Diaphragm ultrasound in patients with prolonged weaning from mechanical ventilation.

Background: Several publications have examined diaphragmatic ultrasound using two-dimensional (2D) parameters in the context of weaning from mechanical ventilation (MV) and extubation. However, the studied cohorts had rather short duration of ventilation. Examinations on patients with prolonged weaning after long-term ventilation were missing. It was the aim of this study to assess of the diaphragm and peripheral musculature of patients undergoing prolonged weaning creating a chronological sequence of ultrasonic parameters during the course of weaning. Methods: This study was carried out as a monocentric, prospective observational cross-sectional study. Patients in prolonged weaning who were transferred to a specialized weaning unit were eligible for inclusion if they were ventilated invasively by means of an endotracheal tube or tracheal cannula and if their expected treatment period was at least 5 days. Diaphragmatic function and one representative peripheral muscle were examined in 50 patients between March 2020 and April 2021. The 2D sonographic parameters of diaphragm and diaphragmatic function consisted of diaphragmatic thickness (Tdi) at the end of inspiration and expiration, the fractional thickening (FT) and the diaphragmatic excursion. Additionally, the M. quadriceps femoris was sonographically assessed at two locations. The difference of measurements between the first and the last measuring timepoint were examined using the Wilcoxon signed-rank test. For a longer chronological sequence, the Friedman's rank sum test with subsequent Wilcoxon-Nemenyi-McDonald-Thompson test for multiple comparisons was carried out. Results: Fifty patients with prolonged weaning were included. The median duration of MV before transfer to the weaning unit was 11.5 [interquartile range (IQR) 10] days. Forty-one patients could be assessed over the full course of weaning, with 38 successfully weaned. Within these 41 patients, the sonographic parameters of the diaphragm slightly increased over the course of weaning indicating an increase in thickness and mobility. Especially parameters which represented an active movement reached statistical significance, i.e., inspiratory Tdi when assessed under spontaneous breathing [begin 3.41 (0.99) vs. end 3.43 (1.31) mm; P=0.01] and diaphragmatic excursion [begin 0.7 (0.8) vs. end 0.9 (0.6) cm; P=0.01]. The presence of positive end-expiratory pressure (PEEP) and pressure support did not influence the sonographic parameters significantly. The M. quadriceps femoris, in contrast, decreased slightly but constantly over the time [lower third: begin 1.36 (0.48) vs. end 1.28 (0.36) cm; P=0.054]. Conclusions: The present study is the first one to longitudinally analyse diaphragmatic ultrasound in patients with prolonged weaning. Sonographic assessment showed that Tdi and excursion increased over the course of prolonged weaning, while the diameter of a representative peripheral muscle decreased. However, the changes are rather small, and data show a wide dispersion. To allow a potential, standardized use of diaphragm ultrasound for diagnostic decision support in prolonged weaning, further studies in this specific patient group are required.

Spectroscopic Evidence for Doubly Hydrogen-Bonded Cationic Dimers in the Solid, Liquid, and Gaseous Phases of Carboxyl-Functionalized Ionic Liquids.

Intermolecular interactions determine whether matter sticks together, gases condense into liquids, or liquids freeze into solids. The most prominent example is hydrogen bonding in water, responsible for the anomalous properties in the liquid phase and polymorphism in ice. The physical properties are also exceptional for ionic liquids (ILs), wherein a delicate balance of Coulomb interactions, hydrogen bonds, and dispersion interactions results in a broad liquid range and the vaporization of ILs as ion pairs. In this study, we show that strong, local, and directional hydrogen bonds govern the structures and arrangements in the solid, liquid, and gaseous phases of carboxyl-functionalized ILs. For that purpose, we explored the H-bonded motifs by X-ray diffraction and attenuated total reflection (ATR) infrared (IR) spectroscopy in the solid state, by ATR and transmission IR spectroscopy in the liquid phase, and by cryogenic ion vibrational predissociation spectroscopy (CIVPS) in the gaseous phase at low temperature. The analysis of the CO stretching bands reveals doubly hydrogen-bonded cationic dimers (c═c), resembling the archetype H-bond motif known for carboxylic acids. The like-charge doubly hydrogen-bonded ion pairs are present in the crystal structure of the IL, survive phase transition into the liquid state, and are still present in the gaseous phase even in (2,1) complexes wherein one counterion is removed and repulsive Coulomb interaction increased. The interpretation of the vibrational spectra is supported by quantum chemical methods. These observations have implications for the fundamental nature of the hydrogen bond between ions of like charge.

Development of a machine learning model for prediction of the duration of unassisted spontaneous breathing in patients during prolonged weaning from mechanical ventilation.

PURPOSE: Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of respiratory musculature. This study aimed to develop a machine learning model to predict the duration of unassisted spontaneous breathing. MATERIALS AND METHODS: Structured clinical data of patients from a specialized weaning unit were used to develop (1) a classifier model to qualitatively predict an increase of duration, (2) a regressor model to quantitatively predict the precise duration of SBTs on the next day, and (3) the duration difference between the current and following day. 61 features, known to influence weaning, were included into a Histogram-based gradient boosting model. The models were trained and evaluated using separated data sets. RESULTS: 18.948 patient-days from 1018 individual patients were included. The classifier model yielded an ROC-AUC of 0.713. The regressor models displayed a mean absolute error of 2:50 h for prediction of absolute durations and 2:47 h for day-to-day difference. CONCLUSIONS: The developed machine learning model showed informed results when predicting the spontaneous breathing capacity of a patient in prolonged weaning, however lacking prognostic quality required for direct translation to clinical use.

A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals.

The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.

Effects of structured protocolized physical therapy on the duration of mechanical ventilation in patients with prolonged weaning.

PURPOSE: 20% of patients with mechanical ventilation (MV) have a prolonged, complex weaning process, often experiencing a condition of ICU-acquired weakness (ICUAW), with a severe decrease in muscle function and restricted long-term prognosis. We aimed to analyze a protocolized, systematic approach of physiotherapy in prolonged weaning patients and hypothesized that the duration of weaning from MV would be shortened. METHODS: ICU patients with prolonged weaning were included before (group 1) and after (group 2) introduction of a quality control measure of a structured and protocolized physiotherapy program. Primary endpoint was the tested dynamometric handgrip strength and the Surgical Intensive Care Unit Optimal Mobilization Score (SOMS). Secondary endpoints were weaning success rate, ventilator-free days, hospital mortality, the prevalence of ICUAW, infections and delirium. RESULTS: 106 patients were included. Both the SOMS and the handgrip test were significantly improved after introducing the program. Despite no differences in weaning success rates at discharge, the total length of MV was significantly shorter in group 2, which also had lower prevalence of infection and higher probability of survival. CONCLUSIONS: Protocolized, systematic physiotherapy resulted in an improvement of the clinical outcome in patients with prolonged weaning. Results were objectifiable with the SOMS and the handgrip test.

Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular. Therefore, traditional machine learning methods like PCA are ill-suited for analysis of EHR-derived patient data. We propose to address these issues with a new methodology based on training a gated recurrent unit (GRU) autoencoder directly on health record data. Our method learns a low-dimensional feature space by training on patient data time series, where the time of each data point is expressed explicitly. We use positional encodings for time, allowing our model to better handle the temporal irregularity of the data. We apply our method to data from the Medical Information Mart for Intensive Care (MIMIC-III). Using our data-derived feature space, we can cluster patients into groups representing major classes of disease patterns. Additionally, we show that our feature space exhibits a rich substructure at multiple scales.

Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support.

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.

[Weaning from invasive ventilation : Challenges in the clinical routine]. / Weaning von invasiver Beatmung : Herausforderungen im klinischen Alltag.

Modern intensive care medicine is caught between the conflicting demands of an efficient but also increasingly more technical intensive care treatment with numerous therapeutic options and, at the same time, an ageing society with increasing morbidity. This is reflected, among other things, in an increasing number of ventilated patients in intensive care units and an increasing proportion of patients for whom ventilation cannot easily be discontinued. Weaning from a ventilator, which can account for more than 50% of the total ventilation time, therefore plays a central role in this process. This main topic article presents the need for strategically wise and holistic actions to minimize the consequences of invasive mechanical ventilation for patients. An attempt is made to shed more light on individual aspects of the ventilation weaning process with high relevance for clinical practice. Especially for prolonged weaning from ventilation, many more concepts are needed than simply ending ventilation.

Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets.

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications. Clinical data structures, patient cohorts, and clinical protocols may be highly biased among hospitals such that sampling of representative learning datasets to learn ML models remains a challenge. As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this study, we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of the two datasets, resulting in an upper bound of the prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e., from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals' datasets and with a high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold-standard datasets that are large and diverse with data from varied application sites.

A training strategy for hybrid models to break the curse of dimensionality.

Mechanistic/data-driven hybrid modeling is a key approach when the mechanistic details of the processes at hand are not sufficiently well understood, but also inferring a model purely from data is too complex. By the integration of first principles into a data-driven approach, hybrid modeling promises a feasible data demand alongside extrapolation. In this work, we introduce a learning strategy for tree-structured hybrid models to perform a binary classification task. Given a set of binary labeled data, the challenge is to use them to develop a model that accurately assesses labels of new unlabeled data. Our strategy employs graph-theoretic methods to analyze the data and deduce a function that maps input features to output labels. Our focus here is on data sets represented by binary features in which the label assessment of unlabeled data points is always extrapolation. Our strategy shows the existence of small sets of data points within given binary data for which knowing the labels allows for extrapolation to the entire valid input space. An implementation of our strategy yields a notable reduction of training-data demand in a binary classification task compared with different supervised machine learning algorithms. As an application, we have fitted a tree-structured hybrid model to the vital status of a cohort of COVID-19 patients requiring intensive-care unit treatment and mechanical ventilation. Our learning strategy yields the existence of patient cohorts for whom knowing the vital status enables extrapolation to the entire valid input space of the developed hybrid model.

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients.

Objective: The attitudes about the usage of artificial intelligence in healthcare are controversial. Unlike the perception of healthcare professionals, the attitudes of patients and their companions have been of less interest so far. In this study, we aimed to investigate the perception of artificial intelligence in healthcare among this highly relevant group along with the influence of digital affinity and sociodemographic factors. Methods: We conducted a cross-sectional study using a paper-based questionnaire with patients and their companions at a German tertiary referral hospital from December 2019 to February 2020. The questionnaire consisted of three sections examining (a) the respondents' technical affinity, (b) their perception of different aspects of artificial intelligence in healthcare and (c) sociodemographic characteristics. Results: From a total of 452 participants, more than 90% already read or heard about artificial intelligence, but only 24% reported good or expert knowledge. Asked on their general perception, 53.18% of the respondents rated the use of artificial intelligence in medicine as positive or very positive, but only 4.77% negative or very negative. The respondents denied concerns about artificial intelligence, but strongly agreed that artificial intelligence must be controlled by a physician. Older patients, women, persons with lower education and technical affinity were more cautious on the healthcare-related artificial intelligence usage. Conclusions: German patients and their companions are open towards the usage of artificial intelligence in healthcare. Although showing only a mediocre knowledge about artificial intelligence, a majority rated artificial intelligence in healthcare as positive. Particularly, patients insist that a physician supervises the artificial intelligence and keeps ultimate responsibility for diagnosis and therapy.

Haemoglobin value and red blood cell transfusions in prolonged weaning from mechanical ventilation: a retrospective observational study.

INTRODUCTION: The role of haemoglobin (Hb) value and red blood cell (RBC) transfusions in prolonged weaning from mechanical ventilation (MV) is still controversial. Pathophysiological considerations recommend a not too restrictive transfusion strategy, whereas adverse effects of transfusions are reported. We aimed to investigate the association between Hb value, RBC transfusion and clinical outcome of patients undergoing prolonged weaning from MV. METHODS: We performed a retrospective, single-centred, observational study including patients being transferred to a specialised weaning unit. Data on demographic characteristics, comorbidities, current and past medical history and the current course of treatment were collected. Weaning failure and mortality were chosen as primary and secondary endpoint, respectively. Differences between transfused and non-transfused patients were analysed. To evaluate the impact of different risk factors including Hb value and RBC transfusion on clinical outcome, a multivariate logistic regression analysis was used. RESULTS: 184 patients from a specialised weaning unit were analysed, of whom 36 (19.6%) failed to be weaned successfully. In-hospital mortality was 18.5%. 90 patients (48.9%) required RBC transfusion during the weaning process, showing a significantly lower Hb value (g/L) (86.3±5.3) than the non-transfusion group (95.8±10.5). In the multivariate regression analysis (OR 3.24; p=0.045), RBC transfusion was associated with weaning failure. However, the transfusion group had characteristics indicating that these patients were still in a more critical state of disease. CONCLUSIONS: In our analysis, the need for RBC transfusion was independently associated with weaning failure. However, it is unclear whether the transfusion itself should be considered an independent risk factor or an additional symptom of a persistent critical patient condition.

[Artificial Intelligence: Infrastructures and Prerequisites at European Level]. / Künstliche Intelligenz: Infrastrukturen und Voraussetzungen auf europäischer Ebene.

The application of artificial intelligence (AI) is often associated with the use of large amounts of data for the construction of AI models and algorithms. This data should ideally comply with the FAIR Data principles, i.e. being findable, accessible, interoperable and reusable. However, the handling of health data poses a particular challenge in this context. In this article, we highlight the challenges of the data usage for AI in medicine using the example of anaesthesia and intensive care medicine. We discuss the current situation but also the obstacles for a wider application of AI in medicine in Europe and give suggestions how to solve the different issues. The article covers different subjects like data protection, research data infrastructures and approval of medical products. Finally, this article shows how it can nevertheless be possible to establish a secure and at the same time effective handling of data for use in AI at the European level despite its unneglectable difficulties.

[Usage of Artificial Intelligence in the Combat against the COVID-19 Pandemic]. / Nutzung von künstlicher Intelligenz zur Bekämpfung der COVID-19-Pandemie.

The COVID-19 pandemic is a global health emergency of historic dimension. In this situation, researchers worldwide wanted to help manage the pandemic by using artificial intelligence (AI). This narrative review aims to describe the usage of AI in the combat against COVID-19. The addressed aspects encompass AI algorithms for analysis of thoracic X-rays or CTs, prediction models for severity and outcome of the disease, AI applications in development of new drugs and vaccines as well as forecasting models for spread of the virus. The review shows, which approaches were pursued, and which were successful.

Thermodynamically Stable Cationic Dimers in Carboxyl-Functionalized Ionic Liquids: The Paradoxical Case of "Anti-Electrostatic" Hydrogen Bonding.

We show that carboxyl-functionalized ionic liquids (ILs) form doubly hydrogen-bonded cationic dimers (c+=c+) despite the repulsive forces between ions of like charge and competing hydrogen bonds between cation and anion (c+-a-). This structural motif as known for formic acid, the archetype of double hydrogen bridges, is present in the solid state of the IL 1-(carboxymethyl)pyridinium bis(trifluoromethylsulfonyl)imide [HOOC-CH2-py][NTf2]. By means of quantum chemical calculations, we explored different hydrogen-bonded isomers of neutral (HOOC-(CH2)n-py+)2(NTf2-)2, single-charged (HOOC-(CH2)n-py+)2(NTf2-), and double-charged (HOOC- (CH2)n-py+)2 complexes for demonstrating the paradoxical case of "anti-electrostatic" hydrogen bonding (AEHB) between ions of like charge. For the pure doubly hydrogen-bonded cationic dimers (HOOC- (CH2)n-py+)2, we report robust kinetic stability for n = 1-4. At n = 5, hydrogen bonding and dispersion fully compensate for the repulsive Coulomb forces between the cations, allowing for the quantification of the two equivalent hydrogen bonds and dispersion interaction in the order of 58.5 and 11 kJmol-1, respectively. For n = 6-8, we calculated negative free energies for temperatures below 47, 80, and 114 K, respectively. Quantum cluster equilibrium (QCE) theory predicts the equilibria between cationic monomers and dimers by considering the intermolecular interaction between the species, leading to thermodynamic stability at even higher temperatures. We rationalize the H-bond characteristics of the cationic dimers by the natural bond orbital (NBO) approach, emphasizing the strong correlation between NBO-based and spectroscopic descriptors, such as NMR chemical shifts and vibrational frequencies.

Speckle tracking ultrasonography as a new tool to assess diaphragmatic function: a feasibility study.

A reliable method of measuring diaphragmatic function at the bedside is still lacking. Widely used two-dimensional (2D) ultrasonographic measurements, such as diaphragm excursion, diaphragm thickness, and fractional thickening (FT) have failed to show clear correlations with diaphragmatic function. A reason for this is that 2D ultrasonographic measurements, like FT, are merely able to measure the deformation of muscular diaphragmatic tissue in the transverse direction, while longitudinal measurements in the direction of contracting muscle fibres are not possible. Speckle tracking ultrasonography, which is widely used in cardiac imaging, overcomes this disadvantage and allows observations of movement in the direction of the contracting muscle fibres, approximating muscle deformation and the deformation velocity. Several studies have evaluated speckle tracking as a promising method to assess diaphragm contractility in healthy subjects. This technical note demonstrates the feasibility of speckle tracking ultrasonography of the diaphragm in a group of 20 patients after an aortocoronary bypass graft procedure. The results presented herein suggest that speckle tracking ultrasonography is able to depict alterations in diaphragmatic function after surgery better than 2D ultrasonographic measurements.

Is sarcopenia a risk factor for reduced diaphragm function following hepatic resection? A study protocol for a prospective observational study.

INTRODUCTION: Sarcopenia is associated with reduced pulmonary function in healthy adults, as well as with increased risk of pneumonia following abdominal surgery. Consequentially, postoperative pneumonia prolongs hospital admission, and increases in-hospital mortality following a range of surgical interventions. Little is known about the function of the diaphragm in the context of sarcopenia and wasting disorders or how its function is influenced by abdominal surgery. Liver surgery induces reactive pleural effusion in most patients, compromising postoperative pulmonary function. We hypothesise that both major hepatic resection and sarcopenia have a measurable impact on diaphragm function. Furthermore, we hypothesise that sarcopenia is associated with reduced preoperative diaphragm function, and that patients with reduced preoperative diaphragm function show a greater decline and reduced recovery of diaphragm function following major hepatic resection. The primary goal of this study is to evaluate whether sarcopenic patients have a reduced diaphragm function prior to major liver resection compared with non-sarcopenic patients, and to evaluate whether sarcopenic patients show a greater reduction in respiratory muscle function following major liver resection when compared with non-sarcopenic patients. METHODS AND ANALYSIS: Transcostal B-mode, M-mode ultrasound and speckle tracking imaging will be used to assess diaphragm function perioperatively in 33 sarcopenic and 33 non-sarcopenic patients undergoing right-sided hemihepatectomy starting 1 day prior to surgery and up to 30 days after surgery. In addition, rectus abdominis and quadriceps femoris muscles thickness will be measured using ultrasound to measure sarcopenia, and pulmonary function will be measured using a hand-held bedside spirometer. Muscle mass will be determined preoperatively using CT-muscle volumetry of abdominal muscle and adipose tissue at the third lumbar vertebra level (L3). Muscle function will be assessed using handgrip strength and physical condition will be measured with a short physical performance battery . A rectus abdominis muscle biopsy will be taken intraoperatively to measure proteolytic and mitochondrial activity as well as inflammation and redox status. Systemic inflammation and sarcopenia biomarkers will be assessed in serum acquired perioperatively. ETHICS AND DISSEMINATION: This trial is open for recruitment. The protocol was approved by the official Independent Medical Ethical Committee at Uniklinik (Rheinish Westphälische Technische Hochschule (RWTH) Aachen (reference EK309-18) in July 2019. Results will be published via international peer-reviewed journals and the findings of the study will be communicated using a comprehensive dissemination strategy aimed at healthcare professionals and patients. TRIAL REGISTRATION NUMBER: ClinicalTrials. gov (EK309-18); Pre-results.

Biometric covariates and outcome in COVID-19 patients: are we looking close enough?

BACKGROUND: The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients. METHODS: We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step. RESULTS: We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group. CONCLUSIONS: The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies.