Evidence map›Paper›PMID 36934216›Full record

ArticleHernia : the journal of hernias and abdominal wall surgery2023

CT-measured hernia parameters can predict component separation: a cross-sectional study from China.

X Du, C Jin, Y Yan, P Sun, Y Shen, Z Pan, T Jiang

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In one paragraph

Article in Hernia : the journal of hernias and abdominal wall surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 8% 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, 2 syntheses or guidelines pooled it, 10 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

7 authors at 3 institutions in 1 country.

X DuDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, No.5 Jingyuan Road, Shijingshan District, Beijing, 100043, China.
C JinDepartment of Hernia and Abdominal Wall Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.5 Jingyuan Road, Shijingshan District, Beijing, 100043, China.
Y YanDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, No.5 Jingyuan Road, Shijingshan District, Beijing, 100043, China.
P SunDepartment of Radiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
Y ShenDepartment of Hernia and Abdominal Wall Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.5 Jingyuan Road, Shijingshan District, Beijing, 100043, China. shenyingmo@126.com.
Z PanDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, No.5 Jingyuan Road, Shijingshan District, Beijing, 100043, China.
T JiangDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, No.5 Jingyuan Road, Shijingshan District, Beijing, 100043, China. jiangt88166@163.com.
Beijing Shijingshan Hospital · CNCapital Medical University · CNBeijing Shijitan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCurrently, there are no reliable preoperative methods for predicting component separation (CS) during incisional hernia repair. By quantitatively measuring preoperative computed tomography (CT) imaging, we aimed to assess the value of hernia defect size, abdominal wall muscle quality, and hernia volume in predicting CS.

methodsThe data of 102 patients who underwent open Rives-Stoppa retro-muscular mesh repair for midline incisional hernia between January 2019 and March 2022 were retrospectively analyzed. The patients were divided into two groups: ''CS group'' patients who required CS to attempt fascial closure, and ''non-CS'' group patients who required only Rives-Stoppa retro-muscular release to achieve fascial closure. Hernia defect width, hernia defect angle, rectus width, abdominal wall muscle area and CT attenuation, hernia volume (HV), and abdominal cavity volume (ACV) were measured on CT images. The rectus width to defect width ratio (RDR), HV/ACV, and HV/peritoneal volume (PV; i.e., HV + ACV) were calculated. Differences between the indices of the two groups were compared. Logistic regression models were applied to analyze the relationships between the above CT parameters and CS. Receiver operator characteristic (ROC) curves were generated to evaluate the potential utility of CT parameters in predicting CS.

resultsOf the102 patients, 69 were in the non-CS group and 33 were in the CS group. Compared with the non-CS group, hernia defect width (P < 0.001), hernia defect angle (P < 0.001), and hernia volume (P < 0.001) were larger in the CS group, while RDR (P < 0.001) was smaller. The abdominal wall muscle area in the CS group was slightly greater than that in the non-CS group (P = 0.046), and there was no significant difference in the CT attenuation of the abdominal wall muscle between the two groups (P = 0.089). Multivariate logistic regression identified hernia defect width (OR 1.815, 95% CI 1.428-2.308, P < 0.001), RDR (OR 0.018, 95% CI 0.003-0.106, P < 0.001), hernia defect angle (OR 1.077, 95% CI 1.042-1.114, P < 0.001), hernia volume (OR 1.002, 95% CI 1.001-1.003, P < 0.001), and CT attenuation of abdominal wall muscle (OR 0.962, 95% CI 0.927-0.998, P = 0.037) as independent predictors of CS. Hernia defect width was the best predictor for CS, with a cut-off point of 9.2 cm and an area under the curve (AUC) of 0.890. The AUCs of RDR, hernia defect angle, hernia volume, and abdominal wall muscle CT attenuation were 0.843, 0.812, 0.747, and 0.572, respectively.

conclusionQuantitative CT measurements are of great value for preoperative prediction of CS. Hernia defect size, hernia volume, and the CT attenuation of abdominal wall muscle are all preoperative predictive indicators of CS.

Indexed as

Abdominal WallHernia, VentralIncisional HerniaCross-Sectional StudiesHerniorrhaphyHumansRetrospective StudiesSurgical MeshTomography, X-Ray ComputedAbdominal wall reconstructionComponent separationComputed tomographyIncisional herniaVentral hernia

Identifiers

PMID36934216
OpenAlexW4327761985

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.