Trial reportBMC pediatrics2018
Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms.
Trial report in BMC pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00209768 (Randomised, Prospective Study of the Use of In-Line Filtration on the Reduction of Complication Rate in Critically Ill Children), which is not on this map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Randomised, Prospective Study of the Use of In-Line Filtration on the Reduction of Complication Rate in Critically Ill Children
Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.Applied clinical informatics · 2025Pooled it
- Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients.The Cochrane database of systematic reviews · 2018Pooled it
- Review
- Multi-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis.Frontiers in pharmacology · 2026Review
- Machine learning model for predicting severe infection in children with idiopathic nephrotic syndrome: multicenter retrospective study.Italian journal of pediatrics · 2025Article
- Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children.PLOS digital health · 2025Review
- Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.Scientific reports · 2025Article
- Comparison of Random Forest and Stepwise Regression for Variable Selection Using Low Prevalence Predictors: A case Study in Paediatric Sepsis.Maternal and child health journal · 2025Article
- Review
- Transforming critical care: the digital revolution's impact on intensive care units.Frontiers in digital health · 2025Review
- Construction and efficacy evaluation of a model for early diagnosis of pediatric sepsis based on LASSO-logistic regression.Frontiers in pediatrics · 2025Article
- A scoping review on pediatric sepsis prediction technologies in healthcare.NPJ digital medicine · 2024Article
- Continuous monitoring of physiological data using the patient vital status fusion score in septic critical care patients.Scientific reports · 2024Article
- Biomarkers for Serious Bacterial Infections in Febrile Children.Biomolecules · 2024Review
- Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis.Open forum infectious diseases · 2022Article
- Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors.Healthcare (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Infection Management in the ICU.Critical care (London, England) · 2022Review
- A Pilot Assessment on the Role of Procalcitonin Dynamic Monitoring in the Early Diagnosis of Infection Post Cardiac Surgery.Frontiers in cardiovascular medicine · 2022Article
- Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis.Children (Basel, Switzerland) · 2021Review
- Neonatal mortality prediction with routinely collected data: a machine learning approach.BMC pediatrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince early antimicrobial therapy is mandatory in septic patients, immediate diagnosis and distinction from non-infectious SIRS is essential but hampered by the similarity of symptoms between both entities. We aimed to develop a diagnostic model for differentiation of sepsis and non-infectious SIRS in critically ill children based on routinely available parameters (baseline characteristics, clinical/laboratory parameters, technical/medical support).
methodsThis is a secondary analysis of a randomized controlled trial conducted at a German tertiary-care pediatric intensive care unit (PICU). Two hundred thirty-eight cases of non-infectious SIRS and 58 cases of sepsis (as defined by IPSCC criteria) were included. We applied a Random Forest approach to identify the best set of predictors out of 44 variables measured at the day of onset of the disease. The developed diagnostic model was validated in a temporal split-sample approach.
resultsA model including four clinical (length of PICU stay until onset of non-infectious SIRS/sepsis, central line, core temperature, number of non-infectious SIRS/sepsis episodes prior to diagnosis) and four laboratory parameters (interleukin-6, platelet count, procalcitonin, CRP) was identified in the training dataset. Validation in the test dataset revealed an AUC of 0.78 (95% CI: 0.70-0.87). Our model was superior to previously proposed biomarkers such as CRP, interleukin-6, procalcitonin or a combination of CRP and procalcitonin (maximum AUC = 0.63; 95% CI: 0.52-0.74). When aiming at a complete identification of sepsis cases (100%; 95% CI: 87-100%), 28% (95% CI: 20-38%) of non-infectious SIRS cases were assorted correctly.
conclusionsOur approach allows early recognition of sepsis with an accuracy superior to previously described biomarkers, and could potentially reduce antibiotic use by 30% in non-infectious SIRS cases. External validation studies are necessary to confirm the generalizability of our approach across populations and treatment practices.
trial registrationClinicalTrials.gov number: NCT00209768; registration date: September 21, 2005.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.