Evidence map›Paper›PMID 40210973›Full record

ArticleNPJ digital medicine2025

Classification of intracranial pressure epochs using a novel machine learning framework.

Rohan Mathur, Sudha Yellapantula, Lin Cheng, Peter Dziedzic, Niteesh Potu, Eusebia Calvillo, Vishank Shah, Austen Lefebvre, Julian Bosel, Elizabeth K Zink and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Rohan Mathur *Division of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA. rmathur2@jhmi.edu.
Sudha Yellapantula *Medical Informatics Corporation, Houston, TX, USA.
Lin Cheng *Division of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Peter DziedzicDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Niteesh PotuDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Eusebia CalvilloDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Vishank ShahDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Austen LefebvreDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Julian BoselDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elizabeth K ZinkDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Susanne MuehlschlegelDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jose I SuarezDivision of Neurosciences Critical Care, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

Program in Postdoctoral Research Training in AnesthesiologyT32GM075774 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI SAPNA R KUDCHADKAR, Emmett E Whitaker · 2006 to 2026
$8.1M
NIGMS NIH HHS T32 GM075774U.S. Department of Health & Human Services | National Institutes of Health (NIH) Award T32- 423 GM075774-16
6 · The paper itself

Abstract

Patients with acute brain injuries are at risk for life threatening elevated intracranial pressure (ICP). External Ventricular Drains (EVDs) are used to measure and treat ICP, which switch between clamped and draining configurations, with accurate ICP data only available during clamped periods. While traditional guidelines focus on mean ICP values, evolving evidence indicates other waveform features may hold prognostic value. However, current machine learning models using ICP waveforms exclude EVD data due to a lack of digital labels indicating the clamped state, markedly limiting their generalizability. We introduce, detail, and validate CICL (Classification of ICP epochs using a machine Learning framework), a semi-supervised approach to classify ICP segments from EVDs as clamped, draining, or noise. This paves the way for multiple applications, including generalizable ICP crisis prediction, potentially benefiting tens of thousands of patients annually and highlights an innovate methodology to label large high frequency physiological time series datasets.

Identifiers

PMID40210973
PMCPMC11986046

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.