Clinical Aid Research Articles

The Epidemiology of Lower Limb Fractures: A Major United Kingdom (UK) Trauma Centre Study.

Understanding the epidemiology and incidence of fractures can help inform policymakers and clinicians about the needs of the population and highlight trends over time, allowing for tailoring of healthcare delivery to the population. This study reports on the lower limb fractures treated at a major trauma centre over a seven-year period. We collected data on fracture locations, age, gender, BMI, hospital admission length, and treatment options of all lower limb fractures treated at a level I trauma centre from January 2015 to December 2021. We included data on the femur, tibia, and fibula, which were each split up into distinct regions.Fractures were subdivided by location and graphed, separated by gender, over age group. Finally, each location area's frequency was graphed over the entire study period. A total of 8,511 patients sustained 8,613 fractures, given an overall incidence of 215.9 fractures per 100,000 patients per year. The mean age was 62.3 years, and 56.3% of patients were female. Fractures of the peri trochanteric region of the femur had the highest mean average age (79.9 years), which was closely followed by fractures of the head and neck of the femur (78.2 years). Fractures of the head and neck of the femur and the peri trochanteric region of the femur also had the highest proportion of females suffering from these fractures (67 and 66% female, respectively). Femur shaft fractures had the lowest average age (36.5 years) and the lowest proportion of female patients (29%). On graphing by location, separated by gender, over age group, overall fractures showed a bi-peak distribution of younger males and older, post-menopausal females having their respective peaks. Three further distinct distributions were observed in individual location fractures. Identifying the relative incidence and demographic associations with lower limb fractures helps highlight a changing population's needs. There is an absence of such study in literature in the United Kingdom (UK) since 2006. Our study's insights and results aid clinicians and policymakers in the creation of guidelines and the distribution of resources based on the most recent information and elucidate changing healthcare service needs for the population.

Evaluating Hymenoptera Venom Allergy Severity: A Data-Centric Comparison of Grading Instruments

Introduction: While a consensus seems to have been reached with regard to the definition of anaphylaxis, there is no universal instrument for scoring allergic reaction severity despite more than 30 having been proposed by the time of writing. This severely hampers comparison of data between studies. While scales have been compared with regard to their utility in grading food-related reactions, no such comparisons have been made for Hymenoptera venom-associated reactions. Methods: The study conducted a retrospective analysis to compare the severity of Hymenoptera venom allergy reactions in 104 participants with suspected Hymenoptera venom allergy. The study applied six grading instruments to each reaction, also evaluating them against the NIAID/FAAN anaphylaxis criteria. Sensitivity, specificity, and receiver operating characteristic area under the curve (AUC) for identifying anaphylaxis were calculated. Severity scales were simplified into “mild,” “moderate,” and “severe” categories. The most common severity grade across the five scales was determined using a custom function to establish a consensus severity grade. Results: The most common culprit insects were honeybees (49.0%). Among the 88 participants with generalized reactions, the highest proportion had involvement of four organ systems. The scales showed high specificity for detecting anaphylaxis, especially when using higher grades of the Mueller, WAO, and Dribin scales. The diagnostic yields (AUC) varied, with the WAO scale having the highest AUC (0.94) for grades 3, 4, and 5. Spearman correlation analysis showed the strongest correlations seen between the Brown and Dribin, Ring and Messmer and Dribin, and Ring and Messmer and Reisman scales. The lowest correlations were observed with the Mueller scale when paired with the WAO, Reisman, and Dribin scales. An inter-rater reliability analysis showed substantial agreement between scales with the same number of grading levels. The agreement was highest for the Brown and Dribin scales, indicating a strong consistency in reaction severity classification across different instruments. Conclusion: While all instruments were effective in stratifying reactions, they showed limitations in differentiating milder phenotypes. The Brown and Dribin scales stood out for their high agreement with the consensus score and sensitivity in identifying anaphylaxis. Our findings suggest that adopting either of these scales could significantly unify the reporting of allergic reactions. We believe the format of an instrument should be tailored to its intended purpose, with clinical decision aids being simpler and research tools being more detailed.

A Literature Review for Detection and Projection of Cardiovascular Disease Using Machine Learning

The heart is a vital organ that is indispensable in ensuring the general health and welfare of individuals. Cardiovascular diseases (CVD) are the major health concern worldwide and a leading cause of death, leaving behind diabetes and cancer. To deal with the problem, it is essential for early detection and prediction of CVDs, which can significantly reduce morbidity and mortality rates. Computer-aided techniques facilitate physicians in the diagnosis of many heart disorders, such as valve dysfunction, heart failure, etc. Living in an "information age," every day million bytes of data are generated, and we can turn these data into knowledge for clinical investigation using the technique of data mining. Machine learning algorithms have shown promising results in predicting heart disease based on different risk parameter. In this study, for the purpose of predicting CVDs, our aim is to appraise and examine the outputs generated by machine learning algorithms including support vector machines, artificial neural network, logistic regression, random forest and decision trees.This literature survey highlights the correctness of different machine learning algorithms in forecasting heart problem and can be used as a basis for building a Clinical decision-making aid to detect and prevent heart disease at an early stage.

Antimicrobial stewardship markers and healthcare-associated pneumonia threshold criteria in UK hospitals: analysis of the MicroGuideTm application.

To address antimicrobial resistance, antimicrobial stewardship (AMS) principles must be implemented and adhered to. Clinical decision aids such as the MicroGuideTM app are an important part of these efforts. We sought to evaluate the consistency of core AMS information and the diversity of classification thresholds for healthcare-associated pneumonia (HAP) in the MicroGuide app. Guidelines in the MicroGuide app were extracted and analysed for content related to AMS and HAP. Guidelines were characterized according to HAP naming classification; community-acquired pneumonia (CAP) classifications were analysed to serve as a comparator group. In total, 115 trusts (119 hospitals) were included. Nearly all hospitals had developed MicroGuide sections on AMS (n = 112/119, 94%) and sepsis management (n = 117/119, 98%). Other AMS sections were outpatient parenteral antimicrobial therapy (47%), antifungal stewardship (70%), critical care (23%) and IV to oral switch therapy (83%). Only 9% of hospitals included guidance on the maximum six key AMS sections identified. HAP definitions varied widely across hospitals with some classifying by time to onset and some classifying by severity or complexity. The largest proportion of HAP guidelines based classification on severity/complexity (n = 69/119, 58%). By contrast, definitions in CAP guidelines were uniform. The high heterogeneity in HAP classification identified suggests inconsistency of practice in identifying thresholds for HAP in the UK. This complicates HAP management and AMS practices. To address HAP in alignment with AMS principles, a comprehensive strategy that prioritizes uniform clinical definitions and thresholds should be developed.

Risk factors and predictive model for prenatal depression: A large retrospective study in China

BackgroundPrenatal depression, associated with adverse effects on mothers and fetuses, has received little attention. We conducted a large-sample study to investigate the risk factors of, and develop a predictive model for, prenatal depression in the Chinese population. MethodsThis study enrolled 14,329 pregnant women who delivered at the West China Second University Hospital, Sichuan University from January 2017 to December 2020. Participants were divided into a training or validation cohort. Multiple variables were collected and selected using univariate logistic regression and least absolute shrinkage and selection operator penalty regression. After multivariate logistic analysis, a predictive model was developed and validated internally and externally. ResultsNine variables (employment, planned pregnancy, pregnancy number, conception methods, gestational diabetes mellitus, twin pregnancy, placenta previa, umbilical cord encirclement, and educational attainment) were identified as independent risk factors for prenatal depression. Receiver operating characteristic curves in both the training and validation cohorts showed excellent discrimination of the predictive model (the area under the curve: 0.746 and 0.732, respectively). LimitationsThe results of this retrospective study may be affected by confounding and information bias. Some important variables were excluded, such as family history of mental disorders. The study was conducted in China; its results may not be generalizable to other regions. ConclusionOur study identified nine significant risk factors for prenatal depression and constructed an accurate predictive model. This model could be applied as a clinical decision aid for individualized risk estimates and prevention of prenatal depression.

MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants.

Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

PurposeAcute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes...

Sliding transformer with uncertainty estimation for vestibular schwannoma automatic segmentation.

Objective.Automated segmentation of vestibular schwannoma (VS) using magnetic resonance imaging (MRI) can enhance clinical efficiency. Though many advanced methods exist for automated VS segmentation, the accuracy is hindered by ambivalent tumor borders and cystic regions in some patients. In addition, these methods provide results that do not indicate segmentation uncertainty, making their translation into clinical workflows difficult due to potential errors. Providing a definitive segmentation result along with segmentation uncertainty or self-confidence is crucial for the conversion of automated segmentation programs to clinical aid diagnostic tools.Approach.To address these issues, we propose a U-shaped cascade transformer structure with a sliding window that utilizes multiple sliding samples, a segmentation head, and an uncertainty head to obtain both the segmentation mask and uncertainty map. We collected multimodal MRI data from 60 clinical patients with VS from Xuanwu Hospital. Each patient case includes T1-weighted images, contrast-enhanced T1-weighted images, T2-weighted images, and a tumor mask. The images exhibit an in-plane resolution ranging from 0.70 × 0.70 to 0.76 × 0.76 mm, an in-plane matrix spanning from 216 × 256 to 284 × 256, a slice thickness varying between 0.50 and 0.80 mm, and a range of slice numbers from 72 to 120.Main results.Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods. On our collected multimodal MRI dataset of clinical VS, our method achieved the dice similarity coefficient (DSC) of 96.08% ± 1.30. On a publicly available VS dataset, our method achieved the mean DSC of 94.23% ± 2.53.Significance.The method efficiently solves the VS segmentation task while providing an uncertainty map of the segmentation results, which helps clinical experts review the segmentation results more efficiently and helps to transform the automated segmentation program into a clinical aid diagnostic tool.

Correlates of Cardiology Nurses' Clinical First Aid Competence in a Selected Hospital in Shandong, China

As a cardiology department is an important place to receive critically ill patients, the improvement of nurses' own quality can significantly improve the first aid competence and resuscitation success rate, thus saving patients' lives. The clinical emergency care competencies of cardiac nurses include the ability to recognize changes in a patient's status in a timely manner during the course of treatment, as well as the ability to carefully assess the situation and make timely judgments. This ability to provide emergency medical care under stress is critical to the overall competence of the nurse. Cardiology nurses should improve their ability to learn on their own, continue to learn new knowledge and techniques, enhance their level of professional benefit, and actively participate in cardiovascular specialty nurse training in order to improve their critical care and emergency care abilities.

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis

The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD. Predictive modeling study. 42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers. Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR. For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival). We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points. All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%. The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids. A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality. Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

Salvage Therapy for Prostate Cancer: AUA/ASTRO/SUO Guideline Part I: Introduction and Treatment Decision-Making at the Time of Suspected Biochemical Recurrence after Radical Prostatectomy.

The summary presented herein covers recommendations on salvage therapy for recurrent prostate cancer intended to facilitate care decisions and aid clinicians in caring for patients who have experienced a recurrence following prior treatment with curative intent. This is Part I of a three-part series focusing on treatment decision-making at the time of suspected biochemical recurrence (BCR) after radical prostatectomy (RP). Please refer to Part II for discussion of treatment delivery for non-metastatic BCR after RP and Part III for discussion of evaluation and management of recurrence after radiotherapy (RT) and focal therapy, regional recurrence, and oligometastasis. The systematic review that informs this Guideline was based on searches in Ovid MEDLINE (1946 to July 21, 2022), Cochrane Central Register of Controlled Trials (through August 2022), and Cochrane Database of Systematic Reviews (through August 2022). Update searches were conducted on July 26, 2023. Searches were supplemented by reviewing electronic database reference lists of relevant articles. In a collaborative effort between AUA, ASTRO, and SUO, the Salvage Therapy for Prostate Cancer Panel developed evidence- and consensus-based statements to provide guidance for the care of patients who experience BCR after initial definitive local therapy for clinically localized disease. Advancing work in the area of diagnostic tools (particularly imaging), biomarkers, radiation delivery, and biological manipulation with the evolving armamentarium of therapeutic agents will undoubtedly present new opportunities for patients to experience long-term control of their cancer while minimizing toxicity.

Salvage Therapy for Prostate Cancer: AUA/ASTRO/SUO Guideline Part III: Salvage Therapy After Radiotherapy or Focal Therapy, Pelvic Nodal Recurrence and Oligometastasis, and Future Directions.

The summary presented herein covers recommendations on salvage therapy for recurrent prostate cancer intended to facilitate care decisions and aid clinicians in caring for patients who have experienced a recurrence following prior treatment with curative intent. This is Part III of a three-part series focusing on evaluation and management of suspected non-metastatic recurrence after radiotherapy (RT) and focal therapy, evaluation and management of regional recurrence, management for molecular imaging metastatic recurrence, and future directions. Please refer to Part I for discussion of treatment decision-making and Part II for discussion of treatment delivery for non-metastatic biochemical recurrence (BCR) after radical prostatectomy (RP). The systematic review that informs this Guideline was based on searches in Ovid MEDLINE (1946 to July 21, 2022), Cochrane Central Register of Controlled Trials (through August 2022), and Cochrane Database of Systematic Reviews (through August 2022). Update searches were conducted on July 26, 2023. Searches were supplemented by reviewing electronic database reference lists of relevant articles. In a collaborative effort between AUA, ASTRO, and SUO, the Salvage Therapy for Prostate Cancer Guideline Panel developed evidence- and consensus-based guideline statements to provide guidance for the care of patients who experience BCR after initial definitive local therapy for clinically localized disease. Continuous and deliberate efforts for multidisciplinary care in prostate cancer will be required to optimize and improve the oncologic and functional outcomes of patients treated with salvage therapies in the future.

Salvage Therapy for Prostate Cancer: AUA/ASTRO/SUO Guideline Part II: Treatment Delivery for Non-metastatic Biochemical Recurrence After Primary Radical Prostatectomy.

The summary presented herein covers recommendations on salvage therapy for recurrent prostate cancer intended to facilitate care decisions and aid clinicians in caring for patients who have experienced a recurrence following prior treatment with curative intent. This is Part II of a three-part series focusing on treatment delivery for non-metastatic biochemical recurrence (BCR) after primary radical prostatectomy (RP). Please refer to Part I for discussion of treatment decision-making and Part III for discussion of evaluation and management of recurrence after radiotherapy (RT) and focal therapy, regional recurrence, and oligometastasis. The systematic review that informs this Guideline was based on searches in Ovid MEDLINE (1946 to July 21, 2022), Cochrane Central Register of Controlled Trials (through August 2022), and Cochrane Database of Systematic Reviews (through August 2022). Update searches were conducted on July 26, 2023. Searches were supplemented by reviewing electronic database reference lists of relevant articles. In a collaborative effort between AUA, ASTRO, and SUO, the Salvage Therapy for Prostate Cancer Panel developed evidence- and consensus-based guideline statements to provide guidance for the care of patients who experience BCR after initial definitive local therapy for clinically localized disease. Optimizing and personalizing the approach to salvage therapy remains an ongoing area of work in the field of genitourinary oncology and represents an area of research and clinical care that requires well-coordinated, multi-disciplinary efforts.

Evaluating the efficacy and mechanisms of Hua-Zhuo-Ning-Fu-Decoction on psoriasis using integrated bioinformatics analysis and metabolomics

Ethnopharmacological relevanceHua Zhuo Ning Fu Decoction (HZD) is an empirical prescription from traditional Chinese medicine that shows excellent clinical results for psoriasis patients. Uncertainty lingered over HZD's potential anti-psoriasis mechanisms. Aim of the studyThe study's objective is to investigate the pharmacological processes and therapeutic effects of HZD on psoriasis. Materials and methodsIn the initial phase of the study, an investigation was conducted to assess the effects of HZD on psoriasis-afflicted mice using an imiquimod (IMQ)-induced murine model. The experimental mice were randomly allocated to different groups, including the IMQ-induced model group, the control group, the HZD therapy groups with varying dosage levels (low, medium, and high), and Dexamethasone (DEX, the positive control medicine) group. Bioinformatics analysis and molecular docking were subsequently employed to identify the primary components and molecular targets associated with the therapeutic action of HZD in the context of psoriasis. Additionally, to find the impacts on metabolite regulation, plasma metabolomics based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was used. It's interesting to note that the combined mechanisms from metabolomics were examined in tandem with the targets. In vivo tests were the last step in validating the potential mechanism. Throughout the trial, the following data were recorded: body weight, psoriasis area and severity index (PASI). The molecular targets connected to HZD's anti-psoriasis activities were revealed using histological examination, western blot (WB), and ELISA investigation. ResultsIn mice induced with IMQ, HZD shown good anti-psoriasis effects in terms of PASI score and epidermal acanthosis. 95 HZD targets and 77 bioactive chemicals connected to psoriasis were found by bioinformatics research; of these, 7 key targets (EPHX2, PLA2G2A, TBXAS1, MAOA, ALDH1A3, ADH1A, and ADH1B) were linked to the mechanisms of HZD, the combination degree of which was finally expressed by the score of docking. In addition, HZD regulated nine metabolites. In line with this, HZD modified three metabolic pathways. Additionally, a combined examination of 7 key targets and 9 metabolites suggested that the metabolism of arachidonic acid might be the key metabolic route, which was identified by ELISA analysis. The in vivo investigation shown that HZD could control cytokines associated to inflammation (IL-10, TGF-β, IL-17A, and IL-23), as well as important antioxidant system markers (ROS, GSH, and MDA). Moreover, HZD controlled iron levels and the expression of ferroptosis-related proteins (ACSL4 and GPX4), suggesting that ferroptosis played a crucial role in this process. ConclusionsOur findings demonstrated the whole mechanism and anti-psoriasis effectiveness of HZD, which will promote its clinical application and aid in the investigation of new bioactive components of HZD against psoriasis.

Development and Validation of a Nomogram for Predicting Survival Based on Ferritin and Transferrin Ratio in Breast Cancer Patients.

Our objective is to develop a predictive model utilizing the ferritin and transferrin ratio (FTR) and clinical factors to forecast overall survival (OS) in breast cancer (BC) patients. We conducted a retrospective analysis of clinical data from 2858 BC patients diagnosed between 2013 and 2021. Subsequently, the cohort of 2858 BC patients underwent random assignment into distinct subsets: a training cohort comprising 2002 patients and a validation cohort comprising 856 patients, maintaining a proportional ratio of 7:3. Employing multivariable Cox regression analysis within the training cohort, we derived a prognostic nomogram. The predictive performance was assessed using calibration curves, C-index, and decision curve analysis. The final prognostic model included the TNM stage, subtype, hemoglobin levels, and the ferritin-transferrin ratio. The nomogram achieved a C-index of .794 (95% CI: .777-.810). The nomogram demonstrated superior predictive accuracy for OS at 3, 5, and 7 years for BC, with area under the time-dependent curves of .812, .782, and .773, respectively. These values notably outperformed those of the conventional TNM stage. Decision curve analysis reaffirmed the greater net benefit of our nomogram compared to the TNM stage. These findings were subsequently validated in the independent validation cohort. The FTR-based prognostic model may predict a patient's OS better than the TNM stage in a clinical setting. The nomogram can provide an early, affordable, and reliable tool for survival prediction, as well as aid clinicians in treatment option-making and prognosis evaluation. However, further multi-center prospective trials are required to confirm the reliability of the existing nomogram.

Crescent calculator: A webtool enabling objective decision-making for assessment of IgA nephropathy immune activity throughout the disease course

IgA nephropathy (IgAN) is an immune-mediated glomerulonephritis, posing a challenge for the long-term management. It is crucial to monitor the disease’s activity over the disease course. Crescent lesions have been known as an active lesion associated with immune activity. We aimed to develop the Crescent Calculator to aid clinicians in making timely and well-informed decisions throughout the long-term disease course, such as renal biopsies and immunosuppressive therapy.1,761 patients with biopsy-proven IgAN were recruited from four medical centers in Zhejiang Province, China. 16.9% presented crescent lesions. UPCR, URBC, eGFR and C4 were independently associated with the crescent lesions. By incorporating these variables, the Crescent Calculator was constructed to estimate the likelihood of crescent lesions. The predictor achieved AUC values of over 0.82 in two independent testing datasets. In addition, to fulfill varied clinical needs, multiple classification modes were established.The Crescent Calculator was developed to estimate the risk of crescent lesions for patients with IgAN, assisting clinicians in making timely, objective, and well-informed decisions regarding the need for renal biopsies and more appropriate use of immunosuppressive therapy in patients with IgAN.

Generalizability of predictive models for Clostridioides difficile infection, severity and recurrence at an urban safety-net hospital

IntroductionPredictive models for Clostridioides difficile (C. difficile) infection can identify high risk patients and aid clinicians in preventing infection. Issues of generalisability regarding current predictive models have been acknowledged but to our knowledge have never been quantified. MethodsThrough case-control sampling from an urban safety-net hospital, we created C. difficile infection, severity, and recurrence predictive models using multivariate logistic regression. Models were validated using 5-fold cross-validation and inverse probability weights (IPW) based on two different catchment area definitions were used to improve external validity. Akaike Information Criterion (AIC), area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity with bootstrapped confidence intervals were used to assess and compare model fit and performance. ResultsChanges in performance before and after weighting were small across all models; although, differences were more apparent after weighting the recurrence model (AUROCs of 0.78, 0.76, 0.71 for the unweighted and two weighted models, respectively). Overall, the infection model performed the best (AUROC=0.82 [95%CI: 0.78, 0.85]) followed by the recurrence model (AUROC=0.78 [95%CI: 0.69, 0.86]) and then the severity model (AUROC=0.70 [95%CI: 0.63, 0.78]. ConclusionsThe performance of our models after weighting did not change drastically suggesting that our model predicting C. difficile infection, severity, and recurrence may not be impacted by patient selection factors; however, other researchers may want to consider addressing these catchment forces using IPW.

Visualizing Severity of Alopecia Tool (SALT) scores in the clinical setting using patient images from a clinical trial

AbstractBackgroundThe Severity of Alopecia Tool (SALT) is a standardized method for quantifying scalp hair loss in alopecia areata (AA). SALT scores can be used to guide treatment decisions and are widely used as eligibility criteria and endpoints for clinical trials in AA. However, clinicians may be unfamiliar with assessing and envisioning SALT scores in practice.ObjectivesTo aid clinicians in the determination and application of SALT scores in a clinical setting, this manuscript seeks to contextualize SALT scores using patient images from a clinical trial of adults with severe AA.MethodsImages from 722 patients enrolled in BRAVE‐AA1, a phase 2/3 study of baricitinib in adults with severe AA (SALT score ≥50; ≥50% scalp hair loss), were obtained at baseline and Weeks 12, 36, and 52 and compiled into a repository. Photographs were selected to represent SALT scores across the full range of disease (SALT scores 0–100) and to demonstrate the progression of SALT scores during the course of treatment.ResultsImages of six patients depict the range of SALT scores (0–100). Photographs are of male and female patients of different ages (21–69) and races (Asian, Black, White) with varying extent, density, and patterns of hair loss. Images of two additional patients demonstrate the use of SALT to monitor treatment progress, showing distinct patterns and timing of clinical response over 52 weeks of therapy.ConclusionsThe SALT is widely used in clinical trials for AA, but clinicians may lack familiarity. Presented patient images show SALT scores commonly used as eligibility criteria and endpoints in clinical trials, which may be useful in identifying patients eligible for systemic treatment and in visualizing therapeutic response.

Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.

Epilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure detection algorithm is of great significance. Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to better handle brain signals and benefit real-world, low-power applications. However, automatic epilepsy seizure detection rarely considers SNNs. In this article, we have explored SNNs for cross-patient seizure detection and discovered that SNNs can achieve comparable state-of-the-art performance or a performance that is even better than artificial neural networks (ANNs). We propose an EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals. We extensively evaluate the performance of different SNN structures, training methods, and time settings, which builds a solid basis for understanding and evaluation of SNNs in seizure detection. Moreover, we show that our EESNN model can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation. These results show the potential for building high-performance, low-power neuromorphic systems for seizure detection and also broaden real-world application scenarios of SNNs.

Orthopedic Injuries Caused by Electric Scooters: A Systematic Review

Background: Orthopedic-related injuries are a significant proportion of electric scooter (e-scooter) injuries that present to hospitals. There is a lack of global regulatory consensus for e-scooter use. Aims: We aimed to synthesize existing data on orthopedic injuries due to e-scooters and identify modifiable risk factors that will help guide policymaking, reduce hospital burden, and aid clinicians in the management of these injuries. Methods: A systematic review of the literature was conducted to identify studies pertaining to e-scooter injuries. The U.S. National Library of Medicine (PubMed/MEDLINE), EMBASE, and the Cochrane Database of Systematic Reviews were queried for publications from January 1980 to November 2022, utilizing keywords pertinent to electric scooter, orthopedic or musculoskeletal injuries, incidence, and prevalence. Results: In total, 30 studies met inclusion criteria. Among all riders, 5.5% were helmeted. Among those injured, 26.3% were intoxicated at the time of injury, 7.6% required inpatient admission, and 20.5% required surgery. The most common orthopedic injuries were to the distal extremities, with wrist fractures being the most reported upper extremity fractures (average 14.9%) and ankle fractures being the most reported lower extremity fractures (average 8.3%). Conclusions: Orthopedic injuries caused by using e-scooters are commonly encountered in the hospital setting. These injuries may continue to rise with the increased accessibility of e-scooters. Lawmakers should instill strict usage guidelines to help prevent injuries from e-scooter use.