units (ICUs). We combine attention mechanism, bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) to analyse clinical time series data, aiming to enhance prediction accuracy. The effectiveness of our model is measured using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC), utilising data from the 2019 PhysioNet Challenge. Upon assessing the performance of the AT-BiLSTM-CNN model throughout prediction windows of 4, 8, and 12 h, we observed its exceptional performance in comparison with existing leading techniques. It achieved average AUROCs of 0.88, 0.85, and 0.84 for the predictions made 4, 8, and 12 h before sepsis onset, respectively. This research contributes significantly to the development of smart clinical support systems, potentially offering lifesaving interventions for septic patients at critical moments.

Despite their increased secondary value for developing applications and knowledge gain, routine, harmonized and standardized datasets are often not available in Pediatrics. We propose a data integration pipeline towards an interoperable routine dataset in pediatric intensive care medicine. Our three-level approach involves identifying relevant data from primary source systems, developing local data integration processes, and converting data into a standardized, interoperable format using openEHR. We modeled 15 openEHR templates and established 31 interoperable ETL processes, resulting in anonymized, standardized data of about 4,200 pediatric patients that were loaded into a harmonized database. Based on our pipeline and templates, we successfully integrated the first part of this data in our openEHR data repository. We seek to inspire other pediatric intensive care units to adopt similar approaches, with the aim of breaking down heterogenous data silos and promoting secondary use of routine data.

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.

Background: Systemic inflammatory response syndrome (SIRS) is a common phenomenon after pediatric cardiac surgery for congenital heart disease, affecting one-third of children and significantly prolonging PICU stay. Several factors, such as cardiopulmonary bypass (CPB) time or fresh frozen plasma (FFP) supplementation, pose a potential risk for the development of SIRS in patients after cardiac surgery.

BACKGROUND To embrace the need for freely accessible training data sets originating from the real world, in the ELISE project, we integrate source data from a pediatric intensive care unit and provide it to researchers. OBJECTIVE We present our vision, initial results and steps on a trail towards an evolutionary open pediatric intensive care data set. METHODS Our evolution plan for the data set comprises three steps. The final data set will include raw clinical data and labels on critical outcomes such as organ dysfunction and sepsis, generated automatically by computerized and well-evaluated methods. RESULTS First step resulted in an initial version data set available in a central repository. CONCLUSIONS Our approach has great potential to provide a comprehensive open intensive care data set labeled for critical pediatric outcomes and, thus, contributing to overcome the current lack of real-world pediatric intensive care data usable for training data-driven algorithms.