Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness

In this prospective observational study, we investigate the role of transactive memory and speaking up in human-AI teams comprising 180 intensive care (ICU) physicians and nurses working with AI in a simulated clinical environment. Our findings indicate that interactions with AI agents differ significantly from human interactions, as accessing information from AI agents is positively linked to a team’s ability to generate novel hypotheses and demonstrate speaking-up behavior, but only in higher-performing teams. Conversely, accessing information from human team members is negatively associated with these aspects, regardless of team performance. This study is a valuable contribution to the expanding field of research on human-AI teams and team science in general, as it emphasizes the necessity of incorporating AI agents as knowledge sources in a team’s transactive memory system, as well as highlighting their role as catalysts for speaking up. Practical implications include suggestions for the design of future AI systems and human-AI team training in healthcare and beyond

Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness

In this prospective observational study, we investigate the role of transactive memory and speaking up in human-AI teams comprising 180 intensive care (ICU) physicians and nurses working with AI in a simulated clinical environment. Our findings indicate that interactions with AI agents differ significantly from human interactions, as accessing information from AI agents is positively linked to a team’s ability to generate novel hypotheses and demonstrate speaking-up behavior, but only in higher-performing teams. Conversely, accessing information from human team members is negatively associated with these aspects, regardless of team performance. This study is a valuable contribution to the expanding field of research on human-AI teams and team science in general, as it emphasizes the necessity of incorporating AI agents as knowledge sources in a team’s transactive memory system, as well as highlighting their role as catalysts for speaking up. Practical implications include suggestions for the design of future AI systems and human-AI team training in healthcare and beyond

Pressure-adjusted venting eliminates start-up delays and compensates for vertical position of syringe infusion pumps used for microinfusion

Microinfusions are commonly used for the administration of catecholamines, but start-up delays pose a problem for reliable and timely drug delivery. Recent findings show that venting of the syringe infusion pump with draining of fluid to ambient pressure before directing the flow towards the central venous catheter does not counteract start-up delays. With the aim to reduce start-up delays, this study compared fluid delivery during start-up of syringe infusion pumps without venting, with ambient pressure venting, and with central venous pressure (CVP)-adjusted venting. Start-up fluid delivery from syringe pumps using a microinfusion of 1 mL/h was assessed by means of liquid flow measurement at 10, 60, 180 and 360 s after opening the stopcock and starting the pump. Assessments were performed using no venting, ambient pressure venting or CVP-adjusted venting, with the pump placed either at zero, − 43 cm or + 43 cm level and exposed to a simulated CVP of 10 mmHg. Measured fluid delivery was closest to the calculated fluid delivery for CVP-adjusted venting (87% to 100% at the different timepoints). The largest deviations were found for ambient pressure venting (− 1151% to + 82%). At 360 s after start-up 72% to 92% of expected fluid volumes were delivered without venting, 46% to 82% with ambient pressure venting and 96% to 99% with CVP-adjusted venting. CVP-adjusted venting demonstrated consistent results across vertical pump placements (p = 0.485), whereas the other methods had significant variances (p < 0.001 for both). In conclusion, CVP-adjusted venting effectively eliminates imprecise drug delivery and start-up delays when using microinfusions

Coagulation side effects of enzymatic debridement in burned patients

Objectives Bromelain-based enzymatic debridement has emerged as a valuable option to the standard surgical intervention for debridement in burn injuries. Adverse effects on coagulation parameters after enzymatic debridement have been described. The purpose of this study was to compare the effect of enzymatic and surgical debridement on coagulation. Methods Between 03/2017 and 02/2021 patients with burn injuries with a total body surface area (TBSA) ≥ 1% were included in the study. Patients were categorized into two groups: the surgically debrided group and the enzymatically debrided group. Coagulation parameters were assessed daily for the first seven days of hospitalization. Results In total 132 patients with a mean TBSA of 17% were included in this study, of which 66 received enzymatic debridement and 66 received regular surgical-debridement. Patients receiving enzymatic debridement presented significantly higher factor-V concentration values over the first seven days after admission (p = 0.05). Conclusion Enzymatic debridement in burned patients does not appear to increase the risk of coagulation abnormalities compared with the regular surgical approach

Critically ill COVID-19 patients with neutralizing autoantibodies against type I interferons have increased risk of herpesvirus disease

Autoantibodies neutralizing the antiviral action of type I interferons (IFNs) have been associated with predisposition to severe Coronavirus Disease 2019 (COVID-19). Here, we screened for such autoantibodies in 103 critically ill COVID-19 patients in a tertiary intensive care unit (ICU) in Switzerland. Eleven patients (10.7%), but no healthy donors, had neutralizing anti-IFNα or anti-IFNα/anti-IFNω IgG in plasma/serum, but anti-IFN IgM or IgA was rare. One patient had non-neutralizing anti-IFNα IgG. Strikingly, all patients with plasma anti-IFNα IgG also had anti-IFNα IgG in tracheobronchial secretions, identifying these autoantibodies at anatomical sites relevant for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Longitudinal analyses revealed patient heterogeneity in terms of increasing, decreasing, or stable anti-IFN IgG levels throughout the length of hospitalization. Notably, presence of anti-IFN autoantibodies in this critically ill COVID-19 cohort appeared to predict herpesvirus disease (caused by herpes simplex viruses types 1 and 2 (HSV-1/-2) and/or cytomegalovirus (CMV)), which has been linked to worse clinical outcomes. Indeed, all 7 tested COVID-19 patients with anti-IFN IgG in our cohort (100%) suffered from one or more herpesviruses, and analysis revealed that these patients were more likely to experience CMV than COVID-19 patients without anti-IFN autoantibodies, even when adjusting for age, gender, and systemic steroid treatment (odds ratio (OR) 7.28, 95% confidence interval (CI) 1.14 to 46.31, p = 0.036). As the IFN system deficiency caused by neutralizing anti-IFN autoantibodies likely directly and indirectly exacerbates the likelihood of latent herpesvirus reactivations in critically ill patients, early diagnosis of anti-IFN IgG could be rapidly used to inform risk-group stratification and treatment options. Trial Registration: ClinicalTrials.gov Identifier: NCT04410263

Antimicrobial susceptibility patterns of respiratory Gram-negative bacterial isolates from COVID-19 patients in Switzerland

BACKGROUND Bacterial superinfections associated with COVID-19 are common in ventilated ICU patients and impact morbidity and lethality. However, the contribution of antimicrobial resistance to the manifestation of bacterial infections in these patients has yet to be elucidated. METHODS We collected 70 Gram-negative bacterial strains, isolated from the lower respiratory tract of ventilated COVID-19 patients in Zurich, Switzerland between March and May 2020. Species identification was performed using MALDI-TOF; antibiotic susceptibility profiles were determined by EUCAST disk diffusion and CLSI broth microdilution assays. Selected Pseudomonas aeruginosa isolates were analyzed by whole-genome sequencing. RESULTS Pseudomonas aeruginosa (46%) and Enterobacterales (36%) comprised the two largest etiologic groups. Drug resistance in P. aeruginosa isolates was high for piperacillin/tazobactam (65.6%), cefepime (56.3%), ceftazidime (46.9%) and meropenem (50.0%). Enterobacterales isolates showed slightly lower levels of resistance to piperacillin/tazobactam (32%), ceftriaxone (32%), and ceftazidime (36%). All P. aeruginosa isolates and 96% of Enterobacterales isolates were susceptible to aminoglycosides, with apramycin found to provide best-in-class coverage. Genotypic analysis of consecutive P. aeruginosa isolates in one patient revealed a frameshift mutation in the transcriptional regulator nalC that coincided with a phenotypic shift in susceptibility to β-lactams and quinolones. CONCLUSIONS Considerable levels of antimicrobial resistance may have contributed to the manifestation of bacterial superinfections in ventilated COVID-19 patients, and may in some cases mandate consecutive adaptation of antibiotic therapy. High susceptibility to amikacin and apramycin suggests that aminoglycosides may remain an effective second-line treatment of ventilator-associated bacterial pneumonia, provided efficacious drug exposure in lungs can be achieved

Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness

In this prospective observational study, we investigate the role of transactive memory and speaking up in human-AI teams comprising 180 intensive care (ICU) physicians and nurses working with AI in a simulated clinical environment. Our findings indicate that interactions with AI agents differ significantly from human interactions, as accessing information from AI agents is positively linked to a team’s ability to generate novel hypotheses and demonstrate speaking-up behavior, but only in higher-performing teams. Conversely, accessing information from human team members is negatively associated with these aspects, regardless of team performance. This study is a valuable contribution to the expanding field of research on human-AI teams and team science in general, as it emphasizes the necessity of incorporating AI agents as knowledge sources in a team’s transactive memory system, as well as highlighting their role as catalysts for speaking up. Practical implications include suggestions for the design of future AI systems and human-AI team training in healthcare and beyond.ISSN:1664-107

Key Factors in Decision Making for ECLS: A Binational Factorial Survey

Background Extracorporeal life support (ECLS) provides support to patients with cardiopulmonary failure refractory to conventional therapy. While ECLS is potentially life-saving, it is associated with severe complications; decision making to initiate ECLS must, therefore, carefully consider which patients ECLS potentially benefits despite its consequences. Objective To answer 2 questions: First, which medically relevant patient factors influence decisions to initiate ECLS? Second, what are factors relevant to decisions to withdraw a running ECLS treatment? Methods We conducted a factorial survey among 420 physicians from 111 hospitals in Switzerland and Germany. The study included 2 scenarios: 1 explored willingness to initiate ECLS, and 1 explored willingness to withdraw a running ECLS treatment. Each participant responded to 5 different vignettes for each scenario. Vignettes were analyzed using mixed-effects regression models with random intercepts. Results Factors in the vignettes such as patients’ age, treatment costs, therapeutic goal, comorbidities, and neurological outcome significantly influenced the decision to initiate ECLS. When it came to the decision to withdraw ECLS, patients’ age, days on ECLS, criteria for discontinuation, condition of the patient, comorbidities, and neurological outcome were significant factors. In both scenarios, patients’ age and neurological outcome were the most influential factors. Conclusions This study provided insights into physicians’ decision making processes about ECLS initiation and withdrawal. Patients’ age and neurological status were the strongest factors influencing decisions regarding initiation of ECLS as well as for ECLS withdrawal. The findings may contribute to a more refined understanding of complex decision making for ECLS

Incidence and Time Point of Sepsis Detection as Related to Different Sepsis Definitions in Severely Burned Patients and Their Accompanying Time Course of Pro-Inflammatory Biomarkers

BACKGROUND Diagnosis of sepsis in burn patients remains difficult for various reasons. One major problem is the definition of sepsis itself. Therefore, previous and current sepsis definitions are a matter of ongoing validation, but a well-defined consensus on which clinical and laboratory parameters to incorporate in such a definition is lacking. The aim of the present study was to compare the incidence and time-related occurrence of septic events according to different definitions as well as their accompanying time course of pro-inflammatory biomarkers. METHODS Across the first 14 days after admission, the incidence and time point of sepsis according to three different definitions (Sepsis-3, Sepsis American Burns Association [ABA] 2007, Sepsis Zurich Burn Center) were assessed on a daily basis in adult burn patients with total body surface area (TBSA) ≥15% admitted to the Zurich Burn Center between May 2015 and October 2018. In order to investigate how well daily drawn proinflammatory biomarkers (white blood cells (WBCs), C-reactive protein (CRP), procalcitonin (PCT), and novel pancreatic stone protein (PSP)) reflect the progression of sepsis depending on its type of definition, a longitudinal mixed model analysis was performed across the first 14 days for septic and non-septic patients. Additionally, the relative increase of biomarker levels 24, 48, and 72 h prior to a septic event was analyzed for each definition used. RESULTS In our cohort of 90 severely burned patients, Sepsis-3 identified 46 patients (51.1%) as septic, while ABA 2007 and the Zurich Burn Center definition counted 33 patients (36.7%) and 24 patients (26.6%), respectively. Sepsis-3 detected sepsis about 1 day earlier than Sepsis ABA 2007 (p < 0.001) and about 0.5 days earlier than Sepsis Zurich Burn Center (p = 0.04). The course of pro-inflammatory biomarkers was largely unaffected by the type of sepsis definition. Irrespective of the sepsis definition, PSP was the only marker to demonstrate a highly significant interaction between time and group (sepsis versus no sepsis) (p < 0.001) with a 3.3-5.5-fold increase within 72 h before the event of sepsis, whereas CRP, PCT, and WBC showed only mild undulations. CONCLUSIONS Despite the ongoing dilemma of how to define sepsis in burn patients, a continually calculated SOFA score as used in Sepsis-3 is advantageous to early identify a patient's detrimental progression to sepsis. Inclusion of biomarkers, such as PSP, may help support the burn specialist's diagnosis of sepsis and could improve the diagnostic performance of current and future definitions in burn patients