Optimal diagnostic strategies for pleural diseases and identifying high-risk patients

INTRODUCTION

Pleural diseases encompass a broad range of conditions with diverse and heterogenous etiologies. Diagnostics in pleural diseases thus represents a challenging field with a wide array of available testing to distinguish between the numerous causes of pleural disease. Nonetheless, deploying best practice diagnostics in this area is essential in reducing both duration o the investigation pathway and symptom burden.

AREAS COVERED

This article critically appraises the optimal diagnostic strategies and pathway in patients with pleural disease, reviewing the latest evidence and key practice points in achieving a treatable diagnosis in patients with pleural disease. We also cover future and novel directions that are likely to influence pleural diagnostics in the near future. PubMed was searched for articles related to pleural diagnostics (search terms below), with the date ranges including June 2012 to June 2022.

EXPERT OPINION

No single test will ever be sufficient to provide a diagnosis in pleural conditions. The key to reducing procedure burden and duration to diagnosis lies in personalizing the investigation pathway to patients and deploying tests with the highest diagnostic yield early (such as pleural biopsy in infection and malignancy). Novel biomarkers may also allow earlier diagnostic precision in the near future.

Achieving Molecular Profiling in Pleural Biopsies: A Multicenter, Retrospective Cohort Study

BACKGROUND

Pleural biopsy findings offer greater diagnostic sensitivity in malignant pleural effusions compared with pleural fluid. The adequacy of pleural biopsy techniques in achieving molecular marker status has not been studied, and such information (termed "actionable" histology) is critical in providing a rational, efficient, and evidence-based approach to diagnostic investigation.

RESEARCH QUESTION

What is the adequacy of various pleural biopsy techniques at providing adequate molecular diagnostic information to guide treatment in malignant pleural effusions?

STUDY DESIGN AND METHODS

This study analyzed anonymized data on 183 patients from four sites across three countries in whom pleural biopsy results had confirmed a malignant diagnosis and molecular profiling was relevant for the diagnosed cancer type. The primary outcome measure was adequacy of pleural biopsy for achieving molecular marker status. Secondary outcomes included clinical factors predictive of achieving a molecular diagnosis.

RESULTS

The median age of patients was 71 years (interquartile range, 63-78 years), with 92 of 183 (50%) male. Of the 183 procedures, 105 (57%) were local anesthetic thoracoscopies (LAT), 12 (7%) were CT scan guided, and 66 (36%) were ultrasound guided. Successful molecular marker analysis was associated with mode of biopsy, with LAT having the highst yield and ultrasound-guided biopsy the lowest (LAT vs CT scan guided vs ultrasound guided: LAT yield, 95%; CT scan guided, 86%; and ultrasound guided, 77% [P = .004]). Biopsy technique and size of biopsy sample were independently associated with successful molecular marker analysis. LAT had an adjusted OR for successful diagnosis of 30.16 (95% CI, 3.15-288.56; P = .003) and biopsy sample size an OR of 1.18 (95% CI, 1.02-1.37) per millimeter increase in tissue sample size (P < .03).

INTERPRETATION

Although previous studies have shown comparable overall diagnostic yields, in the modern era of targeted therapies, this study found that LAT offers far superior results to image-guided techniques at achieving molecular profiling and remains the optimal diagnostic tool.

Timing of Tracheostomy for Prolonged Respiratory Wean in Critically Ill Coronavirus Disease 2019 Patients: A Machine Learning Approach

OBJECTIVES

To propose the optimal timing to consider tracheostomy insertion for weaning of mechanically ventilated patients recovering from coronavirus disease 2019 pneumonia. We investigated the relationship between duration of mechanical ventilation prior to tracheostomy insertion and in-hospital mortality. In addition, we present a machine learning approach to facilitate decision-making.

DESIGN

Prospective cohort study.

SETTING

Guy's & St Thomas' Hospital, London, United Kingdom.

PATIENTS

Consecutive patients admitted with acute respiratory failure secondary to coronavirus disease 2019 requiring mechanical ventilation between March 3, 2020, and May 5, 2020.

INTERVENTIONS

Baseline characteristics and temporal trends in markers of disease severity were prospectively recorded. Tracheostomy was performed for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. Decision tree was constructed using C4.5 algorithm, and its classification performance has been evaluated by a leave-one-out cross-validation technique.

MEASUREMENTS AND MAIN RESULTS

One-hundred seventy-six patients required mechanical ventilation for acute respiratory failure, of which 87 patients (49.4%) underwent tracheostomy. We identified that optimal timing for tracheostomy insertion is between day 13 and day 17. Presence of fibrosis on CT scan (odds ratio, 13.26; 95% CI [3.61-48.91]; p ≤ 0.0001) and Pao2:Fio2 ratio (odds ratio, 0.98; 95% CI [0.95-0.99]; p = 0.008) were independently associated with tracheostomy insertion. Cox multiple regression analysis showed that chronic obstructive pulmonary disease (hazard ratio, 6.56; 95% CI [1.04-41.59]; p = 0.046), ischemic heart disease (hazard ratio, 4.62; 95% CI [1.19-17.87]; p = 0.027), positive end-expiratory pressure (hazard ratio, 1.26; 95% CI [1.02-1.57]; p = 0.034), Pao2:Fio2 ratio (hazard ratio, 0.98; 95% CI [0.97-0.99]; p = 0.003), and C-reactive protein (hazard ratio, 1.01; 95% CI [1-1.01]; p = 0.005) were independent late predictors of in-hospital mortality.

CONCLUSIONS

We propose that the optimal window for consideration of tracheostomy for ventilatory weaning is between day 13 and 17. Late predictors of mortality may serve as adverse factors when considering tracheostomy, and our decision tree provides a degree of decision support for clinicians.