Suveer Singh

Background: Bronchial washing can be inefficient or unsafe due to aerosolisation, poor yield and loss of sample. The setup and connectivity of sampling containers is often challenging. Furthermore, the process of bronchoscopy in the intensive care unit is inadequately reported. We tested whether a novel closed bronchoscopy sampling system is safer and more efficient than the standard sampling technique. We recorded variation in bronchoscopy practice in mechanically ventilated patients. Methods: A multicentre randomised controlled trial saw 188 bronchoscopies performed in mechanically ventilated patients requiring diagnostic sampling. The closed-system Ambu bronchosampler (ABS) was compared to standard sampling containers (SSC) with single-use bronchoscopes (Ambu A4) for primary outcomes of safety (spill, unplanned disconnection) and efficiency (ease of connectivity, setup, loss of sample and % volume retrieved), secondary efficiency outcomes and the tertiary outcomes of the bronchoscopy process. Results: ABS was safer than SSC; spills 0% versus 23.7% (0/94 vs 22/93), p<0.001; unplanned tube disconnections 5.3% vs 21.3% (5/95 vs 20/93), p<0.001; unplanned container disconnections 2.1% vs 7.5% (2/95 vs 7/93), p=0.08. Efficiency was significantly higher in ABS than SSC (acceptable/(very) easy) setup 98.9% vs 82.8% (94/95 vs 77/93), connectivity 100% vs 54.7% (88/88 vs 47/86), upright 98.9% vs 10.8% (94/95 vs 10/93) and sample loss 11.6% vs 48.4% (11/95 vs 45/93), all p0.2). Bronchoscopy for infection in 80.9%, therapeutic lavage in 51% and infrequently otherwise. Prior imaging in 93.2%, preprocedural checklists/timeouts in 83.5% and post-procedure chest X-rays in 78.2%. Mandatory ventilation was most common peribronchoscopy. Neuromuscular blockade in 73.8% and recruitment manoeuvres in 34.7%. The bronchoscope was not removed intraprocedurally in 80.2% of cases. Positive microbiology was found in 45.7% of samples. Adverse events were rare (<5%). Conclusions: Closed-system bronchoscopic sampling was safer (less spill and unplanned disconnections) and more efficient (setup, connectivity and loss prevention) than standard containers. Closed-system diagnostic sampling devices should be the standard during bronchoscopy. Trial registration number: ClinicalTrials.gov NCT05964075 .

Introduction: Flexible bronchoscopy is an essential procedural skill to master for the management of respiratory illness. Bronchoscopy simulators hasten the attainment of competencies. Whether augmented reality (AR) offers advantages over virtual reality (VR) during training is unknown. We tested this premise in novice bronchoscopists by assessing end-of-training performance after mastery learning on either simulator. Methods: This is a single-centre, single-blinded, randomised control trial. Thirty bronchoscopists were randomised to a VR (n = 15) or AR (n = 15) simulator training group. Each had up to 2 h to practice full (diagnostic completeness [DC]/18 segments), orderly (structured progress [SP]), and timely (procedure time [PT]) segmental navigation using pre-defined mastery learning protocols. Experts assessed recorded complete test bronchoscopies on 2 different endobronchial airway training models (BronchoBoy®, Koken). Cognitive load during training was compared. Results: The VR group outperformed the AR group in the first test (BronchoBoy), marginally for DC (median difference, p value) (1, p = 0.0022) but not SP (1, 0.6887), PT (46 s, 0.8119), or mean intersegmental time (MIT) (2.15 s, 0.9760). In the second test (Koken), the AR group (trained on the Koken) was faster PT (−48 s, 0.0377) but no different from VR in DC, SP, or MIT. The VR group trained for more time than AR (90+/−30 min vs. 80+/−30 min, p = 0.0468). The VR group had a significantly lower mental demand in training than AR (median difference, p value) (15 NASA-TLX points, p = 0.023) but higher physical demand score (35 NASA-TLX points, p = 0.004). Conclusion: Training by novices on VR or AR bronchoscopy simulators led to thorough, timely, and moderately ordered test segmental navigation. VR training portended high and slightly more thorough navigation, with lower mental but higher physical demand and longer time-spent-training. Further comparative studies of VR and AR bronchoscopic skills training should seek preferential and predictive factors for optimal clinical proficiency.

Background: Point of care tests (POCTs) offer accurate rapid diagnostics for infection, but not reduced antibiotic overuse in antibiotic stewardship (ABS) studies. Prescribing behaviour shaped by clinical experience may influence antibiotic decisions more than test performance. Understanding prescribing behavioural differences may inform ABS education. Objectives: To find out whether antibiotic decision making differ among medical students and intensive care clinicians when offered POCT use. Methods: An observational study depicted four simulated clinical vignettes of hospital acquired pneumonia. Clinicians and students decided to STOP or continue antibiotics, before and after a PCR-POCT result (negative for infection). Four clinico-biological (WBC/CRP) trajectories were tested: ‘clinical-biological improvement’, ‘clinical improvement/biological worsening’, ‘clinical worsening/biological improvement’ and ‘clinical-biological worsening’. STOP decisions, POCT requests and willingness to stop antibiotics were compared between groups using Chi-squared analysis, Wilcoxon-rank and logistic regression analyses. Results: Eighty-eight students and 70 clinicians participated. Pre-POCT, students stopped antibiotics less than clinicians (42% versus 53%, P = 0.007); most markedly in ‘clinical improvement/biological worsening’ (36% versus 73%, P < 0.001). Both groups requested POCT equally (65% versus 67%, P = 0.65). Negative POCT results raised student STOP rates to those of clinicians (70% versus 67%, P = 0.466); the greatest rise being in ‘clinical improvement/biological worsening’ (P = 0.006). Conclusions: Infection-detecting POCTs (negative) improved students’ antibiotic stop rates to the level of clinicians, particularly in cases of clinico-biological ambiguity. A requested and negative POCT result can reduce (over)cautious prescribing, especially with ambiguous trajectories. Such simulated clinical infection vignettes offer a learning tool to improve antimicrobial judgement, and confidence in POCT driven decision making.

Background: Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators’ skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems in bronchoscopy.Methods: A systematic review was performed using MEDLINE, Embase and Scopus databases, focusing on two terms: bronchoscopy and AI. All studies had to evaluate their AI against human ratings. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI).Results: 1196 studies were identified, with 20 passing the eligibility criteria. The studies could be divided into three categories: nine studies in airway anatomy and navigation, seven studies in computer-aided detection and classification of nodules in endobronchial ultrasound, and four studies in rapid on-site evaluation. 16 were assessment studies, with 12 showing equal performance and four showing superior performance of AI compared with human ratings. Four studies within airway anatomy implemented their AI, all favouring AI guidance to no AI guidance. The methodological quality of the studies was moderate (mean MERSQI 12.9 points, out of a maximum 18 points).Interpretation: 20 studies developed AI systems, with only four examining the implementation of their AI. The four studies were all within airway navigation and favoured AI to no AI in a simulated setting. Future implementation studies are warranted to test for the clinical effect of AI systems within bronchoscopy.

OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory competencies. A new artificial intelligence (AI) navigational system using augmented reality – the Ambu Broncho Simulator – can guide bronchoscopy training. Is training with the AI system comparable to bedside, expert tutor instruction in improving bronchoscopy performance? DESIGN: A nonblinded, parallel group randomized controlled trial was conducted. SETTING: The study was conducted in a simulated setting at an academic university hospital. SUBJECTS: Critical-care physicians were invited to take part in the study. INTERVENTIONS: Forty participants received 30 minutes of bronchoscopy training, either guided by AI only (artificial intelligence group [AIG]) or by expert tutor feedback (expert tutor group [ETG]). All participants performed a final full navigation bronchoscopy performance test and completed a cognitive load questionnaire, the NASA Task Load Index . MEASUREMENTS AND MAIN RESULTS: Mean intersegmental time (MIT = PT/DC), diagnostic completeness (DC), procedure time (PT), structured progress (SP), and number of segments revisited (SR) were measured. The primary outcome measure assessed was MIT, a measure of bronchoscopic performance efficiency. The secondary outcome measures were DC, PT, SP, and SR. Nineteen participants were randomized to the AIG and 21 participants to the ETG. MIT, PT, and SR were significantly better in the AIG compared to the ETG (median difference, p): MIT (–7.9 s, 0.027), PT (–77 s, 0.022), SR (–7 segments, 0.019); all showing moderate effect sizes (0.35, 0.36, and 0.37, respectively) as per Cohen’s classification. There was no significant difference between the groups for all other final test measures. CONCLUSIONS: Training using an AI system resulted in faster and more efficient bronchoscopy performance by critical-care physicians when compared to expert human tutor instruction. This could change the future of bronchoscopy training in critical care and warrants validation in patients through clinical studies.