ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 8 citations in OpenAlex.
- Morphological changes in the arterial pressure waveform following hemodynamic therapies in critical care: A clinical proof-of-concept study in older adults.Physiological reports · 2026Observational
- Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis.Journal of clinical medicine · 2026Review
- Blood pressure response index trajectories identify distinct hemodynamic phenotypes and predict mortality in septic shock: a two-database retrospective cohort study.Critical care (London, England) · 2026Article
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.Critical care (London, England) · 2025Article
- Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.Critical care (London, England) · 2024Article
- Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data.Scientific reports · 2024Article
- Predicting patient decompensation from continuous physiologic monitoring in the emergency department.NPJ digital medicine · 2023Article
- Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.iScience · 2022Article
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
The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjective and requires manual intervention. This study utilizes MIMIC III and associated matched waveform datasets across the entire ICU stay duration of each patient to develop prediction models for assessing fluid responsiveness in sepsis patients. We developed a pipeline to extract high frequency continuous waveform data and included waveform features in the prediction models. Comparing across five machine learning models, random forest performed the best when no waveform information is added (AUC = 0.84), with mean arterial blood pressure and age identified as key factors. After incorporation of features from physiologic waveforms, logistic regression with L1 penalty provided consistent performance and high interpretability, achieving an accuracy of 0.89 and F1 score of 0.90.
Indexed as
Identifiers
33936436PMC8075451W3159071081What 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.