Evidence map›Paper›PMID 33001400›Full record

Trial reportJournal of clinical monitoring and computing2021

A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus.

Jia Long, Deshun Sun, Xi Zhou, Xianjian Huang, Jiani Hu, Jun Xia, Guang Yang

Open access · bronzeAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of clinical monitoring and computing, 2021. 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
0.2field-weighted citation impact, top 53% of its field
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, 4 citations in OpenAlex.

  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

7 authors at 4 institutions in 3 countries.

Jia Long *Department of Radiology, Pinghu Hospital Shenzhen University, Shenzhen, China.
Deshun Sun *Shenzhen Key Laboratory of Tissue Engineering, Shenzhen Laboratory of Digital Orthopedic Engineering, Guangdong Provincial Research Center for Artificial Intelligence and Digital Orthopedic Technology, Shenzhen Second People's Hospital, Health Science Center, The First Hospital Affiliated To Shenzhen University, Shenzhen, 518035, China.
Xi ZhouDepartment of Radiology, Shenzhen Second People's Hospital, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Xianjian HuangDepartment of Neurosurgery, Shenzhen Second People's Hospital, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, China.
Jiani HuDepartment of Radiology, School of Medicine, Wayne State University, Detroit, MI, 48201, USA.
Jun XiaDepartment of Radiology, Shenzhen Second People's Hospital, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen, China. xiajun@email.szu.edu.cn.ORCID http://orcid.org/0000-0002-5689-0343
Guang YangCardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK.
Shenzhen Second People's Hospital · CNRoyal Brompton Hospital · GBShenzhen University · CNWayne State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To develop and validate a mathematical model for predicting intracranial pressure (ICP) noninvasively using phase-contrast cine MRI (PC-MRI). We performed a retrospective analysis of PC-MRI from patients with communicating hydrocephalus (n = 138). The patients were recruited from Shenzhen Second People's Hospital between November 2017 and April 2020, and randomly allocated into training (n = 97) and independent validation (n = 41) groups. All participants underwent lumbar puncture and PC-MRI in order to evaluate ICP and cerebrospinal fluid (CSF) parameters (i.e., aqueduct diameter and flow velocity), respectively. A novel ICP-predicting model was then developed based on the nonlinear relationships between the CSF parameters, using the Levenberg-Marquardt and general global optimisation methods. There was no significant difference in baseline demographic characteristics between the training and independent validation groups. The accuracy of the model for predicting ICP was 0.899 in the training cohort (n = 97) and 0.861 in the independent validation cohort (n = 41). We obtained an ICP-predicting model that showed excellent performance in the noninvasive diagnosis of clinically significant communicating hydrocephalus.

Indexed as

HydrocephalusIntracranial PressureHumansMagnetic Resonance ImagingModels, TheoreticalRetrospective StudiesCerebrospinal fluid parametersCommunicating hydrocephalusIntracranial pressureLevenberg–marquardt optimisationPhase-contrast cine MRI

Identifiers

PMID33001400
PMCPMC7528454
OpenAlexW3089884147

What OpenQuestion holds

Textmetadata
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.