Tool For Early Identification Research Articles

Cause for Concern? The Relationship Between Language Skills in Toddlers and Later Reading Skills in School

AbstractThe purpose of this longitudinal study is to investigate the association between poor language skills in toddlers and later reading skills. In contrast to earlier research, the present study used an authentic assessment of language skills conducted by staff in early childhood education and care as a tool for early identification. The participants were 515 Norwegian monolingual toddlers who were assessed when they were 33 months, 7 years, and 10 years. The results showed a small correlation between language skills identified at 33 months and later reading skills. The toddlers with the 10% poorest language skills were overrepresented in the 10% with the poorest decoding skills in 2nd grade and reading comprehension in 5th grade. Furthermore, 67.3% of toddlers with poor language skills attained below-average reading scores in 5th grade. These findings support the importance of paying attention to children in the lowest 10%, even at the age of 33 months. Furthermore, the results show that authentic assessments in the early years conducted by staff in early childhood education and care can make an important contribution to identifying toddlers in need of extra language stimulation in early childhood education.

Copper toxicity on Eisenia fetida in a vineyard soil: a combined study with standard tests, genotoxicity assessment and gut metagenomic analysis

Copper (Cu) toxicity is a pressing concern for several soils, especially in organic viticulture. The objective of this work was to assess Cu toxicity on the non-target organism Eisenia fetida, employing both traditional and novel tools for early identification of Cu-induced damages. In addition to traditional tests like avoidance and reproductive toxicity experiments, other tests such as the single cell gel electrophoresis (SCGE) and gut microbiome analysis were evaluated to identify early and more sensitive pollution biomarkers. Four sub-lethal Cu concentrations were studied, and the results showed strong dose-dependent responses by the earthworm avoidance test and the exceeding of habitat threshold limit at the higher Cu doses. An inverse proportionality was observed between reproductive output and soil Cu concentration. Bioaccumulation was not detected in earthworms; soil concentrations of potentially bioavailable Cu were not affected by E. fetida presence or by time. On the contrary, the SCGE test revealed dose-dependent genotoxicity for the ‘tail length’ parameter already at the second day of Cu exposition. Gut microbiome analysis a modulation of microbial composition, with the most aboundant families being Pectobateriaceae, Comamonadaceae and Microscillaceae. Bacillaceae increased over time and showed adaptability to copper up to 165 mg/kg, while at the highest dose even the sensitive Acetobacteriaceae family was affected. The research provided new insights into the ecotoxicity of Cu sub-lethal doses highlighting both alterations at earthworms’ cellular level and changes in their gut microbiota.

Prediction of Subclinical and Clinical Multiple Organ Failure Dysfunction in Breast Cancer Patients-A Review Using AI Tools.

This review explores the interconnection between precursor lesions of breast cancer (typical ductal hyperplasia, atypical ductal/lobular hyperplasia) and the subclinical of multiple organ failure syndrome, both representing early stages marked by alterations preceding clinical symptoms, undetectable through conventional diagnostic methods. Addressing the question "Why patients with breast cancer exhibit a tendency to deteriorate", this study investigates the biological progression from a subclinical multiple organ failure syndrome, characterized by insidious but indisputable lesions, to an acute (clinical) state resembling a cascade akin to a waterfall or domino effect, often culminating in the patient's demise. A comprehensive literature search was conducted using PubMed, Google Scholar, and Scopus databases in October 2023, employing keywords such as "MODS", "SIRS", "sepsis", "pathophysiology of MODS", "MODS in cancer patients", "multiple organ failure", "risk factors", "cancer", "ICU", "quality of life", and "breast cancer". Supplementary references were extracted from the retrieved articles. This study emphasizes the importance of early identification and prevention of the multiple organ failure cascade at the inception of the malignant state, aiming to enhance the quality of life and extend survival. This pursuit contributes to a deeper understanding of risk factors and viable therapeutic options. Despite the existence of the subclinical multiple organ failure syndrome, current diagnostic methodologies remain inadequate, prompting consideration of AI as an increasingly crucial tool for early identification in the diagnostic process.

Development and validation of a nomogram predictive model for cerebral small vessel disease: a comprehensive retrospective analysis.

Cerebral small vessel disease (CSVD) is a significant contributor to stroke, intracerebral hemorrhages, and vascular dementia, particularly in the elderly. Early diagnosis remains challenging. This study aimed to develop and validate a novel nomogram for the early diagnosis of cerebral small vessel disease (CSVD). We focused on integrating cerebrovascular risk factors and blood biochemical markers to identify individuals at high risk of CSVD, thus enabling early intervention. In a retrospective study conducted at the neurology department of the Affiliated Hospital of Hebei University from January 2020 to June 2022, 587 patients were enrolled. The patients were randomly divided into a training set (70%, n = 412) and a validation set (30%, n = 175). The nomogram was developed using multivariable logistic regression analysis, with variables selected through the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA). Out of 88 analyzed biomarkers, 32 showed significant differences between the CSVD and non-CSVD groups. The LASSO regression identified 12 significant indicators, with nine being independent clinical predictors of CSVD. The AUC-ROC values of the nomogram were 0.849 (95% CI: 0.821-0.894) in the training set and 0.863 (95% CI: 0.810-0.917) in the validation set, indicating excellent discriminative ability. Calibration plots demonstrated good agreement between predicted and observed probabilities in both sets. DCA showed that the nomogram had significant clinical utility. The study successfully developed a nomogram predictive model for CSVD, incorporating nine clinical predictive factors. This model offers a valuable tool for early identification and risk assessment of CSVD, potentially enhancing clinical decision-making and patient outcomes.

A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment.

Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1-L4 vertebrae derived from DXA (r = 0.91, p < 0.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R2 value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.

The collaborative development of a local authority emotionally based school non-attendance (Ebsna) early identification tool

ABSTRACT Emotionally based school non-attendance (EBSNA) difficulties negatively impact children and young people (CYP) and their families at many levels. EBSNA is complex, often involving individual and contextual risk factors which may require a school-wide or multi-agency response. This action research study in one UK local authority explored how collaborative working with stakeholders, guided by the RADIO model, facilitated the co-production of an early identification of needs tool (EINT) for identifying potential emotion-based attendance difficulties. A research stakeholder group (RSG n = 9) from local educational services worked jointly to develop the tool. The process of developing, piloting, and evaluating the EINT with local schools, services and families is described. Content analysis of ongoing planning and review meetings, an evaluative focus group, and interview data highlighted the opportunities and challenges of working collaboratively. The EINT was positively evaluated and perceived to facilitate home-school discussions and support a contextually relevant EBSNA approach.

Investigation of Nutritional Factors and Malnutrition Risk Prediction Model in Hospitalized Patients with Systemic Lupus Erythematosus in China.

Nutritional status is a critical indicator of overall health and immune function, significantly influencing treatment outcomes. Despite its importance, the nutritional status of patients with systemic lupus erythematosus (SLE) often receives insufficient attention. This study aims to evaluate the nutritional status of patients with SLE, identify factors associated with malnutrition, and develop a risk prediction model for malnutrition in this population. We collected clinical data from a convenience sample of SLE patients at a general hospital in Ningxia Province, China, between January and December 2022. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for malnutrition. A risk prediction model was constructed and evaluated using the receiver operating characteristic (ROC) curve. This study included 420 patients with SLE (mean age: 41.43 years, 91.7% women), of whom 46.2% were malnourished based on their serum albumin levels. Multivariate logistic regression analysis identified monthly income (OR=0.192, P<0.05), sleep quality (OR=2.559, P<0.05), kidney involvement (OR=4.269, P<0.05), disease activity (OR=2.743, P<0.05), leukocyte count (OR=1.576, P<0.05), lymphocyte count (OR=0.393, P<0.05), hemoglobin (OR=0.972, P<0.05), complement C3 (OR=0.802, P<0.05), and complement C4 (OR=0.493, P<0.05) as independent risk factors for malnutrition. The prediction model showed good predictive value with an area under the ROC curve of 0.895 (95% CI: 0.823-0.840), sensitivity of 0.907, and specificity of 0.827. The Hosmer-Lemeshow test indicated a good model fit (χ²=10.779, P=0.215). Malnutrition is a significant concern among SLE patients, influenced by a range of socioeconomic and clinical factors. Our risk prediction model, with its high sensitivity and specificity, provides a robust tool for early identification of malnutrition in this population. Implementing this model in clinical practice can guide healthcare providers in prioritizing at-risk patients, enabling proactive nutritional interventions that could potentially improve clinical outcomes, enhance quality of life, and reduce healthcare costs associated with SLE.

Cognitive equilibrium and instability : Lyapunov stability analysis in mental health research

Mental well-being is often seen as a fragile state, akin to a tightrope walk of balance. In our paper, “Cognitive Equilibrium and Instability: Lyapunov Stability Analysis in Mental Health Research,” we explore the nuanced dance between consistency and change in the realm of cognitive function. Drawing on Lyapunov Stability principles from the study of dynamic systems, we offer new perspectives on mental health mechanics. We propose that the mind is in a state of perpetual flux, constantly adjusting to a spectrum of influences to maintain cognitive balance. Our work involves identifying these points of balance and assessing their robustness using Lyapunov Stability Analysis. We examine how various factors, such as stress, environmental changes, and biological variations, can disrupt this balance, possibly leading to states of well-being or illness. Through our models, we illuminate the evolution and path of mental health conditions. Our methodology is a synthesis of theoretical models and real-world data, including neuroimaging, clinical, and psychological evaluations. Case studies in our paper demonstrate the application of our models to conditions like anxiety, depression, and bipolar disorder, revealing the fluid nature of these ailments. This work goes on to discuss the practical implications of our findings in the clinical setting. By identifying pivotal points of potential instability, our model serves as a tool for early identification of mental health concerns, guiding the creation of specific therapeutic interventions. Additionally, our work supports a tailored approach to mental health care, appreciating the individual cognitive patterns unique to each person. Our paper contributes to the burgeoning field of computational psychiatry, blending mathematical analysis with a perspective centered on the human experience. It sets the stage for future interdisciplinary research aimed at decoding the intricacies of the human psyche.

Construction and Validation of a Nomogram to Identify the Risk of Cavitation in Pulmonary Tuberculosis.

The present study aimed to construct and validate a nomogram based on clinical metrics to identify CPTB. The present study retrospectively recruited pulmonary tuberculosis (PTB) patients admitted to Jiashan County First People's Hospital in China from November 2018 to September 2023. PTB patients were classified into the CPTB group and the non-CPTB group based on chest computed tomography findings, and were randomly allocated to the training set (70%) and the validation cohort (30%). The training set and validation set were used to establish and validate nomogram, respectively. Multivariate logistic regression analysis (MLSA) was used to identify the independent risk factors for CPTB in patients with PTB. Statistically significant variables in the MLSA were then used to construct a nomogram predicting CPTB in patients with PTB. The receiver operating characteristic (ROC) curve, calibration curve analysis (CCA), and decision curve analysis (DCA) were used for the evaluation of the nomogram. A total of 293 PTB patients, including 208 in the training set (85 CPTB) and 85 in the validation set (33 CPTB\), were included in this study. Stepwise MLSA showed that sputum smear (≥2+), smoking(yes), glycosylated hemoglobin A1c(HbA1c), hemoglobin (HB), and systemic inflammatory response index (SIRI) were independent risk factors for the development of cavitation in patients with PTB. The nomogram identifying the high-risk CPTB patients was successfully established and showed a strong predictive capacity, with area under the curves (AUCs) of 0.875 (95% CI:0.806-0.909) and 0.848 (95% CI:0.751-0.946) in the training set and validation set respectively. In addition, the CCA and DCA corroborated the nomogram's high level of accuracy and clinical applicability within both the training and validation sets. The constructed nomogram, consisting of sputum smear positivity, smoking, HbA1C, HB, and SIRI, serves as a practical and effective tool for early identification and personalized management of CPTB.

Cincinnati Prehospital Stroke Scale (CPSS) as a Screening Tool for Early Identification of Cerebral Large Vessel Occlusions; a Systematic Review and Meta-analysis.

Large vessel occlusion (LVO) strokes are associated with worse functional outcomes and higher mortality rates. In the present systematic review and meta-analysis, we evaluated the diagnostic yield of the Cincinnati Prehospital Stroke Scale (CPSS) in detecting LVO. We performed an extensive systematic search among online databases including Medline, Embase, Web of Science, and Scopus, until July 31st, 2023. We also conducted a manual search on Google and Google scholar, along with citation tracking to supplement the systematic search in retrieving all studies that evaluated the diagnostic accuracy of the CPSS in detecting LVO among patients suspected to stroke. Fourteen studies were included in the present meta-analysis. CPSS showed the sensitivity of 97% (95% CI: 87%-99%) and the specificity of 17% (95% CI: 4%-54%) at the cut-off point of ≥1. The optimal threshold was determined to be ≥2, with a sensitivity of 82% (95% CI: 74%-88%) and specificity of 62% (95% CI: 48%-74%) in detecting LVO. At the highest cut-off point of ≥3, the CPSS had the lowest sensitivity of 60% (95% CI: 51%-69%) and the highest specificity of 81% (95% CI: 71%-88%). Sensitivity analyses showed the robustness of the results regardless of study population, inclusion of hemorrhagic stroke patients, pre-hospital or in-hospital settings, and the definition of LVO. A very low level of evidence demonstrated that CPSS, with a threshold set at ≥2, is a useful tool for identifying LVO stroke and directing patients to CSCs, both in prehospital and in-hospital settings.

Cardiovascular Disease Prediction Using Gradient Boosting Classifier

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.

Prognostic accuracy of SOFA, MEWS, and SIRS criteria in predicting the mortality rate of patients with sepsis: A meta-analysis.

In recent years, some studies classified patients with sepsis and predicted their mortality by using some evaluation scales. Several studies reported significant differences in the predictive values of several tools, and the non-uniformity of the cut-off value. To determine and compare the prognostic accuracy of Sequential Organ Failure Assessment (SOFA) score, Modified Early Warning Score (MEWS), and Systemic Inflammatory Response Syndrome (SIRS) criteria in predicting the mortality of patients with sepsis. This study comprised of systematic literature review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We searched PubMed, Embase, Web of Science and Cochrane Library databases from their establishment to July 31, 2022. The research articles published in the index journals provide sufficient data (true positive, false positive, true negative, and false negative results) for patients with sepsis. The combined sensitivity and specificity of the 95% confidence interval (CI) were calculated using the bivariate random effect model (BRM). The hierarchical overall subject working characteristics (HSROC) curve was drawn to evaluate the accuracy of the overall prognosis. Data of 55 088 patients from 32 studies were included in this meta-analysis. SOFA had an intermediate sensitivity of 0.73 (95% CI: 0.67-0.78) and a specificity of 0.70 (0.63-0.76). SIRS criteria had the highest sensitivity of 0.75 (0.66-0.82) and the lowest specificity of 0.40 (0.29-0.52). MEWS had the lowest sensitivity of 0.49 (0.40-0.59) and the highest specificity of 0.82 (0.78-0.86). Among SOFA, MEWS, and SIRS criteria, SOFA showed moderate sensitivity and specificity for predicting mortality in patients with sepsis, the highest sensitivity of SIRS and the strongest specificity of MEWS for predicting mortality in patients with sepsis. The future research direction is to combine the relevant indicators of MEWS and SIRS to develop a measurement tool with high reliability and validity. The review provides useful insights into the prognostic accuracy of different assessment tools in predicting mortality in sepsis patients, which will help clinicians choose the most appropriate tool for early identification and treatment of sepsis. The findings may also contribute to the development of more accurate and reliable prognostic models for sepsis.

Development and Validation of a Model for Postpancreatectomy Hemorrhage Risk

Postpancreatectomy hemorrhage (PPH) due to postoperative pancreatic fistula (POPF) is a life-threatening complication after pancreatoduodenectomy. However, there is no prediction tool for early identification of patients at high risk of late PPH. To develop and validate a prediction model for PPH. This retrospective prognostic study included consecutive patients with clinically relevant POPF who underwent pancreatoduodenectomy from January 1, 2009, to May 20, 2023, at the University Hospital Mannheim (derivation cohort), and from January 1, 2012, to May 31, 2022, at the University Hospital Dresden (validation cohort). Data analysis was performed from May 30 to July 29, 2023. Clinical and radiologic features of PPH. Accuracy of a predictive risk score of PPH. A multivariate prediction model-the hemorrhage risk score (HRS)-was established in the derivation cohort (n = 139) and validated in the validation cohort (n = 154). A total of 293 patients (187 [64%] men; median age, 69 [IQR, 60-76] years) were included. The HRS comprised 4 variables with associations: sentinel bleeding (odds ratio [OR], 35.10; 95% CI, 5.58-221.00; P < .001), drain fluid culture positive for Candida species (OR, 14.40; 95% CI, 2.24-92.20; P < .001), and radiologic proof of rim enhancement of (OR, 12.00; 95% CI, 2.08-69.50; P = .006) or gas within (OR, 12.10; 95% CI, 2.22-65.50; P = .004) a peripancreatic fluid collection. Two risk categories were identified with patients at low risk (0-1 points) and high risk (≥2 points) to develop PPH. Patients with PPH were predicted accurately in the derivation cohort (C index, 0.97) and validation cohort (C index 0.83). The need for more invasive PPH management (74% vs 34%; P < .001) and severe complications (49% vs 23%; P < .001) were more frequent in high-risk patients compared with low-risk patients. In this retrospective prognostic study, a robust prediction model for PPH was developed and validated. This tool may facilitate early identification of patients at high risk for PPH.

Novel nomograms to predict risk and prognosis in hospitalized patients with severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging and life-threatening infectious disease caused by SFTS virus. Although recent studies have reported the use of nomograms based on demographic and laboratory data to predict the prognosis of SFTS, no study has included viral load, which is an important factor that influences the prognosis, when compared with other risk factors. Therefore, this study aimed to develop a model that predicts SFTS prognosis before it reaches the critical illness stage and to compare the predictive ability of groups with and without viral load. Two hundred patients with SFTS were enrolled between June 2018 and August 2023. Data were sourced from the first laboratory results at admission, and two nomograms for mortality risk were developed using multivariate logistic regression to identify the risk variables for poor prognosis in these patients. We calculated the area under the receiver operating characteristic curve (AUC) for the two nomograms to assess their discrimination, and predictive abilities were compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The multivariate logistic regression analysis identified four independent risk factors: age, bleeding manifestations, prolonged activated partial thromboplastin time, and viral load. Based on these factors, a final nomogram predicting mortality risk in patients with SFTS was constructed; in addition, a simplified nomogram was constructed excluding the viral load. The AUC [0.926, 95% confidence interval (CI): 0.882-0.970 vs. 0.882, 95% CI: 35 0.823-0.942], NRI (0.143, 95% CI, 0.036-0.285), and IDI (0.124, 95% CI, 0.061-0.186) were calculated and compared between the two models. The calibration curves of the two models showed excellent concordance, and decision curve analysis was used to quantify the net benefit at different threshold probabilities. Two critical risk nomograms were developed based on the indicators for early prediction of mortality risk in patients with SFTS, and enhanced predictive accuracy was observed in the model that incorporated the viral load. The models developed will provide frontline clinicians with a convenient tool for early identification of critically ill patients and initiation of a better personalized treatment in a timely manner.

COPD: pulmonary vascular volume associated with cardiac structure and function

BackgroundEarly recognition of cardiac dysfunction in patients with chronic obstructive pulmonary disease (COPD) may prevent future cardiac impairment and improve prognosis. Quantitative assessment of subsegmental and segmental vessel volume by Computed Tomographic (CT) imaging can provide a surrogate of pulmonary vascular remodeling. We aimed to examine the relationship between lung segmental- and subsegmental vessel volume, and echocardiographic measures of cardiac structure and function in patients with COPD.MethodsWe studied 205 participants with COPD, included in a large cohort study of cardiovascular disease in COPD patients. Participants had an available CT scan and echocardiogram. Artificial intelligence (AI) algorithms calculated the subsegmental vessel fraction as the vascular volume in vessels below 10 mm2 in cross-sectional area, indexed to total intrapulmonary vessel volume. Linear regressions were conducted, and standardized ß-coefficients were calculated. Scatterplots were created to visualize the continuous correlations between the vessel fractions and echocardiographic parameters.ResultsWe found that lower subsegmental vessel fraction and higher segmental vessel volume were correlated with higher left ventricular (LV) mass, LV diastolic dysfunction, and inferior vena cava (IVC) dilatation. Subsegmental vessel fraction was correlated with right ventricular (RV) remodeling, while segmental vessel fraction was correlated with higher pulmonary pressure. Measures of LV mass and right atrial pressure displayed the strongest correlations with pulmonary vasculature measures.ConclusionPulmonary vascular remodeling in patients with COPD, may negatively affect cardiac structure and function. AI-identified remodeling in pulmonary vasculature may provide a tool for early identification of COPD patients at higher risk for cardiac impairment.

Risk Factors and Prognostic Scoring Model for 28-Day Mortality in Hematologic Patients with Stenotrophomonas M altophilia Bacteremia

Objective: Patients with hematologic diseases are at a higher risk for Stenotrophomonasmaltophilia(SM) bacteremia, which poses significant burden on morbidity and mortality. The aim of this study was to evaluate the early predictors of 28-day mortality in hematologic patients with SM bacteremia and to construct a reliable prediction model which can be used to identify patients at higher risk for mortality. Methods: This retrospective analysis of 131 hospitalized patients diagnosed with SM bacteremia was conducted in a tertiary blood disease center in Tianjin, China, from January 2012 to June 2023. Univariate analysis and multivariate logistic regression analysis were used to identify prognostic risk factors and develop a prognostic mortality scoring system and internally validated by bootstrap method (resampling = 1000). Results: The overall 28-day mortality rate in our study was 29.77% (39/131). The mean age was 45 (IQR 30.5-56.0) years and 70 individuals (53.44%) were diagnosed with myeloid malignancies. The existing condition severity-related scores did not perform well in predicting 28-day mortality for the patients in our population (AUROC values: SOFA: 0.842; ECOG: 0.831; APACHE Ⅱ: 0.709; SAPS Ⅱ: 0.675; aCCI: 0.654; Pitt score: 0.629). Through univariate and multivariate analysis, we identified four independent risk factors associated with 28-day mortality: SOFA score (OR = 3.67, 95% CI 1.99-6.78, P &amp;lt; 0.001; score = corresponding SOFA score), age ≥ 60 years (OR = 6.95, 95% CI 1.44-33.55, P = 0.0158; score = 2), bloody sputum or hemoptysis (OR = 5.32, 95% CI 1.08-26.29, P = 0.0402; score = 2) and neutropenia duration ≥ 25 days (OR = 3.92, 95% CI 1.01-15.26, P = 0.0487; score = 1). These factors were used to develop a prognostic scoring model, which yielded good discrimination (AUC = 0.889, 95% CI: 0.829-0.949) and calibration (Hosmer-Lemeshow goodness-of-fit test P = 0.998), with similar performance in the internal bootstrap validation (AUC = 0.888). Furthermore, all patients were divided into the high-risk group (≥ 7 points) and the low-risk group (&amp;lt; 7 points) based on the model and compared using Kaplan-Meier curves and log-rank tests. Additionally, we founded that the use of trimethoprim-sulfamethoxazole (TMP/SMX) after infection was associated with a better prognosis ( P = 0.012). Conclusions: The prediction model, utilizing SOFA score, age, bloody sputum or hemoptysis and neutropenia duration, proves to be an effective tool for early identification of hematologic patients with SM bacteremia at high risk of mortality.

Development and validation of prediction models for poor sleep quality among older adults in the post-COVID-19 pandemic era

Background Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks. Materials and Methods The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors. Results The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence. Conclusions This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period.

LM-DNN: pre-trained DNN with LSTM and cross Fold validation for detecting viral pneumonia from chest CT

ABSTRACT Some of the viruses may cause lung parenchyma and airway involvement. Usually, viral pneumonia causes ground-glass opacities, bilateral peripheral distribution, consolidation, vascular thickening, and reticular opacity. These features are common in COVID-19 rather than Non-Covid-19 viral pneumonia. However, in advanced cases, COVID-19 viral pneumonia may cause organising pneumonia and fibrosis of the lung. Atypical findings of Non-Covid-19 pneumonia have included central peripheral distribution, pleural effusion, lymphadenopathy, nodules, tree-in-bud opacities, and pneumothorax. Therefore, differentiating Non-Covid-19 pneumonia from COVID-19 pneumonia at chest computed tomography (CT) is necessary. In that case, CT scans of the thorax are one of the essential tools for early identification and future prognosis of viral pneumonia. We have proposed a Computer-Aided Diagnostic (CAD) system that can detect features of chest CT using a Deep Neural Network (DNN) with Long Short-Term Memory (LSTM). Transfer learning using pre-trained DNN models (ResNet50, VGG19, InceptionV3, Xception, DenseNet121, and VGG16) is applied to retain both high-level and low-level features effectively. The deep features are passed to the LSTM layer. The LSTM is utilised as a classifier and detects long short-term dependencies. The proposed method employs a hybrid DNN-LSTM network for automatic detection to take advantage of the uniqueness of the two models. The proposed models are trained with common and different features present in the chest CT of COVID-19 and Non-Covid-19 viral pneumonia. The 5-fold cross-validation (CV) method validated and tested the proposed model. The proposed DNN model’s performance is quite improved with LSTM and CV. As a result, the proposed LM-DNN (VGG16+LSTM+CV) model has achieved the classification test accuracy of 91.58% and specificity of 93.86%, which offers superior performance with state-of-the-art. Also, the DenseNet121+LSTM+CV model has reached the classification test accuracy of 90.1% and sensitivity of 92%.

Using interleukin-18, Eotaxin-1, and Eotaxin-3 to identify brain injury in neonates with congenital heart disease

ObjectivesSerum biomarkers for brain injury in neonates with congenital heart disease (CHD) provide a bedside tool for early identification and intervention. In this preliminary study, we aim to evaluate IL-18, Eotaxin-1 and Eotaxin-3 as biomarkers for the detection of brain injury in neonates with CHD. MethodsWe prospectively enrolled seven neonates diagnosed in-utero with CHD and obtained serum samples at birth, before and after surgery. Samples were analyzed using a human cytokine/chemokine multiplex assay. Brain injury was diagnosed on brain MRI before surgery. ResultsSamples from seven neonates at four time points before surgery and three time points after surgery were analyzed. A significant difference was found in neonates with brain injury compared to CHD neonates without. Elevations in interleukin (IL)-18 pre- and post-operative (p = 0.007), IL-18 pre-operative (p = 0.046), Eotaxin-1 pre-operative (p = 0.011), and Eotaxin-3 pre- and post-operative (p = 0.026) were found in CHD neonates with brain injury. ConclusionThis is the first published report on the use IL-18, Eotaxin-1, and Eotaxin-3 in the detection of brain injury for neonates with CHD. These biomarkers may provide an actionable target for neuroprotection through immunomodulation. Larger cohorts are needed to determine the significance and clinical utility of these biomarkers.

The association of positive sepsis PCRs tests with patient mortality, length of stay, and antibiotic management in a medical intensive care unit

Background: Polymerase chain reaction (PCR) testing amplifies a specific DNA segment through heat cycling to identify pathogens for the diagnosis of infection. This testing allows for rapid detection of pathogens, before conventional blood culture results become available. The high mortality associated with severe sepsis and septic shock stresses the importance of early diagnosis and initiation of early antibiotic therapy. This study analyzed the association of the sepsis PCR results and antibiotic management, length of stay, and mortality outcomes in a medical intensive care unit (MICU).&#x0D; Methods: This study is a retrospective, cross-sectional study on patients diagnosed with severe sepsis and septic shock based on ICD-10 codes, aged 18 years and older, admitted to the MICU at University Medical Center, Lubbock, Texas, between December 2016 to December 2020.&#x0D; Results: Clinical information from 268 patients with the diagnosis of sepsis or septic shock was collected and analyzed. The mean age was 60.9 ± 15.6 years with a predominance of men (144, 53.7%) and Caucasian race (193,73.4%). A total of 101 patients (37.6%) had positive PCR results; overall, PCR test results had a diagnostic sensitivity of 91.2 %. The concordance between positive blood culture and positive PCR was 93.3 % (p &lt;.0001). There was a significant correlation between PCR positivity and increased serum lactate levels (p=0.03), changes in antibiotics (P&lt;0.00010), and increased mortality rate (p=0.04). There was no significant correlation between PCR positivity and length of stay (p=0.84).&#x0D; Conclusion: In this study, PCR testing was an accurate tool for early identification of bacterial pathogens. A positive PCR was associated with higher serum lactate levels, higher mortality rates, and an increased frequency of antibiotic changes but was not associated with shorter ICU length of stay.&#x0D; Key words: Sepsis, septic shock, PCR testing, outcomes