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Absent pulmonary valve syndrome (APVS); the combination of tetralogy of Fallot (TOF) with agenesis of the pulmonary valve, is a relatively rare cardiac malformation. Despite the anatomic similarity with classic TOF, the pathophysiology is strikingly different. Data on 10 patients (3 male, 7 female) with APVS, treated between January 1978 and December 1995, were retrospectively reviewed. During this period a total of 2920 children underwent correction of a variety of congenital cardiac anomalies, of which 246 patients (8%) had a correction for TOF. Two patients with APVS presented within the first four months of life with severe cardiorespiratory distress and required several operative procedures. The remaining eight patients had only mild to moderate respiratory and/or cardiac symptoms and elective intracardiac repair was performed on those between the ages of 10 months and 9.5 years. Associated cardiac anomalies seen in five patients included aberrant coronary artery, absent or interrupted left pulmonary artery, partial AVSD and aberrant azygos continuation. In those electively corrected, the strategies used were ventriculotomy (7), pulmonary homograft (3) and aneurysmorrhaphy (2). There were two deaths, one in each group of patients, as a result of progressive respiratory insufficiency and cardiac tamponade, respectively. The follow-up of the eight survivors ranged from 2 to 11 years (median 6.75). All have a normal effort tolerance; only one child is on digoxin therapy, and one child continues to suffer bronchospastis episodes. Our experience with infants with this lesion is limited but underlines the different approaches required, depending on the age of presentation. [ABSTRACT FROM AUTHOR]

Abstract Negative-pressure wound therapy, commercially known as vacuum-assisted closure (V.A.C.®) therapy, has become one of the most popular (and efficacious) interim (prior to flap reconstruction) or definite methods of managing deep sternal wound infection. Complications such as profuse bleeding, which may occur during negative-pressure therapy but not necessarily due to it, are often attributed to a single factor and reported as such. However, despite the wealth of clinical experience internationally available, information regarding certain simple considerations is still lacking. Garnering information on all the factors that could possibly influence the outcome has become more difficult due to a (fortunate) decrease in the incidence of deep sternal wound infection. If more insight is to be gained from fewer clinical cases, then various potentially confounding factors should be fully disclosed before complications can be attributed to the technique itself or improvements to negative-pressure wound therapy for deep sternal wound infection can be accepted as evidence-based and the guidelines for its use adapted. The authors propose the adoption of a simple checklist in such cases.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment., Competing Interests: Conflicts of Interest No conflicts of interest to disclose.

Background: Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations. Exploring the use of machine learning (ML) on magnetic resonance imaging to predict IDH mutation status shows promise but encounters challenges in generalizability and translation into clinical practice because most studies either use single institution or homogeneous datasets for model training and validation. Our study aims to bridge this gap by using multi-institution data for model validation., Methods: This retrospective study utilizes data from large, annotated datasets for internal (377 cases from Yale New Haven Hospitals) and external validation (207 cases from facilities outside Yale New Haven Health). The 6-step research process includes image acquisition, semi-automated tumor segmentation, feature extraction, model building with feature selection, internal validation, and external validation. An extreme gradient boosting ML model predicted the IDH mutation status, confirmed by immunohistochemistry., Results: The ML model demonstrated high performance, with an Area under the Curve (AUC), Accuracy, Sensitivity, and Specificity in internal validation of 0.862, 0.865, 0.885, and 0.713, and external validation of 0.835, 0.851, 0.850, and 0.847., Conclusions: The ML model, built on a heterogeneous dataset, provided robust results in external validation for the prediction task, emphasizing its potential clinical utility. Future research should explore expanding its applicability and validation in diverse global healthcare settings., Competing Interests: MingDe Lin is an employee and stockholder of Visage Imaging, Inc., and unrelated to this work, receives funding from NIH/NCI R01 CA206180 and is a board member of Tau Beta Pi Engineering Honor Society. Khaled Bousabarah is an employee of Visage Imaging, GmbH. Michael Sabel is a consultant for Novocure and Codman., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow., (© 2024. The Author(s).)

Background: Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods., Methods: An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images., Results: There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD., Conclusions: Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management., Competing Interests: M.L. is an employee and stockholder of Visage Imaging, Inc., and unrelated to this work, receives funding from NIH/NCI R01 CA206180 and NIH/NCI R01 CA275188. K.B. is an employee of Visage Imaging GmbH., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status., (© 2023. The Author(s).)

Background: The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor., Purpose: We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging., Data Sources: Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science., Study Selection: Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria., Data Analysis: We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines., Data Synthesis: Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles., Limitations: The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias., Conclusions: While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques., (© 2023 by American Journal of Neuroradiology.)

Stereotactic radiotherapy (SRT) is the standard of care treatment for brain metastases (METS) today. Nevertheless, there is limited understanding of how posttreatment lesional volumetric changes may assist prediction of lesional outcome. This is partly due to the paucity of volumetric segmentation tools. Edema alone can cause significant clinical symptoms and, therefore, needs independent study along with standard measurements of contrast-enhancing tumors. In this study, we aimed to compare volumetric changes of edema to RANO-BM-based measurements of contrast-enhancing lesion size. Patients with NSCLC METS ≥10 mm on post-contrast T1-weighted image and treated with SRT had measurements for up to seven follow-up scans using a PACS-integrated tool segmenting the peritumoral FLAIR hyperintense volume. Two-dimensional contrast-enhancing and volumetric edema changes were compared by creating treatment response curves. Fifty NSCLC METS were included in the study. The initial median peritumoral edema volume post-SRT relative to pre-SRT baseline was 37% (IQR 8-114%). Most of the lesions with edema volume reduction post-SRT experienced no increase in edema during the study. In over 50% of METS, the pattern of edema volume change was different than the pattern of contrast-enhancing lesion change at different timepoints, which was defined as incongruent. Lesions demonstrating incongruence at the first follow-up were more likely to progress subsequently. Therefore, edema assessment of METS post-SRT provides critical additional information to RANO-BM.

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow., Competing Interests: M.S.A. has collaborations with Visage Imaging, Inc., Blue Earth Diagnostics, Telix, and AAA. She also has a KL2 TR00186 grant from the NCATS foundation. M.L. is an employee and stockholder of Visage Imaging, Inc., and unrelated to this work, receives funding from NIH/NCI R01 CA206180 and NIH/NCI R01 CA275188. W.H. and M.W. are employees and stockholders of Visage Imaging GmbH. K.B. is an employee of Visage Imaging GmbH. C.K. receives royalties from Primal Pictures 3D Informa, has grant funding from the NIH, and has received the Core Curriculum grant from the American Society of Head and Neck Radiology, all unrelated to this work. The remaining co-authors do not have any competing interests.

Nearly 10% of children and adolescents in the United States experience migraine. Pharmacologic treatment of migraine in adolescents is limited due to only few US Food and Drug Administration (FDA)-approved medications, limited efficacy, or lack of tolerability. Remote Electrical Neuromodulation (REN) is a nonpharmacologic abortive treatment for migraine, cleared by the FDA for patients aged 12 years and above. This study evaluated real-world efficacy of REN in adolescents aged 12 to 17 years. Real-world data were collected from patients aged 12 to 17 years treated with the REN device (Nerivio) from January 1, 2021, to May 31, 2022. Study's end points included consistent efficacy two hours after treatment, use of REN as a standalone versus as an adjunct therapy, treatment intensity, and safety. Of 1629 adolescents included in the study, consistent response in at least 50% of treatments at two hours posttreatment was achieved by 60.3% of patients for pain relief, 26.3% for pain freedom, 66.3% for functional disability relief, and 41.2% for functional disability freedom. Of 2365 treatments in which medication usage was reported, REN was used as standalone therapy in 64.4% of the treatments, REN was combined with over-the-counter medications in 18.6%, and it was combined with prescription medications in 17%. Mean treatment intensity from 13,716 treatments was 28.5% (±13.6%) of the max stimulator output. Only three device-related adverse events were reported, all minor. This real-world analysis demonstrates the persistent efficacy of REN for abortive treatment of migraine in adolescents, extending findings of prior clinical trials in adolescents and real-world studies in adults., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Treatment of brain metastases can be tailored to individual lesions with treatments such as stereotactic radiosurgery. Accurate surveillance of lesions is a prerequisite but challenging in patients with multiple lesions and prior imaging studies, in a process that is laborious and time consuming. We aimed to longitudinally track several lesions using a PACS-integrated lesion tracking tool (LTT) to evaluate the efficiency of a PACS-integrated lesion tracking workflow, and characterize the prevalence of heterogenous response (HeR) to treatment after Gamma Knife (GK)., Methods: We selected a group of brain metastases patients treated with GK at our institution. We used a PACS-integrated LTT to track the treatment response of each lesion after first GK intervention to maximally seven diagnostic follow-up scans. We evaluated the efficiency of this tool by comparing the number of clicks necessary to complete this task with and without the tool and examined the prevalence of HeR in treatment., Results: A cohort of eighty patients was selected and 494 lesions were measured and tracked longitudinally for a mean follow-up time of 374 days after first GK. Use of LTT significantly decreased number of necessary clicks. 81.7% of patients had HeR to treatment at the end of follow-up. The prevalence increased with increasing number of lesions., Conclusions: Lesions in a single patient often differ in their response to treatment, highlighting the importance of individual lesion size assessments for further treatment planning. PACS-integrated lesion tracking enables efficient lesion surveillance workflow and specific and objective result reports to treating clinicians., (© The Author(s) 2022. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature., Methods: We performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD., Results: We found that while many articles were published on ML-based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice., Conclusions: In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias., (© The Author(s) 2022. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

Background: Recent studies show that substantial percentage of patients experienced worsening of health related quality of life (HRQoL) 1 year after cardiac surgery. The aim of this study is to identify risk factors that interfere with improvement of HRQoL., Methods: From December 2015 till July 2017 a prospective single centre observational study was carried out in 1920 patients participated who underwent non-salvage cardiac surgery. All patients were requested to complete a Short Form 36 (SF-36) questionnaire before and 1 year after surgery. Primary aim of the study was to identify risk factors for non-recovery in the physical domain of the SF-36 in all cardiac surgery patients. Secondary aim was to identify identical risk factors in patients with isolated coronary artery bypass grafting., Results: After cardiac surgery, the questionnaires for physical and mental health were completed by respectively 803 and 807 patients. Median age was 69[62-75] years, and 77% was male. In comparison to the preoperative status, 176 patients (21.9%) did not display an improvement in the SF-36 physical domain score 1 year after cardiac surgery. In a multivariate analysis independent risk factors for non-recovery in the SF-36 physical domain were baseline SF36 physical domain score (OR 0.954[0.942-0.965], P < 0.001), diabetes (OR 0.437 [0.265-0.720], P 0.001), female sex (OR 0.492 [0.307-0.789], P 0.003), post-operative infection (OR 0.240 [0.109-0.525], P < 0.001) and PCI within 1 year (OR 0.113 [0.036-0.349], P < 0.001) For isolated CABG, 23.2% of patients did not display an improvement in the physical domain score and risk factors appeared to be identical., Conclusions: Twenty two percent of all cardiac surgery patients did not show an improvement in the physical domain score of the HRQoL between the preoperative period and 1 year after surgery. Independent risk factors for non-recovery after cardiac surgery were baseline SF-36 physical domain score, diabetes, female sex, any postoperative infection and the need for PCI in the first year. Further research is needed to tailor the patient selection procedure prior to surgery and potentially modify risk factors in the perioperative process., Trial Registration: Due to type of study not applicable. https://www.ccmo.nl/metcs/erkende-metcs/regionale-toetsingscommissie-patientgebonden-onderzoek .

Background: A low plasma glutamine level was found in 34% of patients after elective cardiothoracic surgery. This could be a result of the inflammation caused by surgical stress or the use of extracorporeal circulation (ECC). But it is also possible that plasma glutamine levels were already lowered before surgery and reflect an impaired metabolic state and a higher likelihood to develop complications. In the present study plasma glutamine levels were measured before and after cardiac surgery and we questioned whether there is a relation between plasma glutamine levels and duration of ECC and the occurrence of postoperative infections., Methods: We performed a single-centre prospective, observational study in a closed-format, 20-bed, mixed ICU in a tertiary teaching hospital. We included consecutive patients after elective cardiac surgery with use of extracorporeal circulation. Blood samples were collected on the day prior to surgery and at admission on the ICU. The study was approved by the local Medical Ethics Committee (Regional Review Committee Patient-related Research, Medical Centre Leeuwarden, nWMO 115, April 28th 2015)., Results: Ninety patients were included. Pre-operative plasma glutamine level was 0.42 ± 0.10 mmol/l and post-operative 0.38 ± 0.09 mmol/l (p < 0.001). There was no relation between duration of extracorporeal circulation or aortic occlusion time and changes in plasma glutamine levels. A logistic regression analysis showed a significant correlation between the presence of a positive culture during the post-operative course and pre-operative plasma glutamine levels (p = 0.04)., Conclusion: Plasma glutamine levels are significantly lower just after cardiac surgery compared to pre-operative levels. We did not find a relation between the decrease in plasma glutamine levels and the duration of extracorporeal circulation or aortic clamp time. There was a correlation between pre-operative plasma glutamine levels and the presence of a positive culture after cardiac surgery., Trial Registration: ClinicalTrials.gov, number NCT02444780 .

The search for the ideal priming fluid continues as more evidence is discovered about side effects of volume expanders. With the availability of modern, balanced hydroxyethyl starch (HES) solutions with less side effects than former HES solutions, we considered to replace our gelatin- (modified gelatin) based extracorporeal circuit prime for a HES (130/.42) prime. Therefore, we studied the influence of two colloidal priming fluids on postoperative coagulation in patients undergoing cardiac surgery. The primary endpoint was to compare clot formation time between the HES group and the gelatin group with rotational thromboelastometry (ROTEM). Additionally we compared colloid osmotic pressure and fluid balance of both groups. Forty patients, undergoing elective first time coronary artery bypass grafting or single-valve surgery, were included in this prospectively randomized open-label pilot study. Laboratory data and ROTEM data were collected and analyzed for differences between the two groups. ROTEM data show significantly more prolongation in Extem clot formation time and significant more decrease in Extem alpha in the HES group. Fibtem maximum clot firmness was significantly smaller in the HES group; this was consistent with fibrinogen concentration measurement, which decreased more in the HES group than in the gelatin group and recovered more over time in the gelatin group. We found no significant difference in colloid. osmotic pressure between the groups. In this trial, HES (130/.42) impairs coagulation significantly more compared with gelatin. These differences in influence on coagulation did not lead to a difference in blood loss or fluid balance, so clinical relevance could not be proven.