Predicting malignant pleural effusion during diagnostic pleuroscopy with biopsy: A prospective multicentre study.


Journal

Respirology (Carlton, Vic.)
ISSN: 1440-1843
Titre abrégé: Respirology
Pays: Australia
ID NLM: 9616368

Informations de publication

Date de publication:
05 2022
Historique:
revised: 12 01 2022
received: 25 10 2021
accepted: 08 02 2022
pubmed: 19 2 2022
medline: 20 4 2022
entrez: 18 2 2022
Statut: ppublish

Résumé

Pleuroscopy with pleural biopsy has a high sensitivity for malignant pleural effusion (MPE). Because MPEs tend to recur, concurrent diagnosis and treatment of MPE during pleuroscopy is desired. However, proceeding directly to treatment at the time of pleuroscopy requires confidence in the on-site diagnosis. The study's primary objective was to create a predictive model to estimate the probability of MPE during pleuroscopy. A prospective observational multicentre cohort study of consecutive patients undergoing pleuroscopy was conducted. We used a logistic regression model to evaluate the probability of MPE with relation to visual assessment, rapid on-site evaluation (ROSE) of touch preparation and presence of pleural nodules/masses on computed tomography (CT). To assess the model's prediction accuracy, a bootstrapped training/testing approach was utilized to estimate the cross-validated area under the receiver operating characteristic curve. Of the 201 patients included in the study, 103 had MPE. Logistic regression showed that higher level of malignancy on visual assessment is associated with higher odds of MPE (OR = 34.68, 95% CI = 9.17-131.14, p < 0.001). The logistic regression also showed that higher level of malignancy on ROSE of touch preparation is associated with higher odds of MPE (OR = 11.63, 95% CI = 3.85-35.16, p < 0.001). Presence of pleural nodules/masses on CT is associated with higher odds of MPE (OR = 6.61, 95% CI = 1.97-22.1, p = 0.002). A multivariable logistic regression model of final pathologic status with relation to visual assessment, ROSE of touch preparation and presence of pleural nodules/masses on CT had a cross-validated AUC of 0.94 (95% CI = 0.91-0.97). A prediction model using visual assessment, ROSE of touch preparation and CT scan findings demonstrated excellent predictive accuracy for MPE. Further validation studies are needed to confirm our findings.

Sections du résumé

BACKGROUND AND OBJECTIVE
Pleuroscopy with pleural biopsy has a high sensitivity for malignant pleural effusion (MPE). Because MPEs tend to recur, concurrent diagnosis and treatment of MPE during pleuroscopy is desired. However, proceeding directly to treatment at the time of pleuroscopy requires confidence in the on-site diagnosis. The study's primary objective was to create a predictive model to estimate the probability of MPE during pleuroscopy.
METHODS
A prospective observational multicentre cohort study of consecutive patients undergoing pleuroscopy was conducted. We used a logistic regression model to evaluate the probability of MPE with relation to visual assessment, rapid on-site evaluation (ROSE) of touch preparation and presence of pleural nodules/masses on computed tomography (CT). To assess the model's prediction accuracy, a bootstrapped training/testing approach was utilized to estimate the cross-validated area under the receiver operating characteristic curve.
RESULTS
Of the 201 patients included in the study, 103 had MPE. Logistic regression showed that higher level of malignancy on visual assessment is associated with higher odds of MPE (OR = 34.68, 95% CI = 9.17-131.14, p < 0.001). The logistic regression also showed that higher level of malignancy on ROSE of touch preparation is associated with higher odds of MPE (OR = 11.63, 95% CI = 3.85-35.16, p < 0.001). Presence of pleural nodules/masses on CT is associated with higher odds of MPE (OR = 6.61, 95% CI = 1.97-22.1, p = 0.002). A multivariable logistic regression model of final pathologic status with relation to visual assessment, ROSE of touch preparation and presence of pleural nodules/masses on CT had a cross-validated AUC of 0.94 (95% CI = 0.91-0.97).
CONCLUSION
A prediction model using visual assessment, ROSE of touch preparation and CT scan findings demonstrated excellent predictive accuracy for MPE. Further validation studies are needed to confirm our findings.

Identifiants

pubmed: 35178828
doi: 10.1111/resp.14232
doi:

Types de publication

Journal Article Multicenter Study Observational Study Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

350-356

Subventions

Organisme : Cancer Center Support Grant (CCSG)
ID : P30CA016672

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2022 Asian Pacific Society of Respirology.

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Auteurs

Horiana B Grosu (HB)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Ryan Kern (R)

Pulmonary Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Fabien Maldonado (F)

Division of Allergy, Pulmonary And Critical Care Medicine, Vanderbilt University, Nashville, Tennessee, USA.

Roberto Casal (R)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Clark R Andersen (CR)

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Liang Li (L)

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Georgie Eapen (G)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

David Ost (D)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Carlos Jimenez (C)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Frangiskos Frangopoulos (F)

Pulmonary Department, Nicosia General Hospital, Nicosia, Cyprus.

Bruce Sabath (B)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Erik Vakil (E)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Audra Schwalk (A)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Mathieu Marcoux (M)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Ala Eddin Sagar (AE)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Faria Nasim (F)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Julie Lin (J)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Moiz Salahudin (M)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Hasan Muhammad Arain (HM)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Laila Noor (L)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Diana Montanez (D)

Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

John Stewart (J)

Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

John Mullon (J)

Pulmonary Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Michalis Michael (M)

Cytopathology Department, Nicosia General Hospital, Nicosia, Cyprus.

Ilias Porfyridis (I)

Pulmonary Department, Nicosia General Hospital, Nicosia, Cyprus.

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