Ost, David E.

Abstract INTRODUCTION Guidelines recommend non-surgical biopsy for indeterminate pulmonary lesions > 8 mm. Shape-sensing robotic-assisted bronchoscopy (ssRAB) is growing in use to biopsy small pulmonary nodules; however current literature is limited to single center studies with limited follow-up. This study, PRECIsE, assessed safety and performance of the first ssRAB iteration across multiple sites and nascent users. METHODS Prospective, multicenter, observational study evaluating ssRAB without cone-beam CT guidance in patients with nodules 10–30 mm located in or beyond the sub-segmental airways. Patients were followed for 2 years; the primary endpoint was sensitivity for malignancy with diagnostic yield, and safety as secondary endpoints. Multilevel logistic regression models were used to control for factors associated with sensitivity and diagnostic yield. RESULTS A total of 305 procedures were performed across 6 centers. Median nodule size was 17.0 mm (IQR: 14.0–23.0) with bronchus sign present in 37% of cases. Sensitivity for malignancy through 2 years was 81.3% (95% CI: 75.7, 86.1). Multi-level modelling demonstrated female sex, smaller nodule size, lower lobe location, semi-solid density, and higher body-mass-index were associated with lower sensitivity. Diagnostic yield was 74.1% (95% CI: 68.8, 78.9) according to the ATS/ACCP criteria and 77.5% [95% CI: 72.4, 81.8] according to the intermediate criteria. Multi-level modeling demonstrated a non-significant site/center level effect on ATS/ACCP diagnostic yield (P = .36). Pneumothorax requiring intervention was 1.6% (5/305); bleeding was 1.0% (3/305) with two (2) Nashville grade 2 and one (1) Nashville grade 3 events. CONCLUSIONS The first iteration of ssRAB demonstrated encouraging performance and a strong safety profile among nascent users for the biopsy of small peripheral nodules.

AbstractBackground and objectivePleuroscopy 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.MethodsA 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.ResultsOf 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).ConclusionA 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.

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.

ABSTRACTBackground and objectiveThe main purpose of treatment in patients with malignant pleural effusion (MPE) is symptom palliation. Currently, patients undergo repeat thoracenteses prior to receiving a definitive procedure as clinicians are not aware of the risk factors associated with fluid recurrence. The primary objective of this study was to identify risk factors associated with recurrent symptomatic MPE.MethodsRetrospective multicentre cohort study of patients who underwent first thoracentesis was performed. The primary outcome was time to fluid recurrence requiring intervention in patients with evidence of metastatic disease. We used a cause‐specific hazard model to identify risk factors associated with fluid recurrence. We also developed a predictive model, utilizing Fine–Gray subdistribution hazard model, and externally validated the model.ResultsA total of 988 patients with diagnosed metastatic disease were included. Cumulative incidence of recurrence was high with 30% of patients recurring by day 15. On multivariate analysis, size of the effusion on chest X‐ray (up to the top of the cardiac silhouette (hazard ratio (HR): 1.84, 95% CI: 1.21–2.80, P = 0.004) and above the cardiac silhouette (HR: 2.22, 95% CI: 1.43–3.46, P = 0.0004)), larger amount of pleural fluid drained (HR: 1.06, 95% CI: 1.04–1.07, P < 0.0001) and higher pleural fluid LDH (HR: 1.008, 95% CI: 1.004–1.011, P < 0.0001) were associated with increased hazard of recurrence. Negative cytology (HR: 0.52, 95% CI: 0.43–0.64, P < 0.0001) was associated with decreased hazard of recurrence. The model had low prediction accuracy.ConclusionPleural effusion size, amount of pleural fluid drained, LDH and pleural fluid cytology were found to be risk factors for recurrence.