ArticlePediatric research2026
Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTimely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify children requiring early hemodynamic escalation after fluid resuscitation.
methodsWe performed a retrospective multicenter study using electronic health record data from five pediatric emergency departments (March 2022-February 2025). Children aged 3 months-17 years screened for sepsis who received ≥2 fluid boluses and were vasopressor-naïve at the second bolus were included. The analytic cohort required abnormal age-adjusted vital signs before the first bolus and documented vital signs after the second bolus. Eight predictors were selected from 41 candidate variables via recursive feature elimination to develop a model predicting subsequent vasopressor administration. A super learner framework evaluated 13 algorithms.
resultsAmong 645 eligible patients, 341 met analytic criteria and 88 (25.8%) received vasopressors. Random Forest demonstrated optimal performance (AUROC 0.827 [95% CI 0.777-0.876], AUPRC 0.661). Post-second-bolus mean arterial pressure and baseline MAP severity were the strongest predictors, while blood urea nitrogen was the only retained laboratory variable. Four risk tiers showed vasopressor rates ranging from 6.6% to 63.6%.
conclusionMachine learning accurately identified children at risk for fluid-refractory septic shock and may support timely vasopressor initiation. IMPACT: A machine-learning model using eight routinely available clinical variables accurately predicts vasoactive medication need after two fluid boluses in pediatric suspected sepsis. The model stratifies patients into four calibrated risk tiers with a 9.6-fold gradient in vasoactive medication rates, identifying children at risk for fluid-refractory septic shock. This work addresses a critical clinical decision point, whether to continue fluids or escalate to vasoactive medications, where current guidelines offer limited objective guidance. The tool has potential to support earlier recognition of fluid-refractory shock and optimize resuscitation strategies in pediatric emergency care.
Identifiers
42668278What OpenQuestion holds
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.