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.

The interplay between inflammation and oxidative stress is a vicious circle, potentially resulting in organ damage. Essential micronutrients such as selenium (Se) and zinc (Zn) support anti-oxidative defense systems and are commonly depleted in severe disease. This single-center retrospective study investigated micronutrient levels under Se and Zn supplementation in critically ill patients with COVID-19 induced acute respiratory distress syndrome (ARDS) and explored potential relationships with immunological and clinical parameters. According to intensive care unit (ICU) standard operating procedures, patients received 1.0 mg of intravenous Se daily on top of artificial nutrition, which contained various amounts of Se and Zn. Micronutrients, inflammatory cytokines, lymphocyte subsets and clinical data were extracted from the patient data management system on admission and after 10 to 14 days of treatment. Forty-six patients were screened for eligibility and 22 patients were included in the study. Twenty-one patients (95%) suffered from severe ARDS and 14 patients (64%) survived to ICU discharge. On admission, the majority of patients had low Se status biomarkers and Zn levels, along with elevated inflammatory parameters. Se supplementation significantly elevated Se (p = 0.027) and selenoprotein P levels (SELENOP; p = 0.016) to normal range. Accordingly, glutathione peroxidase 3 (GPx3) activity increased over time (p = 0.021). Se biomarkers, most notably SELENOP, were inversely correlated with CRP (r_s = −0.495), PCT (r_s = −0.413), IL-6 (r_s = −0.429), IL-1β (r_s = −0.440) and IL-10 (r_s = −0.461). Positive associations were found for CD8⁺ T cells (r_s = 0.636), NK cells (r_s = 0.772), total IgG (r_s = 0.493) and PaO₂/FiO₂ ratios (r_s = 0.504). In addition, survivors tended to have higher Se levels after 10 to 14 days compared to non-survivors (p = 0.075). Sufficient Se and Zn levels may potentially be of clinical significance for an adequate immune response in critically ill patients with severe COVID-19 ARDS.

Introduction: Fitness trackers can provide continuous monitoring of vital signs and thus have the potential to become a complementary, mobile and effective tool for early detection of patient deterioration and post-operative complications. Methods: To evaluate potential implementations in acute care setting, we included 36 patients after moderate to major surgery in a recent randomised pilot trial to compare the performance of vital sign monitoring by three different fitness trackers (Apple Watch 7, Garmin Fenix 6pro and Withings ScanWatch) with established standard clinical monitors in post-anaesthesia care units and monitoring wards. Results: During a cumulative period of 56 days, a total of 53,197 heart rate (HR) measurements, as well as 12,219 measurements of the peripheral blood oxygen saturation (SpO₂) and 28,954 respiratory rate (RR) measurements were collected by fitness trackers. Under real-world conditions, HR monitoring was accurate and reliable across all benchmarked devices (r = [0.95;0.98], p < 0.001; Bias = [−0.74 bpm;−0.01 bpm]; MAPE∼2%). However, the performance of SpO₂ (r = [0.21;0.68]; p < 0.001; Bias = [−0.46%;−2.29%]; root-mean-square error = [2.82%;4.1%]) monitoring was substantially inferior. RR measurements could not be obtained for two of the devices, therefore exclusively the accuracy of the Garmin tracker could be evaluated (r = 0.28, p < 0.001; Bias = −1.46/min). Moreover, the time resolution of the vital sign measurements highly depends on the tracking device, ranging from 0.7 to 117.94 data points per hour. Conclusion: According to the results of the present study, tracker devices are generally reliable and accurate for HR monitoring, whereas SpO₂ and RR measurements should be interpreted carefully, considering the clinical context of the respective patients.

Background: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion. Methods: This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019. Results: 837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets. Conclusions: Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables.

Zusammenfassung: Die präoperative Risikoevaluation spielt eine wichtige Rolle für die Sicherheit von Anästhesien. Ziel dieses Artikels ist eine Anleitung zur fokussierten Risikoevaluation und Anordnung weiterer diagnostischer und therapeutischer Maßnahmen bei erwachsenen Patienten vor elektiven, nicht herz-thoraxchirurgischen Eingriffen.