Evidence map›Paper›PMID 40597819›Full record

ArticleBMC medical imaging2025

Development and validation of a risk nomogram predicting pneumothorax requiring chest tube placement post-percutaneous CT-guided lung biopsy.

Masha Bondarenko, Jianxiang Zhang, Ulysis Hugo Baal, Brian Lam, Gunvant Chaudhari, Yoo Jin Lee, Jamie Schroeder, Maya Vella, Brian Haas, Thienkhai Vu and 5 more

Abstract readValidation StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

15 authors.

Masha BondarenkoDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.ORCID http://orcid.org/0009-0006-3471-7095
Jianxiang ZhangUniversity of California Berkeley (UCB), Berkeley, CA, USA.
Ulysis Hugo BaalDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Brian LamDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Gunvant ChaudhariDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.ORCID http://orcid.org/0000-0003-2872-3828
Yoo Jin LeeDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Jamie SchroederDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Maya VellaDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.ORCID http://orcid.org/0000-0002-4816-5563
Brian HaasDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.ORCID http://orcid.org/0000-0001-9409-9213
Thienkhai VuDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Kimberly KallianosDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Jonathan LiuDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Shravan SridharDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Brett ElickerDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA.
Jae Ho SohnDepartment of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 185 Berry Street, Suite 350, Lobby 6, San Francisco, CA, 94107, USA. sohn87@gmail.com.ORCID http://orcid.org/0000-0002-6733-7551

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPneumothorax requiring chest tube after CT-guided transthoracic lung biopsy presents added clinical risk and costs to the healthcare system. Identifying high-risk patients can prompt alternative biopsy modes and/or better preparation for more focused post-procedural care. We aimed to develop and externally validate a risk nomogram for pneumothorax requiring chest tube placement following CT-guided lung biopsy, leveraging quantitative emphysema algorithm.

methodsThis two-center retrospective study included patients who underwent CT-guided lung biopsy from between 1994 and 2023. Data from one hospital was set aside for validation (n = 613). Emphysema severity was quantified and categorized to 3-point scale using a previously published algorithm based on 3×3×3 kernels and Hounsfield thresholding, and a risk calculator was developed using forward variable selection and logistic regression. The model was validated using bootstrapping and Harrell's C-index.

results2,512 patients (mean age, 64.47 years ± 13.38 [standard deviation]; 1250 men) were evaluated, of whom 157 (6.7%) experienced pneumothorax complications requiring chest tube placement. After forward variable selection to reduce the covariates to maximize clinical usability, the risk score was developed using age over 60 (OR 1.80 [1.15-2.93]), non-prone patient position (OR 2.48 [1.63-3.75]), and severe emphysema (OR 1.99 [1.35-2.94]). The nomogram showed a mean absolute error of 0.5% in calibration and Harrell's C-index of 0.664 in discrimination in the internal cohort.

conclusionThe developed nomogram predicts age over 60, non-prone position during biopsy, and severe emphysema to be most predictive of pneumothorax requiring chest tube placement following CT-guided lung biopsy.

Indexed as

Chest TubesImage-Guided BiopsyLungNomogramsPneumothoraxTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsImage-guided biopsyLung neoplasmsPneumothorax

Identifiers

PMID40597819
PMCPMC12211772

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