Deep hypothermic circulatory arrest produces less systemic inflammatory response than low-flow cardiopulmonary bypass. In addition, there is an indication of less fluid accumulation postoperatively.

Introduction: Visualization of B-lines via lung ultrasound provides a non-invasive estimation of pulmonary hydration. Extravascular lung water index (EVLWI) and pulmonary vascular permeability index (PVPI) assessed by transpulmonary thermodilution (TPTD) represent the most validated parameters of lung water and alveolocapillary permeability, but measurement is invasive and expensive. This study aimed to compare the correlations of B-lines scores from extensive 28-sector and simplified 4-sector chest scan with EVLWI and PVPI derived from TPTD in the setting of intensive care unit (primary endpoint). Methods: We performed scoring of 28-sector and 4-sector B-Lines in 50 critically ill patients. TPTD was carried out with the PiCCO-2-device (Pulsion Medical Systems SE, Maquet Getinge Group). Median time exposure for ultrasound procedure was 12 minutes for 28-sector and 4 minutes for 4-sector scan. Results: Primarily, we found close correlations of 28-sector as well as 4-sector B-Lines scores with EVLWI (R2 = 0.895 vs. R2 = 0.880) and PVPI (R2 = 0.760 vs. R2 = 0.742). Both B-lines scores showed high accuracy to identify patients with specific levels of EVLWI and PVPI. The extensive 28-sector B-lines score revealed a moderate advantage compared to simplified 4-sector scan in detecting a normal EVLWI ≤ 7 (28-sector scan: sensitivity = 81.8%, specificity = 94.9%, AUC = 0.939 versus 4-sector scan: sensitivity = 81.8%, specificity = 82.1%, AUC = 0.902). Both protocols were approximately equivalent in prediction of lung edema with EVLWI ≥ 10 (28-sector scan: sensitivity = 88.9%, specificity = 95.7%, AUC = 0.977 versus 4-sector scan: sensitivity = 81.5%, specificity = 91.3%, AUC = 0.958) or severe pulmonary edema with EVLWI ≥ 15 (28-sector scan: sensitivity = 91.7%, specificity = 97.4%, AUC = 0.995 versus 4-sector scan: sensitivity = 91.7%, specificity = 92.1%, AUC = 0.978). As secondary endpoints, our evaluations resulted in significant associations of 28-sector as well as simplified 4-sector B-Lines score with parameters of respiratory function. Conclusion: Both B-line protocols provide accurate non-invasive evaluation of lung water in critically ill patients. The 28-sector scan offers a marginal advantage in prediction of pulmonary edema, but needs substantially more time than 4-sector scan.

Isavuconazole plasma concentrations were measured before and after sustained low-efficiency dialysis (SLED) treatment in 22 critically ill adult patients with probable invasive aspergillosis and underlying hematological malignancies. Isavuconazole levels were significantly lower after SLED treatment (5.73 versus 3.36 μg/ml; P < 0.001). However, even after SLED treatment, isavuconazole concentrations exceeded the in vivo MICs for several relevant Aspergillus species.

A substantial part of COVID-19-patients suffers from multi-organ failure (MOF). We report on an 80-year old patient with pulmonary, renal, circulatory, and hepatic failure. We decided against the use of extracorporeal membrane oxygenation (ECMO) due to old age and a SOFA-score of 13. However, the patient was continuously treated with the extracorporeal multi-organ- “ADVanced Organ Support” (ADVOS) device (ADVITOS GmbH, Munich, Germany). During eight 24h-treatment-sessions blood flow (100–300 mL/min), dialysate flow (160–320 mL/min) and dialysate pH (7.6–9.0) were adapted to optimize arterial PaCO2 and pH. Effective CO2 removal and correction of acidosis could be demonstrated by mean arterial- versus post-dialyzer values of pCO2 (68.7 ± 13.8 vs. 26.9 ± 11.6 mmHg; p < 0.001). The CO2-elimination rate was 48 ± 23mL/min. The initial vasopressor requirement could be reduced in parallel to pH-normalization. Interruptions of ADVOS-treatment repeatedly resulted in reversible deteriorations of paCO2 and pH. After 95 h of continuous extracorporeal decarboxylating therapy the patient had markedly improved circulatory parameters compared to baseline. In the context of secondary pulmonary infection and progressive liver failure, the patient had a sudden cardiac arrest. In accordance with the presumed patient will, we decided against mechanical resuscitation. Irrespective of the outcome we conclude that extracorporeal CO2 removal and multiorgan-support were feasible in this COVID-19-patient. Combined and less invasive approaches such as ADVOS might be considered in old-age-COVID-19 patients with MOF.

Background and Aims: Risk stratification and recommendation to surgery regarding intraductal papillary mucinous neoplasm (IPMN) is currently based on consensus guidelines. Risk stratification from presurgery histology only could potentially be decisive but suffers from the low sensitivity of fine needle aspiration. In this study, we developed and validated a deep learning-based method to distinguish between IPMN with low grade dysplasia and IPMN with high grade dysplasia/invasive carcinoma from endoscopic ultrasound (EUS) images. Patients and methods: For training our model, we acquired a total of 3355 EUS images from 43 patients who underwent pancreatectomy from March 2015 to August 2021. All patients had histologically proven IPMN. We used transfer learning to fine tune a convolutional neural network and to classify “low grade IPMN” from “high grade IPMN/invasive carcinoma”. Our testset consisted of 1823 images from 27 patients, recruiting 11 patients retrospectively, 7 patients prospectively and 9 patients externally. We compared our results with the prediction of international consensus guidelines. Results: Our approach could classify low grade from high grade/invasive carcinoma in the test set with an accuracy of 99.6% [99.5%,99.9%]. Our deep learning model achieved superior accuracy in prediction of the histologic outcome compared to any individual guidelines, which have accuracies between 51.8% [31.9%,71.3%] and 70.3% [49.8,86.2]. Conclusion: This pilot study demonstrates that deep learning in IPMN-EUS images can predict the histological outcome with high accuracy. Keywords: Endoscopic Ultrasound, EUS, IPMN, Deep learning, artificial intelligence

Transpulmonary thermodilution (TPTD)-derived global end-diastolic volume index (GEDVI) is a static marker of preload which better predicted volume responsiveness compared to filling pressures in several studies. GEDVI can be generated with at least two devices: PiCCO and EV-1000. Several studies showed that uncorrected indicator injection into a femoral central venous catheter (CVC) results in a significant overestimation of GEDVI by the PiCCO-device. Therefore, the most recent PiCCO-algorithm corrects for femoral indicator injection. However, there are no systematic data on the impact of femoral indicator injection for the EV-1000 device. Furthermore, the correction algorithm of the PiCCO is poorly validated. Therefore, we prospectively analyzed 14 datasets from 10 patients with TPTD-monitoring undergoing central venous catheter (CVC)- and arterial line exchange. PiCCO was replaced by EV-1000, femoral CVCs were replaced by jugular/subclavian CVCs and vice-versa. For PiCCO, jugular and femoral indicator injection derived GEDVI was comparable when the correct information about femoral catheter site was given (p = 0.251). By contrast, GEDVI derived from femoral indicator injection using the EV-1000 was obviously not corrected and was substantially higher than jugular GEDVI measured by the EV-1000 (846 ± 250 vs. 712 ± 227 ml/m2; p = 0.001). Furthermore, measurements of GEDVI were not comparable between PiCCO and EV-1000 even in case of jugular indicator injection (p = 0.003). This is most probably due to different indexations of the raw value GEDV. EV-1000 could not be recommended to measure GEDVI in case of a femoral CVC. Furthermore, different indexations used by EV-1000 and PiCCO should be considered even in case of a jugular CVC when comparing GEDVI derived from PiCCO and EV-1000.

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IntroductionCardiac function index (CFI) is a trans-pulmonary thermodilution (TPTD)-derived estimate of systolic function. CFI is defined as the ratio of cardiac output divided by global end-diastolic volume GEDV (CFI = CO/GEDV). Several studies demonstrated that the use of femoral venous access results in a marked overestimation of GEDV, while CFI is underestimated. One study suggested a correction formula for femoral venous access that markedly reduced the bias for GEDVI. Therefore, the last PiCCO-algorithm requires information about the CVC-position which suggests a correction of GEDV for femoral access. However, a recent study demonstrated inconsistencies of the last PiCCO algorithm using incorrected GEDV to calculate CFI despite obvious correction of GEDV. Nevertheless, this study was based on mathematical analyses of data displayed in a total of 15 patients equipped with only a femoral, but not with a jugular CVC. Therefore, this study compared CFI derived from the femoral indicator injection TPTD to data derived from jugular indicator injection in 28 patients with both a jugular and a femoral CVC.Methods28 ICU-patients with PiCCO-monitoring were included. Each dataset consisted of three triplicate TPTDs using the jugular venous gold standard access and the femoral access with and without information about the femoral indicator injection to evaluate, if correction for femoral GEDV also pertains to CFI. (CFI_jug: jugular indicator injection; CFI_fem: femoral indicator injection; CFI_fem_cor: femoral indicator injection with correct information about CVC-position; CFI_fem_uncor: femoral indicator injection with uncorrect information about CVC-position; CFI_fem_uncor_form = CFI_fem_uncor * (GEDVI_fem_uncor/GEDVI_fem_cor)).ResultsCFI_fem_uncor was significantly lower than CFI_jug (4.28±1.70 vs. 5.21±1.91 min-1; p<0.001). Similarly, CFI_fem_cor was significantly lower than CFI_jug (4.24±1.62 vs. 5.21±1.91 min-1; p<0.001). This is explained by the finding that CFI_fem_uncor was not different to CFI_fem_cor (4.28±1.70 vs. 4.24±1.62 min-1; p = 0.611). This suggests that correction for femoral CVC does not pertain to CFI. Calculative correction of CFI_fem_uncor by multiplying CFI_fem_uncor by the ratio GEDVI_fem_uncor/GEDVI_jug resulted in CFI_fem_uncor_form which was slightly, but significantly different from the gold standard CFI_jug (5.51±2.00 vs. 5.21±1.91 min-1; p = 0.024). The agreement of measurements classified in the same category of CFI (decreased (<4.5), normal (4.5–6.5) and increased (>6.5 min-1)) was high for CFI_jug and CFI_fem_uncor_form (identical categories in 26 of 28 comparisons; p = 0.49). By contrast, the agreement with CFI_jug was significantly lower for CFI_fem_cor (14 out of 28; p<0.001) and CFI_fem_uncor (15 out of 28; p<0.001).ConclusionsWhile the last PiCCO algorithm obviously corrects GEDVI for femoral indicator injection, this correction is not applied to CFI. Therefore, femoral TPTD indicator injection results in substantially lower values for CFI compared to TPTD using a jugular CVC. N...