Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation.

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.

An embedded intracranial seizure monitor for objective outcome measurements and rhythm identification.

Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior and circadian/multidien rhythm characteristics that are difficult to capture in short clinical follow-ups. This work presents preliminary validation evidence for an implantable neuromodulation system with integrated physiological event monitoring, with an initial focus on seizure tracking for epilepsy. The system was developed to address currently unmet requirements for patients undergoing neuromodulation therapy for neurological disorders, specifically the ability to sense physiological data during stimulation and track events with seconds-level granularity. The system incorporates an interactive software tool to enable optimal configuration of the signal processing chain on an embedded implantable device (the Picostim-DyNeuMo Mk-2) including data ingestion from the device loop recorder, event labeling, generation of filter and classification parameters, as well as summary statistics. When the monitor parameters are optimized, the user can wirelessly update the system for chronic event tracking. The simulated performance of the device was assessed using an in silico model with human data to predict the real-time device performance at tracking recorded seizure activity. The in silico performance was then compared against its performance in an in vitro model to capture the full environmental constraints such as sensing during stimulation at the tissue electrode interface. In vitro modeling demonstrated comparable results to the in silico model, providing verification of the software tool and model. This study provides validation evidence of the suitability of the proposed system for tracking longitudinal seizure activity. Given its flexibility, the event monitor can be adapted to track other disorders with episodic and rhythmic symptoms represented by bioelectrical behavior.Clinical relevance-An implantable neuromodulation system is presented that enables chronic tracking of physiological events in disease. This physiological monitor provides the basis for longitudinal assessments of therapy outcomes for patients, such as those with epilepsy where objective identification of patient seizure activity and rhythms might help guide therapy optimization. The system is configurable for other disease states such as Parkinson's disease and mood disorders.

Multivariable closed-loop control of deep brain stimulation for Parkinson's disease.

Objective. Closed-loop deep brain stimulation (DBS) methods for Parkinson's disease (PD) to-date modulate either stimulation amplitude or frequency to control a single biomarker. While good performance has been demonstrated for symptoms that are correlated with the chosen biomarker, suboptimal regulation can occur for uncorrelated symptoms or when the relationship between biomarker and symptom varies. Control of stimulation-induced side-effects is typically not considered.Approach.A multivariable control architecture is presented to selectively target suppression of either tremor or subthalamic nucleus beta band oscillations. DBS pulse amplitude and duration are modulated to maintain amplitude below a threshold and avoid stimulation of distal large diameter axons associated with stimulation-induced side effects. A supervisor selects between a bank of controllers which modulate DBS pulse amplitude to control rest tremor or beta activity depending on the level of muscle electromyographic (EMG) activity detected. A secondary controller limits pulse amplitude and modulates pulse duration to target smaller diameter axons lying close to the electrode. The control architecture was investigated in a computational model of the PD motor network which simulated the cortico-basal ganglia network, motoneuron pool, EMG and muscle force signals.Main results.Good control of both rest tremor and beta activity was observed with reduced power delivered when compared with conventional open loop stimulation, The supervisor avoided over- or under-stimulation which occurred when using a single controller tuned to one biomarker. When DBS amplitude was constrained, the secondary controller maintained the efficacy of stimulation by increasing pulse duration to compensate for reduced amplitude. Dual parameter control delivered effective control of the target biomarkers, with additional savings in the power delivered.Significance.Non-linear multivariable control can enable targeted suppression of motor symptoms for PD patients. Moreover, dual parameter control facilitates automatic regulation of the stimulation therapeutic dosage to prevent overstimulation, whilst providing additional power savings.

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.

Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.

The Use of Single Photon Emission Computed Tomography in Aerosol Medicine.

Imaging of radiolabeled aerosols provides useful in vivo data on both the initial site of deposition and its subsequent transport by mucociliary clearance and epithelial permeability. Single Photon Emission Computed Tomography (SPECT) uses a gamma camera with multiple rotating heads to produce three-dimensional (3D) images of inhaled radioaerosol labeled with technetium-99m. This enables total lung deposition and its 3D regional distribution to be quantified. Aligned 3D images of lung structure allow deposition data to be related to lung anatomy. Mucociliary clearance or epithelial permeability can be assessed from a time series of SPECT aerosol images. SPECT is slightly superior to planar imaging for measuring total lung deposition. However, it is more complex to use, and for studies where total lung deposition is the endpoint, planar imaging is recommended. However, SPECT has been shown to be clearly superior to planar imaging for assessing regional distribution of aerosol and is the method of choice for this purpose. It therefore has applications in studying the influence of regional deposition on clinical effectiveness and also in validating computer models of deposition. The inability to directly radiolabel drug molecules with 99mTc is a clear disadvantage of SPECT and limits its potential use for pharmacokinetic studies. SPECT provides a wealth of data on aerosol deposition, which has been relatively underused at present. Optimal methods of analyzing and interpreting the data need to be developed. SPECT can also, in principle, provide detailed information of mucociliary clearance and has the potential to significantly improve knowledge of this process and hence clarify the role of clearance as a biomarker.

Bioelectronic Zeitgebers: targeted neuromodulation to re-establish circadian rhythms.

Existing neurostimulation systems implanted for the treatment of neurodegenerative disorders generally deliver invariable therapy parameters, regardless of phase of the sleep/wake cycle. However, there is considerable evidence that brain activity in these conditions varies according to this cycle, with discrete patterns of dysfunction linked to loss of circadian rhythmicity, worse clinical outcomes and impaired patient quality of life. We present a targeted concept of circadian neuromodulation using a novel device platform. This system utilises stimulation of circuits important in sleep and wake regulation, delivering bioelectronic cues (Zeitgebers) aimed at entraining rhythms to more physiological patterns in a personalised and fully configurable manner. Preliminary evidence from its first use in a clinical trial setting, with brainstem arousal circuits as a surgical target, further supports its promising impact on sleep/wake pathology. Data included in this paper highlight its versatility and effectiveness on two different patient phenotypes. In addition to exploring acute and long-term electrophysiological and behavioural effects, we also discuss current caveats and future feature improvements of our proposed system, as well as its potential applicability in modifying disease progression in future therapies.

MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systems.

Sleep Stage Classification (SSC) is a labor-intensive task, requiring experts to examine hours of electrophysiological recordings for manual classification. This is a limiting factor when it comes to leveraging sleep stages for therapeutic purposes. With increasing affordability and expansion of wearable devices, automating SSC may enable deployment of sleep-based therapies at scale. Deep Learning has gained increasing attention as a potential method to automate this process. Previous research has shown accuracy comparable to manual expert scores. However, previous approaches require sizable amount of memory and computational resources. This constrains the ability to classify in real time and deploy models on the edge. To address this gap, we aim to provide a model capable of predicting sleep stages in real-time, without requiring access to external computational sources (e.g., mobile phone, cloud). The algorithm is power efficient to enable use on embedded battery powered systems. Our compact sleep stage classifier can be deployed on most off-the-shelf microcontrollers (MCU) with constrained hardware settings. This is due to the memory footprint of our approach requiring significantly fewer operations. The model was tested on three publicly available data bases and achieved performance comparable to the state of the art, whilst reducing model complexity by orders of magnitude (up to 280 times smaller compared to state of the art). We further optimized the model with quantization of parameters to 8 bits with only an average drop of 0.95% in accuracy. When implemented in firmware, the quantized model achieves a latency of 1.6 seconds on an Arm Cortex-M4 processor, allowing its use for on-line SSC-based therapies.

Diurnal modulation of subthalamic beta oscillatory power in Parkinson's disease patients during deep brain stimulation.

Beta-band activity in the subthalamic local field potential (LFP) is correlated with Parkinson's disease (PD) symptom severity and is the therapeutic target of deep brain stimulation (DBS). While beta fluctuations in PD patients are well characterized on shorter timescales, it is not known how beta activity evolves around the diurnal cycle, outside a clinical setting. Here, we obtained chronic recordings (34 ± 13 days) of subthalamic beta power in PD patients implanted with the Percept DBS device during high-frequency DBS and analysed their diurnal properties as well as sensitivity to artifacts. Time of day explained 41 ± 9% of the variance in beta power (p < 0.001 in all patients), with increased beta during the day and reduced beta at night. Certain movements affected LFP quality, which may have contributed to diurnal patterns in some patients. Future DBS algorithms may benefit from taking such diurnal and artifactual fluctuations in beta power into account.

Embedding digital chronotherapy into bioelectronic medicines.

Biological rhythms pervade physiology and pathophysiology across multiple timescales. Because of the limited sensing and algorithm capabilities of neuromodulation device technology to-date, insight into the influence of these rhythms on the efficacy of bioelectronic medicine has been infeasible. As the development of new devices begins to mitigate previous technology limitations, we propose that future devices should integrate chronobiological considerations in their control structures to maximize the benefits of neuromodulation therapy. We motivate this proposition with preliminary longitudinal data recorded from patients with Parkinson's disease and epilepsy during deep brain stimulation therapy, where periodic symptom biomarkers are synchronized to sub-daily, daily, and longer timescale rhythms. We suggest a physiological control structure for future bioelectronic devices that incorporates time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical control methods and illustrate the concept with initial results from three of our recent case studies using chronotherapy-enabled prototypes.

Effect of an Anterior Cruciate Ligament Rupture on Knee Proprioception Within 2 Years After Conservative and Operative Treatment: A Systematic Review with Meta-Analysis.

BACKGROUND: The anterior cruciate ligament (ACL) plays a major role in knee proprioception and is thus responsible for maintaining knee joint stability and functionality. The available evidence suggests that ACL reconstruction diminishes somatosensory feedback and proprioceptive functioning, which are vital for adequate joint positioning and movement control. OBJECTIVE: The aim of this systematic review and meta-analysis was to investigate the effect of an ACL rupture on knee proprioception after arthroscopic ACL repair surgery or conservative treatment. METHODS: A systematic review with meta-analysis was conducted according to the Preferred Reporting Guidelines for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search was performed in the following databases from inception to 10th October 2020: PubMed, Web of Science, SPORTDiscus, Cochrane Library and Scopus. Randomized and non-randomized studies that evaluated proprioception using the joint position sense (JPS) and threshold to detection of passive motion (TTDPM) techniques at 15°-30° knee flexion with an external healthy control group in a time period between 6 and 24 months post injury or operation were included in the analysis. RESULTS: In total, 4857 studies were identified, from which 11 were included in the final quantitative analysis. The results demonstrated that proprioception after arthroscopic ACL repair surgery was significantly lower than in the healthy control group (JPS: standardized mean difference [SMD] 0.57, 95% confidence interval [CI] 0.27-0.87, p < 0.01, n = 6 studies; TTDPM: SMD 0.77, 95% CI 0.20-1.34, p < 0.01, n = 4 studies). There were no significant differences in proprioception between the conservative treatment group and the healthy control group (JPS: SMD 0.57, 95% CI - 0.69 to 1.84, p = 0.37, n = 4 studies; TTDPM: SMD 0.82, 95% CI - 0.02 to 1.65, p = 0.05, n = 2 studies), although measures for TTDPM were close to statistical significance. CONCLUSION: The findings of the present systematic review and meta-analysis revealed that knee proprioception is persistently compromised 6-24 months following surgical treatment of ACL tears compared with healthy controls. The reduced kinesthetic awareness after ACL surgery is of high relevance for optimizing individual treatment plans in these patients. As the current literature is still scarce about the exact underlying mechanisms, further research is needed. TRIAL REGISTRATION: The present systematic review was registered in PROSPERO (CRD42021198617).

Providing occupational and physical therapy services in a free community-based interprofessional primary care clinic.

Interprofessional care provided in a free community-based clinic that focuses on chronic health conditions and health promotion provides an innovative solution to improve societal health. Many existing clinics provide a range of professions, but few include allied health services such as occupational and physical therapy. This paper provides a description of the development and implementation of an expanded faculty-guided student-led community-based primary care clinic that includes occupational and physical therapists as part of an interprofessional collaborative practice model. A detailed description and explanation of the partners involved, the institutional missions that drive this work, the logistics completed that enabled the 'doors to open,' faculty and student roles, and initial outcomes will be provided. A review of the service delivery model, lessons learned, and future directions for the clinic will also be offered.

Does Knee Arthroscopy for Treatment of Meniscal Damage with Osteoarthritis Delay Knee Replacement Compared to Physical Therapy Alone?

BACKGROUD: To determine patient factors that lead to treatment of meniscal tears with osteoarthritis (OA) with knee arthroscopy (KA) or physical therapy only (PT-only); and to assess differences in clinical outcomes including the time to knee arthroplasty. METHODS: Patients aged ≥ 45 years with OA at meniscal tear diagnosis were followed up from the date of surgery (KA) or first PT visit (PT-only) until partial/total knee replacement surgery, death, disenrollment, or end of study. Demographic and clinical characteristics were compared and used to derive propensity scores. A Cox proportional hazards model was used to estimate the risk of knee replacement surgery and greater healthcare utilization associated with KA vs. PT-only. RESULTS: Among 7,026 patients (KA, 69%; PT-only, 31%), 27% had partial or total knee replacement surgery during follow-up. PT-only patients were older and more likely to be women and had more comorbidities. After accounting for differences between groups, the cumulative incidence of knee replacement was modestly but significantly higher for those who received KA than those who underwent PT-only (hazard ratio, 1.30; 95% confidence interval, 1.17-1.44; p < 0.001), although there was no significant difference in health service utilization, narcotic medication dispenses, or knee injections after initiating treatment. CONCLUSIONS: For patients with meniscal damage complicated by OA, those who underwent KA were 30% more likely to have partial or total knee replacement surgery at any given time than those who had PT alone.

Quantitative Assessment of Regional Mucociliary Clearance in Smokers with Mild-to-Moderate Chronic Obstructive Pulmonary Disease and Chronic Bronchitis from Planar Radionuclide Imaging.

Background: Mucociliary clearance (MCC) rate from the lung has been shown to be reduced in chronic obstructive pulmonary disease (COPD). This study investigates the value of regional clearance measurements in assessing MCC in mild-to-moderate disease. Methods: Measurement of lung MCC using planar gamma camera imaging was performed in three groups: (i) healthy nonsmoking controls (NSCs) (n = 9), (ii) smoking controls (SCs) who were current smokers with normal lung function (n = 10), and (iii) current smokers with mild-to-moderate COPD and bronchitis (n = 15). The mean (±standard deviation) forced expiratory volumes at 1 second (FEV1) for the three groups were 109 (± 18), 94 (± 5), and 78 (± 12), respectively. After inhalation of a technetium-99m labeled aerosol, planar imaging was performed over 4 hours and then at 24 hours. Both lung clearance and tracheobronchial clearance (TBC) (normalized to 24 hours clearance) were calculated for inner and outer lung zones. Inner zone clearance was corrected for input from the outer zone. A novel parameter, the bronchial airways clearance index (BACI), which combined clearance data from both zones, was also evaluated. Regional results were compared with whole lung clearance in the same subjects. Results: Corrected inner zone clearance at 3 hours was not reduced compared with NSC in either SCs or COPD. Outer zone clearance was higher in COPD than in the other groups. Corrected inner zone TBC showed significant reductions in SC and COPD compared with NSC. BACI was significantly reduced in COPD compared with NSC and also correlated with FEV1. The mean BACI for SC was also reduced compared with NSC, but the distribution of results was bimodal, with a significant proportion of subjects having values in the NSC range. Conclusions: Regional MCC demonstrated differences between NSCs, SCs, and subjects with mild-to-moderate COPD, which were not apparent with whole lung measurements.

Self-Tuning Deep Brain Stimulation Controller for Suppression of Beta Oscillations: Analytical Derivation and Numerical Validation.

Closed-loop control strategies for deep brain stimulation (DBS) in Parkinson's disease offer the potential to provide more effective control of patient symptoms and fewer side effects than continuous stimulation, while reducing battery consumption. Most of the closed-loop methods proposed and tested to-date rely on controller parameters, such as controller gains, that remain constant over time. While the controller may operate effectively close to the operating point for which it is set, providing benefits when compared to conventional open-loop DBS, it may perform sub-optimally if the operating conditions evolve. Such changes may result from, for example, diurnal variation in symptoms, disease progression or changes in the properties of the electrode-tissue interface. In contrast, an adaptive or "self-tuning" control mechanism has the potential to accommodate slowly varying changes in system properties over a period of days, months, or years. Such an adaptive mechanism would automatically adjust the controller parameters to maintain the desired performance while limiting side effects, despite changes in the system operating point. In this paper, two neural modeling approaches are utilized to derive and test an adaptive control scheme for closed-loop DBS, whereby the gain of a feedback controller is continuously adjusted to sustain suppression of pathological beta-band oscillatory activity at a desired target level. First, the controller is derived based on a simplified firing-rate model of the reciprocally connected subthalamic nucleus (STN) and globus pallidus (GPe). Its efficacy is shown both when pathological oscillations are generated endogenously within the STN-GPe network and when they arise in response to exogenous cortical STN inputs. To account for more realistic biological features, the control scheme is then tested in a physiologically detailed model of the cortical basal ganglia network, comprised of individual conductance-based spiking neurons, and simulates the coupled DBS electric field and STN local field potential. Compared to proportional feedback methods without gain adaptation, the proposed adaptive controller was able to suppress beta-band oscillations with less power consumption, even as the properties of the controlled system evolve over time due to alterations in the target for beta suppression, beta fluctuations and variations in the electrode impedance.

Trends in Snakebites Related to Texas Tropical Storms and Hurricanes, 2000-2017.

INTRODUCTION: Anecdotal media reports suggest an increase in snakebites after hurricanes. After Hurricane Harvey, several households called Texas poison control centers to report snakebites that occurred when rising water flooded homes. Patterns of snakebite before and after hurricane landfalls have not been well studied. METHODS: We reviewed retrospective surveillance data from the Texas Poison Control Network to examine snakebites possibly related to tropical storms/hurricanes that hit Texas between 2000 and 2017. For that assessment, we compared 2 groups of counties: those designated for individual assistance (impact counties) by the Federal Emergency Management Agency and all others (nonimpact counties). Typically, counties with individual assistance declarations are those in which damage is worse and resident return may be delayed. RESULTS: Eleven named tropical storms/hurricanes struck Texas between 2000 and 2017; 9 received individual assistance declarations. During the 18 y, 2037 snakebites were reported in the 30 d after and the 30 d before landfalls in 9 storms; 132 (6%) occurred poststorm in impact counties, and 13 of 132 (9%) of the case narratives mentioned hurricanes as a contributing factor. Impact counties were not statistically more likely to report snakebites in the 30 d after landfall for any of the 9 storms or overall, nor did we find differences in patient demographic characteristics, type of snake, and care patterns post- and prestorm. CONCLUSIONS: There was no evidence of increases in snakebites after hurricanes in Texas during the study period. More detailed evaluations may be warranted in other regions that experience hurricanes and have venomous snake populations.

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson's Disease.

This study presents a computational model of closed-loop control of deep brain stimulation (DBS) for Parkinson's disease (PD) to investigate clinically viable control schemes for suppressing pathological beta-band activity. Closed-loop DBS for PD has shown promising results in preliminary clinical studies and offers the potential to achieve better control of patient symptoms and side effects with lower power consumption than conventional open-loop DBS. However, extensive testing of algorithms in patients is difficult. The model presented provides a means to explore a range of control algorithms in silico and optimize control parameters before preclinical testing. The model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The performance of on-off and dual-threshold controllers for suppressing beta-band activity by modulating the DBS amplitude were first verified, showing levels of beta suppression and reductions in power consumption comparable with previous clinical studies. Proportional (P) and proportional-integral (PI) closed-loop controllers for amplitude and frequency modulation were then investigated. A simple tuning rule was derived for selecting effective PI controller parameters to target long duration beta bursts while respecting clinical constraints that limit the rate of change of stimulation parameters. Of the controllers tested, PI controllers displayed superior performance for regulating network beta-band activity whilst accounting for clinical considerations. Proportional controllers resulted in undesirable rapid fluctuations of the DBS parameters which may exceed clinically tolerable rate limits. Overall, the PI controller for modulating DBS frequency performed best, reducing the mean error by 83% compared to DBS off and the mean power consumed to 25% of that utilized by open-loop DBS. The network model presented captures sufficient physiological detail to act as a surrogate for preclinical testing of closed-loop DBS algorithms using a clinically accessible biomarker, providing a first step for deriving and testing novel, clinically suitable closed-loop DBS controllers.

The Effect of Symptom Duration on Outcomes After Fusion for Degenerative Spondylolisthesis.

STUDY DESIGN: Longitudinal comparative cohort. OBJECTIVES: To determine if the duration of symptoms in patients with degenerative spondylolisthesis affects postoperative outcomes after 1- or 2-level decompression and fusion. METHODS: Patients undergoing primary surgery for grade 1 degenerative spondylolisthesis at a single Quality Outcomes Database (QOD) participating site were identified. Demographic, surgical and patient-reported outcomes (PROs) data, including baseline and 12-month postoperative Oswestry Disability Index (ODI), back pain (BP, 0-10), leg pain (LP, 0-10), and EuroQOL-5D (EQ-5D) scores were collected. Individual medical records were reviewed for data on duration of symptoms prior to surgery. Patients were stratified into 3 cohorts-those with preoperative symptom duration of less than 1 year, 1 to 2 years, or greater than 2 years. RESULTS: Complete data was available in 123 patients. Significant improvement in ODI, BP, and LP scores were observed in all groups. At 12-month follow-up improvement in ODI, BP, or LP was similar among the cohorts; with a trend toward significance with better improvement in LP scores in patients with a symptom duration of less than 1 year to those with symptom duration greater than 2 years (P = .058). CONCLUSIONS: The duration of symptoms up to 2 years prior to surgery may not be a useful predictor of improvement of back pain or disability scores in patients with spondylolisthesis requiring decompression and fusion. Although there was a positive trend for improvement in leg pain for those with a shorter duration of symptoms, this did not reach statistical significance in our study.