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Summary: Background: No systematic data on the prevalence of, reasons for, and outcomes of hospital admissions in Mongolia are currently available. Methods: In a descriptive analysis, the Mongolian National Hospital Data registry was screened for all hospital admissions in adults from 1 January 2019 to 31 December 2023. The endpoints were the prevalence of and most common reasons for hospital admission, the length of hospital stay, and the hospital mortality rate. Descriptive methods, Chi-square tests, Mann-Whitney-U-tests, and ANOVA on ranks were used for data analysis. Findings: 3,449,083 cases were analysed. The median prevalence of hospital admissions per 100,000 population per year was 20,242 (IQR, 19,412–20,778). The most common main diagnoses were COVID-19 (8.7%; 299,409/3,449,083), chronic tubulo-interstitial nephritis (6.7%; 230,244/3,449,083), chronic arterial hypertension (2.5%; 86,213/3,449,083), chronic ischaemic heart disease (2.3%; 80,071/3,449,083), nerve root/plexus disorders (2.3%; 79,341/3,449,083), hypertensive heart disease (2.1%; 73,597/3,449,083), and lower back pain (1.8%; 60,614/3,449,083). The median length of hospital stay was 7 days (IQR, 6–9) and the hospital mortality rate was 0.66% (22,683/3,449,083). Differences in all endpoints were found between female and male participants, the periods before, during, and after the COVID-19 pandemic, regions of residence, seasons of admission, levels of admission hospitals, and public and private hospitals. Interpretation: During a five-year period including the COVID-19 pandemic, the prevalence of hospital admissions in Mongolian adults was high during the study period. An analysis of the most common diagnoses leading to hospital admission suggested that many diagnoses could be managed without hospital admission in outpatient or primary care settings. Funding: Institutional funding (Mongolian National University of Medical Sciences and the Johannes Kepler University).

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook subtle variations, particularly in the case of artifacts. In this theoretical, proof-of-concept study, we investigated the potential of five different machine learning algorithms [random forests (RFs), gradient boosting methods (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regression] to detect subtle changes in the morphology of synthetically generated ECG beats despite artifacts. Following the generation of a synthetic ECG beat using the standardized McSharry algorithm, the baseline ECG signal was modified by changing the amplitude of different ECG components by 0.01–0.06 mV. In addition, a Gaussian jitter of 0.1–0.3 mV was overlaid to simulate artifacts. Five different machine learning algorithms were then applied to detect differences between the modified ECG beats. The highest discriminatory potency, as assessed by the discriminatory accuracy, was achieved by RFs and GBMs (accuracy of up to 1.0), whereas the least accurate results were obtained by logistic regression (accuracy approximately 10% less). In a second step, a feature importance algorithm (Boruta) was used to discriminate which signal parts were responsible for difference detection. For all comparisons, only signal components that had been modified in advance were used for discretion, demonstrating that the RF model focused on the appropriate signal elements. Our findings highlight the potential of RFs and GBMs as valuable tools for detecting subtle ECG changes despite artifacts, with implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings with clinical data.

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 0.98 ± 0.04, accuracyindividual: 0.96 ± 0.04; AUCROConeout: 0.96 ± 0.05; accuracyoneout: 0.93 ± 0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting.

Abstract Despite being vital in treating intensive-care patients with lung failure, especially COVID-19 patients, Veno-Venous Extra-Corporeal Membrane Oxygenation does not exploit its full potential, leaving ample room for improvement. The objective of this study is to determine the effect of cannula positioning and blood flow on the efficacy of Veno-Venous Extra-Corporeal Membrane Oxygenation, in particular in relationship with blood recirculation. We performed 98 computer simulations of blood flow and oxygen diffusion in a computerized-tomography-segmented right atrium and venae cavae for different positions of the returning and draining cannulae and ECMO flows of 3 L/min and $$5\,\hbox {L}/\hbox {min}$$ 5 L / min . For each configuration we measured how effective Veno-Venous Extra-Corporeal Membrane Oxygenation is at delivering oxygen to the right ventricle and thus to the systemic circulation. The main finding is that VV-ECMO efficacy is largely affected by the ECMO flow (global peak blood saturation: $${92.8}\,\%$$ 92.8 % ; average inter-group saturation gain: 9 percentage points) but only scarcely by the positioning of the cannulae (mean saturation ± standard deviation for the 3 L/min case: $$83.12 \pm 0.49$$ 83.12 ± 0.49 ; for the $$5\,\hbox {L}/\hbox {min}$$ 5 L / min case: $$92.21 \pm 0.37$$ 92.21 ± 0.37 ). An important secondary outcome is that recirculation, more intense with a higher ECMO flow, is less detrimental to the procedure than previously thought. The efficacy of current ECMO procedures is intrinsically limited and fine-tuning the positions of the cannulae, risking infections, offers very little gain. Setting a higher ECMO flow offers the biggest benefit despite mildly increasing blood recirculation.

Abstract Background and objectives Fainting is a well-known side effect of blood donation. Such adverse experiences can diminish the return rate for further blood donations. Identifying factors associated with fainting could help prevent adverse incidents during blood donation. Materials and methods Data of 85,040 blood donations from whole blood and apheresis donors within four consecutive years were included in this retrospective study. Seven different machine learning models (random forests, artificial neural networks, XGradient Boosting, AdaBoost, logistic regression, K nearest neighbors, and support vector machines) for predicting fainting during blood donation were established. The used features derived from the data obtained from the questionnaire every donor has to fill in before the donation and weather data of the day of the donation. Results One thousand seven hundred fifteen fainting reactions were observed in 228 846 blood donations from 88,003 donors over a study period of 48 months. Similar values for all machine learning algorithms investigated for NPV, PPV, AUC, and F1-score were obtained. In general, NPV was above 0.996, whereas PPV was below 0.03. AUC and F1-score were close to 0.9 for all models. Essential features predicting fainting during blood donation were systolic and diastolic blood pressure and ambient temperature, humidity, and barometric pressure. Conclusion Machine-learning algorithms can establish prediction models of fainting in blood donors. These new tools can reduce adverse reactions during blood donation and improve donor safety and minimize negative associations relating to blood donation.

This study presents rice husk silica-reinforced elastomer composites fabricated by wet compounding, exhibiting 10% higher dynamic friction coefficients than the reference composites made by conventional dry compounding. Depending on the fabrication method, the filler micro-dispersion was characterized to correlate the measured friction coefficients with the viscoelastic properties with respect to sliding velocity and substrate lubrication. The effect of viscoelastic properties of the elastomer composites on the adhesion and hysteresis friction coefficients was investigated based on the elastomer friction theory. It was found that the constructed friction master curves showed a reasonable correlation with the theory. This study demonstrates that the rice husk silica can be used as a reinforcing filler for sustainable tires. More importantly, the well-dispersed filler microstructures can ensure the higher friction coefficients of the elastomer composites on rough surfaces, improving the tire performances (e.g., wet traction, cornering, and acceleration) of the ever-growing electric vehicles and future mobility.

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P

Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital’s data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one’s own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls.

OBJECTIVES:. To identify the epidemiology and outcome of adults and children with and without sepsis in a rural sub-Sahara African setting. DESIGN:. A priori planned substudy of a prospective, before-and-after trial. SETTING:. Rural, sub-Sahara African hospital. PATIENTS:. One-thousand four-hundred twelve patients (adults, n = 491; children, n = 921) who were admitted to hospital because of an acute infection. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Demographic, clinical, laboratory data, danger signs, and the presence of sepsis (defined as a quick Sequential Organ Failure Assessment score count ≥ 2) at admission were extracted. Sepsis was observed in 69 adults (14.1%) and 248 children (26.9%). Sepsis patients differed from subjects without sepsis in several demographic and clinical aspects. Malaria was the most frequent type of infection in adults (66.7%) and children (63.7%) with sepsis, followed by suspected bacterial and parasitic infections other than malaria. Adults with sepsis more frequently developed respiratory failure (8.7% vs 2.1%; p = 0.01), had a higher in-hospital mortality (17.4% vs 8.3%; p < 0.001), were less often discharged home (81.2% vs 92.2%; p = 0.007), and had higher median (interquartile range) costs of care (30,300 [19,400–49,900] vs 42,500 Rwandan Francs [27,000–64,400 Rwandan Francs]; p = 0.004) than adults without sepsis. Children with sepsis were less frequently discharged home than children without sepsis (93.1% vs 96.4%; p = 0.046). Malaria and respiratory tract infections claimed the highest absolute numbers of lives. The duration of symptoms before hospital admission did not differ between survivors and nonsurvivors in adults (72 [24–168] vs 96 hr [72–168 hr]; p = 0.27) or children (48 [24–72] vs 36 [24–108 hr]; p = 0.8). Respiratory failure and coma were the most common causes of in-hospital death. CONCLUSIONS:. In addition to suspected bacterial, viral, and fungal infections, malaria and other parasitic infections are common and important causes of sepsis in adults and children admitted to a rural hospital in sub-Sahara Africa. The in-hospital mortality associated with sepsis is substantial, primarily in adults.

Over the last decades, individualized approaches and a better understanding of coagulopathy complexity in end-stage liver disease (ESLD) patients has evolved. The risk of both thrombosis and bleeding during minimally invasive interventions or surgery is associated with a worse outcome in this patient population. Despite deranged quantitative and qualitative coagulation laboratory parameters, prophylactic coagulation management is unnecessary for patients who do not bleed. Transfusion of red blood cells (RBCs) and blood products carries independent risks for morbidity and mortality, including modulation of the immune system with increased risk for nosocomial infections. Optimal coagulation management in these complex patients should be based on the analysis of standard coagulation tests (SCTs) and viscoelastic tests (VETs). VETs represent an individualized approach to patients and can provide information about coagulation dynamics in a concise period of time. This narrative review will deliver the pathophysiology of deranged hemostasis in ESLD, explore the difficulties of evaluating the coagulopathies in liver disease patients, and examine the use of VET assays and management of coagulopathy using coagulation factors. Methods: A selective literature search with PubMed as the central database was performed with the following.

Constrained layer damping treatments have been commonly used for vibration damping. The included viscoelastic material exhibits high damping potential that is retrieved under shear deformation. However, the impact of temperature on the viscoelastic properties and thus on the vibration damping often remains unconsidered. This paper addresses the design of viscoelastic damping under thermal consideration. For this purpose, the frequency and temperature dependent properties of a bromobutyl rubber compound were characterized in a dynamic mechanic thermal analysis. The viscoelastic material properties are employed in a finite element model of a structure with a constrained layer damping treatment. Subsequently, the modal damping of the structural vibrations is analyzed under varying temperature conditions. The analyses show a significant thermal impact on the modal damping. In this context, an approach is presented for compensating the adverse thermal effect on damping by a modified geometric design of the viscoelastic core layer.

Visually evoked potentials (VEPs) are widely used for diagnoses of different neurological diseases. Interestingly, there is limited research about the impact of the stimulus color onto the evoked response. Therefore, in our study we investigated the possibility of automatically classifying the stimulus color. The visual stimuli were selected to be red/black and green/black checkerboard patterns with equal light density. Both of these stimuli were presented in a random manner to nine subjects, while the electroencephalogram was recorded at the occipital lobe. After pre-processing and aligning the evoked potentials, an artificial neural network with one hidden layer was used to investigate the general possibility to automatically classify the stimulus color in three different settings. First, color classification with individually trained models, color classification with a common model, and color classification for each individual volunteer with a model trained on the data of the remaining subjects. With an average accuracy (ACC) of 0.83, the best results were achieved for the individually trained model. Also, the second (mean ACC = 0.76) and third experiments (mean ACC = 0.71) indicated a reasonable predictive accuracy across all subjects. Consequently, machine learning tools are able to appropriately classify stimuli colors based on VEPs. Although further studies are needed to improve the classification performance of our approach, this opens new fields of applications for VEPs.

OBJECTIVE:To assess the value of the inability to walk unassisted to predict hospital mortality in patients with suspected infection in a resource-limited setting. METHODS:This is a post hoc study of a prospective trial performed in rural Rwanda. Patients hospitalized because of a suspected acute infection and who were able to walk unassisted before this disease episode were included. At hospital presentation, the walking status was graded into: 1) can walk unassisted, 2) can walk assisted only, 3) cannot walk. Receiver operating characteristic (ROC) analyses and two-by-two tables were used to determine the sensitivity, specificity, negative and positive predictive values of the inability to walk unassisted to predict in-hospital death. RESULTS:One-thousand-sixty-nine patients were included. Two-hundred-one (18.8%), 315 (29.5%), and 553 (51.7%) subjects could walk unassisted, walk assisted or not walk, respectively. Their hospital mortality was 0%, 3.8% and 6.3%, respectively. The inability to walk unassisted had a low specificity (20%) but was 100% sensitive (CI95%, 90-100%) to predict in-hospital death (p = 0.00007). The value of the inability to walk unassisted to predict in-hospital mortality (AUC ROC, 0.636; CI95%, 0.564-0.707) was comparable to that of the qSOFA score (AUC ROC, 0.622; CI95% 0.524-0.728). Fifteen (7.5%), 34 (10.8%) and 167 (30.2%) patients who could walk unassisted, walk assisted or not walk presented with a qSOFA score count ≥2 points, respectively (p

Introduction Observational studies have demonstrated an association between vitamin D deficiency and increased risk of morbidity and mortality in critically ill patients. Cohort studies and pilot trials have suggested promising beneficial effects of vitamin D replacement in the critical ill, at least in patients with severe vitamin D deficiency. As vitamin D is a simple, low-cost and safe intervention, it has potential to improve survival in critically ill patients.Methods and analysis In this randomised, placebo-controlled, double-blind, multicentre, international trial, 2400 adult patients with severe vitamin D deficiency (25-hydroxyvitamin D≤12 ng/mL) will be randomised in a 1:1 ratio by www.randomizer.at to receive a loading dose of 540 000 IU cholecalciferol within 72 hours after intensive care unit (ICU) admission, followed by 4000 IU daily for 90 days or placebo. Hypercalcaemia may occur as a side effect, but is monitored by regular checks of the calcium level. The primary outcome is all-cause mortality at 28 days after randomisation. Secondary outcomes are: ICU, hospital, 90-day and 1-year mortality; hospital and ICU length of stay, change in organ dysfunction on day 5 as measured by Sequential Organ Function Assessment (SOFA) score, number of organ failures; hospital and ICU readmission until day 90; discharge destination, self-reported infections requiring antibiotics until day 90 and health-related quality of life. Recruitment status is ongoing.Ethics and dissemination National ethical approval was obtained by the Ethics Committee of the University of Graz for Austria, Erasme University Brussels (Belgium) and University Hospital Frankfurt (Germany), and will further be gained according to individual national processes. On completion, results will be published in a peer-reviewed scientific journal. The study findings will be presented at national and international meetings with abstracts online.Trial registration NCT03188796, EudraCT-No: 2016-002460-13.