Hemodynamic Impairment of Blood Pressure and Stroke Mechanisms in Symptomatic Intracranial Atherosclerotic Stenosis.

BACKGROUND: Hemodynamic impairment of blood pressure may play a crucial role in determining the mechanisms of stroke in symptomatic intracranial atherosclerotic stenosis). We aimed to elucidate this issue and assess the impacts of modifications to blood pressure on hemodynamic impairment. METHODS: From the Third China National Stroke Registry III, computed fluid dynamics modeling was performed using the Newton-Krylov-Schwarz method in 339 patients with symptomatic intracranial atherosclerotic stenosis during 2015 to 2018. The major exposures were translesional systolic blood pressure (SBP) drop and poststenotic mean arterial pressure (MAP), and the major study outcomes were cortex-involved infarcts and borderzone-involved infarcts, respectively. Multivariate logistic regression models and the bootstrap resampling method were utilized, adjusting for demographics and medical histories. RESULTS: In all, 184 (54.3%) cortex-involved infarcts and 70 (20.6%) borderzone-involved infarcts were identified. In multivariate logistic model, the upper quartile of SBP drop correlated with increased cortex-involved infarcts (odds ratio, 1.92 [95% CI, 1.03-3.57]; bootstrap analysis odds ratio, 2.07 [95% CI, 1.09-3.93]), and the lower quartile of poststenotic MAP may correlate with increased borderzone-involved infarcts (odds ratio, 2.07 [95% CI, 0.95-4.51]; bootstrap analysis odds ratio, 2.38 [95% CI, 1.04-5.45]). Restricted cubic spline analysis revealed a consistent upward trajectory of the relationship between translesional SBP drop and cortex-involved infarcts, while a downward trajectory between poststenotic MAP and borderzone-involved infarcts. SBP drop correlated with poststenotic MAP negatively (rs=-0.765; P<0.001). In generating hemodynamic impairment, simulating blood pressure modifications suggested that ensuring adequate blood pressure to maintain sufficient poststenotic MAP appears preferable to the reverse approach, due to the prolonged plateau period in the association between the translesional SBP drop and cortex-involved infarcts and the relatively short plateau period characterizing the correlation between poststenotic MAP and borderzone-involved infarcts. CONCLUSIONS: This research elucidates the role of hemodynamic impairment of blood pressure in symptomatic intracranial atherosclerotic stenosis-related stroke mechanisms, underscoring the necessity to conduct hemodynamic assessments when managing blood pressure in symptomatic intracranial atherosclerotic stenosis.

Variability in short-term mortality following repair of ruptured abdominal aortic aneurysms across centers and physicians.

BACKGROUND: Variation in the care management of repairs for ruptured infrarenal abdominal aortic aneurysms between centers and physicians, such as procedural volumes, may explain differences in mortality outcomes. First, we quantified the center and physician variability associated with 30- and 90-day mortality risk after ruptured open surgical repair (rOSR) and ruptured endovascular aneurysm repair (rEVAR). Second, we explored wheter part of this variability was attributable to procedural volume at the center and physician levels. METHODS: Two cohorts including rOSR and rEVAR procedures between 2013 and 2019 were analyzed from the Vascular Quality Initiative database. Thirty- and 90-day all-cause mortality rates were derived from linked Medicare claims data. The median odds ratio (MOR) (median mortality risk from low- to high-risk cluster) and intraclass correlation coefficient (ICC) (variability attributable to each cluster) for 30- and 90-day mortality risks associated with center and physician variability were derived using patient-level adjusted multilevel logistic regression models. Procedural volume was calculated at the center and physician levels and stratified by quartiles. The models were sequentially adjusted for volumes, and the difference in ICCs (without vs with accounting for volume) was calculated to describe the center and physician variability in mortality risk attributable to volumes. RESULTS: We included 450 rOSRs (mean age, 74.5 ± 7.6 years; 23.5% female) and 752 rEVARs (76.4 ± 8.4 years; 26.1% female). After rOSRs, the 30- and 90-day mortality rates were 32.9% and 38.7%, respectively. No variability across centers and physicians was noted (30- and 90-day MORs ≈1 and ICCs ≈0%). Neither center nor physician volume was associated with 30-day (P = .477 and P = .796) or 90-day mortality (P = .098 and P = .559). After rEVAR, the 30- and 90-day mortality rates were 21.3% and 25.5%, respectively. Significant center variability (30-day MOR, 1.82 [95% confidence interval (CI), 1.33-2.22]; ICC, 11% [95% CI, 2%-36%]; and 90-day MOR, 1.76 [95% CI, 1.37-2.09]; ICC, 10% [95% CI, 3%-30%]), but negligeable variability across physicians (30- and 90-day MORs ≈1 and ICCs ≈0%) were noted. Neither center nor physician volume were associated with 30-day (P = .076 and P = .336) or 90-day mortality risk (P = .066 and P = .584). The center variability attributable to procedural volumes was negligeable (difference in ICCs, 1% for 30-day mortality; 0% for 90-day mortality). CONCLUSIONS: Variability in practice from center to center was associated with short-term mortality outcomes in rEVAR, but not for rOSR. Physician variability was not associated with short-term mortality for rOSR or rEVAR. Annualized center and physician volumes did not significantly explain these associations. Further work is needed to identify center-level factors affecting the quality of care and outcomes for ruptured abdominal aortic aneurysms.

Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally?

OBJECTIVES: Prognostic and diagnostic models must work in their intended clinical setting, proven via "external evaluation", preferably by authors uninvolved with model development. By systematic review, we determined the proportion of models published in high-impact radiological journals that are evaluated subsequently. METHODS: We hand-searched three radiological journals for multivariable diagnostic/prognostic models 2013-2015 inclusive, developed using regression. We assessed completeness of data presentation to allow subsequent external evaluation. We then searched literature to August 2022 to identify external evaluations of these index models. RESULTS: We identified 98 index studies (73 prognostic; 25 diagnostic) describing 145 models. Only 15 (15%) index studies presented an evaluation (two external). No model was updated. Only 20 (20%) studies presented a model equation. Just 7 (15%) studies developing Cox models presented a risk table, and just 4 (9%) presented the baseline hazard. Two (4%) studies developing non-Cox models presented the intercept. Just 20 (20%) articles presented a Kaplan-Meier curve of the final model. The 98 index studies attracted 4224 citations (including 559 self-citations), median 28 per study. We identified just six (6%) subsequent external evaluations of an index model, five of which were external evaluations by researchers uninvolved with model development, and from a different institution. CONCLUSIONS: Very few prognostic or diagnostic models published in radiological literature are evaluated externally, suggesting wasted research effort and resources. Authors' published models should present data sufficient to allow external evaluation by others. To achieve clinical utility, researchers should concentrate on model evaluation and updating rather than continual redevelopment. CLINICAL RELEVANCE STATEMENT: The large majority of prognostic and diagnostic models published in high-impact radiological journals are never evaluated. It would be more efficient for researchers to evaluate existing models rather than practice continual redevelopment. KEY POINTS: • Systematic review of highly cited radiological literature identified few diagnostic or prognostic models that were evaluated subsequently by researchers uninvolved with the original model. • Published radiological models frequently omit important information necessary for others to perform an external evaluation: Only 20% of studies presented a model equation or nomogram. • A large proportion of research citing published models focuses on redevelopment and ignores evaluation and updating, which would be a more efficient use of research resources.

Added value of arterial enhancement fraction derived from dual-energy computed tomography for preoperative diagnosis of cervical lymph node metastasis in papillary thyroid cancer: initial results.

OBJECTIVES: To explore the added value of arterial enhancement fraction (AEF) derived from dual-energy computed tomography CT (DECT) to conventional image features for diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC). METHODS: A total of 273 cervical LNs (153 non-metastatic and 120 metastatic) were recruited from 92 patients with PTC. Qualitative image features of LNs were assessed. Both single-energy CT (SECT)-derived AEF (AEFS) and DECT-derived AEF (AEFD) were calculated. Correlation between AEFD and AEFS was determined using Pearson's correlation coefficient. Multivariate logistic regression analysis with the forward variable selection method was used to build three models (conventional features, conventional features + AEFS, and conventional features + AEFD). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. RESULTS: Abnormal enhancement, calcification, and cystic change were chosen to build model 1 and the model provided moderate diagnostic performance with an area under the ROC curve (AUC) of 0.675. Metastatic LNs demonstrated both significantly higher AEFD (1.14 vs 0.48; p < 0.001) and AEFS (1.08 vs 0.38; p < 0.001) than non-metastatic LNs. AEFD correlated well with AEFS (r = 0.802; p < 0.001), and exhibited comparable performance with AEFS (AUC, 0.867 vs 0.852; p = 0.628). Combining CT image features with AEFS (model 2) and AEFD (model 3) could significantly improve diagnostic performances (AUC, 0.865 vs 0.675; AUC, 0.883 vs 0.675; both p < 0.001). CONCLUSIONS: AEFD correlated well with AEFS, and exhibited comparable performance with AEFS. Integrating qualitative CT image features with both AEFS and AEFD could further improve the ability in diagnosing cervical LN metastasis in PTC. CLINICAL RELEVANCE STATEMENT: Arterial enhancement fraction (AEF) values, especially AEF derived from dual-energy computed tomography, can help to diagnose cervical lymph node metastasis in patients with papillary thyroid cancer, and complement conventional CT image features for improved clinical decision making. KEY POINTS: • Metastatic cervical lymph nodes (LNs) demonstrated significantly higher arterial enhancement fraction (AEF) derived from dual-energy computed tomography (DECT) and single-energy CT (SECT)-derived AEF (AEFS) than non-metastatic LNs in patients with papillary thyroid cancer. • DECT-derived AEF (AEFD) correlated significantly with AEFS, and exhibited comparable performance with AEFS. • Integrating qualitative CT images features with both AEFS and AEFD could further improve the differential ability.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings. AIM: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI. METHODS: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance. RESULTS: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results. CONCLUSION: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Developing a prediction model to identify people with severe mental illness without regular contact to their GP - a study based on data from the Danish national registers.

INTRODUCTION: People with severe mental illness (SMI) face a higher risk of premature mortality due to physical morbidity compared to the general population. Establishing regular contact with a general practitioner (GP) can mitigate this risk, yet barriers to healthcare access persist. Population initiatives to overcome these barriers require efficient identification of those persons in need. OBJECTIVE: To develop a predictive model to identify persons with SMI not attending a GP regularly. METHOD: For individuals with psychotic disorder, bipolar disorder, or severe depression between 2011 and 2016 (n = 48,804), GP contacts from 2016 to 2018 were retrieved. Two logistic regression models using demographic and clinical data from Danish national registers predicted severe mental illness without GP contact. Model 1 retained significant main effect variables, while Model 2 included significant bivariate interactions. Goodness-of-fit and discriminating ability were evaluated using Hosmer-Lemeshow (HL) test and area under the receiver operating characteristic curve (AUC), respectively, via cross-validation. RESULTS: The simple model retained 11 main effects, while the expanded model included 13 main effects and 10 bivariate interactions after backward elimination. HL tests were non-significant for both models (p = 0.50 for the simple model and p = 0.68 for the extended model). Their respective AUC values were 0.789 and 0.790. CONCLUSION: Leveraging Danish national register data, we developed two predictive models to identify SMI individuals without GP contact. The extended model had slightly better model performance than the simple model. Our study may help to identify persons with SMI not engaging with primary care which could enhance health and treatment outcomes in this group.

Validation of the Saga Fall Injury Risk Model.

Background: Predicting fall injuries can mitigate the sequelae of falls and potentially utilize medical resources effectively. This study aimed to externally validate the accuracy of the Saga Fall Injury Risk Model (SFIRM), consisting of six factors including age, sex, emergency transport, medical referral letter, Bedriddenness Rank, and history of falls, assessed upon admission. Methods: This was a two-center, prospective, observational study. We included inpatients aged 20 years or older in two hospitals, an acute and a chronic care hospital, from October 2018 to September 2019. The predictive performance of the model was evaluated by calculating the area under the curve (AUC), 95% confidence interval (CI), and shrinkage coefficient of the entire study population. The minimum sample size of this study was 2,235 cases. Results: A total of 3,549 patients, with a median age of 78 years, were included in the analysis, and men accounted for 47.9% of all the patients. Among these, 35 (0.99%) had fall injuries. The performance of the SFIRM, as measured by the AUC, was 0.721 (95% CI: 0.662-0.781). The observed fall incidence closely aligned with the predicted incidence calculated using the SFIRM, with a shrinkage coefficient of 0.867. Conclusions: The external validation of the SFIRM in this two-center, prospective study showed good discrimination and calibration. This model can be easily applied upon admission and is valuable for fall injury prediction.

A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors.

OBJECTIVE: Preeclampsia (PE) is a pregnancy-related multi-organ disease and a significant cause of incidence rate and mortality of pregnant women and newborns worldwide. Delivery remains the only available treatment for PE. This study aims to establish a dynamic prediction model for PE. METHODS: A total of 737 patients who visited our hospital from January 2021 to June 2022 were identified according to the inclusion and exclusion criteria, forming the primary dataset. Additionally, 176 singleton pregnant women who visited our hospital from July 2022 to November 2022 comprised the verification set. We investigated different gestational weeks of sFlt-1/PLGF (soluble FMS-like tyrosine kinase-1, placental growth factor) ratio combined with maternal characteristics and routine prenatal laboratory results in order to predict PE in each trimester. Multivariate logistic regression was used to establish the prediction model for PE at different gestational weeks. The discrimination, calibration, and clinical validity were utilized to evaluate predictive models as well as models in external validation queues. RESULTS: At 20-24 weeks, the obtained prediction model for PE yielded an area under the curve of 0.568 (95% confidence interval, 0.479-0.657). At 25-29 weeks, the obtained prediction model for PE yielded an area under the curve of 0.773 (95% confidence interval, 0.703-0.842)and 0.731 (95% confidence interval, 0.653-0.809) at 30-34 weeks. After adding maternal factors, uterine artery pulsation index(Ut-IP), and other laboratory indicators to the sFlt-1/PLGF ratio, the predicted performance of PE improved. It found that the AUC improved to 0.826(95% confidence interval, 0.748 ∼ 0.904) at 20-24 weeks, 0.879 (95% confidence interval, 0.823 ∼ 0.935) at 25-29 weeks, and 0.862(95% confidence interval, 0.799 ∼ 0.925) at 30-34 weeks.The calibration plot of the prediction model indicates good predictive accuracy between the predicted probability of PE and the observed probability. Furthermore, decision-curve analysis showed an excellent clinical application value of the models. CONCLUSION: Using the sFlt-1/PLGF ratio combined with multiple factors at 25-29 weeks can effectively predict PE, but the significance of re-examination in late pregnancy is not significant.

Outcomes of pregnancies complicated by cirrhosis: a retrospective cohort study.

BACKGROUND: Although pregnancy complicated by liver cirrhosis is rare, women with cirrhosis experience increased adverse pregnancy outcomes. This study aimed to evaluate pregnancy outcomes in women with liver cirrhosis and develop a predictive model using maternal factors for preterm birth in such pregnancies. METHODS: A retrospective analysis was conducted on pregnancy outcomes of a cirrhosis group (n = 43) and a non-cirrhosis group (n = 172) in a university hospital between 2010 and 2022. Logistic regression evaluated pregnancy outcomes, and a forward stepwise logistic regression model was designed to predict preterm birth in pregnant women with cirrhosis. The model's predictive performance was evaluated using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). RESULTS: The incidence of cirrhosis during pregnancy was 0.06% (50/81,554). Pregnant women with cirrhosis faced increased risks of cesarean section, preterm birth, intrahepatic cholestasis of pregnancy, thrombocytopenia, and postpartum hemorrhage. In pregnant women with cirrhosis, preterm birth risk significantly increased at an incidence rate of 46.51% (20/43). According to the prediction model, the key predictors of preterm birth in pregnant women with cirrhosis were intrahepatic cholestasis of pregnancy and total bilirubin. The model demonstrated accurate prediction, with an AUC of 0.847, yielding a model accuracy of 81.4%. CONCLUSIONS: Pregnant women with cirrhosis face a heightened risk of adverse obstetric outcomes, particularly an increased incidence of preterm birth. The preliminary evidence shows that the regression model established in our study can use the identified key predictors to predict preterm birth in pregnant women with cirrhosis, with high accuracy.

Assessment of nonlinear dose-response relationships via nonparametric regression.

We propose a simple approach to assess whether a nonlinear parametric model is appropriate to depict the dose-response relationships and whether two parametric models can be applied to fit a dataset via nonparametric regression. The proposed approach can compensate for the ANOVA, which is sometimes conservative, and is very easy to implement. We illustrate the performance by analyzing experimental examples and a small simulation study.

Factors associated with depressive symptoms among cancer patients: a nationwide cross-sectional study.

OBJECTIVE: Research on factors contributing to depressive symptoms in cancer patients at a national level, encompassing a comprehensive set of variables was limited. This study aimed to address this gap by identifying the factors associated with depressive symptoms among cancer patients through a nationwide cross-sectional analysis. METHODS: Various factors, including demographic, socioeconomic, behavioral patterns, general and self-rated health status, chronic conditions, dietary habits, and cancer-related factors, were examined. Data was from the National Health and Nutrition Examination Survey. Univariate and multivariate logistic regression analyses were performed to identify associated factors. The receiver-operating characteristic (ROC) curve was used to evaluate the performance of the logistic model. RESULTS: The findings showed that five sociodemographic factors, two behavioral styles, self-rated health status, comorbid arthritis, two dietary factors and two cancer-related factors were strongly associated with depressive symptoms. Compared with those aged 20-39 years, cancer individuals aged 40-59 years (OR = 0.48, P < 0.05) and those 60 years or older (OR = 0.18, P < 0.05) had lower odds of depression. Positive factors included being never married (OR = 1.98, P < 0.05), widowed, divorced or separated (OR = 1.75, P < 0.05), unemployment (OR = 1.87, P < 0.05), current smoking (OR = 1.84, P < 0.05), inadequate sleep (OR = 1.96, P < 0.05), comorbid arthritis (OR = 1.79, P < 0.05), and poor self-rated health status (OR = 3.53, P < 0.05). No significant association was identified between the Healthy Eating Index 2015 and the Dietary Inflammatory Index with depression (P > 0.05). Shorter cancer diagnosis duration was associated with reduced odds of depression (P < 0.05). The logistic model had an area under the curve of 0.870 (95% CI: 0.846-0.894, P < 0.05). CONCLUSIONS: Cancer patients should receive enhanced family and social support while cultivating a healthy lifestyle and diet. Incorporating plenty of fruits, greens, and beans is highly recommended, along with establishing a comprehensive health management framework.

The effects of potentially traumatic events on the recovery from pre-existing anxiety and depression symptomatology and the risk of PTSD.

AIM: The extent to which recent potentially traumatic events (PTEs) hinder the recovery from pre-existing mental health problems is largely unknown. The same applies to the extent to which non-recovery from pre-existing mental health problems increases the risk of posttraumatic stress disorder (PTSD). The aim of the present study is to gain insight in these effects. METHODS: Data were extracted from six annual surveys of the Dutch population-based Victims in Modern Society (VICTIMS) study. Of the adult respondents who participated in two subsequent surveys (labeled T1 and T2, n = 6942), those with severe anxiety and depression symptoms (ADS) at T1 (n = 487) were selected. We distinguished respondents exposed to PTEs (PTE-group, n = 162) and not exposed to PTEs (comparison group, n = 325) between T1 and T2. We applied five indicators of recovery [based on the Reliable Change Index (RCI), degrees of symptom reduction, and the cut-off score at T2]. Differences in the recovery from ADS and probable PTSD at T2 were examined using multivariate logistic regression. RESULTS: The PTE group less often recovered from severe ADS between T1 and T2 than the comparison group according to all five indicators of recovery, while controlling for 11 different variables (0.40 ≤ adjusted OR's ≤ 0.66). Those in the PTE group who did not recover, considerably more often suffered from probable PTSD at T2 (63%-82%) than those who did recover (0%-29%; 8.96 ≤ adjusted OR ≤ 26.33). CONCLUSION: Recent potentially traumatic events hinder the recovery from pre-existing anxiety and depression symptomatology and thereby increase the risk of probable PTSD.

Healthcare professionals' digital health competence profiles and associated factors: A cross-sectional study.

AIMS: To identify healthcare professionals' digital health competence profiles and explore associated factors to digital health competence in healthcare settings. DESIGN: A cross-sectional study. METHODS: Data were collected from 817 healthcare professionals from nine organizations with an electronic questionnaire by using Digital Health Competence instrument (42 items) and Aspects Associated with Digital Health instrument (15 items) between 1st March and 31st July 2022. K-means clustering was used to describe digital health competence profiles. Binary logistic regression analysis was used to explore associated factors. RESULTS: Analysis revealed three digital health competence profiles: A - high competence (n = 336), B - intermediate competence (n = 352) and C - low competence (n = 129). Between the profiles, digital health competence showed significant differences (p < .001). Recent graduation year, working in outpatient environments and leader or specialist position were associated with higher digital health competence. Organizational practices and the influence from colleagues improved competence in human-centred remote counselling, digital solutions as part of work, competence in utilizing and evaluating digital solutions and ethical competence. Support from management improved digital solutions as part of work and ethical competence. CONCLUSION: Nursing and allied health professionals working in other than outpatient environments should be specifically acknowledged when digital health competence development initiatives are designed and targeted. The positive influence from colleagues could be harnessed by enhancing their involvement in digital health competence development methods such as orientation, mentoring or coaching. Additionally, managers should take a stronger role in supporting different areas of digital health competence. IMPACT: This was the first study that explored healthcare professionals' digital health competence profiles and associated factors. The detection of healthcare professionals' digital health competence profiles guides the development of digital health education according to different needs in healthcare environments. REPORTING METHOD: The study has adhered to STROBE guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Factors related to suicidal ideation of schizophrenia patients in China: a study based on decision tree and logistic regression model.

This study aimed to investigate the factors associated with suicidal ideation in schizophrenia patients in China using decision tree and logistic regression models. From October 2020 to March 2022, patients with schizophrenia were chosen from Chifeng Anding Hospital and Daqing Third Hospital in Heilongjiang Province. A total of 300 patients with schizophrenia who met the inclusion criteria were investigated by questionnaire. The questionnaire covered general data, suicidal ideation, childhood trauma, social support, depressive symptoms and psychological resilience. Logistic regression analysis revealed that childhood trauma and depressive symptoms were risk factors for suicidal ideation in schizophrenia (OR = 2.330, 95%CI: 1.177 ~ 4.614; OR = 10.619, 95%CI: 5.199 ~ 21.688), while psychological resilience was a protective factor for suicidal ideation in schizophrenia (OR = 0.173, 95%CI: 0.073 ~ 0.409). The results of the decision tree model analysis demonstrated that depressive symptoms, psychological resilience and childhood trauma were influential factors for suicidal ideation in patients with schizophrenia (p < 0.05). The area under the ROC for the logistic regression model and the decision tree model were 0.868 (95% CI: 0.821 ~ 0.916) and 0.863 (95% CI: 0.814 ~ 0.912) respectively, indicating excellent accuracy of the models. Meanwhile, the logistic regression model had a sensitivity of 0.834 and a specificity of 0.743 when the Youden index was at its maximum. The decision tree model had a sensitivity of 0.768 and a specificity of 0.8. Decision trees in combination with logistic regression models are of high value in the study of factors influencing suicidal ideation in schizophrenia patients.

Diagnostic accuracy of clinical signs to detect erosive tooth wear in its early phase.

BACKGROUND: Agreement exists about most of the clinical features of erosive tooth wear, though no evidence supports their validity in diagnosing the condition. OBJECTIVE: This study aimed to determine the accuracy of clinical signs for diagnosing erosive tooth wear in a young adult general population. METHODS: We conducted a cross-sectional study of dental students. In the first session, two examiners independently determined the presence of erosive tooth wear based on glazed enamel surfaces, morphological changes on non-occlusal surfaces, flattening of convex areas, or any type of concavity. In the second session, one examiner recorded the presence of clinical signs according to the Tooth Wear Evaluation System. The diagnostic accuracy of each clinical sign, both alone and combined, was assessed by calculating their sensitivity and specificity for detecting erosive tooth wear and performing multivariate logistic regression models. RESULTS: Of the 147 participants (78 women and 69 men; median age, 22 years) we included, 76.2% had erosive tooth wear. The single clinical signs with greatest balance between the sensitivity and specificity were 'convex areas flatten' (63% and 71%, respectively) and 'dull surface' (47% and 89%, respectively). Multivariate logistic regression revealed that 'preservation of the enamel cuff' (odds ratio, 22) and the combination of 'smooth silky shining, silky glazed appearance, and dull surface' (odds ratio, 68) had the best predictive values. CONCLUSIONS: The most accurate clinical signs for detecting early erosive tooth wear were dull surface, flattened convex areas and preservation of the enamel cuff.

A Statistical Physics Approach to Understanding the Adsorption of Methylene Blue onto Cobalt Oxide Nanoparticles.

The production of cobalt oxide nanoparticles and their use in the adsorption of methylene blue (MB) from solution is described in the paper. The X-ray diffraction patterns show that the synthesized cobalt oxide nanoparticles have a crystalline cubic structure. The study of the adsorption of methylene blue onto the cobalt oxide nanoparticles involved determining the contact time and initial concentration of the adsorption of MB on the adsorbent. The kinetics of adsorption were analyzed using two kinetic models (pseudo-first order and pseudo-second order), and the pseudo-second-order model was found to be the most appropriate for describing the behavior of the adsorption. This study indicates that the MLTS (monolayer with the same number of molecules per site) model is the most suitable model for describing methylene blue/cobalt oxide systems, and the parameter values help to further understand the adsorption process with the steric parameters. Indicating that methylene blue is horizontally adsorbed onto the surface of the cobalt oxide, which is bonded to two different receptor sites. Regarding the temperature effect, it was found that the adsorption capacity increased, with the experimental value ranging from 313.7 to 405.3 mg g-1, while the MLTS predicted 313.32 and 408.16 mg g-1. From the thermodynamic functions, high entropy was found around 280 mg L-1 concentration. For all concentrations and temperatures examined, the Gibbs free energy and enthalpy of adsorption were found to be negative and positive, respectively, suggesting that the system is spontaneous and endothermic. According to this study's findings, methylene blue adsorption onto cobalt oxide nanoparticles happens via the creation of a monolayer, in which the same amount of molecules are adsorbed at two distinct locations. The findings shed light on the methylene blue adsorption process onto cobalt oxide nanoparticles, which have a variety of uses, including the remediation of wastewater.

Risk factors predicting the need for intensive care unit admission within forty-eight hours of emergency department presentation: A case-control study.

BACKGROUND: Patients admitted from the emergency department to the wards, who progress to a critically unwell state, may require expeditious admission to the intensive care unit. It can be argued that earlier recognition of such patients, to facilitate prompt transfer to intensive care, could be linked to more favourable clinical outcomes. Nevertheless, this can be clinically challenging, and there are currently no established evidence-based methods for predicting the need for intensive care in the future. OBJECTIVES: We aimed to analyse the emergency department data to describe the characteristics of patients who required an intensive care admission within 48 h of presentation. Secondly, we planned to test the feasibility of using this data to identify the associated risk factors for developing a predictive model. METHODS: We designed a retrospective case-control study. Cases were patients admitted to intensive care within 48 h of their emergency department presentation. Controls were patients who did not need an intensive care admission. Groups were matched based on age, gender, admission calendar month, and diagnosis. To identify the associated variables, we used a conditional logistic regression model. RESULTS: Compared to controls, cases were more likely to be obese, and smokers and had a higher prevalence of cardiovascular (39 [35.1%] vs 20 [18%], p = 0.004) and respiratory diagnoses (45 [40.5%] vs 25 [22.5%], p = 0.004). They received more medical emergency team reviews (53 [47.8%] vs 24 [21.6%], p < 0.001), and more patients had an acute resuscitation plan (31 [27.9%] vs 15 [13.5%], p = 0.008). The predictive model showed that having acute resuscitation plans, cardiovascular and respiratory diagnoses, and receiving medical emergency team reviews were strongly associated with having an intensive care admission within 48 h of presentation. CONCLUSIONS: Our study used emergency department data to provide a detailed description of patients who had an intensive care unit admission within 48 h of their presentation. It demonstrated the feasibility of using such data to identify the associated risk factors to develop a predictive model.

Trends in Disparities in Advanced Neuroimaging Utilization in Acute Stroke: A Population-Based Study.

BACKGROUND: Our primary objective was to evaluate if disparities in race, sex, age, and socioeconomic status (SES) exist in utilization of advanced neuroimaging in year 2015 in a population-based study. Our secondary objective was to identify the disparity trends and overall imaging utilization as compared with years 2005 and 2010. METHODS: This was a retrospective, population-based study that utilized the GCNKSS (Greater Cincinnati/Northern Kentucky Stroke Study) data. Patients with stroke and transient ischemic attack were identified in the years 2005, 2010, and 2015 in a metropolitan population of 1.3 million. The proportion of imaging use within 2 days of stroke/transient ischemic attack onset or hospital admission date was computed. SES determined by the percentage below the poverty level within a given respondent's US census tract of residence was dichotomized. Multivariable logistic regression was used to determine the odds of advanced neuroimaging use (computed tomography angiogram/magnetic resonance imaging/magnetic resonance angiogram) for age, race, gender, and SES. RESULTS: There was a total of 10 526 stroke/transient ischemic attack events in the combined study year periods of 2005, 2010, and 2015. The utilization of advanced imaging progressively increased (48% in 2005, 63% in 2010, and 75% in 2015 [P<0.001]). In the combined study year multivariable model, advanced imaging was associated with age and SES. Younger patients (≤55 years) were more likely to have advanced imaging compared with older patients (adjusted odds ratio, 1.85 [95% CI, 1.62-2.12]; P<0.01), and low SES patients were less likely to have advanced imaging compared with high SES (adjusted odds ratio, 0.83 [95% CI, 0.75-0.93]; P<0.01). A significant interaction was found between age and race. Stratified by age, the adjusted odds of advanced imaging were higher for Black patients compared with White patients among older patients (>55 years; adjusted odds ratio, 1.34 [95% CI, 1.15-1.57]; P<0.01), but no racial differences among the young. CONCLUSIONS: Racial, age, and SES-related disparities exist in the utilization of advanced neuroimaging for patients with acute stroke. There was no evidence of a change in trend of these disparities between the study periods.

Relationships of Hemoglobin Concentration, Ischemic Lesions, and Clinical Outcomes in Patients With Intracerebral Hemorrhage.

BACKGROUND: Hemoglobin concentration and diffusion-weighted imaging (DWI) ischemic lesions are separately known to be associated with poor intracerebral hemorrhage (ICH) outcomes. While hemoglobin concentrations have known relationships with ischemic stroke, it is unclear whether hemoglobin concentration is associated with DWI ischemic lesions after ICH. We sought to investigate the hypothesis that hemoglobin concentrations would associate with DWI lesions after ICH and further investigated their relationships with clinical outcomes. METHODS: Supratentorial ICH patients enrolled between 2010 and 2016 to a prospective, multicenter, observational cohort study (ERICH study [Ethnic/Racial Variations of Intracerebral Hemorrhage]) were assessed. Patients from this study with baseline, admission hemoglobin, and hospitalization magnetic resonance imaging were analyzed. Hemoglobin was examined as the primary exposure variable defined as a continuous variable (g/dL). Magnetic resonance imaging DWI ischemic lesion presence was assessed as the primary radiographic outcome. Primary analyses assessed relationships of hemoglobin with DWI lesions. Secondary analyses assessed relationships of DWI lesions with poor 3-month outcomes (modified Rankin Scale score, 4-6). These analyses were performed using separate multivariable logistic regression models adjusting for relevant covariates. RESULTS: Of 917 patients with ICH analyzed, mean baseline hemoglobin was 13.8 g/dL (±1.9), 60% were deep ICH, and DWI lesions were identified in 27% of the cohort. In our primary analyses, increased hemoglobin, defined as a continuous variable, was associated with DWI lesions (adjusted odds ratio, 1.21 per 1 g/dL change in hemoglobin [95% CI, 1.07-1.37]) after adjusting for sex, race, ICH severity, time to magnetic resonance imaging, and acute blood pressure change. In secondary analyses, DWI lesions were associated with poor 3-month outcomes (adjusted odds ratio, 1.83 [95% CI, 1.24-2.69]) after adjusting for similar covariates. CONCLUSIONS: We identified novel relationships between higher baseline hemoglobin concentrations and DWI ischemic lesions in patients with ICH. Further studies are required to clarify the role of hemoglobin concentration on both cerebral small vessel disease pathophysiology and ICH outcomes.