Consolidating malignant pleural and peritoneal services during the COVID-19 response

Pneumothorax Trends from 2010-2020 from a Large-Volume Pleural Unit

INTRODUCTION

Previous studies in 2018 and 2022 have suggested increasing inpatient burden of pneumothorax and widespread variation in management. Local trends have never been elucidated. Northumbria Healthcare NHS Foundation Trust (NHCT) has a well-established pleural service, serving just over 600,000. Thus, we set up a local retrospective study to look at trends in pneumothorax presentation, management strategies, length of stay, and recurrence.

METHODS

A coding search for 'pneumothorax' was performed for all patients attending NHCT between 2010 and 2020 was performed with local Caldicott approval. A total of 1840 notes were analysed to exclude iatrogenic, traumatic, and paediatric events. After excluding those cases, 580 remained for further analysis, consisting of 183 primary pneumothoraces (PSP) and 397 secondary pneumothoraces (SSP).

RESULTS

Median age for PSP was 26.5 years (IQR 17) with 69% male, and for SSP 68 years (IQR 11.5), 62% male; 23.5% of PSP and 8.6% of SSP were never smokers. The proportion of smokers and ex-smokers has not really changed over time: > 65% every year have been smokers or ex-smokers. Yearly pneumothorax incidence shows a downward trend for PSP but upwards for SSP. Median length of stay (LoS) for PSP was 2 days (IQR 2), and SSP 5 days (IQR 8), with a clear downward trend. From 2010 to 2015 > 50% PSP were managed with drain, but in 2019-2020 at least 50% managed conservatively, with a significant reduction in aspiration. Trends of recurrence for PSP are increasing, whereas for SSP is decreasing. Seventy-six (20 PSP, 56 SSP) went for surgery at the index time with 5.3% recurrence (20% recurrence in those without surgery).

CONCLUSIONS

This is the first known analysis of pneumothorax trends in a large trust in the northeast of England. The data in this study have certain limitations, including the lack of information on the size of pneumothorax and frailty indicators that may influence the decision for conservative management. Additionally, there is a reliance on clinical coding, which can introduce potential inaccuracies, and not all patient notes were accessible for analysis. Updated larger datasets should help elucidate trends better.

Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis

Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90±0.08 and a specificity of 0.96±0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound.

A review of the outcomes of rigid medical thoracoscopy in a large UK district general hospital

Objectives

Local anesthetic medical thoracoscopy (LAT) is a well-established diagnostic, therapeutic, and preventative intervention in undiagnosed pleural effusions with a high diagnostic sensitivity and low complication rates. There is a large variability in practice. We describe a nine-year experience in a large district general hospital in England.

Methods

Two hundred seventy-five patients had LAT between January 2010 and December 2018. Data on outcomes and complications were obtained from the patients’ notes, electronic records, laboratory, and radiographic findings.

Results

The main diagnoses were malignant pleural mesothelioma (MPM) (n=110, 40%), chronic inflammation/fibrinous pleuritis (77, 28%), lung cancer (26, 9.5%), and breast cancer (16, 6%). LAT failed to diagnose cancer in 7/275 patients (false-negative rate 2.5%, diagnostic sensitivity 97.5%). Out of the 105 patients with chronic inflammation/fibrinous pleuritis or atypical proliferative processes, 21 (20%) were subsequently diagnosed with malignancy. Talcum pleurodesis was performed in 146 patients, and was successful in 86%. Seventy eight (28%) patients had trapped lung; 27 of those had a repeat procedure. The median length of stay was 3.96 days. There was one hospital death (0.3% mortality). Complications of LAT included pleural (3, 1%) and wound infections (4, 1.4%), persistent air leaks (9, 3.2%), subcutaneous emphysema (10, 3.6%), and tumor extension to the access port (1, 0.3%).

Conclusions

In this cohort, LAT was safe, effective, and enabled high diagnostic sensitivity. Further areas of study include optimal sedation and anesthetic pathways and combining LAT with indwelling pleural catheters (IPC).