Background: Anemia remains one of the most common comorbidities in intensive care patients worldwide. The cause of anemia is often multifactorial and triggered by underlying disease, comorbidities, and iatrogenic factors, such as diagnostic phlebotomies. As anemia is associated with a worse outcome, especially in intensive care patients, unnecessary iatrogenic blood loss must be avoided. Therefore, this scoping review addresses the amount of blood loss during routine phlebotomies in adult (>17 years) intensive care patients and whether there are factors that need to be improved in terms of patient blood management (PBM). Methods: A systematic search of the Medline Database via PubMed was conducted according to PRISMA guidelines. The reported daily blood volume for diagnostics and other relevant information from eligible studies were charted. Results: A total of 2167 studies were identified in our search, of which 38 studies met the inclusion criteria (9 interventional studies and 29 observational studies). The majority of the studies were conducted in the US (37%) and Canada (13%). An increasing interest to reduce iatrogenic blood loss has been observed since 2015. Phlebotomized blood volume per patient per day was up to 377 mL. All interventional trials showed that the use of pediatric-sized blood collection tubes can significantly reduce the daily amount of blood drawn. Conclusion: Iatrogenic blood loss for diagnostic purposes contributes significantly to the development and exacerbation of hospital-acquired anemia. Therefore, a comprehensive PBM in intensive care is urgently needed to reduce avoidable blood loss, including blood-sparing techniques, regular advanced training, and small-volume blood collection tubes.
Artificial intelligence (AI) is predicted to play an increasingly important role in perioperative medicine in the very near future. However, little is known about what anesthesiologists know and think about AI in this context. This is important because the successful introduction of new technologies depends on the understanding and cooperation of end users. We sought to investigate how much anesthesiologists know about AI and what they think about the introduction of AI-based technologies into the clinical setting. In order to better understand what anesthesiologists think of AI, we recruited 21 anesthesiologists from 2 university hospitals for face-to-face structured interviews. The interview transcripts were subdivided sentence-by-sentence into discrete statements, and statements were then grouped into key themes. Subsequently, a survey of closed questions based on these themes was sent to 70 anesthesiologists from 3 university hospitals for rating. In the interviews, the base level of knowledge of AI was good at 86 of 90 statements (96%), although awareness of the potential applications of AI in anesthesia was poor at only 7 of 42 statements (17%). Regarding the implementation of AI in anesthesia, statements were split roughly evenly between pros (46 of 105, 44%) and cons (59 of 105, 56%). Interviewees considered that AI could usefully be used in diverse tasks such as risk stratification, the prediction of vital sign changes, or as a treatment guide. The validity of these themes was probed in a follow-up survey of 70 anesthesiologists with a response rate of 70%, which confirmed an overall positive view of AI in this group. Anesthesiologists hold a range of opinions, both positive and negative, regarding the application of AI in their field of work. Survey-based studies do not always uncover the full breadth of nuance of opinion amongst clinicians. Engagement with specific concerns, both technical and ethical, will prove important as this technology moves from research to the clinic.
Background Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. Objective However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. Methods We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). Results All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Conclusions Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. Trial Registration ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881
Patient monitoring is the foundation of intensive care medicine. High workload and information overload can impair situation awareness of staff, thus leading to loss of important information about patients’ conditions. To facilitate mental processing of patient monitoring data, we developed the Visual-Patient-avatar Intensive Care Unit (ICU), a virtual patient model animated from vital signs and patient installation data. It incorporates user-centred design principles to foster situation awareness. This study investigated the avatar’s effects on information transfer measured by performance, diagnostic confidence and perceived workload. This computer-based study compared Visual-Patient-avatar ICU and conventional monitor modality for the first time. We recruited 25 nurses and 25 physicians from five centres. The participants completed an equal number of scenarios in both modalities. Information transfer, as the primary outcome, was defined as correctly assessing vital signs and installations. Secondary outcomes included diagnostic confidence and perceived workload. For analysis, we used mixed models and matched odds ratios. Comparing 250 within-subject cases revealed that Visual-Patient-avatar ICU led to a higher rate of correctly assessed vital signs and installations [rate ratio (RR) 1.25; 95% CI 1.19–1.31; P < 0.001], strengthened diagnostic confidence [odds ratio (OR) 3.32; 95% CI 2.15–5.11, P < 0.001] and lowered perceived workload (coefficient − 7.62; 95% CI − 9.17 to − 6.07; P < 0.001) than conventional modality. Using Visual-Patient-avatar ICU, participants retrieved more information with higher diagnostic confidence and lower perceived workload compared to the current industry standard monitor.
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