The PAncreatic Surgery Composite Endpoint PACE - Development and Validation of a Clinically Relevant Endpoint Requiring Lower Sample Sizes.

OBJECTIVE: To provide a composite endpoint in pancreatic surgery. SUMMARY BACKGROUND DATA: Single endpoints in prospective and randomized studies have become impractical due to their low frequency and the marginal benefit of new interventions. METHODS: Data from prospective studies were used to develop (n=1273) and validate (n=544) a composite endpoint based on postoperative pancreatic fistula, post-pancreatectomy hemorrhage as well as reoperation and reinterventions. All patients had pancreatectomies of different extents. The association of the developed PAncreatic surgery Composite Endpoint (PACE) with prolonged length of hospital stay (LOS) >75th percentile and mortality was assessed. A single-institution database was used for external validation (n = 2666). Sample size calculations were made for single outcomes and the composite endpoint. RESULTS: In the internal validation cohort, the PACE demonstrated an AUC of 78.0%, a sensitivity of 90.4% and a specificity of 67.6% in predicting a prolonged LOS. In the external cohort, the AUC was 76.9%, the sensitivity 73.8% and the specificity 80.1%. The 90-day mortality rate was significantly different for patients with a positive versus a negative PACE both in the development and internal validation cohort (5.1% vs 0.9%; P< 0.001), as well as in the external validation cohort (8.5% vs 1.2%, P< 0.001). The PACE enabled sample size reductions of up to 80.5% compared to single outcomes. CONCLUSION: The PACE performed well in predicting prolonged hospital stays and can be used as a standardized and clinically relevant endpoint for future prospective trials enabling lower sample sizes and therefore improved feasibility compared to single outcome parameters.

Polypropylene Degradation on Co-Rotating Twin-Screw Extruders.

Nowadays, usable plastic materials with defined properties are created by blending additives into the base polymer. This is the main task of compounding on co-rotating twin-screw extruders. The thermal and mechanical stress occurring in the process leads to a mostly irreversible damage to the material. Consequently, the properties of the polymer melt and the subsequent product are affected. The material degradation of polypropylene (PP) on a 28 mm twin-screw extruder has already been studied and modeled at Kunststofftechnik Paderborn. In this work, the transferability of the previous results to other machine sizes and polypropylene compounds were investigated experimentally. Therefore, pure polypropylene was processed with screw diameters of 25 mm and 45 mm. Furthermore, polypropylene compounds with titanium dioxide as well as carbon fibers were considered on a 28 mm extruder. In the course of the evaluation of the pure polypropylene, the melt flow rates of the samples were measured and the molar masses were calculated on this basis. The compounds were analyzed by gel permeation chromatography. As in the previous investigations, high rotational speeds, low throughputs and high melt temperatures lead to a higher material degradation. In addition, it is illustrated that the previously developed model for the calculation of material degradation is generally able to predict the degradation even for different machine sizes by adjusting the process coefficients. In summary, this article shows that compounders can use the recommendations for action and the calculation model for the material degradation of polypropylene, irrespective of the machine size, to design processes that are gentle on the material.

New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.

Preeclampsia, a multisystem disorder in pregnancy, is still one of the main causes of maternal morbidity and mortality. Due to a lack of a causative therapy, an accurate prediction of women at risk for the disease and its associated adverse outcomes is of utmost importance to tailor care. In the past two decades, there have been successful improvements in screening as well as in the prediction of the disease in high-risk women. This is due to, among other things, the introduction of biomarkers such as the sFlt-1/PlGF ratio. Recently, the traditional definition of preeclampsia has been expanded based on new insights into the pathophysiology and conclusive evidence on the ability of angiogenic biomarkers to improve detection of preeclampsia-associated maternal and fetal adverse events.However, with the widespread availability of digital solutions, such as decision support algorithms and remote monitoring devices, a chance for a further improvement of care arises. Two lines of research and application are promising: First, on the patient side, home monitoring has the potential to transform the traditional care pathway. The importance of the ability to input and access data remotely is a key learning from the COVID-19 pandemic. Second, on the physician side, machine-learning-based decision support algorithms have been shown to improve precision in clinical decision-making. The integration of signals from patient-side remote monitoring devices into predictive algorithms that power physician-side decision support tools offers a chance to further improve care.The purpose of this review is to summarize the recent advances in prediction, diagnosis and monitoring of preeclampsia and its associated adverse outcomes. We will review the potential impact of the ability to access to clinical data via remote monitoring. In the combination of advanced, machine learning-based risk calculation and remote monitoring lies an unused potential that allows for a truly patient-centered care.

Pharmacogenetics of human sulfotransferases and impact of amino acid exchange on Phase II drug metabolism.

Sulfotransferases (SULTs) are Phase II drug-metabolizing enzymes (DMEs) catalyzing the sulfation of a variety of endogenous compounds, natural products, and drugs. Various drugs, such as nonsteroidal anti-inflammatory drugs (NSAIDS) can inhibit SULTs, affecting drug-drug interactions. Several polymorphisms have been identified for SULTs that might be crucial for interindividual variability in drug response and toxicity or for increased disease risk. Here, we review current knowledge on non-synonymous single nucleotide polymorphisms (nsSNPs) of human SULTs, focusing on the coded SULT allozymes and molecular mechanisms explaining their variable activity, which is essential for personalized medicine. We discuss the structural and dynamic bases of key amino acid (AA) variants implicated in the impacts on drug metabolism in the case of SULT1A1, as revealed by molecular modeling approaches.

Impact of Sarcopenia and Bone Mineral Density on Implant Failure after Dorsal Instrumentation in Patients with Osteoporotic Vertebral Fractures.

Background and Objectives: Age-related loss of bone and muscle mass are signs of frailty and are associated with an increased risk of falls and consecutive vertebral fractures. Management often necessitates fusion surgery. We determined the impacts of sarcopenia and bone density on implant failures (IFs) and complications in patients with spondylodesis due to osteoporotic vertebral fractures (OVFs). Materials and Methods: Patients diagnosed with an OVF according to the osteoporotic fracture classification (OF) undergoing spinal instrumentation surgery between 2011 and 2020 were included in our study. The skeletal muscle area (SMA) was measured at the third lumbar vertebra (L3) level using axial CT images. SMA z-scores were calculated for the optimal height and body mass index (BMI) adjustment (zSMAHT). The loss of muscle function was assessed via measurement of myosteatosis (skeletal muscle radiodensity, SMD) using axial CT scans. The bone mineral density (BMD) was determined at L3 in Hounsfield units (HU). Results: A total of 68 patients with OVFs underwent instrumentation in 244 segments (mean age 73.7 ± 7.9 years, 60.3% female). The median time of follow-up was 14.1 ± 15.5 months. Sarcopenia was detected in 28 patients (47.1%), myosteatosis in 45 patients (66.2%), and osteoporosis in 49 patients (72%). The presence of sarcopenia was independent of chronological age (p = 0.77) but correlated with BMI (p = 0.005). The zSMAHT was significantly lower in patients suffering from an IF (p = 0.0092). Sarcopenia (OR 4.511, 95% CI 1.459-13.04, p = 0.0092) and osteoporosis (OR 9.50, 95% CI 1.497 to 104.7, p = 0.014) increased the likelihood of an IF. Using multivariate analysis revealed that the zSMAHT (p = 0.0057) and BMD (p = 0.0041) were significantly related to IF occurrence. Conclusion: Herein, we established sarcopenic obesity as the main determinant for the occurrence of an IF after instrumentation for OVF. To a lesser degree, osteoporosis was associated with impaired implant longevity. Therefore, measuring the SMA and BMD using an axial CT of the lumbar spine might help to prevent an IF in spinal fusion surgery via early detection and treatment of sarcopenia and osteoporosis.

A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes.

BACKGROUND: Preeclampsia presents a highly prevalent burden on pregnant women with an estimated incidence of 2% to 5%. Preeclampsia increases the maternal risk of death 20-fold and is one of the main causes of perinatal morbidity and mortality. Novel biomarkers, such as soluble fms-like tyrosine kinase-1 and placental growth factor in addition to a wide span of conventional clinical data (medical history, physical symptoms, laboratory parameters, etc.), present an excellent basis for the application of early-detection machine-learning models. OBJECTIVE: This study aimed to develop, train, and test an automated machine-learning model for the prediction of adverse outcomes in patients with suspected preeclampsia. STUDY DESIGN: Our real-world dataset of 1647 (2472 samples) women was retrospectively recruited from women who presented to the Department of Obstetrics at the Charité - Universitätsmedizin Berlin, Berlin, Germany, between July 2010 and March 2019. After standardization and data cleaning, we calculated additional features regarding the biomarkers soluble fms-like tyrosine kinase-1 and placental growth factor and sonography data (umbilical artery pulsatility index, middle cerebral artery pulsatility index, mean uterine artery pulsatility index), resulting in a total of 114 features. The target metric was the occurrence of adverse outcomes throughout the remaining pregnancy and 2 weeks after delivery. We trained 2 different models, a gradient-boosted tree and a random forest classifier. Hyperparameter training was performed using a grid search approach. All results were evaluated via a 10 × 10-fold cross-validation regimen. RESULTS: We obtained metrics for the 2 naive machine-learning models. A gradient-boosted tree model was performed with a positive predictive value of 88%±6%, a negative predictive value of 89%±3%, a sensitivity of 66%±5%, a specificity of 97%±2%, an overall accuracy of 89%±3%, an area under the receiver operating characteristic curve of 0.82±0.03, an F1 score of 0.76±0.04, and a threat score of 0.61±0.05. The random forest classifier returned an equal positive predictive value (88%±6%) and specificity (97%±1%) while performing slightly inferior on the other available metrics. Applying differential cutoffs instead of a naive cutoff for positive prediction at ≥0.5 for the classifier's results yielded additional increases in performance. CONCLUSION: Machine-learning techniques were a valid approach to improve the prediction of adverse outcomes in pregnant women at high risk of preeclampsia vs current clinical standard techniques. Furthermore, we presented an automated system that did not rely on manual tuning or adjustments.

The Effects of Gas Saturation of Electrolytes on the Performance and Durability of Lithium-Ion Batteries.

Traces of species in batteries are known to impact battery performance. The effects of gas species, although often reported in the electrolyte and evolving during operation, have not been systematically studied to date and are therefore barely understood. This study reveals and compares the effects of different gases on the charge-discharge characteristics, cycling stability and impedances of lithium-ion batteries. All investigated gases have been previously reported in lithium-ion batteries and are thus worth investigating: Ar, CO2 , CO, C2 H4 , C2 H2 , H2 , CH4 and O2 . Gas-electrolyte composition has a significant influence on formation, coulombic and energy efficiencies, C-rate capability, and aging. Particularly, CO2 and O2 showed a higher C-rate capability and a decrease in irreversible capacity loss during the first cycle compared to Ar. Similar discharge capacities and aging behaviors are observed for CO, C2 H4 and CH4 . Acetylene showed a large decrease in performance and cycle stability. Furthermore, electrochemical impedance spectroscopy revealed that the gases mainly contribute to changes in charge transfer processes, whereas the effects on resistance and solid electrolyte interphase performance were minor. Compared to all other gas-electrolyte mixtures, the use of CO2 saturated electrolyte showed a remarkable increase in all performance parameters including lifetime.