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PurposeThe novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants.MethodsWe retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics.ResultsThe dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42-65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77-0.85 and AUPRC of 0.51-0.60 for variants.ConclusionThe tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts.

Introduction: Scientific studies have focused on patient-related characteristics as predictors of length of stay in forensic psychiatry. However, little attention has been paid to the specificities of forensic psychiatric settings. This study aims to test whether differences in forensic admissions transcend individual factors by comparing length of stay between different psychiatric units, controlling for hospital characteristics and patient characteristics. Methods: The dataset was derived from a forensic documentation system containing a wide range of information on forensic psychiatric patients. N = 594 patients with schizophrenia spectrum disorders discharged from 6 forensic hospitals in southern Germany were included in a linear mixed regression model. Linear mixed models were calculated, allowing for the simultaneous estimation of variance between patients and hospitals. Results: The final regression model explained 49% of the total variance. The only statistically significant patient-related predictors were age at admission, education and severity of the index offence. Hospital differences explained 41% (ICC) of the variance in length of stay without finding a significant hospital effect in the data. Discussion: Previous research has primarily analyzed predictors of length of stay in terms of individual patient characteristics. This work suggests that variables other than patient-related factors need to be considered when assessing the length of stay in forensic units. Further multi-center studies are needed to gain a better understanding of how forensic psychiatric hospitals and other institutional influences affect length of stay. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this work is to develop a deep learning (DL) system for rapidly and accurately screening for intraocular tumor (IOT), retinal detachment (RD), vitreous hemorrhage (VH), and posterior scleral staphyloma (PSS) using ocular B-scan ultrasound images. Methods: Ultrasound images from five clinically confirmed categories, including vitreous hemorrhage, retinal detachment, intraocular tumor, posterior scleral staphyloma, and normal eyes, were used to develop and evaluate a fine-grained classification system (the Dual-Path Lesion Attention Network, DPLA-Net). Images were derived from five centers scanned by different sonographers and divided into training, validation, and test sets in a ratio of 7:1:2. Two senior ophthalmologists and four junior ophthalmologists were recruited to evaluate the system's performance. Results: This multi-center cross-sectional study was conducted in six hospitals in China. A total of 6054 ultrasound images were collected; 4758 images were used for the training and validation of the system, and 1296 images were used as a testing set. DPLA-Net achieved a mean accuracy of 0.943 in the testing set, and the area under the curve was 0.988 for IOT, 0.997 for RD, 0.994 for PSS, 0.988 for VH, and 0.993 for normal. With the help of DPLA-Net, the accuracy of the four junior ophthalmologists improved from 0.696 (95% confidence interval [CI] 0.684–0.707) to 0.919 (95% CI 0.912–0.926, p < 0.001), and the time used for classifying each image reduced from 16.84 ± 2.34 s to 10.09 ± 1.79 s. Conclusions: The proposed DPLA-Net showed high accuracy for screening and classifying multiple ophthalmic diseases using B-scan ultrasound images across mutiple centers. Moreover, the system can promote the efficiency of classification by ophthalmologists. [ABSTRACT FROM AUTHOR]

Abstract Introduction The aim of this work is to develop a deep learning (DL) system for rapidly and accurately screening for intraocular tumor (IOT), retinal detachment (RD), vitreous hemorrhage (VH), and posterior scleral staphyloma (PSS) using ocular B-scan ultrasound images. Methods Ultrasound images from five clinically confirmed categories, including vitreous hemorrhage, retinal detachment, intraocular tumor, posterior scleral staphyloma, and normal eyes, were used to develop and evaluate a fine-grained classification system (the Dual-Path Lesion Attention Network, DPLA-Net). Images were derived from five centers scanned by different sonographers and divided into training, validation, and test sets in a ratio of 7:1:2. Two senior ophthalmologists and four junior ophthalmologists were recruited to evaluate the system's performance. Results This multi-center cross-sectional study was conducted in six hospitals in China. A total of 6054 ultrasound images were collected; 4758 images were used for the training and validation of the system, and 1296 images were used as a testing set. DPLA-Net achieved a mean accuracy of 0.943 in the testing set, and the area under the curve was 0.988 for IOT, 0.997 for RD, 0.994 for PSS, 0.988 for VH, and 0.993 for normal. With the help of DPLA-Net, the accuracy of the four junior ophthalmologists improved from 0.696 (95% confidence interval [CI] 0.684–0.707) to 0.919 (95% CI 0.912–0.926, p

Analyzing treatment or exposure effect is a major research theme in scientific studies. In the current big-data era where multiple sources of data are available, it is of interest to perform a synthesized analysis of treatment effects by integrating information from different data sources or studies. However, studies may contain heterogeneous and incomplete covariate sets, and individual data therein may not be accessible. We apply and extend the generalized meta-analysis method to integrate summary results (e.g., regression coefficients) of outcome and treatment (propensity score, PS) regression analyses across different datasets that may contain heterogeneous covariate sets. The proposed integrated analysis utilizes a reference dataset, which contains data on the complete set of covariates. The asymptotic distribution for the proposed integrated estimator is established. Simulations reveal that the proposed estimator performs well. We apply the proposed method to obtain the causal effect of waist circumference on hypertension by integrating two existing outcomes and PS regression analyses with different sets of covariates. [ABSTRACT FROM AUTHOR]

Hypothermia is highly common in patients undergoing gynecological surgeries under general anesthesia, so the length of hospitalization and even the risk of mortality are substantially increased. Our aim was to develop a simple and practical model to preoperatively identify gynecological surgery patients at risk of intraoperative hypothermia. In this retrospective study, we collected data from 802 patients who underwent gynecological surgery at three medical centers from June 2022 to August 2023. We further allocated the patients to a training group, an internal validation group, or an external validation group. The preliminary predictive factors for intraoperative hypothermia in gynecological patients were determined using the least absolute shrinkage and selection operator (LASSO) method. The final predictive factors were subsequently identified through multivariate logistic regression analysis, and a nomogram for predicting the occurrence of hypothermia was established. A total of 802 patients were included, with 314 patients in the training cohort (mean age 48.5 ± 12.6 years), 130 patients in the internal validation cohort (mean age 49.9 ± 12.5 years), and 358 patients in the external validation cohort (mean age 47.6 ± 14.0 years). LASSO regression and multivariate logistic regression analyses indicated that body mass index, minimally invasive surgery, baseline heart rate, baseline body temperature, history of previous surgery, and aspartate aminotransferase level were associated with intraoperative hypothermia in gynecological surgery patients. This nomogram was constructed based on these six variables, with a C-index of 0.712 for the training cohort. We established a practical predictive model that can be used to preoperatively predict the occurrence of hypothermia in gynecological surgery patients. Clinical trial registration: chictr.org.cn , identifier ChiCTR2300071859. • How can the occurrence of intraoperative hypothermia be predicted before surgery in gynecological surgery patients? • To address this issue, we conducted a multicenter cohort study of 802 patients who underwent gynecological surgery. • A powerful model was established for the early identification of patients prone to intraoperative hypothermia. [ABSTRACT FROM AUTHOR]

Backgroud and objectives: The implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model's performance was compared by evaluating the impact of two distinct feature selection methods. Methods: A retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, y analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model's classification performance was assessed through F1-score and area under the curve (AUC) values. Results: The model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible. Conclusion: Based on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists. [ABSTRACT FROM AUTHOR]

IntroductionScientific studies have focused on patient-related characteristics as predictors of length of stay in forensic psychiatry. However, little attention has been paid to the specificities of forensic psychiatric settings. This study aims to test whether differences in forensic admissions transcend individual factors by comparing length of stay between different psychiatric units, controlling for hospital characteristics and patient characteristics.MethodsThe dataset was derived from a forensic documentation system containing a wide range of information on forensic psychiatric patients. N = 594 patients with schizophrenia spectrum disorders discharged from 6 forensic hospitals in southern Germany were included in a linear mixed regression model. Linear mixed models were calculated, allowing for the simultaneous estimation of variance between patients and hospitals.ResultsThe final regression model explained 49% of the total variance. The only statistically significant patient-related predictors were age at admission, education and severity of the index offence. Hospital differences explained 41% (ICC) of the variance in length of stay without finding a significant hospital effect in the data.DiscussionPrevious research has primarily analyzed predictors of length of stay in terms of individual patient characteristics. This work suggests that variables other than patient-related factors need to be considered when assessing the length of stay in forensic units. Further multi-center studies are needed to gain a better understanding of how forensic psychiatric hospitals and other institutional influences affect length of stay.

Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants. Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics. Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42–65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77–0.85 and AUPRC of 0.51–0.60 for variants. Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts.

Objective: This study aimed to explore the signal detection method for allergic reactions induced by inpatient iodixanol injection. Methods: A database of 3,719,217 hospitalized patients from 20 large Chinese general hospitals was processed and analyzed using the prescription sequence symmetry analysis (PSSA) method. Results: 126,680 inpatients who used iodixanol and were concurrently treated with anti-allergic drugs were analyzed. In the medical records of these patients, only 32 had documented iodixanol allergies. Statistical analysis identified 22 drugs in 4 categories--calcium preparations, adrenergic/dopaminergic agents, glucocorticoids, and antihistamines--as marker drugs. With time intervals of 3, 7, and 28 days, the adjusted sequence ratios (aSRs) for all anti-allergics and the 4 categories were greater than 1. The 7-day aSRs were 2.12 (95% CI: 2.08-2.15), 1.70 (95% CI: 1.68-1.73), 3.85 (95% confidence interval [CI]: 3.75-2.30), 2.30 (95% CI: 2.26-2.35), and 1.95 (95% CI: 1.89-2.02), respectively. The proportions of adverse drug events indicated by each signal were as follows: all anti-allergics (2.92%-3%), calcium gluconate (0.19%-0.52%), adrenergic/dopaminergic agents (2.20%-3.37%), glucocorticoids (3.13%-3.76%), and antihistamines (1.05%-1.32%). Conclusion: This first multi-center Chinese inpatient database study detected iodixanol-induced allergy signals, revealing that reactions may be much higher than those in collected spontaneous reports. Iodixanol risk exposure was closer to actual pharmaceutical care findings. PSSA application with =7-day intervals appears better suited for monitoring late allergic reaction signals with these drugs. [ABSTRACT FROM AUTHOR]

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Quality sleep plays a crucial role in maintaining good health. Nevertheless, sleep disruption is a common and complex issue after a stroke. It can increase the likelihood of stroke recurrence by influencing modifiable risk factors. However, there is currently a lack of research on the latent classes or clusters of sleep quality and its predictive factors among stroke patients. This study aims to identify latent classes of sleep quality and explore the predictive factors associated with different sleep quality clusters among stroke patients. A total of 500 participants were recruited through cluster random sampling from January 2023 to May 2023. Latent profile analysis was conducted to identify latent classes of sleep quality within the sample of stroke patients. Additionally, multinomial regression analyses were employed to investigate the predictors associated with the different latent classes identified in the analysis. Out of the 500 participants, 458 (91.6 %) completed the survey, and 71 % of them reported experiencing sleep problems. The analysis revealed three latent profile classes: the "good sleep quality-deficient duration" group (65.4 %), the "moderate sleep quality-more disturbances" group (14.1 %), and the "poor sleep quality-low efficiency" group (20.5 %). Factors associated with sleep quality were identified. Protective factors for sleep quality included being male, having the TOAST type of large-artery atherosclerosis, having a good education, high household income, no family history of stroke, residing in rural areas, and having better environmental and social support (all p < 0.05). Risk factors for sleep quality included smoking, high perceived stress, and a greater number of comorbidities (all p < 0.05). This study has successfully identified three distinct latent profile classes of sleep quality and their associated predictors among stroke patients in China. The findings offer both theoretical guidance and practical insights for the development of targeted intervention programs aimed at enhancing the sleep quality of stroke patients. • Sleep quality is a crucial role in maintaining good health. • Little is known about the latent classes of sleep quality among stroke patients. • Identify this can offer theoretical guidance for sleep quality intervention. [ABSTRACT FROM AUTHOR]

To investigate the possible risk factors for death at post-treatment in children with acute lymphoblastic leukemia (ALL). A multivariate competing risk analysis was performed to retrospectively analyze the data of children with ALL who died after treatment with CCCG-ALL-2015 in China and to determine the possible risk factors for death at post-treatment in children with ALL. Age at the first diagnosis of ≥10 years; final risk level of high-risk; D19 minimal residual disease (MRD) (≥0.01%) and D46 MRD (≥0.01%); genetic abnormalities, such as KMT2A-rearrangement, c-Myc rearrangement, and PDGFRB rearrangement; and the presence of CNS3 (all P values, <0.05) were identified as independent risk factors, whereas the risk level at the first diagnosis of low-risk (LR) and ETV6::RUNX1 positivity was considered as independent protective factors of death in children with ALL. Among the 471 cases of death, 45 cases were treated with CCCG-ALL-2015 only, and 163 (34.61%) were treatment-related, with 62.42% due to severe infections. 55.83% of treatment-related mortality (TRM) occurred in the early phase of treatment (induction phase). TRM has a significant impact on the overall survival of pediatric patients with ALL. Moreover, the CCCG-ALL-2015 regimen has a better safety profile for treating children with ALL, with rates close to those in developed countries (registration number: ChiCTR-IPR-14005706; date of registration: June 4, 2014). [ABSTRACT FROM AUTHOR]

Backgroud and objectivesThe implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model’s performance was compared by evaluating the impact of two distinct feature selection methods.MethodsA retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, γ analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model’s classification performance was assessed through F1-score and area under the curve (AUC) values.ResultsThe model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1-score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible.ConclusionBased on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists.

Objective:This study aimed to explore the signal detection method for allergic reactions induced by inpatient iodixanol injection.Methods:A database of 3,719,217 hospitalized patients from 20 large Chinese general hospitals was processed and analyzed using the prescription sequence symmetry analysis (PSSA) method.Results:126,680 inpatients who used iodixanol and were concurrently treated with anti-allergic drugs were analyzed. In the medical records of these patients, only 32 had documented iodixanol allergies. Statistical analysis identified 22 drugs in 4 categories—calcium preparations, adrenergic/dopaminergic agents, glucocorticoids, and antihistamines—as marker drugs. With time intervals of 3, 7, and 28 days, the adjusted sequence ratios (aSRs) for all anti-allergics and the 4 categories were greater than 1. The 7-day aSRs were 2.12 (95% CI: 2.08–2.15), 1.70 (95% CI: 1.68–1.73), 3.85 (95% confidence interval [CI]: 3.75–2.30), 2.30 (95% CI: 2.26–2.35), and 1.95 (95% CI: 1.89–2.02), respectively. The proportions of adverse drug events indicated by each signal were as follows: all anti-allergics (2.92%–3%), calcium gluconate (0.19%–0.52%), adrenergic/dopaminergic agents (2.20%–3.37%), glucocorticoids (3.13%–3.76%), and antihistamines (1.05%–1.32%).Conclusion:This first multi-center Chinese inpatient database study detected iodixanol-induced allergy signals, revealing that reactions may be much higher than those in collected spontaneous reports. Iodixanol risk exposure was closer to actual pharmaceutical care findings. PSSA application with ≤7-day intervals appears better suited for monitoring late allergic reaction signals with these drugs.

Analyzing treatment or exposure effect is a major research theme in scientific studies. In the current big-data era where multiple sources of data are available, it is of interest to perform a synthesized analysis of treatment effects by integrating information from different data sources or studies. However, studies may contain heterogeneous and incomplete covariate sets, and individual data therein may not be accessible. We apply and extend the generalized meta-analysis method to integrate summary results (e.g., regression coefficients) of outcome and treatment (propensity score, PS) regression analyses across different datasets that may contain heterogeneous covariate sets. The proposed integrated analysis utilizes a reference dataset, which contains data on the complete set of covariates. The asymptotic distribution for the proposed integrated estimator is established. Simulations reveal that the proposed estimator performs well. We apply the proposed method to obtain the causal effect of waist circumference on hypertension by integrating two existing outcomes and PS regression analyses with different sets of covariates.

ABSTRACT Cryptococcus gattii (C. gattii) is emerging as a life-threatening fungus worldwide. However, few data were available in China. In this study, 27 isolates and 32 patient information were collected from several hospitals in China, together with previously reported data (80 cases), to describe the geographic distribution of C. gattii. Molecular identification and genetic diversity analysis of the isolates were conducted using multilocus sequence typing, while the microbiological and virulence characteristics were explored through in vitro phenotypic tests and in vivo animal experiments. The findings revealed that patients infected with C. gattii were mainly immunocompetent males, with most showing symptoms of central nervous system involvement. Isolated strains were predominantly distributed in tropical and subtropical regions, with VGI genotype predominance. These strains exhibited marked genetic diversity, identifying 25 distinct sequence types (STs) among the 57 isolates (available data), including three novel STs (ST565, ST567, and ST568). In vitro assays unveiled significant differences between VGI and VGII in growth capacity at 39°C, capsule diameter, melanin production, UV resistance, and antioxidative capacity, with VGII displaying greater resilience. Integrating animal experiments and clinical prognosis showed that pathogenicity did not directly correlate with in vitro virulence phenotypes or molecular genotypes, underscoring virulence’s complexity. Furthermore, histopathological analysis suggested that lung tissue damage might primarily contribute to mouse mortality, particularly with more pathogenic strains causing extensive lung tissue damage. Our study has the potential to provide valuable data for a comprehensive understanding of the microbiological characteristics of C. gattii in China. IMPORTANCE Our study indicates that the molecular typing of Cryptococcus gattii is unrelated to virulence. The integration of animal experiments and clinical prognosis demonstrated that pathogenicity did not exhibit a direct correlation with in vitro virulence phenotypes or molecular genotypes, emphasizing the intricate nature of virulence. In conclusion, our research holds the potential to provide valuable insights into understanding the microbiological attributes of C. gattii in China.

Objective: The purpose of this study was to collect data on the management of ventilator-associated pneumonia (VAP) in pediatric intensive care units (PICU) in Türkiye and to determine the need for new national pediatric VAP guidelines. Methods: In this multicenter cross-sectional study, an online questionnaire was disseminated via email to PICUs in various cities across Türkiye. One person at each PICU, namely, the clinician who made the treatment decisions, completed the questionnaire. The VAP diagnosis and treatment algorithms of the PICUs were analyzed using the data obtained from the questionnaires. Results: Of the initial 32 PICUs, 30 units in 19 cities completed the questionnaire. The average number of beds in the units was 13.13±6.16, and the number of beds per nurse per shift was 2.13±0.57. The mean duration of mechanical ventilation was 5.8±4.2 days. The mean VAP frequency was 2.81% and the mean VAP rate was 5.04 per 1000 ventilator day. Distal airway culture sampling was performed in 86.7% of the units before antibiotic treatment was initiated. The most common agent was Pseudomonas aeruginosa, followed by Klebsiella pneumonia and Acinetobacter baumannii. When the resistance status of the isolates was analyzed, anti-pseudomonal penicillin resistance was 81.2%, antipseudomonal cephalosporin resistance was 84.5% for Pseudomonas aeruginosa; cefepime and ceftazidime resistance was 80.5% for Klebsiella pneumonia, and carbapenem resistance was 47.5% for Acinetobacter baumannii. A nurse-bed ratio >2 made a significant difference in the VAP rates between the PICUs (p<0.05). Conclusion: Consensus exists regarding the need to reduce VAP in PICUs in Türkiye, and up-to-date national guidelines are essential to maximize the efficiency of PICUs. [ABSTRACT FROM AUTHOR]

• First multi-center study to investigate inhaled medication adherence post-ETI. • Medication adherence was calculated using the Medication Possession Ratio (MPR). • MPR for all inhaled medication was significantly decreased after ETI initiation. Elexacator/tezacaftor/ivacaftor (ETI) has improved cystic fibrosis (CF) outcomes. A reduction in use of maintenance medication after its initiation has been reported. Seventy-one adult people with CF (PwCF) who are followed in three CF centers and completed one year of treatment with ETI were included in this study. Their use of inhaled dornase-α, colistin, tobramycin, aztreonam and levofloxacin during this period was compared with the corresponding use during one year without ETI, using the Medication Possession Ratio (MPR). MPR was significantly decreased after ETI initiation for dornase-α (67±35% vs 48±40%, p <0.001) and for all four inhaled antibiotics together (62±33% vs 41±37%, p <0.001). The findings of this multi-center, retrospective, study suggest that the initiation of ETI significantly leads to decrease in use of standard inhaled medication in PwCF. The significance of this finding in the course of the disease is yet to be investigated by larger prospective clinical trials. [ABSTRACT FROM AUTHOR]

This multi-center point prevalence study evaluated children who were diagnosed as having coronavirus disease 2019 (COVID-19). On February 2nd, 2022, inpatients and outpatients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were included in the study from 12 cities and 24 centers in Turkey. Of 8605 patients on February 2nd, 2022, in participating centers, 706 (8.2%) had COVID-19. The median age of the 706 patients was 92.50 months, 53.4% were female, and 76.7% were inpatients. The three most common symptoms of the patients with COVID-19 were fever (56.6%), cough (41.3%), and fatigue (27.5%). The three most common underlying chronic diseases (UCDs) were asthma (3.4%), neurologic disorders (3.3%), and obesity (2.6%). The SARS-CoV-2-related pneumoniae rate was 10.7%. The COVID-19 vaccination rate was 12.5% in all patients. Among patients aged over 12 years with access to the vaccine given by the Republic of Turkey Ministry of Health, the vaccination rate was 38.7%. Patients with UCDs presented with dyspnea and pneumoniae more frequently than those without UCDs (p < 0.001 for both). The rates of fever, diarrhea, and pneumoniae were higher in patients without COVID-19 vaccinations (p = 0.001, p = 0.012, and p = 0.027). Conclusion: To lessen the effects of the disease, all eligible children should receive the COVID-19 vaccine. The illness may specifically endanger children with UCDs. What is Known: • Children with COVID-19 mainly present with fever and cough, as in adults. • COVID-19 may specifically threaten children with underlying chronic diseases. What is New: • Children with obesity have a higher vaccination rate against COVID-19 than children without obesity. • Among unvaccinated children, fever and pneumoniae might be seen at a higher ratio than among vaccinated children. [ABSTRACT FROM AUTHOR]

This study is nationwide retrospective multi-center study to investigate the incidence and characteristics of blunt cerebrovascular injury (BCVI) in elderly Japanese patients with traumatic cervical spine injuries (CSI) including spinal cord injury (SCI) without major bone injury. The study enrolled 1512 patients (average age: 75.8 ± 6.9 years; 1007 males, 505 females) from 33 nationwide institutions, and 391 (26%) of the participants had digital subtraction angiography and/or computed tomography angiography. Fifty-three patients were diagnosed as having BCVI by angiography. We assessed neurological evaluation, comorbidities and classification of CSI in the elderly patients with/without BCVI and collected 6-month follow-up data on treatment, complications, and patient outcome. We also statistically analyzed the relative risk (RR) and relationship between BCVI and other factors. Significant differences were identified between BCVI (+) (n = 53) and (–) (n = 1459) patients with American Spinal Injury Association Impairment Scale (ASIA) A, C, D, cervical fracture, C3-7 injury level (AO type F and/or C), cervical dislocation, spinal surgery for CSI, tetraplegia type of SCI, and/or head injury. Fifty-three (3.5%) elderly patients had CSI complicated by BCVI including 10 (19%) cases of Denver grade I, four (7%) of grade II, 1 (2%) of grade III, 29 (55%) of grade IV, and nine (17%) of grade V. Sixteen cases were treated by interventional radiology. Rates of mortality and brain infarction from BCVI were 0.13% and 0.40%, respectively. RR of BCVI was significantly higher in the elderly cervical injury patients with head injury, severe neurological deficit, ASIA A (RR: 4.33), cervical fracture at the C3-7 level (RR: 7.39), and cervical dislocation at the C1-6 level (RR: 3.06-7.18). In conclusion, 53 (3.5%) elderly patients were complicated with BCVI. BCVI more frequently complicated head injury, severe neurological deficit (ASIA A or tetraplegia), AO type F, and/or C fractures and cervical dislocation in these patients. Six patients (11%) suffered brain infarction and two patients died from BCVI. [ABSTRACT FROM AUTHOR]

Background: Serum albumin has been demonstrated as prognostic parameter in non-Hodgkin lymphoma (NHL). Primary central nervous system lymphoma (PCNSL) is a rare extranodal NHL with highly aggressive behavior. In this study, we aimed at creating a novel prognostic model for PCNSL based on serum albumin levels. Methods: We compared several commonly used laboratory nutritional parameters for predicting the survival of PCNSL patients using overall survival (OS) for outcome analysis and receiver operating characteristic curve analysis to determine the optimal cut-off values. Parameters associated with OS were evaluated by univariate and multivariate analyses. Independent prognostic parameters for OS were selected for risk stratification, including albumin ≤ 4.1 g/dL, ECOG PS > 1, and LLR > 166.8, which were associated with shorter OS; albumin > 4.1 g/dL, ECOG PS 0–1 and LLR ≤ 166.8, which were associated with longer OS, and five-fold cross-validation was used for evaluating predictive accuracy of identified prognostic model. Results: By univariate analysis, age, ECOG PS, MSKCC score, Lactate dehydrogenase-to-lymphocyte ratio (LLR), total protein, albumin, hemoglobin, and albumin to globulin ratio (AGR) resulted statistically associated with the OS of PCNSL. By multivariate analysis, albumin ≤ 4.1 g/dL, ECOG PS > 1, and LLR > 166.8 were confirmed to be significant predictors of inferior OS. We explored several PCNSL prognostic models based on albumin, ECOG PS and LLR with 1 point assigned to each parameter. Eventually, a novel and effective PCNSL prognostic model based on albumin and ECOG PS successfully classified patients into three risk groups with 5-year survival rates of 47.5%, 36.9%, and 11.9%, respectively. Conclusions: The novel two-factor prognostic model based on albumin and ECOG PS we propose represents a simple but significant prognostic tool for assessing newly diagnosed patients with PCNSL. [ABSTRACT FROM AUTHOR]

BackgroundModerate-to-severe bronchopulmonary dysplasia (msBPD) is a serious complication in preterm infants. We aimed to develop a dynamic nomogram for early prediction of msBPD using perinatal factors in preterm infants born at

Childhood stroke occurs from birth to 18 years of age, ranks among the top ten childhood causes of death, and leaves lifelong neurological impairments. Arterial ischemic stroke in infancy and childhood occurs due to arterial occlusion in the brain, resulting in a focal lesion. Our understanding of mechanisms of injury and repair associated with focal injury in the developing brain remains rudimentary. Neuroimaging can reveal important insights into these mechanisms. In adult stroke population, multi-center neuroimaging studies are common and have accelerated the translation process leading to improvements in treatment and outcome. These studies are centered on the growing evidence that neuroimaging measures and other biomarkers (e.g., from blood and cerebrospinal fluid) can enhance our understanding of mechanisms of risk and injury and be used as complementary outcome markers. These factors have yet to be studied in pediatric stroke because most neuroimaging studies in this population have been conducted in single-centred, small cohorts. By pooling neuroimaging data across multiple sites, larger cohorts of patients can significantly boost study feasibility and power in elucidating mechanisms of brain injury, repair and outcomes. These aims are particularly relevant in pediatric stroke because of the decreased incidence rates and the lack of mechanism-targeted trials.Toward these aims, we developed the Pediatric Stroke Neuroimaging Platform (PEDSNIP) in 2015, funded by The Brain Canada Platform Support Grant, to focus on three identified neuroimaging priorities. These were: developing and harmonizing multisite clinical protocols, creating the infrastructure and methods to import, store and organize the large clinical neuroimaging dataset from multiple sites through the International Pediatric Stroke Study (IPSS), and enabling central searchability. To do this, developed a two-pronged approach that included building 1) A Clinical-MRI Data Repository (standard of care imaging) linked to clinical data and longitudinal outcomes and 2) A Research-MRI neuroimaging data set acquired through our extensive collaborative, multi-center, multidisciplinary network. This dataset was collected prospectively in eight North American centers to test the feasibility and implementation of harmonized advanced Research-MRI, with the addition of clinical information, genetic and proteomic studies, in a cohort of children presenting with acute ischemic stroke.Here we describe the process that enabled the development of PEDSNIP built to provide the infrastructure to support neuroimaging research priorities in pediatric stroke. Having built this Platform, we are now able to utilize the largest neuroimaging and clinical data pool on pediatric stroke data worldwide to conduct hypothesis-driven research. We are actively working on a bioinformatics approach to develop predictive models of risk, injury and repair and accelerate breakthrough discoveries leading to mechanism-targeted treatments that improve outcomes and minimize the burden following childhood stroke. This unique transformational resource for scientists and researchers has the potential to result in a paradigm shift in the management, outcomes and quality of life in children with stroke and their families, with far-reaching benefits for other brain conditions of people across the lifespan.

MRI based multicenter studies which target neurological pathologies affecting the spinal cord and brain – including spinal cord injury (SCI) – require standardized acquisition protocols and image processing methods. We have optimized and applied a multi-parameter mapping (MPM) protocol that simultaneously covers the brain and the cervical cord within a traveling heads study across six clinical centers (Leutritz et al., 2020). The MPM protocol includes quantitative maps (magnetization transfer saturation (MT), proton density (PD), longitudinal (R1), and effective transverse (R2*) relaxation rates) sensitive to myelination, water content, iron concentration, and morphometric measures, such as cross-sectional cord area. Previously, we assessed the repeatability and reproducibility of the brain MPM data acquired in the five healthy participants who underwent two scan-rescans (Leutritz et al., 2020). This study focuses on the cervical cord MPM data derived from the same acquisitions to determine its repeatability and reproducibility in the cervical cord. MPM matrices of the cervical cord were generated and processed using the hMRI and the spinal cord toolbox. To determine reliability of the cervical MPM data, the intra-site (i.e., scan-rescan) coefficient of variation (CoV), inter-site CoV, and bias within region of interests (C1, C2 and C3 levels) were determined. The range of the mean intra- and inter-site CoV of MT, R1 and PD was between 2.5% and 12%, and between 1.1% and 4.0% for the morphometric measures. In conclusion, the cervical MPM data showed a high repeatability and reproducibility for key imaging biomarkers and hence can be employed as a standardized tool in multi-center studies, including clinical trials.

Background/Aims: This study was to determine the test–retest repeatability in quantifying macular capillary perfusion density (CPD, expressed as fractal dimension) using optical coherence tomography angiography (OCTA) in a multi-center setting. Methods: OCTA data were obtained in self-reported healthy subjects from Bascom Palmer Eye Institute at the University of Miami (UM, N = 18) and the University of Pennsylvania (UPenn, N = 22). The right eye of each subject was imaged twice at the first visit and then again at an interval of one week to assess intra-visit and inter-visit repeatability. The macular area of the OCTA-derived capillary perfusion density (OCTA-CPD) was analyzed by custom-made image processing and fractal analysis software. Fractal analysis was performed on the skeletonized microvascular network to yield OCTA-CPD by box-counting to the fractal dimension (Dbox) in the superficial vascular plexus (SVP). Repeatability was assessed by three measures: within-subject standard deviation (Sw), coefficient of variation (CoV) of repeated measures, and intraclass correlation coefficient (ICC). Results: OCTA-CPD from both sites (UM and UPENN) showed good to excellent intra-visit repeatability, as demonstrated by the Sw ≤ 0.004, CoVs ≤ 0.23%, and ICCs ≥ 0.61. Similarly, both sites had good to excellent inter-visit repeatability, as shown by the Sw ≤ 0.005, CoVs ≤ 0.28%, and ICCs ≥ 0.61. The Bland-Altman plots of the intra-visit and inter-visit measurements showed excellent agreements between the paired measurements with minimal biases. Conclusion: Our data showed that comparable high repeatability of OCTA-CPD can be achieved in both research sites using the same device, scan protocol, and image analysis. [ABSTRACT FROM AUTHOR]

Objectives: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).Materials and Methods: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis.Results: In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]).Conclusions: A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%.Key Points: • Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. • Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. • Applying deep learning to an auto-fixed VOI radiomics approach can be valuable. [ABSTRACT FROM AUTHOR]

Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants., Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics., Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42-65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77-0.85 and AUPRC of 0.51-0.60 for variants., Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiao Li reports financial support was provided by National Natural Science Foundation of China. NA reports a relationship with NA that includes:. NA has patent NA pending to NA. NA If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Context: The Spinal Cord Independence Measure is a comprehensive functional rating scale for individuals with spinal cord lesion (SCL)., Objective: To validate the scores of the three subscales of SCIM IV, the fourth version of SCIM, using advanced statistical methods., Study Design: Multi-center cohort study., Setting: Nineteen SCL units in 11 countries., Methods: SCIM developers created SCIM IV following comments by experts, included more accurate definitions of scoring criteria in the SCIM IV form, and adjusted it to assess specific conditions or situations that the third version, SCIM III, does not address. Professional staff members assessed 648 SCL inpatients, using SCIM IV and SCIM III, at admission to rehabilitation, and at discharge. The authors examined the validity and reliability of SCIM IV subscale scores using Rasch analysis., Results: The study included inpatients aged 16-87 years old. SCIM IV subscale scores fit the Rasch model. All item infit and most item outfit mean-square indices were below 1.4; statistically distinct strata of abilities were 2.6-6; most categories were properly ordered; item hierarchy was stable across most clinical subgroups and countries. In a few items, however, we found misfit or category threshold disordering. We found SCIM III and SCIM IV Rasch properties to be comparable., Conclusions: Rasch analysis suggests that the scores of each SCIM IV subscale are reliable and valid. This reinforces the justification for using SCIM IV in clinical practice and research.

Abstract Background Perioperative patients are at risk of developing stress ulcers (SU), which can cause clinically important bleeding. Stress ulcer prophylaxis (SUP) is widely applied to the patients in Intensive care unit (ICU) as well as the general ward, so it may lead to overmedication. However, there have been no surveys regarding SUP knowledge or prescribing habits. Objective Our study assessed the knowledge, attitudes, and prescribing behavior of the surgeons toward perioperative patients regarding SUP and determined factors associated with low knowledge and high level of prescribing behaviors. Methods We performed a cross-sectional survey using questionnaires, randomly sampling 1266 surgeons on their current SUP practices. Results Proton pump inhibitors for SUP were used the most (94%); 43% used lansoprazole. Guideline awareness was inconsistent; the most familiar guideline was the National Medical Journal of China, and 46% were unaware of any guidelines. The predictors of low knowledge score regarding SUP in multivariable analysis were the hospital grade (p = 0.000), the type of hospital (p = 0.044), attendance at continuing education programs (p = 0.037), the awareness of clinical practice guidelines (CPGs) for SUP (p = 0.000). Twenty-one percent of physicians were high prescribers. High prescribing behavior was associated with hospital grade(p = 0.000), education level(p = 0.010) and attendance at continuing education programs (p = 0.000). Conclusion We found that most surgeons used SUP, primarily proton pump inhibitors. However, surgeons knew little about the SUP guidelines, which may lead to insufficient SUP knowledge and overmedication. In addition, hospital grade, the type of hospital and attendance at continuing education programs may also affect the low knowledge of SUP. Hospital grade, education level and attendance at continuing education programs may affect high prescribing behavior.

The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected for the construction of the prognosis risk score model as the prognostic factors of ccRCC, including tumor size, tumor grade, and tumor stage. In the CPTAC (Clinical Proteomic Tumor Analysis Consortium) cohort, the General cohort, the SEER (Surveillance, Epidemiology, and End Results) cohort, and the Huashan cohort, patients with high-risk score had worse clinical outcomes than patients with low-risk score (hazard ratio 5.15, 4.64, 3.96, and 5.15, respectively). Further functional enrichment analysis demonstrated that our machine learning-based risk score was significantly connected with some cell proliferation-related pathways, consisting of DNA repair, cell division, and cell cycle. In summary, we developed and validated a machine learning-based prognosis prediction model, which might contribute to clinical decision-making for patients with ccRCC.

Abstract Background Over the last decades, many high-income countries have successfully implemented assertive outreach mental health services for acute care. Despite evidence that these services entail several benefits for service users, Germany has lagged behind and has been slow in implementing outreach services. In 2018, a new law enabled national mental health care providers to implement team-based crisis intervention services on a regular basis, allowing for different forms of Inpatient Equivalent Home Treatment (IEHT). IEHT is similar to the internationally known Home Treatment or Crisis Resolution Teams. It provides acute psychiatric treatment at the user’s home, similar to inpatient hospital treatment in terms of content, flexibility, and complexity. Methods/design The presented naturalistic, quasi-experimental cohort study will evaluate IEHT in ten hospitals running IEHT services in different German regions. Within a multi-method research approach, it will evaluate stakeholders’ experiences of care, service use, efficacy, costs, treatment processes and implementation processes of IEHT from different perspectives. Quantitative surveys will be used to recruit 360 service users. Subsequently, 180 service users receiving IEHT will be compared with 180 matched statistical ‘twins’ receiving standard inpatient treatment. Assessments will take place at baseline as well as after 6 and 12 months. The primary outcome is the hospital re-admission rate within 12 months. Secondary outcomes include the combined readmission rate, total number of inpatient hospital days, treatment discontinuation rate, quality of life, psycho-social functioning, job integration, recovery, satisfaction with care, shared decision-making, and treatment costs. Additionally, the study will assess the burden of care and satisfaction with care among relatives or informal caregivers. A collaborative research team made up of researchers with and without lived experience of mental distress will conduct qualitative investigations with service users, caregivers and IEHT staff teams to explore critical ingredients and interactions between implementation processes, treatment processes, and outcomes from a stakeholder perspective. Discussion By integrating outcome, process and implementation research as well as different stakeholder perspectives and experiences in one study, this trial captures the various facets of IEHT as a special form of home treatment. Therefore, it allows for an adequate, comprehensive evaluation on different levels of this complex intervention. Trial registration Trial registrations: 1) German Clinical Trials Register (DRKS), DRKS000224769. Registered December 3rd 2020, https://www.drks.de/drks_web/setLocale_EN.do ; 2) ClinicalTrials.gov, Identifier: NCT0474550 . Registered February 9th 2021.

Objective: The purpose of this work was to summarize the real-world safety and efficacy of Pipeline Embolization Device (PED) therapy for small and medium-sized intracranial aneurysms in China. Methods: Patients from the PED in China post-market multi-center registry study (PLUS) with aneurysms smaller than 12 mm were selected. Radiographic outcomes were assessed using digital subtraction angiography. Clinical outcomes included functional outcomes (modified Rankin Scale, MRS) in the early postoperative period (≤ 30 days) and early postoperative complications associated with PED therapy. Results: A total of 652 patients with a combined 754 aneurysms were included in this study (mean age of 53.9 ± 10.3 years, 68.7% women). Mean aneurysm diameter was 6.78 ± 2.67 mm. Of the 687 stents deployed, 99.7% (685/689) were successfully deployed. In this study, 64.7% (488/754) of aneurysms were treated with only the PED, whereas 35.3% (266/754) were subjected to PED-assisted therapy. Radiographic outcome at the last follow-up (median time: seven months) was available for 64.3% (485/754) of the aneurysms. 82.5% (400/485) of aneurysms demonstrated complete occlusion (Raymond Roy Grade I). 81.4% (395/485) of aneurysms were found to meet the study's primary effectiveness outcome. At the early postoperative period, the mRS score was determined to be 0–2 vs. 3-6 in 98.2% (640/652) vs. 1.8% (12/652) of the cases, respectively. The combined major morbidity and mortality rate was 3.2% (21/652). Conclusion: In the largest study of PED therapy for small and medium-sized intracranial aneurysms to date, pipeline-assisted coil embolization was chosen more often than multiple stent implantation for aneurysm treatment, demonstrating good results, high surgical success rates, high occlusion rates, and low morbidity and mortality. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03831672. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to investigate the prevalence of developmental regression in children with Autism Spectrum Disorder (ASD) and to explore its relationship with disease severity. Methods: We finally included 1,027 ASD children aged 2–5 years from 13 cities in China: 138 with regressive ASD and 889 with non-regressive ASD. The Social Responsiveness Scale (SRS), Autism Behavior Checklist (ABC), Child Autism Rating Scale (CARS), and Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) were used to evaluate the core symptoms and developmental status of children in the two groups. Results: Among the 1,027 ASD children eventually included, 138 (13.44%) cases showed regressive behavior and the average regression occurring age was 24.00 (18.00–27.00) months. Among the regressive children, 105 cases (76.09%) had language regression, 79 cases (57.25%) had social regression, and 4 cases (2.90%) had motor regression. The total scores of ABC and the sub-score of sensory and stereotypic behavior (β = 5.122, 95% CI: 0.818, 9.426, P < 0.05; β = 1.104, 95% CI: 0.120, 2.089, P < 0.05; β = 1.388, 95% CI: 0.038, 2.737, P < 0.05), the SRS total scores and the sub-score of autistic mannerisms (β = 4.991, 95% CI: 0.494, 9.487, P < 0.05; β = 1.297, 95% CI: 0.140, 2.453, P < 0.05) of children in the regressive group were all higher than the non-regressive group. The total developmental quotient (DQ) of CNBS-R2016 and the DQ of gross motor, fine motor, adaptive behavior, language (β = −5.827, 95% CI: −11.529, −0.125, P < 0.05) and personal society in the regressive group were lower than the non-regressive group and the proportion of children with intelligent developmental impairment was higher the non-regressive group. Conclusion: Regressive autism is mainly manifested as language and social regression. Children with regressive ASD have more severe core symptoms, lower neurodevelopmental level DQ, and more serious disease degree than children with non-regressive ASD, which requires further etiological examinations and more clinical attention. [ABSTRACT FROM AUTHOR]

ObjectiveThe purpose of this work was to summarize the real-world safety and efficacy of Pipeline Embolization Device (PED) therapy for small and medium-sized intracranial aneurysms in China.MethodsPatients from the PED in China post-market multi-center registry study (PLUS) with aneurysms smaller than 12 mm were selected. Radiographic outcomes were assessed using digital subtraction angiography. Clinical outcomes included functional outcomes (modified Rankin Scale, MRS) in the early postoperative period ( ≤ 30 days) and early postoperative complications associated with PED therapy.ResultsA total of 652 patients with a combined 754 aneurysms were included in this study (mean age of 53.9 ± 10.3 years, 68.7% women). Mean aneurysm diameter was 6.78 ± 2.67 mm. Of the 687 stents deployed, 99.7% (685/689) were successfully deployed. In this study, 64.7% (488/754) of aneurysms were treated with only the PED, whereas 35.3% (266/754) were subjected to PED-assisted therapy. Radiographic outcome at the last follow-up (median time: seven months) was available for 64.3% (485/754) of the aneurysms. 82.5% (400/485) of aneurysms demonstrated complete occlusion (Raymond Roy Grade I). 81.4% (395/485) of aneurysms were found to meet the study's primary effectiveness outcome. At the early postoperative period, the mRS score was determined to be 0–2 vs. 3-6 in 98.2% (640/652) vs. 1.8% (12/652) of the cases, respectively. The combined major morbidity and mortality rate was 3.2% (21/652).ConclusionIn the largest study of PED therapy for small and medium-sized intracranial aneurysms to date, pipeline-assisted coil embolization was chosen more often than multiple stent implantation for aneurysm treatment, demonstrating good results, high surgical success rates, high occlusion rates, and low morbidity and mortality.Clinical Trial Registrationwww.ClinicalTrials.gov, identifier: NCT03831672.

BackgroundHuman coronaviruses (HCoVs) are associated with upper respiratory tract infections. Although studies have analyzed the clinical and epidemiological characteristics of HCoV-associated infections, no multi-center studies have been conducted in Korean children. We aimed to describe the epidemiology and clinical characteristics of HCoV-associated infection in children.MethodsWe retrospectively reviewed medical records of children in whom HCoVs were detected using multiplex reverse transcriptase-polymerase chain reaction amplification in five centers from January 2015 to December 2019.ResultsOverall, 1,096 patients were enrolled. Among them, 654 (59.7%) patients were male. The median age was 1 year [interquartile range (IQR), 0–2 years]. HCoVs were identified mainly in winter (55.9%). HCoV-229E, HCoV-OC43, and HCoOV-NL63 were detected mainly in winter (70.9, 55.8, and 57.4%, respectively), but HCoV-HKU1 was mainly identified in spring (69.7%). HCoV-OC43 (66.0%) was detected most frequently, followed by HCoV-NL63 (33.3%), and HCoV-229E (7.7%). Two different types of HCoVs were co-detected in 18 samples, namely. Alphacoronavirus-betacoronavirus co-infection (n = 13) and, alphacoronavirus-alphacoronavirus co-infection (n = 5). No betacoronavirus-betacoronavirus co-infection was detected. Patients were diagnosed with upper respiratory tract infection (41.4%), pneumonia (16.6%), acute bronchiolitis (15.5%), non-specific febrile illness (13.1%), croup (7.3%), and acute gastroenteritis (5.1%). There were 832 (75.9%) hospitalized patients with a median duration of hospitalization of 4 days (IQR, 3–5 days); 108 (9.9%) patients needed supplemental oxygen with 37 (3.4%) needing high-flow nasal cannula or mechanical ventilation. There were no deaths.ConclusionHCoV-associated infections exhibit marked seasonality with peaks in winter. Patients with lower respiratory tract infection, a history of prematurity, or underlying chronic diseases may progress to a severe course and may need oxygen therapy.

IntroductionThis study aimed to investigate the prevalence of developmental regression in children with Autism Spectrum Disorder (ASD) and to explore its relationship with disease severity.MethodsWe finally included 1,027 ASD children aged 2–5 years from 13 cities in China: 138 with regressive ASD and 889 with non-regressive ASD. The Social Responsiveness Scale (SRS), Autism Behavior Checklist (ABC), Child Autism Rating Scale (CARS), and Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) were used to evaluate the core symptoms and developmental status of children in the two groups.ResultsAmong the 1,027 ASD children eventually included, 138 (13.44%) cases showed regressive behavior and the average regression occurring age was 24.00 (18.00–27.00) months. Among the regressive children, 105 cases (76.09%) had language regression, 79 cases (57.25%) had social regression, and 4 cases (2.90%) had motor regression. The total scores of ABC and the sub-score of sensory and stereotypic behavior (β = 5.122, 95% CI: 0.818, 9.426, P < 0.05; β = 1.104, 95% CI: 0.120, 2.089, P < 0.05; β = 1.388, 95% CI: 0.038, 2.737, P < 0.05), the SRS total scores and the sub-score of autistic mannerisms (β = 4.991, 95% CI: 0.494, 9.487, P < 0.05; β = 1.297, 95% CI: 0.140, 2.453, P < 0.05) of children in the regressive group were all higher than the non-regressive group. The total developmental quotient (DQ) of CNBS-R2016 and the DQ of gross motor, fine motor, adaptive behavior, language (β = −5.827, 95% CI: −11.529, −0.125, P < 0.05) and personal society in the regressive group were lower than the non-regressive group and the proportion of children with intelligent developmental impairment was higher the non-regressive group.ConclusionRegressive autism is mainly manifested as language and social regression. Children with regressive ASD have more severe core symptoms, lower neurodevelopmental level DQ, and more serious disease degree than children with non-regressive ASD, which requires further etiological examinations and more clinical attention.

BackgroundThe accumulation of risk for the development of rheumatoid arthritis (RA) is regarded as a continuum that may start with interacting environmental and genetic factors, proceed with the initiation of autoimmunity, and result in the formation of autoantibodies such as anti-citrullinated peptide antibodies (ACPA). In parallel, at-risk individuals may be asymptomatic or experience joint pain (arthralgia) that is itself non-specific or clinically suspicious for evolving RA, even in the absence of overt arthritis. Optimal strategies for the management of people at-risk of RA, both for symptom control and to delay or prevent progression to classifiable disease, remain poorly understood.MethodsTo help address this, groups of stakeholders from academia, clinical rheumatology, industry and patient research partners have collaborated to advance understanding, define and study different phases of the at-risk state. In this current report we describe different European initiatives in the field and the successful effort to build a European Registry of at-risk people to facilitate observational and interventional research.ResultsWe outline similarities and differences between cohorts of at-risk individuals at institutions spanning several countries, and how to best combine them within the new database. Over the past 2 years, besides building the technical infrastructure, we have agreed on a core set of variables that all partners should strive to collect for harmonization purposes.ConclusionWe emphasize to address this process from different angles and touch on the biologic, epidemiologic, analytic, and regulatory aspects of collaborative studies within a meta-database of people at-risk of RA.

Background: Perioperative patients are at risk of developing stress ulcers (SU), which can cause clinically important bleeding. Stress ulcer prophylaxis (SUP) is widely applied to the patients in Intensive care unit (ICU) as well as the general ward, so it may lead to overmedication. However, there have been no surveys regarding SUP knowledge or prescribing habits.Objective: Our study assessed the knowledge, attitudes, and prescribing behavior of the surgeons toward perioperative patients regarding SUP and determined factors associated with low knowledge and high level of prescribing behaviors.Methods: We performed a cross-sectional survey using questionnaires, randomly sampling 1266 surgeons on their current SUP practices.Results: Proton pump inhibitors for SUP were used the most (94%); 43% used lansoprazole. Guideline awareness was inconsistent; the most familiar guideline was the National Medical Journal of China, and 46% were unaware of any guidelines. The predictors of low knowledge score regarding SUP in multivariable analysis were the hospital grade (p = 0.000), the type of hospital (p = 0.044), attendance at continuing education programs (p = 0.037), the awareness of clinical practice guidelines (CPGs) for SUP (p = 0.000). Twenty-one percent of physicians were high prescribers. High prescribing behavior was associated with hospital grade(p = 0.000), education level(p = 0.010) and attendance at continuing education programs (p = 0.000).Conclusion: We found that most surgeons used SUP, primarily proton pump inhibitors. However, surgeons knew little about the SUP guidelines, which may lead to insufficient SUP knowledge and overmedication. In addition, hospital grade, the type of hospital and attendance at continuing education programs may also affect the low knowledge of SUP. Hospital grade, education level and attendance at continuing education programs may affect high prescribing behavior. [ABSTRACT FROM AUTHOR]

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Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs. [ABSTRACT FROM AUTHOR]

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently. [ABSTRACT FROM AUTHOR]

Microsatellite instability (MSI) is present in 15-20% of primary colorectal cancers. MSI status is assessed to detect Lynch syndrome, guide adjuvant chemotherapy, determine prognosis, and use as a companion test for checkpoint blockade inhibitors. Traditionally, MSI status is determined by immunohistochemistry or molecular methods. The Idylla™ MSI Assay is a fully automated molecular method (including automated result interpretation), using seven novel MSI biomarkers (ACVR2A, BTBD7, DIDO1, MRE11, RYR3, SEC31A, SULF2) and not requiring matched normal tissue. In this real-world global study, 44 clinical centers performed Idylla™ testing on a total of 1301 archived colorectal cancer formalin-fixed, paraffin-embedded (FFPE) tissue sections and compared Idylla™ results against available results from routine diagnostic testing in those sites. MSI mutations detected with the Idylla™ MSI Assay were equally distributed over the seven biomarkers, and 84.48% of the MSI-high samples had ≥ 5 mutated biomarkers, while 98.25% of the microsatellite-stable samples had zero mutated biomarkers. The concordance level between the Idylla™ MSI Assay and immunohistochemistry was 96.39% (988/1025); 17/37 discordant samples were found to be concordant when a third method was used. Compared with routine molecular methods, the concordance level was 98.01% (789/805); third-method analysis found concordance for 8/16 discordant samples. The failure rate of the Idylla™ MSI Assay (0.23%; 3/1301) was lower than that of referenced immunohistochemistry (4.37%; 47/1075) or molecular assays (0.86%; 7/812). In conclusion, lower failure rates and high concordance levels were found between the Idylla™ MSI Assay and routine tests. [ABSTRACT FROM AUTHOR]

Background: Over the last decades, many high-income countries have successfully implemented assertive outreach mental health services for acute care. Despite evidence that these services entail several benefits for service users, Germany has lagged behind and has been slow in implementing outreach services. In 2018, a new law enabled national mental health care providers to implement team-based crisis intervention services on a regular basis, allowing for different forms of Inpatient Equivalent Home Treatment (IEHT). IEHT is similar to the internationally known Home Treatment or Crisis Resolution Teams. It provides acute psychiatric treatment at the user's home, similar to inpatient hospital treatment in terms of content, flexibility, and complexity. Methods/design: The presented naturalistic, quasi-experimental cohort study will evaluate IEHT in ten hospitals running IEHT services in different German regions. Within a multi-method research approach, it will evaluate stakeholders' experiences of care, service use, efficacy, costs, treatment processes and implementation processes of IEHT from different perspectives. Quantitative surveys will be used to recruit 360 service users. Subsequently, 180 service users receiving IEHT will be compared with 180 matched statistical 'twins' receiving standard inpatient treatment. Assessments will take place at baseline as well as after 6 and 12 months. The primary outcome is the hospital re-admission rate within 12 months. Secondary outcomes include the combined readmission rate, total number of inpatient hospital days, treatment discontinuation rate, quality of life, psycho-social functioning, job integration, recovery, satisfaction with care, shared decision-making, and treatment costs. Additionally, the study will assess the burden of care and satisfaction with care among relatives or informal caregivers. A collaborative research team made up of researchers with and without lived experience of mental distress will conduct qualitative investigations with service users, caregivers and IEHT staff teams to explore critical ingredients and interactions between implementation processes, treatment processes, and outcomes from a stakeholder perspective. Discussion: By integrating outcome, process and implementation research as well as different stakeholder perspectives and experiences in one study, this trial captures the various facets of IEHT as a special form of home treatment. Therefore, it allows for an adequate, comprehensive evaluation on different levels of this complex intervention. Trial registration: Trial registrations: 1) German Clinical Trials Register (DRKS), DRKS000224769. Registered December 3rd 2020, https://www.drks.de/drks%5fweb/setLocale%5fEN.do; 2) ClinicalTrials.gov, Identifier: NCT0474550. Registered February 9th 2021. [ABSTRACT FROM AUTHOR]

Purpose: The Severity Indices of Personality Problems 118 (SIPP-118) is a self-report questionnaire that aims to measure core components of (mal)adaptive personality functioning that can change over time. In this study, we aimed to assess the facet strength of the 16 facets across three large clinical samples. Methods: Data from Norwegian and Dutch psychiatric patients were analyzed in this international multi-center study (N1 = 2814, N2 = 4751, N3 = 2217). Bi-factor modeling was used to assess to what degree the SIPP items tap into an overall general factor. The incremental value (distinctiveness) of the facets was studied using proportional reduction in mean squared error (PRMSE) based statistics. Results: The estimated model showed adequate fit. The explained common variance (ECV) attributable to the general factor equaled 50% for all three samples. All but two facets (stable self-image and frustration tolerance) showed sufficient levels of distinctiveness. The findings were observed to be comparable across the three samples. Conclusion: Our findings showed that the general factor was relatively weak, and the facets had a clear incremental value. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess the long-term outcomes in patients treated by thoracic endovascular aortic repair (TEVAR) for blunt thoracic aortic injuries (BTAI)., Materials and Methods: From January 2010 to December 2019, this retrospective observational study was conducted at 3 centers, involving 62 consecutive BTAI patients who underwent TEVAR. Computed tomography angiography scans were planned to be conducted at 6 months post-procedure, and annually thereafter., Results: Technical success was achieved in all 62 procedures (100%), which included cases of dissection (n=35, 56.45%), pseudoaneurysm (n=20, 32.26%), and rupture (n=7, 11.29%). Mean injury severity score was 31.66±8.30. A total of 21 supra-arch branches were revascularized by chimney technique, with 12 cases involving the left subclavian artery (LSA) and 9 cases involving the left common carotid artery. In addition, 11 LSAs were covered during the procedure. The in-hospital mortality rate was 1.61% (n=1). The mean follow-up time was 86.82±30.58 months. The all-cause follow-up mortality rate was 3.28% (n=2). Stenosis or occlusion of 3 supra-arch branches (4.92%) was identified at follow-up, with 2 cases (3.28%) requiring re-intervention. No spinal cord ischemia, endoleak, or migration was observed., Conclusions: Despite only including patients with long-term follow-up, this study confirms the long-term safety and effectiveness of TEVAR for BTAI. For young BTAI patients, as the thoracic aorta increases with age, longer follow-up is needed to observe the potential mismatch between the endograft and the aorta., Clinical Impact: This study confirms the long-term safety and effectiveness of endovascular treatment for blunt thoracic aortic injury (BTAI). For young BTAI patients, as the thoracic aorta increases with age, longer follow-up is needed to observe the potential mismatch between the endograft and the aorta. Through a remarkably extended follow-up period (86.82±30.58 months) conducted at multiple centers in China, this study confirms the long-term safety and effectiveness of endovascular treatment for BTAI., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.