Evidence map›Paper›PMID 33459859›Full record

ArticleEuropean radiology2021

2D shear wave elastography is better than transient elastography in predicting post-hepatectomy complication after resection.

Dong Ho Lee, Eun Sun Lee, Jae Seok Bae, Jae Young Lee, Joon Koo Han, Nam-Joon Yi, Kwang-Woong Lee, Kyung-Suk Suh, Haeryoung Kim, Kyung Bun Lee and 1 more

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Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 27% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 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

11 authors at 4 institutions in 1 country.

Dong Ho LeeDepartment of Radiology, Seoul National University Hospital, 101 Daehakro, Jongno-gu, Seoul, 03080, South Korea.
Eun Sun LeeDepartment of Radiology, Chung-Ang University Hospital, Seoul, South Korea.
Jae Seok BaeDepartment of Radiology, Seoul National University Hospital, 101 Daehakro, Jongno-gu, Seoul, 03080, South Korea.
Jae Young LeeDepartment of Radiology, Seoul National University Hospital, 101 Daehakro, Jongno-gu, Seoul, 03080, South Korea. leejy4u@gmail.com.
Joon Koo HanDepartment of Radiology, Seoul National University Hospital, 101 Daehakro, Jongno-gu, Seoul, 03080, South Korea.
Nam-Joon YiDepartment of Surgery, Seoul National University College of Medicine, Seoul, South Korea.
Kwang-Woong LeeDepartment of Surgery, Seoul National University College of Medicine, Seoul, South Korea.
Kyung-Suk SuhDepartment of Surgery, Seoul National University College of Medicine, Seoul, South Korea.
Haeryoung KimDepartment of Pathology, Seoul National University College of Medicine, Seoul, South Korea.
Kyung Bun LeeDepartment of Pathology, Seoul National University College of Medicine, Seoul, South Korea.
Byung Ihn ChoiDepartment of Radiology, Chung-Ang University Hospital, Seoul, South Korea.
Seoul National University · KRSeoul National University Hospital · KRChung-Ang University · KRChung-Ang University Hospital · KR

Funding

Canon Medical System 2017-01
6 · The paper itself

Abstract

objectivesBoth transient elastography (TE) and 2D shear wave elastography (SWE) are accurate methods to evaluate liver fibrosis. We aimed to evaluate the diagnostic performance of 2D-SWE in predicting post-hepatectomy complication and to compare it with TE.

methodsWe prospectively enrolled 125 patients with liver tumors. Liver stiffness (LS) (kilopascal [kPa]) was measured using both TE and 2D-SWE before surgery. All post-operative complication was evaluated using the comprehensive complication index (CCI), and CCI ≥ 26.2 was defined as severe complication. Logistic regression analysis was performed to identify predictive factors for severe complication. Receiver operating characteristic analysis was used to evaluate the diagnostic performance of TE/2D-SWE in detecting liver fibrosis and severe complication.

resultsSevere complication developed in 18 patients. The median LS in patients with severe complication was significantly higher for both 2D-SWE (11.4 kPa vs. 7.0 kPa, p < 0.001) and TE (8.9 kPa vs. 6.2 kPa, p = 0.009). LS obtained from 2D-SWE was a significant factor correlated with severe complication (odds ratio: 1.27 per kPa [1.10-1.46], p = 0.001). The diagnostic performance of 2D-SWE was significantly higher than that of TE in detecting both ≥F3 (p = 0.024) and F4 (p = 0.048). The area under the curve of 2D-SWE to predict severe complication was 0.854, significantly higher than 0.692 of TE (p = 0.004). The optimal cut-off LS from 2D-SWE to predict severe complication was 8.6 kPa, with sensitivity of 88.9% (16/18) and specificity of 73.8% (79/107).

conclusionLS obtained from 2D-SWE was a significant predictive factor for severe complication, and 2D-SWE showed significantly a better diagnostic performance than TE in detecting liver fibrosis and severe complication. KEY POINTS: • The diagnostic performance of 2D-SWE was significantly higher than that of TE in detecting both ≥ F3 (AUC: 0.853 vs. 0.779, p = 0.024) and F4 (AUC: 0.929 vs. 0.872, p = 0.048). • Liver stiffness value obtained from 2D-SWE was a significant factor correlated with the development of severe complication defined as CCI ≥ 26.2 after hepatic resection for liver tumors (odds ratio: 1.27 per kPa [1.10-1.46], p = 0.001). • 2D-SWE provided significantly a better diagnostic performance in predicting severe complication after hepatic resection than TE (AUC for 2D-SWE: 0.853 vs. AUC for TE: 0.692, p = 0.004).

Indexed as

Elasticity Imaging TechniquesHepatectomyHumansLiverLiver CirrhosisProspective StudiesElasticity imaging techniquesHepatectomyPost-operative complications

Identifiers

PMID33459859
OpenAlexW3122382443

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Read underepoch 390

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.