Using a Walking Test to Assess Firefighter Fitness.

Tactical populations face increased risk on the job, and it is known that firefighters have high levels of cardiac-related death. Aerobic fitness is a modifiable cardiac risk factor, but many fire stations lack the proper equipment to easily assess aerobic fitness levels of their firefighters. Additionally, many fire stations lack wellness programs to hold firefighters accountable for maintaining their fitness levels. Purpose: We assessed the validity of the submaximal 6-minute walk test (6MWT) as a measure of aerobic capacity compared to a maximal treadmill test and the submaximal Gerkin protocol. Methods: Twenty-four firefighters (19 male, 5 female, 34.8 ± 9.7 years; 38.1 ± 3.6 kg·m-2) completed the 6MWT, the submaximal Gerkin protocol, and a maximal treadmill test. Data were analyzed with Bland-Altman plots and correlation analysis. Results: We found equivalence between the 6MWT and directly measured VO2max and between the 6MWT and Gerkin protocol using Bland-Altman plots. In our cohort, the 6MWT underestimated VO2max (31.57 ml·kg-1·min-1) compared to directly measured VO2max (38.1 ml·kg-1·min-1) by 17% and to the Gerkin (40.48 ml·kg-1·min-1) by 22%. Conclusion: Considering its equivalence, using the 6MWT could be a more accessible way to quantify aerobic capacity in firefighters. Despite underestimation, having an easy to administer protocol may encourage more fire stations to assess pre- and post- fitness levels regularly.

Integration and evaluation of chest X-ray artificial intelligence in clinical practice.

Purpose: To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice. Approach: In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice. Results: The alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful. Conclusions: The AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.

Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.

RATIONALE AND OBJECTIVES: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool. MATERIALS AND METHODS: A retrospective data set including 2000 de-identified images from intensive care unit patients was split into 1488 for training and 512 for testing. AI was developed to automatically identify the ETT, trachea, and carina using semantically embedded neural networks that combine a declarative knowledge base with deep neural networks. To check the ETT tip placement, a "safe zone" was computed as the region inside the trachea and 3-7 cm above the carina. Two AI outputs were evaluated: (1) ETT overlay, (2) ETT misplacement alert messages. Clinically relevant performance metrics were compared against prespecified thresholds of >85% overlay accuracy and positive predictive value (PPV) > 30% and negative predictive value NPV > 95% for alerts to move into clinical validation. RESULTS: An ETT was present in 285 of 512 test cases. The AI detected 95% (271/285) of ETTs, 233 (86%) of these with accurate tip localization. The system (correctly) did not generate an ETT overlay in 221/227 CXRs where the tube was absent for an overall overlay accuracy of 89% (454/512). The alert messages indicating that either the ETT was misplaced or not detected had a PPV of 83% (265/320) and NPV of 98% (188/192). CONCLUSION: The chest X-ray AI met prespecified performance thresholds to move into clinical validation.

Effect of Extended Benzannelation Orientation on Bergman and Related Cyclizations of Isomeric Quinoxalenediynes.

A combined computational and experimental study was conducted to examine the effect of extended benzannelation orientation on C1-C5 and C1-C6 cyclization of acyclic quinoxalenediynes. Calculations (mPW1PW91/cc-pVTZ//mPW1PW91/6-31G(d,p)) on terminal and phenylethynyl-substituted 5,6-diethynylquinoxaline and 6,7-diethynylquinoxaline showed C1-C6 Bergman cyclization as the favored thermodynamic reaction pathway, with larger C1-C6 preference for the angular quinoxalenediynes due to gain of a new aromatic sextet. Kinetic studies, as a function of 1,4-cyclohexadiene concentration, revealed retro-Bergman ring opening predominates over hydrogen atom abstraction (k-1 > k2) for 6,7-diethynylquinoxaline while 5,6-diethynylquinoxaline undergoes irreversible Bergman cyclization indicative of a large retro-Bergman ring opening barrier (k2 > k-1). The effect of extended linear versus angular benzannelation on reaction pathway shows in the contrasting photocyclizations of phenylethynyl derivatives. While angular 5,6-diethynylquinoxalines gave exclusive C1-C6 photocyclization, linear 6,7-diethynylquinoxaline afforded C1-C5 fulvene products. Computed singlet-triplet gaps and biradical stabilization energies indicated weak interaction between the nitrogen lone pair and proximal radical center in angular 5,6-diethynylquinoxalines. The overall data indicates extended angular benzannelation effectively renders Bergman cyclization irreversible due to favorable aromatic stabilization energy, while extended linear benzannelation results in increased retro-Bergman ring opening, allowing C1-C5 cyclization to become a competitive reaction channel.