Managing malignant pleural effusion

Wahla, Ali S.; Uzbeck, Mateen; Sameed, Yaser Abu El; Zoumot, Zaid

doi:10.3949/ccjm.86a.17095

Cited by 8 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The high morbidity of MPE continues to rise and therefore causes a heavy health care burden [ 28 ]. An ability to distinguish between MPE and BPE with considerable accuracy is clinically significant to avoid the deferred diagnosis of MPE [ [29] , [30] , [31] , [32] ]. Thus, this clinical study aiming to precisely differentiate MPE from BPE was of great public health implications.…”

Section: Discussionmentioning

confidence: 99%

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Tian

Huang

et al. 2021

Translational Oncology

View full text Add to dashboard Cite

Our study aimed to explore the applicability of deep learning and machine learning techniques to distinguish MPE from BPE. We initially used a retrospective cohort with 726 PE patients to train and test the predictive performances of the driverless artificial intelligence (AI), and then stacked with a deep learning and five machine learning models, namely gradient boosting machine (GBM), extreme gradient boosting (XGBoost), extremely randomized trees (XRT), distributed random forest (DRF), and generalized linear models (GLM). Furthermore, a prospective cohort with 172 PE patients was applied to detect the external validity of the predictive models. The area under the curve (AUC) in the training, test and validation set were deep learning (0.995, 0.848, 0.917), GBM (0.981, 0.910, 0.951), XGBoost (0.933, 0.916, 0.935), XRT (0.927, 0.909, 0.963), DRF (0.906, 0.809, 0.969), and GLM (0.898, 0.866, 0.892), respectively. Although the Deep Learning model had the highest AUC in the training set (AUC = 0.995), GBM demonstrated stable and high predictive efficiency in three data sets. The final AI model by stacked ensemble yielded optimal diagnostic performance with AUC of 0.991, 0.912 and 0.953 in the training, test and validation sets, respectively. Using the driverless AI framework based on the routinely collected clinical data could significantly improve diagnostic performance in distinguishing MPE from BPE.

show abstract

Section: Discussionmentioning

confidence: 99%

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Tian

Huang

et al. 2021

Translational Oncology

View full text Add to dashboard Cite

show abstract

“…Currently, palliative care is mainly used for pleural malignancy, aiming to improve patient quality of life, relieve dyspnea and/or chest pain, and prolong patient survival as long as possible. 16 …”

Section: Discussionmentioning

confidence: 99%

A Case of Pneumonia Masking Pleural Malignancy

Xu¹,

Tian²,

Huang³

et al. 2022

OTT

View full text Add to dashboard Cite

The invasion of the pleural membrane by a malignant pleural tumor can lead to the production of malignant pleural effusion (MPE), resulting in the symptoms of dyspnea, and some patients have cough, sputum and other symptoms, which are easily confused with pneumonia. In this case, the initial diagnosis of the patient is pneumonia, and the final diagnosis is pneumonia combined with pleural malignancy. Therefore, if the patient has unexplained symptoms of bloody pleural effusion, it is necessary to examine for malignant tumors and should actively perform thoracentesis and drainage, look for malignant cells in the pleural effusion cell precipitation, evaluate the nature of pleural effusion, conduct pleural biopsy tissue examination, and determine the type and source of lung malignancy by the combined application of cell block technology and immunohistochemistry. Take the cytological examination results in pleural effusion seriously, and finally, surgical or immunotherapy can be performed.

show abstract

“…Tunneled/Indwelling Pleural Catheter Insertion of a narrow, soft silicone IPC with a oneway valve has become a first-line therapy for MPE. These catheters allow palliative management of drainage at home with 89% to 100% improvement in symptoms (Wahla et al, 2019). Indwelling pleural catheters are minimally invasive, can be inserted in outpatient settings, and reduce the need for frequent travel to healthcare settings for thoracentesis (Feller-Kopman et al, 2018).…”

Section: Malignant Pleural Effusionmentioning

confidence: 99%

Indwelling Pleural Catheters for Malignant Pleural Effusion

Vrtis

DeCesare

Day

2021

Home Healthcare Now

View full text Add to dashboard Cite

Malignant pleural effusion (MPE) resulting from metastatic spread to the pleura frequently occurs in patients with primary lung, breast, hematological, gastrointestinal, and gynecological cancers. These effusions tend to reaccumulate quickly, and the patient requires increasingly frequent thoracentesis. An indwelling pleural catheter allows for dramatic improvement in quality of life as the patient has the power to ease her/his own suffering by draining the effusion at home when shortness of breath and/or chest pain intensifies. Patients with MPE need home healthcare support to address symptom management related to complications of advanced metastatic cancer and antineoplasm treatment regimens. The financial obstacles for the home healthcare agency are explored by using agency supply costs, per visit costs, and the patient-driven groupings reimbursement mode grouper to estimate reimbursement. Care for a home healthcare patient with MPE costs Medicare approximately $64.50 per day, markedly less than costs for hospitalization and outpatient thoracentesis. Unfortunately, agencies must absorb the cost of vacuum drainage bottles. Whereas a small positive balance of $291 was estimated for the first 30-day posthospital episode, losses were estimated at $1,185 to $1,633 for subsequent 30-day episodes. Absorbing these costs has become extremely difficult as home healthcare agencies are experiencing unprecedented COVID-19 infection control and staffing-related costs.

show abstract

Managing malignant pleural effusion

Cited by 8 publications

References 26 publications

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Driverless artificial intelligence framework for the identification of malignant pleural effusion

A Case of Pneumonia Masking Pleural Malignancy

Indwelling Pleural Catheters for Malignant Pleural Effusion

Contact Info

Product

Resources

About