Diagnosis Of Neurological Disorders Research Articles

Kappa Free Light Chains in Cerebrospinal Fluid in Inflammatory and Non-Inflammatory Neurological Diseases.

Background: Oligoclonal bands represent intrathecal immunoglobulin G (IgG) synthesis and play an important role in the diagnosis of multiple sclerosis (MS). Kappa free light chains (KFLC) are increasingly recognized as an additional biomarker for intrathecal Ig synthesis. However, there are limited data on KFLC in neurological diseases other than MS. Methods: This study, conducted at two centers, retrospectively enrolled 346 non-MS patients. A total of 182 patients were diagnosed with non-inflammatory and 84 with inflammatory neurological diseases other than MS. A further 80 patients were classified as symptomatic controls. Intrathecal KFLC production was determined using different approaches: KFLC index, Reiber’s diagram, Presslauer’s exponential curve, and Senel’s linear curve. Results: Matching results of oligoclonal bands and KFLC (Reiber’s diagram) were frequently observed (93%). The Reiber’s diagram for KFLC detected intrathecal KFLC synthesis in an additional 7% of the patient samples investigated (4% non-inflammatory; 3% inflammatory), which was not found by oligoclonal band detection. Conclusions: The determination of both biomarkers (KFLC and oligoclonal bands) is recommended for routine diagnosis and differentiation of non-inflammatory and inflammatory neurological diseases. Due to the high sensitivity and physiological considerations, the assessment of KFLC in the Reiber’s diagram should be preferred to other evaluation methods.

Enhanced sensitivity of dilute aqueous adrenaline solution with an asymmetric hexagonal ring structure in the terahertz frequencies

Quantitative detection of neurotransmitters in aqueous environment is crucial for the early diagnosis of many neurological disorders. Terahertz waves, as a non-contact and non-labeling tool, have demonstrated large potentials in quantitative biosensing. Although the detection of trace-amount analyte has been achieved with terahertz metamaterials in the recent decades, most studies have been focused on dried samples. Here, a hexagonal asymmetric metamaterial sensor was designed and fabricated for aqueous solution sensing with terahertz waves in the reflection geometry. An absorption enhancement of 43 was determined from the simulation. Dilute adrenaline solutions ranging from 30 µM to 0.6 mM were measured on our sensor using a commercial terahertz time-domain spectroscopy system, and the effective absorption was found to be linearly correlated with the concentration (R2 = 0.81). Furthermore, we found that as the concentration becomes higher (>0.6 mM), a non-linear relationship starts to take place, which confirmed the previous theory on the extended solvation shell that can be probed on the picosecond scale. Our sensor, without the need of high-power and stable terahertz sources, has enabled the detection of subtle absorption changes induced by the solvation dynamics.

Spinel structured MgAl2O4 nanoparticles as a low-cost and stable SERS substrate for rapid simultaneous detection of neurological drugs in biofluids

There is an urgent need for exploring reliable and low-cost techniques for simultaneous detection of neurological drugs to facilitate timely diagnosis and treatment of various neurological disorders. In this work, we demonstrate a low cost, one-step synthesis of spinel magnesium aluminate (MgAl2O4) nanoparticles as a chemically stable substrate for rapid, simultaneous detection of neurological drugs like nicotine, gabapentin, caffeine and carbamazepine in the biofluids via surface-enhanced Raman scattering (SERS) technique. The substrate exhibits a limit of detection (LOD) of 0.18 ng/mL, 0.78 μg/mL, 0.1 μg/mL and 0.81 μg/mL with an enhancement factor (EF) of 1.61 × 107, 9.70 × 107, 7.72 × 107 and 1.52 × 107 towards detection of nicotine, gabapentin, caffeine and carbamazepine, respectively with an accumulation time of 20 s. The substrate exhibits an excellent selectivity and a wide dynamic range of detection towards the drug molecules in presence of 0.1 mM of ascorbic acid (AA), NaCl, KCl, CaCl2, urea, and glucose. This excellent performance can be ascribed to the interaction between the metallic sties of the MgAl2O4 cubic spinel structure and the drug molecules which leads to the resonance Raman scattering-like effect through the reconstruction of the energy bandgap closer to the photon energy of the excitation laser. Simultaneous recognition of drug molecules in the blood serum samples with reliable recovery percentages is also achieved using this platform thus, proving its efficacy for developing perovskite materials-based SERS detection useful in bioanalytical applications.

Comprehensive Account of Sodium Imaging and Spectroscopy for Brain Research

Sodium (23Na) is a vital component of neuronal cells and plays a key role in various signal transmission processes. Hence, information on sodium distribution in the brain using magnetic resonance imaging (MRI) provides useful information on neuronal health. 23Na MRI and MR spectroscopy (MRS) improve the diagnosis, prognosis, and clinical monitoring of neurological diseases but confront some inherent limitations that lead to low signal-to-noise ratio, longer scan time, and diminished partial volume effects. Recent advancements in multinuclear MR technology have helped in further exploration in this domain. We aim to provide a comprehensive description of 23Na MRI and MRS for brain research including the following aspects: (a) theoretical background for understanding 23Na MRI and MRS fundamentals; (b) technological advancements of 23Na MRI with respect to pulse sequences, RF coils, and sodium compartmentalization; (c) applications of 23Na MRI in the early diagnosis and prognosis of various neurological disorders; (d) structural-chronological evolution of sodium spectroscopy in terms of its numerous applications in human studies; (e) the data-processing tools utilized in the quantitation of sodium in the respective anatomical regions.

A high-sensitive dopamine electrochemical sensor based on multilayer Ti3C2 MXene, graphitized multi-walled carbon nanotubes and ZnO nanospheres

Dopamine is one of the most important neurotransmitters in the human body, and its sensitive detection and quantification are important for the diagnosis, prevention, and treatment of related neurological diseases. Herein, a highly-sensitive dopamine electrochemical sensor has been constructed based on the two-dimensional transition metal carbide Ti3C2 MXene, graphitized multi-walled carbon nanotubes and ZnO nanospheres. Dopamine presents a standard reversible redox reaction on the sensor, and density flooding theory has been successfully applied to reveal the redox process of dopamine. Under optimal experimental conditions, the sensor exhibits a wide linear range (0.01–30 μM), low limit of detection (3.2 nM) and high sensitivity (16 A/M). Furthermore, the sensor has excellent stability and anti-interference ability, satisfactory accuracy in human serum samples, which can be used for the detection of actual samples. This work has provided a novel method for detecting dopamine and a new idea for the application of MXene and metal oxide semiconductor in electrochemical sensors.

Diagnosis of Functional Weakness and Functional Gait Disorders in Children and Adolescents

Functional neurological disorder is neurological dysfunction not primarily explained by pathophysiologic or structural abnormalities and can present in children and adolescents with limb weakness, gait abnormality, non-epileptic seizures or sensory changes. In this review article we focus primarily on the diagnosis of functional limb weakness and functional gait disorders, and how to differentiate functional neurological disorders from structural or pathologic neurological presentations of weakness or gait disturbance. Detailed history and attentive observation of a patient outside of the formal neurological examination can be pertinent to identifying inconsistency and incongruency in keeping with functional neurological presentations. Understanding of structural and physiologic neurological pathology is required to identify non-anatomical and non-pathological features consistent with a diagnosis of functional neurological disorder. Diagnosis is made on recognition of positive clinical features of a functional disorder on examination and is not based primarily on exclusion of a pathologic neurological disorder. Specific tests can be performed to elicit pathognomonic findings supportive of a diagnosis of functional limb weakness and gait disorders.

Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions.

Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.

Applications of nanodiamonds in the diagnosis and treatment of neurological diseases

Applications of nanodiamonds in the diagnosis and treatment of neurological diseases

Advancing the diagnosis of repeat expansion disorders

Genomic technologies are transforming health care, with next-generation sequencing providing an important tool that underpins diagnostics, gene discovery, and the mechanistic understanding of disease. 1 Rehm HL Evolving health care through personal genomics. Nat Rev Genet. 2017; 18: 259-267 Google Scholar However, next-generation sequencing has had limited success in bringing the power of genomic medicine to neurological disorders caused by pathogenic repeat expansions. 2 Ashley EA Towards precision medicine. Nat Rev Genet. 2016; 17: 507-522 Google Scholar The development of research tools to identify repeat expansions in next-generation sequencing data has begun to address this deficit. 3 Bahlo M Bennett MF Degorski P Tankard RM Delatycki MB Lockhart PJ Recent advances in the detection of repeat expansions with short-read next-generation sequencing. F1000Res. 2018; 7: 736 Google Scholar In The Lancet Neurology, Kristina Ibañez and colleagues 4 Ibañez K Polke J Hagelstrom RT et al. Whole genome sequencing for the diagnosis of neurological repeat expansion disorders in the UK: a retrospective diagnostic accuracy and prospective clinical validation study. Lancet Neurol. 2022; 21: 232-243 Google Scholar present compelling evidence of translation to clinical use, showing the utility of genome sequencing to diagnose neurogenetic repeat expansion disorders. Whole genome sequencing for the diagnosis of neurological repeat expansion disorders in the UK: a retrospective diagnostic accuracy and prospective clinical validation studyIn our study, whole genome sequencing for the detection of repeat expansions showed high sensitivity and specificity, and it led to identification of neurological repeat expansion disorders in previously undiagnosed patients. These findings support implementation of whole genome sequencing in clinical laboratories for diagnosis of patients who have a neurological presentation consistent with a repeat expansion disorder. Full-Text PDF Open Access

Whole genome sequencing for the diagnosis of neurological repeat expansion disorders in the UK: a retrospective diagnostic accuracy and prospective clinical validation study

SummaryBackgroundRepeat expansion disorders affect about 1 in 3000 individuals and are clinically heterogeneous diseases caused by expansions of short tandem DNA repeats. Genetic testing is often locus-specific, resulting in underdiagnosis of people who have atypical clinical presentations, especially in paediatric patients without a previous positive family history. Whole genome sequencing is increasingly used as a first-line test for other rare genetic disorders, and we aimed to assess its performance in the diagnosis of patients with neurological repeat expansion disorders.MethodsWe retrospectively assessed the diagnostic accuracy of whole genome sequencing to detect the most common repeat expansion loci associated with neurological outcomes (AR, ATN1, ATXN1, ATXN2, ATXN3, ATXN7, C9orf72, CACNA1A, DMPK, FMR1, FXN, HTT, and TBP) using samples obtained within the National Health Service in England from patients who were suspected of having neurological disorders; previous PCR test results were used as the reference standard. The clinical accuracy of whole genome sequencing to detect repeat expansions was prospectively examined in previously genetically tested and undiagnosed patients recruited in 2013–17 to the 100 000 Genomes Project in the UK, who were suspected of having a genetic neurological disorder (familial or early-onset forms of ataxia, neuropathy, spastic paraplegia, dementia, motor neuron disease, parkinsonian movement disorders, intellectual disability, or neuromuscular disorders). If a repeat expansion call was made using whole genome sequencing, PCR was used to confirm the result.FindingsThe diagnostic accuracy of whole genome sequencing to detect repeat expansions was evaluated against 793 PCR tests previously performed within the NHS from 404 patients. Whole genome sequencing correctly classified 215 of 221 expanded alleles and 1316 of 1321 non-expanded alleles, showing 97·3% sensitivity (95% CI 94·2–99·0) and 99·6% specificity (99·1–99·9) across the 13 disease-associated loci when compared with PCR test results. In samples from 11 631 patients in the 100 000 Genomes Project, whole genome sequencing identified 81 repeat expansions, which were also tested by PCR: 68 were confirmed as repeat expansions in the full pathogenic range, 11 were non-pathogenic intermediate expansions or premutations, and two were non-expanded repeats (16% false discovery rate).InterpretationIn our study, whole genome sequencing for the detection of repeat expansions showed high sensitivity and specificity, and it led to identification of neurological repeat expansion disorders in previously undiagnosed patients. These findings support implementation of whole genome sequencing in clinical laboratories for diagnosis of patients who have a neurological presentation consistent with a repeat expansion disorder.FundingMedical Research Council, Department of Health and Social Care, National Health Service England, National Institute for Health Research, and Illumina.

Whole-exome sequencing confirms implication of VPS13D as a potential cause of progressive spastic ataxia

BackgroundVPS13D is a large ubiquitin-binding protein playing an essential role in mitophagy by regulating mitochondrial fission. Recently, VPS13D biallelic pathogenic variants have been reported in patients displaying variable neurological phenotypes, with an autosomic recessive inheritance.The objectives of the study were to determine the genetic etiology of a patient with early onset sporadic progressive spastic ataxia, and to investigate the pathogenicity of VPS13D variants through functional studies on patient’s skin fibroblasts.Case presentationWe report the case of a 51-year-old patient with spastic ataxia, with an acute onset of the disease at age 7. Walking difficulties slowly worsened over time, with the use of a wheelchair since age 26. We have used trio-based whole-exome sequencing (WES) to identify genes associated with spastic ataxia. The impact of the identified variants on mitochondrial function was assessed in patient’s fibroblasts by imaging mitochondrial network and measuring level of individual OXPHOS complex subunits. Compound heterozygous variants were identified in VPS13D: c.946C > T, p.Arg316* and c.12416C > T, p.(Ala4139Val). Primary fibroblasts obtained from this patient revealed an altered mitochondrial morphology, and a decrease in levels of proteins from complex I, III and IV.ConclusionsOur findings confirmed implication of VPS13D in spastic ataxia and provided further support for mitochondrial defects in patient’s skin fibroblasts with VPS13D variants. This report of long-term follow up showed a slowly progressive course of the spastic paraplegia with cerebellar features. Furthermore, the performed functional studies could be used as biomarker helping diagnosis of VPS13D-related neurological disorders when molecular results are uneasy to interpret.

Improved Priority-based Congestion Control Protocol for Multi-Access Edge Computing (MAEC) Using IoT-based Wearable Devices for Neurological Diseases Diagnosis

Stroke is one of the fatal diseases that affect the brain and causes death within 3 to 10 hours. However, most of the deaths caused by a stroke can be avoided with the identification of the nature of stroke and react to it in a timely manner by intelligent health systems. Internet of Things (IoT) has become an important aspect in medical industry for monitoring of stroke related data/information using various wearable devices. Moreover, Multiple-Access Edge Computing (MAEC) is playing a major role for processing, analysing, and storing of data which leads to several researchers to compete in improving the mechanism of congestion control. In this paper, an improved Priority-based Congestion Control Protocol (iPCCP) is proposed for obtaining increased throughput, decreasing delay, effective resource utilization, and longer network lifetime by optimal energy consumption among IoT based sensor nodes. The proposed method categorizes the data-traffic into emergency and normal data. The packet delivery rate is considered for the normal data-traffic and retains the size of the buffer to improve the throughput and avoids the packet drops due to congestion. The energy consumption and network traffic load is reduced using the data aggregation and filtration technique. For emergency situations, priority-based routing scheme is used to have greater throughput and lesser delay. The performance of the proposed technique outperforms in term of traffic load, lifespan, energy consumption, and network throughput and simulation results are compared with other existing methods to show the improvement of the proposed work.

A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics.

Neurodegenerative disorders are primarily characterized by neuron loss. The most common neurodegenerative disorders include Alzheimer’s and Parkinson’s disease. Although there are several medicines currently approved for managing neurodegenerative disorders, a large majority of them only help with associated symptoms. This lack of pathogenesis-targeting therapies is primarily due to the restrictive effects of the blood–brain barrier (BBB), which keeps close to 99% of all “foreign substances” out of the brain. Since their discovery, nanoparticles have been successfully used for targeted delivery into many organs, including the brain. This review briefly describes the pathophysiology of Alzheimer’s, Parkinson’s disease, and amyotrophic lateral sclerosis, and their current management approaches. We then highlight the major challenges of brain-drug delivery, followed by the role of nanotherapeutics for the diagnosis and treatment of various neurological disorders.

Blood GFAP as an emerging biomarker in brain and spinal cord disorders.

Blood-derived biomarkers for brain and spinal cord diseases are urgently needed. The introduction of highly sensitive immunoassays led to a rapid increase in the number of potential blood-derived biomarkers for diagnosis and monitoring of neurological disorders. In 2018, the FDA authorized a blood test for clinical use in the evaluation of mild traumatic brain injury (TBI). The test measures levels of the astrocytic intermediate filament glial fibrillary acidic protein (GFAP) and neuroaxonal marker ubiquitin carboxy-terminal hydrolase L1. In TBI, blood GFAP levels are correlated with clinical severity and extent of intracranial pathology. Evidence also indicates that blood GFAP levels hold the potential to reflect, and might enable prediction of, worsening of disability in individuals with progressive multiple sclerosis. A growing body of evidence suggests that blood GFAP levels can be used to detect even subtle injury to the CNS. Most importantly, the successful completion of the ongoing validation of point-of-care platforms for blood GFAP might ameliorate the decision algorithms for acute neurological diseases, such as TBI and stroke, with important economic implications. In this Review, we provide a systematic overview of the evidence regarding the utility of blood GFAP as a biomarker in neurological diseases. We propose a model for GFAP concentration dynamics in different conditions and discuss the limitations that hamper the widespread use of GFAP in the clinical setting. In our opinion, the clinical use of blood GFAP measurements has the potential to contribute to accelerated diagnosis and improved prognostication, and represents an important step forward in the era of precision medicine.

ADAMTS9-AS2 Promotes Angiogenesis of Brain Microvascular Endothelial Cells Through Regulating miR-185-5p/IGFBP-2 Axis in Ischemic Stroke.

Ischemic stroke is a common disease threatening human health. ADAMTS9-AS2 is a lncRNA that has been widely studied in tumors, but not in ischemic stroke. The purpose of this study was to investigate the role and potential molecular mechanism of ADAMTS9-AS2 in endothelial cell function after ischemic stroke in vivo and in vitro. The results showed that ADAMTS9-AS2 was decreased in the plasma of acute ischemic stroke (AIS) patients and in the brain tissue and plasma of MCAO mice, and the low expression of ADAMTS9-AS2 was associated with the increase in infarct size. Besides, compared with the control group, MCAO treatment slightly promoted angiogenesis, which was enhanced by the overexpression of ADAMTS9-AS2. Molecular mechanism results indicated that ADAMTS9-AS2 and miR-185-5p affected the pathological process of ischemic stroke through ceRNA mechanism, and IGFBP-2 was a downstream target gene of miR-185-5p. These findings demonstrated that ADAMTS9-AS2 promoted angiogenesis through regulating miR-185-5p/IGFBP-2 axis, suggesting that ADAMTS9-AS2 may be a potential marker for the diagnosis and treatment of neurological diseases.

Exploring Neurofilament Light Chain and Exosomes in the Genetic Forms of Frontotemporal Dementia.

Differential diagnosis of neurological disorders and their subtype classification are challenging without specific biomarkers. Genetic forms of these disorders, typified by an autosomal dominant family history, could offer a window to identify potential biomarkers by exploring the presymptomatic stages of the disease. Frontotemporal dementia (FTD) is the second cause of dementia with an age of onset < 65, and its most common mutations are in GRN, C9orf72, and MAPT genes. Several studies have demonstrated that the main proteins involved in FTD pathogenesis can be secreted in exosomes, a specific subtype of extracellular vesicles able to transfer biomolecules between cells avoiding cell-to-cell contact. Neurofilament light chain (NfL) levels in central nervous system have been advocated as biomarkers of axonal injury. NfL concentrations have been found increased in FTD and have been related to disease severity and prognosis. Little information on the relationship between NfL and exosomes in FTD has been collected, deriving mainly from traumatic brain injury. Current review deals with this matter in the attempt to provide an updated discussion of the role of NfL and exosomes as biomarkers of genetic forms of FTD.

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions.

Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.

MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis

PurposeRecently, functional brain networks (FBN) have been used for the classification of neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder diagnosis with FBN is a challenging task due to the high heterogeneity in subjects and the noise correlations in brain networks. Meanwhile, it is challenging for the existing deep learning models to provide interpretable insights into the brain network. We propose a machine learning approach for the classification of neurological disorders while providing an interpretable framework. MethodIn this paper, we build upon graph neural network in order to learn effective representations for brain networks in an end-to-end fashion. Specifically, we present a prior brain structure learning-guided multi-view graph convolutional neural network (MVS-GCN), which collaborates the graph structure learning and multi-task graph embedding learning to improve the classification performance and identify the potential functional subnetworks. ResultsTo demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results indicate that our MVS-GCN can achieve enhanced performance compared with state-of-the-art methods. Notably, MVS-GCN achieves an average accuracy/AUC of 69.38%/69.01% on the ABIDE dataset. Moreover, the obtained results from our model show high consistency with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed MVS-GCN model. ConclusionThe proposed MVS-GCN method performs a graph embedding learning from the multi-views graph embedding learning perspective while considering eliminating the heterogeneity in brain networks and enhancing the feature representation of functional subnetworks, which can capture the essential embeddings to improve the classification performance of brain disorder diagnosis. The code is available at https://github.com/GuangqiWen/MVS-GCN.

Pre-Operative Predictors for Discharge to Post-Acute Care Facilities After Total Knee Arthroplasty.

Joint replacement surgery is in increasing demand and is the most common inpatient surgery for Medicare beneficiaries. The venue for post-operative rehabilitation, including early outpatient therapy after surgery, influences recovery and quality of life. As part of a comprehensive total joint program at Kaiser Permanente Colorado, we developed and validated a predictive model to anticipate and plan the disposition for rehabilitation of our patients after total knee arthroplasty (TKA). We analyzed data for TKA patients who completed a pre-operative Total Knee Risk Assessment in 2017 (the model development cohort) or during the first 6 months of 2018 (the model validation cohort). The Total Knee Risk Assessment, which is used to guide disposition for rehabilitation, included questions in mobility, social, and environment domains. Multivariable logistic regression was used to predict discharge to post-acute care facilities (PACFs) (ie, skilled nursing facilities or acute rehabilitation centers). Data for a total of 1481 and 631 patients who underwent TKA were analyzed in the development and validation cohorts, respectively. Ninety-three patients (6.3%) in the development cohort and 22 patients (3.5%) in the validation cohort were discharged to PACFs. Eight risk factors for discharge to PACFs were included in the final multivariable model. Patients with a diagnosis of neurological disorder and with a mobility/balance issue had the greatest chance of discharge to PACFs. This validated predictive model for discharge disposition following TKA may be used as a tool in shared decision-making and discharge planning for patients undergoing TKA.

Palda: Public Dataset and Algorithms for Facial Imaging and Diagnosis of Neurological Disorders

Palda: Public Dataset and Algorithms for Facial Imaging and Diagnosis of Neurological Disorders