Quick Assessment Research Articles

Prevalence and predictors of multidrug-resistant bacteremia in liver cirrhosis.

Improved knowledge of local epidemiology and predicting risk factors of multidrug-resistant (MDR) bacteria are required to optimize the management of infections. This study examined local epidemiology and antibiotic resistance patterns of liver cirrhosis (LC) patients and evaluated the predictors of MDR bacteremia in Korea. This was a retrospective study including 140 LC patients diagnosed with bacteremia between January 2017 and December 2022. Local epidemiology and antibiotic resistance patterns and the determinants of MDR bacteremia were analyzed using logistic regression analysis. The most frequently isolated bacteria, from the bloodstream, were Escherichia coli (n = 45, 31.7%) and Klebsiella spp. (n = 35, 24.6%). Thirty-four isolates (23.9%) were MDR, and extended-spectrum beta-lactamase E. coli (52.9%) and methicillin-resistant Staphylococcus aureus (17.6%) were the most commonly isolated MDR bacteria. When Enterococcus spp. were cultured, the majority were MDR (MDR 83.3% vs. 16.7%, p = 0.003), particularly vancomycin-susceptible Enterococcus faecium. Antibiotics administration within 30 days and/or nosocomial infection was a significant predictor of MDR bacteremia (OR: 3.40, 95% CI: 1.24-9.27, p = 0.02). MDR bacteremia was not predicted by sepsis predictors, such as positive systemic inflammatory response syndrome (SIRS) or quick Sequential Organ Failure Assessment (qSOFA). More than 70% of strains that can be treated with a third-generation cephalosporin have been cultured. In cirrhotic patients, antibiotic administration within 30 days and/or nosocomial infection are predictors of MDR bacteremia; therefore, empirical administration of broad-spectrum antibiotics should be considered when these risk factors are present.

Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities

ObjectiveIn this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score. DesignThis retrospective cohort study was conducted between 2016 and 2021. SettingTwo university hospitals in Brazil. ParticipantsPatients with sepsis. InterventionsSeveral clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities. Main variable of interestIn-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis. ResultsA total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction. ConclusionsSeveral risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.

Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities

ObjectiveIn this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score. DesignThis retrospective cohort study was conducted between 2016 and 2021. SettingTwo university hospitals in Brazil. ParticipantsPatients with sepsis. InterventionsSeveral clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities. Main variable of interestIn-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis. ResultsA total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction. ConclusionsSeveral risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.

Diastolic/systolic blood pressure ratio for predicting febrile children with sepsis and progress to septic shock in the emergency department

ObjectiveGiven the scarcity of studies analyzing the clinical predictors of pediatric septic cases that would progress to septic shock, this study aimed to determine strong predictors for pediatric emergency department (PED) patients with sepsis at risk for septic shock and mortality.MethodsWe conducted chart reviews of patients with ≥ 2 age-adjusted quick Sequential Organ Failure Assessment score (qSOFA) criteria to recognize patients with an infectious disease in two tertiary PEDs between January 1, 2021, and April 30, 2022. The age range of included patients was 1 month to 18 years. The primary outcome was development of septic shock within 48 h of PED attendance. The secondary outcome was sepsis-related 28-day mortality. Initial important variables in the PED and hemodynamics with the highest and lowest values during the first 24 h of admission were also analyzed.ResultsOverall, 417 patients were admitted because of sepsis and met the eligibility criteria for the study. Forty-nine cases progressed to septic shock within 48 h after admission and 368 were discharged without progression. General demographics, laboratory data, and hemodynamics were analyzed by multivariate analysis. Only the minimum diastolic blood pressure/systolic blood pressure ratio (D/S ratio) during the first 24 h after admission remained as an independent predictor of progression to septic shock and 28-day mortality. The best cutoff values of the D/S ratio for predicting septic shock and 28-day mortality were 0.52 and 0.47, respectively.ConclusionsThe D/S ratio is a practical bedside scoring system in the PED and had good discriminative ability in predicting the progression of septic shock and in-hospital mortality in PED patients. Further validation is essential in other settings.

Characteristics and outcomes of patients admitted to intensive care units in Uganda: a descriptive nationwide multicentre prospective study

Intensive care unit (ICU) mortality rates have decreased over time. However, in low-and lower-middle income countries (LMICs), there remains an excess ICU mortality with limited understanding of patient characteristics, treatments, and outcomes from small single centre studies. We aimed therefore, to describe the characteristics, therapies and outcomes of patients admitted to all intensive care units in Uganda. A nationwide prospective observational study including all patients admitted Uganda’s ICUs with available daily charts was conducted from 8th January 2018 to 1st April 2018. Socio-demographics and clinical characteristics including worst vital signs in the first 24 h of admission were recorded with calculation of the National Early Warning Score (NEWS-2) and quick Sequential Organ Function Assessment (qSOFA) score. ICU interventions were recorded during the ICU stay and patients were followed up to 28 days in ICU. The primary outcome was 28 day ICU mortality. Three-hundred fifty-one patients were analysed with mean age 39 (24.1) years, 205 (58.4%) males with 197 (56%) surgical admissions. The commonest indication for ICU admission was postoperative care (42.9%), 214 (61%) had at least one comorbidity, with hypertension 104 (48.6%) most prevalent and 35 (10%) HIV positive. The 28 day ICU mortality was 90/351 (25.6%) with a median ICU stay of 3 (1–7) days. The highest probability of death occurred during the first 10 days with more non-survivors receiving mechanical ventilation (80% vs 34%; p < 0.001), sedation/paralysis (70% vs 50%; p < 0.001), inotropic/vasopressor support (56.7% vs 22.2%; p < 0.001) and renal replacement therapy (14.4% vs 4.2%; p < 0.001). Independent predictors of ICU mortality included mechanical ventilation (HR 3.34, 95% CI 1.48–7.52), sedation/paralysis (HR 2.68, 95% CI 1.39–5.16), inotropes/vasopressor (HR 3.17,95% CI 1.89–5.29) and an HIV positive status (HR 2.28, 95% CI 1.14–4.56). This study provides a comprehensive description of ICU patient characteristics, treatment patterns, and outcomes in Uganda. It not only adds to the global body of knowledge on ICU care in resource-limited settings but also serves as a foundation for future research and policy initiatives aimed at optimizing ICU care in Sub-Saharan Africa.

Worry as Important “Feelers” in Clinical Anesthesia Practice: A Mixed-Methods Study

PurposeWorry is an intuitive sense that goes beyond logical reasoning and is valuable in situations where patients’ conditions are rapidly changing or when objective data may not fully capture the complexity of a patient’s situation. Nurse anesthetists’ subjective reasons for worry are quite vague as they are valued inconsistently and not accurately expressed. This study aimed to identify factors playing a role in the emergence of worry during anesthesia practice to clarify its concept. DesignMixed-methods design consisting of quantitative online surveys followed by qualitative focus group interviews including Dutch nurse anesthetists. MethodsBoth quantitative and qualitative thematic analyses were performed, followed by data and methodological triangulation to enhance the validity and credibility of findings and mitigate the presence of bias. FindingsSurveys (N = 102) were analyzed, and 14 nurse anesthetists participated in the focus group interviews. A total of 89% of the survey respondents reported that at least once have had the feeling of worry, of which 92% use worry during clinical anesthesia practice. Worry was mentioned to be a vital element during anesthesia practice that makes it possible to take precautionary actions to change the anesthetic care plan in a changing situation or patient deterioration. ConclusionsWhile a clear definition of worry could not be given, it is a valuable element of anesthesia practice as it serves as a catalyst for critical thinking, problem-solving, clinical reasoning, and decision-making. Use of the feeling of worry alongside technological systems to make an informed decision is crucial. Technology has significantly improved the ability of health care providers to detect and respond to patient deterioration promptly, but it is crucial for nurse anesthetists to use their feeling of worry or intuition alongside technological systems and evidence-based practice to ensure quick assessments or judgments based on experience, knowledge, and observations in clinical practice.

Development of the VEGANScreener, a Tool for a Quick Diet Quality Assessment among Vegans in Europe.

Plant-based diets are not inherently healthy. Similar to omnivorous diets, they may contain excessive amounts of sugar, sodium, and saturated fats, or lack diversity. Moreover, vegans might be at risk of inadequate intake of certain vitamins and minerals commonly found in foods that they avoid. We developed the VEGANScreener, a tool designed to assess the diet quality of vegans in Europe. Our approach combined best practices in developing diet quality metrics with scale development approaches and involved the following: (a) narrative literature synthesis, (b) evidence evaluation by an international panel of experts, and (c) translation of evidence into a diet screener. We employed a modified Delphi technique to gather opinions from an international expert panel. Twenty-five experts in the fields of nutrition, epidemiology, preventive medicine, and diet assessment participated in the first round, and nineteen participated in the subsequent round. Initially, these experts provided feedback on a pool of 38 proposed items from the literature review. Consequently, 35 revised items, with 17 having multiple versions, were suggested for further consideration. In the second round, 29 items were retained, and any residual issues were addressed in the final consensus meeting. The ultimate screener draft encompassed 29 questions, with 17 focusing on foods and nutrients to promote, and 12 addressing foods and nutrients to limit. The screener contained 24 food-based and 5 nutrient-based questions. We elucidated the development process of the VEGANScreener, a novel diet quality screener for vegans. Future endeavors involve contrasting the VEGANScreener against benchmark diet assessment methodologies and nutritional biomarkers and testing its acceptance. Once validated, this instrument holds potential for deployment as a self-assessment application for vegans and as a preliminary dietary screening and counseling tool in healthcare settings.

Prognostic Accuracy of Screening Tools for Clinical Deterioration in Adults With Suspected Sepsis in Northeastern Thailand: A Cohort Validation Study.

We sought to assess the performance of commonly used clinical scoring systems to predict imminent clinical deterioration in patients hospitalized with suspected infection in rural Thailand. Patients with suspected infection were prospectively enrolled within 24 hours of admission to a referral hospital in northeastern Thailand between 2013 and 2017. In patients not requiring intensive medical interventions, multiple enrollment scores were calculated including the National Early Warning Score (NEWS), the Modified Early Warning Score, Between the Flags, and the quick Sequential Organ Failure Assessment score. Scores were tested for predictive accuracy of clinical deterioration, defined as a new requirement of mechanical ventilation, vasoactive medications, intensive care unit admission, and/or death approximately 1 day after enrollment. The association of each score with clinical deterioration was evaluated by means of logistic regression, and discrimination was assessed by generating area under the receiver operating characteristic curve. Of 4989 enrolled patients, 2680 met criteria for secondary analysis, and 100 of 2680 (4%) experienced clinical deterioration within 1 day after enrollment. NEWS had the highest discrimination for predicting clinical deterioration (area under the receiver operating characteristic curve, 0.78 [95% confidence interval, .74-.83]) compared with the Modified Early Warning Score (0.67 [.63-.73]; P < .001), quick Sequential Organ Failure Assessment (0.65 [.60-.70]; P < .001), and Between the Flags (0.69 [.64-.75]; P < .001). NEWS ≥5 yielded optimal sensitivity and specificity for clinical deterioration prediction. In patients hospitalized with suspected infection in a resource-limited setting in Southeast Asia, NEWS can identify patients at risk of imminent clinical deterioration with greater accuracy than other clinical scoring systems.

E-WASTE FACILITY LOCATOR

Integrated mobile application and website offer a holistic e-waste management solution with multilingual, chatbot-driven assistance. Users can efficiently locate the nearest e-waste facility using path optimization and, upon device input, instantly view the rewards they'll receive post-disposal, which may include cryptocurrency, vouchers, NFTs, or cashback points, based on valuable materials recovered. To enhance user awareness, our platform delivers educational pop-ups related to users' recent search history, educating them about harmful and valuable components in their old devices. Furthermore, users can utilize AI and ML for quick assessments by uploading device pictures. In the spirit of sustainability, the platform allows users to not only dispose of their old devices but also purchase materials recovered from e-waste disposal. We are committed to cost-effective development, ensuring an economical yet environmentally conscious service that encompasses both a mobile app and website.

Diversified Filtering Mechanism for Evaluation Indicators of Urban Road Renewal Schemes

As global urban infrastructure renewal progresses, the urgent need for sustainability-driven urban renewal intensifies amidst resource scarcity and environmental concerns. Effective evaluation and decision making regarding urban road renewal schemes are prerequisites for their successful implementation. However, variation in the prioritization of indicators in project evaluations and the poor adaptability of existing frameworks hinder the quick assessment of diverse projects. To address this issue, this paper proposes a scheme evaluation framework with embedded renewal project features comprising four modules. Following the initial construction of a sustainability-driven evaluation system, an indicator-filtering mechanism combining the Latent Dirichlet Allocation (LDA) model with a text similarity algorithm is developed. The Entropy Weight-–TOPSIS method is then employed to derive the final optimal decision based on selected indicators. Applying the decision framework to the G15 Jialiu Widening and Reconstruction Project in Shanghai, China, indicators are reduced by 48.3%, with the optimal scheme decision consistent with the traditional Entropy Weight–TOPSIS method. The framework is robust and enhances decision efficiency, filling theoretical gaps in existing indicator-filtering mechanisms.

Development of two questionnaires for the assessment of knowledge and self-management in patients with chronic-obstructive pulmonary disease (COPD)

As with other chronic diseases, the course of chronic obstructive pulmonary disease (COPD) can be expected to be positively influenced if patients are well informed about their disease and undertake appropriate self-management. Assessments of the level of knowledge and management that are comparable should benefit from structured, systematically developed questionnaires. These, however, have not been published in Germany. A total of 310 patients with COPD were recruited from three pneumological practices and one hospital to develop the questionnaires. Based on statistical criteria and content assessments by medical specialists, two questionnaires on knowledge (17 questions) and self-management (25 questions) were developed by selecting and modifying questions from published studies and training programs. In addition, two short versions with 5 and 3 questions were created to enable a quick assessment of the patients' knowledge and self-management. All questionnaires also included a visual analogue scale for self-assessment of knowledge and self-management. The statistical procedures for systematically guided selection comprised correlation and regression analyses. The questionnaires revealed considerable knowledge deficits in many patients and remarkably unsystematic, incoherent knowledge. The extent of this knowledge was negatively correlated with higher age and positively correlated with participation in training programs; this also applied to self-management. Correlations between the answers to the knowledge questions were higher in patients who had participated in training programs. The visual analogue scales for self-assessment of knowledge and management always correlated with the total number of correct answers. The questionnaires on knowledge and self-management in patients with COPD could be used in outpatient settings, including by non-medical staff, in order to quickly identify and correct deficits or as a reason to recommend training programs. The short versions and the analogue scales for self-assessment can give at least first hints. Potentially, training programs should focus more on promoting the coherence of knowledge through better understanding, as this presumably favors long-term knowledge. Older patients and those with a low level of education appear to be particularly in need of specially adapted training programs.

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools.

Rheological models are usually used to predict foamed fluid viscosity; however, obtaining the model constants under various conditions is challenging. Hence, this paper investigated the effect of different variables on foam rheology, such as shear rate, temperature, pressure, surfactant types, gas phase, and salinity, using a high-pressure high-temperature foam rheometer. Power-law, Bingham plastic, and Casson fluid models fit the experimental data well. Therefore, the data were fed to different machine learning techniques to evaluate the rheological model constants with different features. In this study, seven different machine learning techniques have been applied to predict the rheological models' constants, including decision tree, random forest, XGBoost (XGB), adaptive gradient boosting, gradient boosting, support vector regression, and voting regression. We evaluated the performance of our machine learning models using the coefficient of determination (R2), cross-plots, root-mean-square error, and average absolute percentage error. Based on the prediction outcomes, the XGB model outperformed the other ML models. The XGB model exhibited remarkably low error rates, achieving a prediction accuracy of 95% under ideal conditions. Furthermore, our prediction results demonstrated that the Casson model accurately captured the rheological behavior of the foam. Additionally, we used Pearson's correlation coefficients to assess the significance of various properties in relation to the constants within the rheological models. It is evident that the XGB model makes predictions with nearly all features contributing significantly, while other machine learning techniques rely more heavily on specific features over others. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serves as a quick assessment tool.

When failure is not an option: a police firearms training concept for improving decision-making in shoot/don't shoot scenarios.

Sometimes, policing requires a quick and correct assessment of potentially hazardous situations. The training of tactical gaze control and visual attention, and its positive impact on efficient shoot/don't shoot decisions in police cadets' use of firearms has recently been demonstrated. On this basis, we designed an individual videobased police firearms training that was grounded on the Four-Component Instructional Design Model (4C/ID). We shifted toward an individual blended learning approach where we applied an intervention training focused on situational awareness, tactical gaze control, and visual attention. In a preregistered lab experiment, N = 45 senior police officers were randomly allocated to the intervention training or an active control training that resembled a traditional police firearms training. Both groups watched a self-produced educational video before proceeding to the practical training in our indoor firing range. In a pre- and post-test, they engaged in realistic shoot/don't shoot video scenarios. Both groups did very well regarding decision-making, the optimal muzzle position, and the tactical conduct to keep both eyes open before shooting. Although both groups performed on a comparable level in the pre-test's shoot scenarios, the intervention group significantly improved their response times and time until the first hit. Overall, we were able to provide an adapted, didactically based police firearms training that supplements current standards. We demonstrated that experts are still susceptible to innovative training concepts and therefore substantiate the recommendation to devote more attention to approaches that emphasize the importance of situational awareness, tactical gaze control, and visual attention in police firearms training.

Assessment of a novel BLOOMY score for predicting mortality in hospitalised adults with bloodstream infection

PurposeA German multicentre study BLOOMY was the first to use machine learning approach to develop mortality prediction scores for bloodstream infection (BSI) patients, but the scores have not been assessed in other cohorts. Our aim was to assess how the BLOOMY 14-day and 6-month scores estimate mortality in our cohort of 497 cases with BSI.MethodsClinical data, laboratory data, and patient outcome were gathered retrospectively from patient records. The scores were calculated as presented in the BLOOMY study with the exception in the day of the evaluation.ResultsIn our cohort, BLOOMY 14-day score estimated death by day 14 with an area under curve (AUC) of 0.87 (95% Confidence Interval 0.80–0.94). Using ≥ 6 points as a cutoff, sensitivity was 68.8%, specificity 88.1%, positive predictive value (PPV) 39.3%, and negative predictive value (NPV) 96.2%. These results were similar in the original BLOOMY cohort and outweighed both quick Sepsis-Related Organ Failure Assessment (AUC 0.76) and Pitt Bacteraemia Score (AUC 0.79) in our cohort. BLOOMY 6-month score to estimate 6-month mortality had an AUC of 0.79 (0.73–0.85). Using ≥ 6 points as a cutoff, sensitivity was 98.3%, specificity 10.7%, PPV 25.7%, and NPV 95.2%. AUCs of 6-month score to estimate 1-year and 5-year mortality were 0.80 (0.74–0.85) and 0.77 (0.73–0.82), respectively.ConclusionThe BLOOMY 14-day and 6-month scores performed well in the estimations of mortality in our cohort and exceeded some established scores, but their adoption in clinical work remains to be seen.

Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework.

Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.

Geometrical method for a fast practical static stability region evaluation of a smart microgrid

This paper introduces an innovative approach to the offline Static Security Assessment (SSA) of smart MicroGrids (MG). By defining a specific region in the operating hyperspace, the stability of a MG is evaluated in a practical way, through the study of violations of voltage limits settled by protection relays. This method determines whether operating points, generated by Economic Dispatch algorithms, are feasible or not. Unlike traditional methods, it doesn't require continuous training on large datasets if the system topology doesn’t change. The method proactively explores the operating region offline, enabling quick assessment of operational security. By analyzing the feasibility region in the hyperspace from a geometrical perspective, it provides rapid insights of the system security status. This eliminates the need for computationally expensive trajectory studies involving complex equations and online iterative procedures, as applied in energy function approaches and conventional methods like the continuation load flow. The methodology is tested by simulation on a case study reproducing a subset of the experimental MG installed at the PrInCE Laboratory of the Polytechnic University of Bari.

Dynamic analysis of acute deterioration in chronic liver disease patients using modified quick sequential organ failure assessment.

Quick sequential organ failure assessment (qSOFA) is believed to identify patients at risk of poor outcomes in those with suspected infection. We aimed to evaluate the ability of modified qSOFA (m-qSOFA) to identify high-risk patients among those with acutely deteriorated chronic liver disease (CLD), especially those with acute-onchronic liver failure (ACLF). We used data from both the Korean Acute-on-Chronic Liver Failure (KACLiF) and the Asian Pacific Association for the Study of the Liver ACLF Research Consortium (AARC) cohorts. qSOFA was modified by replacing the Glasgow Coma Scale with hepatic encephalopathy, and an m-qSOFA ≥2 was considered high. Patients with high m-qSOFA had a significantly lower 1-month transplant-free survival (TFS) in both cohorts and higher organ failure development in KACLiF than those with low m-qSOFA (Ps<0.05). Subgroup analysis by ACLF showed that patients with high m-qSOFA had lower TFS than those with low m-qSOFA. m-qSOFA was an independent prognostic factor (hazard ratios, HR=2.604, 95% confidence interval, CI 1.353-5.013, P=0.004 in KACLiF and HR=1.904, 95% CI 1.484- 2.442, P<0.001 in AARC). The patients with low m-qSOFA at baseline but high m-qSOFA on day 7 had a significantly lower 1-month TFS than those with high m-qSOFA at baseline but low m-qSOFA on day 7 (52.6% vs. 89.4%, P<0.001 in KACLiF and 26.9% vs. 61.5%, P<0.001 in AARC). Baseline and dynamic changes in m-qSOFA may identify patients with a high risk of developing organ failure and short-term mortality among CLD patients with acute deterioration.

A novel stability equation for the estimation of the factor of safety for homogeneous dry finite slopes

AbstractThis paper introduces a novel closed‐form equation (surrogate model) for approximating the Morgenstern–Price estimate of the factor of safety of homogeneous dry finite slopes with circular failure surfaces. Unlike typically used methods, the proposed equation does not require the definition of a critical failure surface, splitting the soil mass into slices, or the iterative reduction of soil resistance to the limit state. It can be easily programmed into calculators or computers and accurately determines the minimum factor of safety based on the shear strength parameters and slope geometry—meaning that it is ideally suited for integration into reliability calculations. The equation is determined parametrically for various soil parameters and slope geometry using the Morgenstern–Price method and compares favorably with conventional techniques, such as slope stability charts, limit equilibrium, and strength reduction methods (SRM). From a practical perspective, the proposed equation greatly simplifies the analysis of the slope stability as the creation of a numerical model is not required and is suited to use in the feasibility stage of a project, for example, for large linear infrastructure projects with differing slope geometries or in situations where a quick assessment is desired.

Value of modified qSOFA, glucose and lactate in predicting prognosis in children with sepsis in the PICU

The purpose was to investigate how well age-adjusted modified quick Sequential Organ Failure Assessment (qSOFA) scores paired with blood glucose and lactate levels predict the outcomes of septicemic children in the pediatric intensive care unit (PICU). One hundred children who were diagnosed with sepsis and septic shock in the PICU of Henan Children’s Hospital were eligible, and other 20 patients in the same hospital at different times were selected as a validation set. Respiratory rate (RR), heart rate (HR), capillary refill time (CRT), and Alert, Voice, Pain, Unresponsive (AVPU) scale were included in the age-adjusted modified qSOFA scoring criteria for scoring. The primary outcome was 28-day all-cause mortality. The predictive values were evaluated by the ROC curve. In the sepsis group, 50 patients were male, and 50 patients were female. The 28-day all-cause mortality rate was 52%. Fifty-one patients with age-adjusted modified qSOFA scores >1. The serum lactate level was 2.4 mmol/L, and the blood glucose level was 9.3 mmol/L. The AUCs for the age-adjusted modified qSOFA score, serum lactate and blood glucose levels for the prediction of 28-day all-cause mortality in children with sepsis were 0.719, 0.719 and 0.737, respectively. The cut-off values were one point, 3.8 mmol/L and 10 mmol/L, respectively. The AUC of the age-adjusted modified qSOFA score for the validation set of was 0.925. When the three indices were combined, the AUC was 0.817, the Hosmer–Lemeshow goodness-of-fit test showed χ 2 = 2.428 and p = .965. When children with sepsis are admitted to the ICU, we recommend performing rapid scoring and rapid bedside lactate and glucose testing to determine the early prognosis.

Association between chest X-ray score and clinical outcome in COVID-19 patients: A study on modified radiographic assessment of lung edema score (mRALE) in Indonesia.

Radiological examinations such as chest X-rays (CXR) play a crucial role in the early diagnosis and determining disease severity in coronavirus disease 2019 (COVID-19). Various CXR scoring systems have been developed to quantitively assess lung abnormalities in COVID-19 patients, including CXR modified radiographic assessment of lung edema (mRALE). The aim of this study was to determine the relationship between mRALE scores and clinical outcome (mortality), as well as to identify the correlation between mRALE score and the severity of hypoxia (PaO2/FiO2 ratio). A retrospective cohort study was conducted among hospitalized COVID-19 patients at Dr. Soetomo General Academic Hospital Surabaya, Indonesia, from February to April 2022. All CXR data at initial admission were scored using the mRALE scoring system, and the clinical outcomes at the end of hospitalization were recorded. Of the total 178 COVID-19 patients, 62.9% survived after completing the treatment. Patients within non-survived had significantly higher quick sequential organ failure assessment (qSOFA) score (p<0.001), lower PaO2/FiO2 ratio (p=0.004), and higher blood urea nitrogen (p<0.001), serum creatinine (p<0.008) and serum glutamic oxaloacetic transaminase (p=0.001) levels. There was a significant relationship between mRALE score and clinical outcome (survived vs deceased) (p=0.024; contingency coefficient of 0.184); and mRALE score of ≥2.5 served as a risk factor for mortality among COVID-19 patients (relative risk of 1.624). There was a significant negative correlation between the mRALE score and PaO2/FiO2 ratio based on the Spearman correlation test (r=-0.346; p<0.001). The findings highlight that the initial mRALE score may serve as an independent predictor of mortality among hospitalized COVID-19 patients as well as proves its potential prognostic role in the management of COVID-19.