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Health·G Space·Evidence-backed gain·Published 2026-08-31

Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis

Abstract: Background Timely 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. Methods We 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 in…

TRV-2026-0934Peer-reviewedPermanent record — cite & verify
Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis

Vital Signs Vol. 17 No. 1, January 1991 by U.S. Naval Hospital Orlando. Public domain

The quick read

Researchers developed a machine-learning risk stratification tool using routine EHR data from five pediatric emergency departments to predict need for vasoactive medication after two-bolus fluid resuscitation in suspected sepsis. Among 341 children meeting analytic criteria, 25.8% received vasopressors, and a Random Forest model achieved AUROC 0.827 and AUPRC 0.661 with four risk tiers.

The tool addresses the fluid-refractory decision point where guidelines offer limited objective guidance, potentially supporting earlier vasopressor initiation. As of the August 2026 publication, results reflect retrospective model development and internal validation, not prospective deployment or measured impact on time-to-vasopressor or outcomes.

Main points
  • Retrospective multicenter study used EHR data from five pediatric emergency departments March 2022-February 2025.
  • Analytic cohort was 341 children aged 3 months-17 years screened for sepsis who received 2 fluid boluses and were vasopressor-naafve at second bolus, with 88 (25.8%) receiving vasopressors.
  • Eight predictors selected from 41 candidates via recursive feature elimination; super learner evaluated 13 algorithms.
  • Post-second-bolus mean arterial pressure and baseline MAP severity were strongest predictors; blood urea nitrogen was only retained laboratory variable.
Gain

A Random Forest model using eight routinely available variables predicted subsequent vasopressor need after two fluid boluses in pediatric suspected sepsis with AUROC 0.827 and stratified patients into four tiers with a 6.6% to 63.6% gradient.

The rundown

The study included children 3 months-17 years screened for sepsis who received at least two fluid boluses and were vasopressor-naafve at the second bolus, requiring abnormal age-adjusted vital signs before first bolus and documented vitals after second. From 645 eligible patients, 341 met analytic criteria.

A super learner framework evaluated 13 algorithms after recursive feature elimination selected eight predictors from 41 candidates. Random Forest was optimal, and the model produced four calibrated risk tiers to inform the decision whether to continue fluids or escalate to vasoactive medications.

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