Implementation of a Rapid Sequence Intubation Checklist Improves First-Pass Success and Reduces Peri-Intubation Hypoxia in Air Medical Transport.

OBJECTIVE: Rapid sequence intubation (RSI) is a critical skill commonly performed by air medical teams in the United States. To improve safety and reduce potential patient harm, checklists have been implemented by various institutions in intensive care units, emergency departments, and even prehospital air medical programs. However, the literature suggests that checklist use before RSI has not shown improvement in clinically important outcomes in the hospital. It is unclear if RSI checklist use by air medical crews in prehospital environments confers any clinically important benefit. METHODS: This institutional review board-approved project is a before-and-after observational study conducted within a large helicopter ambulance company. The RSI checklist was used by flight crewmembers (flight paramedic/nurse) for over 3 years. Data were evaluated for 8 quarters before and 8 quarters after checklist implementation, spanning December 2014 to March 2019. Data were collected, including the self-reported use of the checklist during intubation attempts, the reason for intubation, and correlation with difficult airway predictors (HEAVEN [Hypoxemia, Extremes of size, Anatomic disruption, Vomit, Exsanguination, Neck mobility/Neurologic injury] criteria), and compared with airway management before the implementation of the checklist. The primary outcome was improved first-pass success (FPS) when compared among those who received RSI before the checklist versus those who received RSI with the checklist. The secondary outcome was a definitive airway sans hypoxia improvement noted on the first pass among adult patients as measured before and after RSI checklist implementation. Post-RSI outcome scenarios were recorded to analyze and validate the effectiveness of the checklist. RESULTS: Ten thousand four hundred five intubations were attempted during the study. FPS was achieved in 90.9% of patients before RSI checklist implementation, and 93.3% achieved FPS postimplementation of the RSI checklist (P ≤ .001). In the preimplementation epoch, 36.2% of patients had no HEAVEN predictors versus 31.5% after RSI checklist implementation. These data showed that before RSI checklist implementation, airways were defined as less difficult than after implementation. CONCLUSION: The implementation of a standardized RSI checklist provided a better identification of deterring factors, affording efficient and accurate actions promoting FPS. Our data suggest that when a difficult airway is identified, using the RSI checklist improves FPS, thereby reducing adverse events.

Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis.

Feline infectious peritonitis (FIP) is a severe feline coronavirus-associated syndrome in cats, which is invariably fatal without anti-viral treatment. In the majority of non-effusive FIP cases encountered in practice, confirmatory diagnostic testing is not undertaken and reliance is given to the interpretation of valuable, but essentially non-specific, clinical signs and laboratory markers. We hypothesised that it may be feasible to develop a machine learning (ML) approach which may be applied to the analysis of clinical data to aid in the diagnosis of disease. A dataset encompassing 1939 suspected FIP cases was scored for clinical suspicion of FIP on the basis of history, signalment, clinical signs and laboratory results, using published guidelines, comprising 683 FIP (35.2%), and 1256 non-FIP (64.8%) cases. This dataset was used to train, validate and evaluate two diagnostic machine learning ensemble models. These models, which analysed signalment and laboratory data alone, allowed the accurate discrimination of FIP and non-FIP cases in line with expert opinion. To evaluate whether these models may have value as a diagnostic tool, they were applied to a collection of 80 cases for which the FIP status had been confirmed (FIP: n = 58 (72.5%), non-FIP: n = 22 (27.5%)). Both ensemble models detected FIP with an accuracy of 97.5%, an area under the curve (AUC) of 0.969, sensitivity of 95.45% and specificity of 98.28%. This work demonstrates that, in principle, ML can be usefully applied to the diagnosis of non-effusive FIP. Further work is required before ML may be deployed in the laboratory as a diagnostic tool, such as training models on datasets of confirmed cases and accounting for inter-laboratory variation. Nevertheless, these results illustrate the potential benefit of applying ML to standardising and accelerating the interpretation of clinical pathology data, thereby improving the diagnostic utility of existing laboratory tests.

Endometriosis first presenting in pleural fluid cytology.

Most patients with thoracic endometriosis present with catamenial pneumothorax, a rare condition in which recurrent episodes occur within 72 h before or after the start of menstruation. We report a case of thoracic endometriosis presenting with recurrent bloody pleural effusions without pneumothorax diagnosed on pleural fluid cytology. We describe the cytomorphology and immunoprofile of thoracic endometriosis and discuss the differential diagnoses, including neoplastic processes. We also highlight the importance of communication with clinicians for timeliness of diagnosis and treatment, especially when thoracic endometriosis is not suspected.