Evidence map›Paper›PMID 37059905›Full record

ArticleEuropean radiology2023

Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based nomogram.

Su Young Yun, Kyu Sung Choi, Chong Hyun Suh, Soo Chin Kim, Hwon Heo, Woo Hyun Shim, Sungyang Jo, Sun Ju Chung, Jae-Sung Lim, Jae-Hong Lee and 6 more

Abstract read
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Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

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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

16 authors at 3 institutions in 1 country.

Su Young YunDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Kyu Sung ChoiDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Chong Hyun SuhDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. chonghyunsuh@amc.seoul.kr.ORCID http://orcid.org/0000-0002-4737-0530
Soo Chin KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Hwon HeoDepartment of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Woo Hyun ShimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sungyang JoDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sun Ju ChungDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Jae-Sung LimDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Jae-Hong LeeDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Donghyun KimDepartment of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Seon-Ok KimDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Wooseok JungVUNO Inc., Seoul, Republic of Korea.
Ho Sung KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sang Joon KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Ji-Hoon KimDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Ulsan College · KRSeoul National University Hospital · KRInje University Busan Paik Hospital · KR

Funding

National Research Foundation of Korea NRF-2021R1C1C1014413
6 · The paper itself

Abstract

objectivesTo develop and validate a nomogram based on MRI features for predicting iNPH.

methodsPatients aged  ≥ 60 years (clinically diagnosed with iNPH, Parkinson's disease, or Alzheimer's disease or healthy controls) who underwent MRI including three-dimensional T1-weighted volumetric MRI were retrospectively identified from two tertiary referral hospitals (one hospital for derivation set and the other for validation set). Clinical and imaging features for iNPH were assessed. Deep learning-based brain segmentation software was used for 3D volumetry. A prediction model was developed using logistic regression and transformed into a nomogram. The performance of the nomogram was assessed with respect to discrimination and calibration abilities. The nomogram was internally and externally validated.

resultsA total of 452 patients (mean age ± SD, 73.2 ± 6.5 years; 200 men) were evaluated as the derivation set. One hundred eleven and 341 patients were categorized into the iNPH and non-iNPH groups, respectively. In multivariable analysis, high-convexity tightness (odds ratio [OR], 35.1; 95% CI: 4.5, 275.5), callosal angle  < 90° (OR, 12.5; 95% CI: 3.1, 50.0), and normalized lateral ventricle volume (OR, 4.2; 95% CI: 2.7, 6.7) were associated with iNPH. The nomogram combining these three variables showed an area under the curve of 0.995 (95% CI: 0.991, 0.999) in the study sample, 0.994 (95% CI: 0.990, 0.998) in the internal validation sample, and 0.969 (95% CI: 0.940, 0.997) in the external validation sample.

conclusionA brain morphometry-based nomogram including high-convexity tightness, callosal angle  < 90°, and normalized lateral ventricle volume can help accurately estimate the probability of iNPH. KEY POINTS: • The nomogram with MRI findings (high-convexity tightness, callosal angle, and normalized lateral ventricle volume) helped in predicting the probability of idiopathic normal-pressure hydrocephalus. • The nomogram may facilitate the prediction of idiopathic normal-pressure hydrocephalus and consequently avoid unnecessary invasive procedures such as the cerebrospinal fluid tap test, drainage test, and cerebrospinal fluid shunt surgery.

Indexed as

Alzheimer DiseaseHydrocephalus, Normal PressureAgedBrainHumansMagnetic Resonance ImagingMaleNomogramsRetrospective StudiesDeep learningHydrocephalusNomogramsNormal pressure

Identifiers

PMID37059905
OpenAlexW4365503191

What OpenQuestion holds

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Registered trials

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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.