Assessment In Clinical Settings Research Articles

Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.

We aim to assess the severity of spatial neglect through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test - Conventional (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients. Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined Spatio-Temporal Network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset. Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations. These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.

Artificial Neural Network-based Model for Predicting Cardiologists’ Over-apron Dose in CATHLABs

Abstract Aim: The radiation dose that cardiologists receive in the catheterization laboratory is influenced by various factors. Handling high-stress tasks in interventional cardiology departments may cause physicians to overlook the use of dosimeters. Therefore, it is essential to develop a model for predicting cardiologists’ radiation exposure. Materials and Methods: This study developed an artificial neural network (ANN) model to predict the over-apron radiation dose received by cardiologists during catheterization procedures, using dose area product (DAP) values. Leveraging a validated Monte Carlo simulation program, we generated data from simulations with varying spectra (70, 81, and 90 kVp) and tube orientations, resulting in 125 unique scenarios. We then used these data to train a multilayer perceptron neural network with four input features: DAP, energy spectrum, tube angulation, and the resulting cardiologist’s dose. Results: The model demonstrated high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 μSv, outperforming a traditional linear regression model, which had an R-value of 0.48 and an RMSE of 18.15 μSv. This significant improvement highlights the effectiveness of advanced techniques such as ANNs in accurately predicting occupational radiation doses. Conclusion: This study underscores the potential of ANN models for accurate radiation dose prediction, enhancing safety protocols, and providing a reliable tool for real-time exposure assessment in clinical settings. Future research should focus on broader validation and integration into real-time monitoring systems.

Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

Abstract Background Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification. Objective To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges. Method In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software. Results In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction. Conclusion The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.

A French multicenter analytical evaluation of the automated Lumipulse G sNfL blood assay (Fujirebio®) and its comparison to four other immunoassays for serum neurofilament light chain assessment in clinical settings

ObjectivesMeasurement of serum neurofilament light chain (sNfL) protein is becoming a key biomarker for many neurological diseases. Several immunoassays have been developed to meet these clinical needs, revealing significant differences in terms of variability and results. Here, we propose a French multicenter comparison of 5 sNfL assays. Methods6 replicates of 3 pools with low (10 pg/mL), medium (30 pg/mL) and high (100 pg/mL) sNfL values and one replicate of 12 samples with growing sNfL values were analyzed by six independent French clinical laboratories. The analytical performances of the sNfL blood assay (Fujirebio®) on Lumipulse G were first evaluated then compared to four other immunoassays: NF-light V2 (Quanterix®) on SiMOA HD-X, Human NF-L (Biotechne®) on Ella, R-Plex Human Neurofilament L (MSD®) on Sector 2400; manual ELISA test using Uman Diagnostic/Quanterix®. ResultsInter-center comparison of the Lumipulse blood assay revealed limited but significant differences in the mean sNfL values across low, medium, and high pools between each city (p < 0.001) and between the two different batches used. Coefficients of variation of pools ranged from 2.0 to 16.9 %. Z-score of sNfL results of the 12 samples ranged from −1.70 to +1.71. Inter-technique comparison showed a systematic difference of sNfL values, with a overestimation of MSD and Ella over other tests. Nonetheless, results were all significantly correlated (p < 0.001). ConclusionThe automated Lumipulse assay produced comparable sNfL values across laboratories; but further adjustments are needed to harmonize sNfL results. Biologists and physicians should be aware of the variability in results between different immunoassay suppliers.

Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12–15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Assessment of Valance Emotional State Using EEG-EDA Coupling and Explainable Classifiers.

Emotion influences human life and impacts daily life activities. During emotional processes, physiological signals interact with each other instead of functioning separately. Although unimodal and multimodal approaches have been explored for emotion classification, there is a lack of inclusion of central and peripheral nervous system signal interaction-based approaches. In this study, an attempt has been made to characterize valance emotional states using Electroencephalogram (EEG)- Electrodermal activity (EDA) based coupling. For this, multimodal signals are obtained from the publicly available DEAP database (n=32 subjects). The EEG signals are decomposed into θ, α, β, and bands and EDA signals are decomposed into phasic and tonic components. Then two EEG, three EDA, and two EEG-EDA coupling-based features are extracted and applied to three classifiers namely Random Forest (RF), Linear discriminant analysis, and Adaptive boosting. In addition, SHAP analysis is performed to explain classifiers' performance with respect to features. The result shows that the proposed approach is able to classify valence emotional states. The feature combination of EEG, EDA, and EEG-EDA coupling-based features with an RF classifier performs best with an F1-score of 68.21%. SHAP analysis in frontal electrodes with γ band obtained better discrimination among different valance states. This study underscores the significance of the coupling studies of EEG with EDA in classifying emotion. Therefore, the proposed approach can be extended to emotional state assessment in clinical settings.

Using a simplified version of Shaughnessy's guideline trustworthiness tool (G-TRUST) to help clinicians choose the most relevant guidelines for their practice

Reliance on trusted secondary sources, such as clinical practice guidelines, has emerged as a pragmatic solution for evidence-based practice. This editorial underscores the necessity of adapting medical education to equip clinicians with skills aligned with practical clinical demands and introduces a streamlined version of Shaughnessy's G-TRUST tool tailored for swift guideline assessment in clinical settings.

RAD-IQMRI: A benchmark for MRI image quality assessment

Magnetic resonance imaging (MRI) is susceptible to visual artifacts that can degrade the perceptual image quality, potentially leading to inaccurate or inefficient diagnoses in clinical practice. It is critical to evaluate the perceptual image quality and build this technique into clinical solutions. In a previous study, an MRI database was created for image quality assessment (IQA), where various types of MRI artifacts with different degrees of degradation were simulated. Application specialists assessed the image quality; however, radiologists’ perception of MRI image quality remains unknown. To make IQA clinically relevant, in this paper we conduct a new subjective experiment where 13 radiologists rated the quality of images contained in the MRI database. Based on this subjective IQA benchmark named RAD-IQMRI, we evaluate the performance of state-of-the-art objective IQA models, providing insights into their application for MRI image quality assessment in clinical settings.

Optimizing time-in-target-range assessment for blood pressure: insights from a large-scale study with continual cuffless monitoring.

Blood pressure (BP) time-in-target-range (TTR) is an emerging predictor of cardiovascular risk. Conventional BP methods are fundamentally unable to provide an optimal assessment of TTR, using irregular measurements separated by lengthy intervals. We investigated the optimal duration and frequency for reliable, practical TTR assessment in clinical settings using continual monitoring. This retrospective study analyzed 2.3 million BP readings from 5,189 European home users (55 ± 11 years, 82% male, BMI 28.0 ± 5.8) using a cuffless BP monitor (Aktiia SA). Systolic BP (SBP) data over 15 consecutive days were assessed (29 ± 11 readings/subject/24-h; 434 + 132 readings/subject/15-day). Subjects were classified into risk-related TTR groups based on 15-day SBP data (24-h, target 90-125 mmHg; ≥6 daytime readings). Various measurement frequencies and durations (1-14 days; 24-h/daytime; 2, 4 or ≥ 6 readings/day) were compared to this reference. Two specific configurations paralleling ambulatory ("One-Day-24 h") and home ("One-Week-Daytime") BP monitoring were selected for detailed analysis. The reference TTR classified 63.0% of the subjects as high risk, 19.0% intermediate, and 18.0% low. "One-Day-24 h" schedule inaccurately classified 26% of subjects compared to the reference TTR, and "One-Week-Daytime" schedule inaccurately classified 45%. Classification accuracy with both schedules was high for subjects with very low or very high reference TTR, but poor otherwise. Accuracy of ≥90% in TTR classification only occurred with 7 days of continual 24-h monitoring. For the first time, with the benefit of a cuffless device that measures BP with sufficient frequency and duration, practical use of TTR is enabled as a potentially enhanced metric to manage hypertension.

An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings.

A comparative study of ProRithm and standard monitoring techniques for non-invasive blood pressure measurement using photoplethysmography and electrocardiography signals through artificial intelligence/machine learning methods

Background: Multi-parameter monitoring devices are essential for providing real-time patient data, which is crucial for effective healthcare interventions. This clinical trial evaluated the accuracy of the ProRithm beat-to-beat cuffless device for arterial blood pressure monitoring, comparing it with a standard sphygmomanometer. Methods: This observational study included 30 subjects aged 18 and above. Systolic and diastolic blood pressure measurements from both the ProRithm device and the Philips Monitor were compared using statistical analysis. Results: The analysis revealed no statistically significant differences between the ProRithm device and the manual method. In comparison with manual measurements using a sphygmomanometer, the mean systolic blood pressure was 131.2 mmHg with ProRithm it was 129.3 mmHg. Similarly, with the manual method, while the mean diastolic blood pressure was 76.2 mmHg and with ProRithm it was 75.9 mmHg. Conclusions: This study indicates that portable, small-sized devices like ProRithm, which facilitate remote monitoring, are effective for real-time blood pressure assessment in clinical settings.

Identifying optimized assessment of nerve damage during chemotherapy.

12016 Background: Chemotherapy-induced peripheral neurotoxicity (CIPN) is a significant and persisting toxicity of anticancer treatments, impacting dose delivery and quality of life. There is a lack of consensus on the optimal method of CIPN assessment in clinical settings. This study compared the validity and responsiveness of patient reported outcome measures (PROMs) against neurological, neurophysiological and sensory assessment approaches of CIPN. Methods: A multi-centre dual study design evaluated patients treated with neurotoxic chemotherapy across two cohorts: patients commencing treatment assessed prospectively and patients who completed treatment assessed cross-sectionally. CIPN was assessed via PROMs (EORTC-CIPN20, FACT/GOG-Ntx, PRO-CTCAE), neurological and neurophysiological assessment (Total Neuropathy Score, sural and tibial compound nerve amplitudes) and sensory functional measures (Grating orientation, Von Frey monofilament and 2-Point discrimination tasks). Convergent and known-groups validity were assessed cross-sectionally following treatment completion and responsiveness was evaluated prospectively during treatment. Neurological, neurophysiological, and sensory outcome measure scores were compared between high and low CIPN symptom reporters. Results: A total of 1,033 patients were recruited, incorporating 1,623 assessments. PROMs demonstrated superior ability to identify CIPN (convergent validity; α = 0.75-0.85, all P &lt; 0.001), to discriminate between CIPN severity (known-groups validity; all P &lt; 0.001) and to detect changes in CIPN development (responsiveness; Cohen’s d = 0.65-0.83) compared to neurological, neurophysiological and sensory assessment approaches. These other measures did not achieve threshold for convergent validity (α &lt; 0.7). and did not demonstrate acceptable responsiveness (Cohen’s d &lt; 0.5). Neurological, neurophysiological, and sensory outcome measures were significantly impaired in patients who were high CIPN symptom reporters compared to low (all P &lt; 0.05). Conclusions: PROMs represent a valid method of CIPN assessment, with preferential measurement properties over other approaches to assessing neuropathy. Adoption of PROMs in clinical practice will allow for accurate representation and early recognition of CIPN, leading to reduced long term morbidity in this key long-term toxicity in cancer survivors.

Atherogenic index of plasma as a predictor of coronary artery disease: a cohort study in south of Iran

BackgroundCoronary artery disease (CAD) is an atherosclerotic disease of an inflammatory nature. Previous studies examining the relationship between triglycerides and high-density lipoprotein cholesterol have highlighted the importance of plasma atherogenic index (AIP) as an important predictor of coronary heart disease. However, due to the lack of adequate information on this topic, this study aimed to investigate the relationship between AIP and coronary heart disease risk.ResultsThis study included 2,226 women and 1,690 men aged 35–70 years who participated in the Bandar Kong Cohort study and met the eligibility criteria. The data was collected using a checklist and questionnaires, which were designed by experienced individuals. After participants completed a registration form and gave informed consent, face-to-face interviews were conducted by trained experts. The validity and reliability of the questionnaire had been verified by the national cohort team prior to its use. The Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1400.171) approved the study. Data from the initial cohort survey using SPSS software version 25, were analyzed to include several factors, including age, sex, smoking status, body mass index (BMI), physical activity level, socioeconomic status, AIP, systolic blood pressure, and diastolic blood pressure. The prevalence of coronary heart disease was found to be 7.5% higher in people with a BMI of 25 or higher. Also, Individuals with low physical activity had a higher prevalence. Individuals with CAD had significantly higher mean values for the AIP, age, systolic blood pressure, and diastolic blood pressure (0.46, 57.50, 128.43, and 81.10, respectively) compared to those without CAD. Furthermore, patients with CAD had lower years of education (2649.45 and 3.59) than individuals without CAD (P < 0.05). Importantly, our findings showed that AIP increased the odds ratio of coronary heart disease by 1.86 as an independent risk factor.ConclusionsBased on our investigation, the AIP is a valuable and independent predictive risk factor for coronary artery disease. This index can be utilized effectively due to its accessibility and affordability, making it a promising tool for risk assessment in clinical settings.

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {CHA}_2\ ext {DS}_2\ ext {-VASc}$$\\end{document} score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

The Association of Unmet Palliative Care Needs and Physical Frailty With Clinical Outcomes: A Prospective Study of Adults With Heart Failure.

People with heart failure, particularly those who are physically frail, experience complex needs that can be addressed by palliative care (PC). However, we have a limited understanding of how the intersection of unmet PC needs and physical frailty contributes to health-related quality of life (HRQOL) and risk for hospitalization or mortality. In this study, we sought to examine the association of unmet PC needs and physical frailty with clinical outcomes (baseline HRQOL and hospitalizations or mortality at 6 months). We recruited a convenience sample of community-dwelling persons with heart failure from an urban hospital system who were older than 50 years and hospitalized in the last year. We measured physical frailty using the FRAIL scale (nonfrail, 0-2; frail, 3-5), PC needs using the Integrated Palliative Outcome Scale (range, 0-58; higher scores indicating higher needs), and HRQOL using the Kansas City Cardiomyopathy Questionnaire (range, 0-100; higher scores indicate higher HRQOL). We performed multivariable linear regression to test the relationships between physical frailty, PC needs, and HRQOL, and multivariable logistic regression for associations with all-cause 6-month hospitalization or mortality. We also performed an exploratory analysis of 4 PC needs/frailty groups (high PC needs/frail, high PC needs/nonfrail, low PC needs/frail, low PC needs/nonfrail) with outcomes. In our overall sample (n = 298), mean (SD) age was 68 (9.8) years, 37% were women (n = 108), 28% identified as Black/African American (n = 84), and 65% had heart failure with preserved ejection fraction (n = 194). Mean PC needs score was 19.7, and frail participants (n = 130, 44%) had a significantly higher mean PC needs score than nonfrail participants (P < .001). Those with higher PC needs (Integrated Palliative Care Outcome Scale ≥ 20) had significantly worse HRQOL (P < .001) and increased odds of hospitalization or mortality (odds ratio, 2.5; P < .01) compared with those with lower PC needs, adjusting for covariates. Physically frail participants had significantly worse HRQOL (P < .001) and higher odds of hospitalization or mortality at 6 months (odds ratio, 2.6; P < .01) than nonfrail participants, adjusting for covariates. In an exploratory analysis, physically frail participants with high PC needs had the lowest HRQOL score, with an average score of 28.6 points lower (P < .001) and 4.6 times higher odds of hospitalization or mortality (95% confidence interval, 2.03-10.43; P < .001) than low-needs/nonfrail participants. Higher unmet PC needs and physical frailty, separately and in combination, were associated with lower HRQOL and higher odds of hospitalization or mortality. Self-reported PC needs and physical frailty assessment in clinical settings may improve identification of patients at the highest risk for poor HRQOL and hospitalization or mortality amenable to PC intervention.

Thoracic Society of Australia and New Zealand position statement: The safe clinical use of sputum induction for bio-sampling of the lower airways in children and adults.

Sputum induction is widely used in clinical settings for collection of biological samples from the lower airways. However, in recent years sputum induction has been associated with serious adverse events and even death. This position statement was commissioned by the Thoracic Society of Australia and New Zealand to address major adverse events of two deaths associated with sputum induction that have occurred in Australia in 2021, and outlines best practice for the safe use of sputum induction. The statement resulted from systematic literature searches by a multi-disciplinary group including respiratory physicians, nurses and physiotherapists (paediatric and adults focused). Consumers had input to an advanced draft of the position statement. The position statement covers indications for sputum induction, informed consent, scope of practice of personnel administering the procedure, infection control considerations, details about the sputum induction procedure, safety considerations and risk assessment in clinical settings.

Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study

Background Quantifying pain and the balance between nociception and anti-nociception (NANB) in sedated patients is challenging. Traditional opioid titration methods overlook individual differences, while existing indices like the Noxious Stimulation Response Index (NSRI) lack correlation with effect-site concentration (Ce). The Normalized Pulse Volume (NPV), used in polygraphs, has potential for pain quantification but is underexplored. This study aimed to assess NPV’s efficacy as a pain monitoring tool compared to Ce and to explore its potential in various clinical settings. Methods The study included 39 patients undergoing surgery under total intravenous anesthesia from July 2013 to May 2014. Selection criteria were an American Society of Anesthesiologists physical status classification system (ASA score) of 1 or 2 and surgeries with minimal fluid resuscitation or blood loss. Exclusion criteria were significant posture changes, massive hemorrhage, and high perfusion index variation. NPV and Ce were measured using the Masimo SET adult SpO2 sensor. Results Out of 39 patients, 9 were excluded. NPV at recovery of spontaneous respiration (RoR) was 2.62 (95% CI: 2.26–2.98) with a coefficient of variation (CoV) of 36.3%, while total Ce was 1.48 ng/ml (95% CI: 1.14–1.84) with a CoV of 62.4%. NPV showed a narrower CoV than Ce (p &lt; 0.05, 1.93*10−5), indicating less variability. NPV outperformed Ce in predicting RoR, suggesting a more accurate reflection of NANB balance. Its superiority in stable measurement underlines its potential as a reliable pain indicator. The study’s limitations include temporal differences in NPV and Ce calculations, affecting comparative analysis. Conclusion NPV demonstrates promise as an objective, reliable indicator of pain or NANB, showing a strong correlation with Ce. Its application could improve pain assessments in clinical settings, optimizing patient care and analgesic administration. Future research should integrate NPV with other vital signs for a comprehensive pain monitoring system.

Genomic disparity impacts variant classification of cancer susceptibility genes in Turkish breast cancer patients.

Turkish genome is underrepresented in large genomic databases. This study aims to evaluate the effect of allele frequency in the Turkish population in determining the clinical utility of germline findings in breast cancer, including invasive lobular carcinoma (ILC), mixed invasive ductal and lobular carcinoma (IDC-L), and ductal carcinoma (DC). Two clinic-based cohorts from the Umraniye Research and Training Hospital (URTH) were used in this study: a cohort consisting of 132 women with breast cancer and a non-cancer cohort consisting of 492 participants. The evaluation of the germline landscape was performed by analysis of 27 cancer genes. The frequency and type of variants in the breast cancer cohort were compared to those in the non-cancer cohort to investigate the effect of population genetics. The variant allele frequencies in Turkish Variome and gnomAD were statistically evaluated. The genetic analysis identified 121 variants in the breast cancer cohort (actionable = 32, VUS = 89) and 223 variants in the non-cancer cohort (actionable = 25, VUS = 188). The occurrence of 21 variants in both suggested a possible genetic population effect. Evaluation of allele frequency of 121 variants from the breast cancer cohort showed 22% had a significantly higher value in Turkish Variome compared to gnomAD (p < 0.0001, 95% CI) with a mean difference of 60 times (ranging from 1.37-354.4). After adjusting for variant allele frequency using the ancestry-appropriate database, 6.7% (5/75) of VUS was reclassified to likely benign. To our knowledge, this is the first study of population genetic effects in breast cancer subtypes in Turkish women. Our findings underscore the need for a large genomic database representing Turkish population-specific variants. It further highlights the significance of the ancestry-appropriate population database for accurate variant assessment in clinical settings.

Inertial Sensor Gait Analysis of Trendelenburg Gait in Patients Who Have Hip Osteoarthritis

BackgroundGait abnormalities such as Trendelenburg gait (TG) in patients who have hip osteoarthritis (OA) have traditionally been evaluated using clinicians’ visual assessment. Recent advances in portable inertial gait sensors offer more sensitive, quantitative methods for gait assessment in clinical settings. This study sought to compare sensor-derived metrics in a cohort of hip OA patients when stratified by clinical TG severity. MethodsThere were 42 patients who had hip OA and were grouped by TG severity (mild, moderate, and severe) through visual assessment by a single arthroplasty surgeon who had > 30 years of experience. After informed consent, wireless inertial sensors placed at the midpoint of the intercristal line collected gait parameters including pelvic shift, support time, toe-off symmetry, impact, and cadence. Clinical data on hip strength, range of motion, and Kellgren-Lawrence grade were collected. ResultsWorsening TG severity had a higher mean Kellgren-Lawrence grade (2.5 versus 3.2 versus 3.4; P = .014) and reduced passive hip abduction (P = .004). Severe TG group demonstrated predominantly contralateral pelvic shift (n = 9 of 10, 90.0%), while ipsilateral shift was more frequently detected in moderate (n = 10 of 18, 55.6%) and mild groups (n = 9 of 14, 64.3%; P = .021). Contralateral single support time bias was greatest in severe TG (35.7% versus 50.0 versus 90.0%; P = .027). Asymmetric toe-off, impact, and support times were observed in all groups. ConclusionsTraditional understanding of TG is that truncal shift occurs to the ipsilateral side. Using sensor-based measurements, the present study demonstrates a shift of the weight-bearing axis toward the contralateral side with increasing TG severity, which has not been previously described. Inertial sensors are feasible, quantitative gait measuring tools, and may reveal subtle patterns not readily discernible by traditional methods.

Prognostic accuracy of 70 individual frailty biomarkers in predicting mortality in the Canadian Longitudinal Study on Aging

The frailty index (FI) uses a deficit accumulation approach to derive a single, comprehensive, and replicable indicator of age-related health status. Yet, many researchers continue to seek a single “frailty biomarker” to facilitate clinical screening. We investigated the prognostic accuracy of 70 individual biomarkers in predicting mortality, comparing each with a composite FI. A total of 29,341 individuals from the comprehensive cohort of the Canadian Longitudinal Study on Aging were included (mean, 59.4 ± 9.9 years; 50.3% female). Twenty-three blood-based biomarkers and 47 test-based biomarkers (e.g., physical, cardiac, cardiology) were examined. Two composite FIs were derived: FI-Blood and FI-Examination. Mortality status was ascertained using provincial vital statistics linkages and contact with next of kin. Areas under the curve were calculated to compare prognostic accuracy across models (i.e., age, sex, biomarker, FI) in predicting mortality. Compared to an age-sex only model, the addition of individual biomarkers demonstrated improved model fit for 24/70 biomarkers (11 blood, 13 test-based). Inclusion of FI-Blood or FI-Examination improved mortality prediction when compared to any of the 70 biomarker-age-sex models. Individual addition of seven biomarkers (walking speed, chair rise, time up and go, pulse, red blood cell distribution width, C-reactive protein, white blood cells) demonstrated an improved fit when added to the age-sex-FI model. FI scores had better mortality risk prediction than any biomarker. Although seven biomarkers demonstrated improved prognostic accuracy when considered alongside an FI score, all biomarkers had worse prognostic accuracy on their own. Rather than a single biomarker test, implementation of routine FI assessment in clinical settings may provide a more accurate and reliable screening tool to identify those at increased risk of adverse outcomes.