Daily Ards Research Analysis

INTRODUCTION: Few artificial intelligence (AI) clinical decision support systems (CDSSs) are ever evaluated in practice. Although some signal of clinical effectiveness may be needed to justify AI deployment and testing, such data are typically unavailable in early-stage research. This conundrum is especially relevant in the intensive care unit (ICU), where conditions like sepsis and acute respiratory distress syndrome (ARDS) require high-stakes decisions. Our group developed the AI ventilator assistant (AVA), a novel AI CDSS for patients with sepsis ARDS receiving invasive mechanical ventilation. But the promising results of predictive performance estimates are not sufficient to assess AVA's clinical safety and appropriateness prior to future evaluation and deployment. Therefore, we propose a Clinician Turing Test as a novel validation approach to determine whether clinicians can distinguish AVA-generated treatment recommendations from those enacted by real human clinicians. If AVA's recommendations are consistently indistinguishable from those of real clinicians, thereby 'passing' this Turing test, this would provide a strong preclinical signal of safety and appropriateness. METHODS AND ANALYSIS: This multisite, randomised, electronic, vignette-based Phase 1b study will use a Clinician Turing Test design. We aim to recruit 350 critical care clinicians, including physicians and advanced practice providers from six US hospitals. Participants will review nine clinical vignettes of patients with sepsis and ARDS derived from the Molecular Epidemiology of Severe Sepsis in the ICU cohort and an associated profile of a suggested treatment plan. For each participant-vignette combination, the source of the treatment profile will be randomly assigned (AI-generated by AVA vs the actually enacted treatment from real human clinicians) in a 1:1 allocation. The primary endpoint is the participants' accuracy in identifying whether a treatment profile was AI-generated or human-generated, assessed using equivalence testing through a mixed-effects logistic regression model with random effects for participants and vignettes. Secondarily, a fitted binary classifier will assess discrimination ability using the C-statistic. Secondary endpoints include clinicians' perceptions of the safety and appropriateness of the treatment profiles, confidence in distinguishing AI-generated and human-generated recommendations, interest in AI CDSSs for sepsis and ventilator management and the time to complete the survey. This novel Phase 1b design provides preliminary but essential information about an AI CDSS's clinical appropriateness without the risk or cost of actual deployment, thereby informing decisions about future clinical implementation and evaluation in real clinical environments. ETHICS AND DISSEMINATION: This protocol was approved by the Institutional Review Board of the University of Pennsylvania (Protocol #858201). Results are expected in 2026 and will be submitted for publication in peer-reviewed journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: NCT07025096.

OBJECTIVE: Acute respiratory distress syndrome (ARDS) is a common complication in patients with non-pulmonary sepsis. Early identification and prediction of the occurrence of ARDS in non-pulmonary sepsis patients are of vital importance for timely intervention and improving the prognosis of these patients. MATERIALS AND METHODS: 482 patients were included in this study. The Recursive Feature Elimination (RFE) method was employed to identify the key variables related to the prognosis of sepsis. The selected variables were used to construct nine different machine learning prediction models. To evaluate the performance of the model, we employed the Receiver Operating Characteristic (ROC) Curve, calibration curve, and Decision Curve Analysis (DCA). The clinical significance of the model was further analyzed through Shapley Additive Explanations (SHAP) analysis. RESULTS: Through the RFE method, the final selected 11 variables. In the training set and test set, the AUC of the LightGBM model was 0.954 (95% CI: 0.933-0.973) and 0.923 (95% CI: 0.864-0.967) respectively. In this study, the calibration curve of the LightGBM model was close to the diagonal, indicating that its probability predictions were relatively reliable. In the DCA curves, the LightGBM model consistently maintained the highest net gain within the threshold range of 0-0.4, indicating LightGBM has greater clinical practical value. Through SHAP analysis, it was found that the SOFA score, PaO2/FiO2 ratio, lactate level, creatinine, and SAPS II score were the five most important features in the model prediction. CONCLUSION: In this study, a machine learning model based on inflammatory indicators and blood gas parameters was successfully developed and validated to predict the risk of ARDS in patients with non-pulmonary sepsis.

BACKGROUND: Leptospirosis is a widespread zoonotic, infectious disease associated with abortion, stillbirth, as well as liver and kidney failure. Leptospiral Pulmonary Haemorrhage Syndrome (LPHS) has increasingly been reported in human and canine patients infected by Leptospira and is associated with a high fatality rate. In equine medicine, pulmonary haemorrhage has mainly been described in foals with leptospiral infections, but rarely in adult horses. OBJECTIVES: To characterise the clinicopathological features of pulmonary haemorrhage as a distinct disease entity in adult horses with leptospirosis, termed Equine Leptospiral Pulmonary Haemorrhage Syndrome. STUDY DESIGN: Retrospective case series. METHODS: The clinical presentation, with blood biochemical, tracheobronchoscopic, ultrasonographic, and radiographic findings, as well as treatment and outcomes, is described in six adult horses. Leptospiral infection was confirmed by urinary PCR analysis and paired serology. RESULTS: Cases had pulmonary haemorrhage accompanied by concurrent azotaemia. Thoracic radiographs revealed a structured interstitial pattern, with marked accentuation in the caudodorsal lung fields in all cases. Leptospiral infection was confirmed in 5/6 horses by urinary PCR analysis, and in all horses by early seroconversion. Four cases survived to hospital discharge. MAIN LIMITATIONS: Small case series, incomplete clinical data, limited long-term follow-up. CONCLUSIONS: The term Equine Leptospiral Pulmonary Haemorrhage Syndrome is proposed to designate equine leptospirosis characterised by acute respiratory distress caused by pulmonary haemorrhage associated with blood biochemical disturbances including hyponatraemic and hypochloraemic azotaemia and increased serum amyloid A concentrations. The exact pathophysiology of pulmonary haemorrhage in equine leptospirosis remains incompletely elucidated. Urinary PCR analysis and paired serum microagglutination assays were useful to diagnose active leptospiral infection. The diagnostic benefit of tracheobronchial secretions remains an area for further investigation considering its potential source as a biohazard.