Accuracy of Subthalamic Nucleus Electrode Implantation in Deep Brain Stimulation Surgery for Parkinson's Disease Treatment and Affecting Factors: Outcomes at Two Centers in Vietnam.

BACKGROUND: Deep brain stimulation (DBS) surgery for Parkinson's Disease (PD) has become more and more popular in Vietnam. However, the accuracy of implantation and affecting factors are under investigation. The objective of this study is to evaluate the accuracy of the subthalamic nucleus (STN)-DBS electrode implantation technique for treatment PD at Nguyen Tri Phuong Hospital and University Medical Center. To investigate factors related to accuracy. METHODS: We carried out a retrospective analysis of 58 patients with advanced PD who underwent STN-DBS surgery at Nguyen Tri Phuong Hospital and University Medical Center in Ho Chi Minh City, Viet Nam between June 2014 and July 2021 (115 leads total). All patients underwent the procedure with standard frame-based techniques under local anesthesia with microelectrode recording and macrostimuation test. RESULTS: Twenty-six female (44.8%) and thirty-two male (55.2%) patients with a mean age of 60.4 ± 8.3 years old (40-76 years) were included. Of total of 115 electrodes implanted, the mean target error (ΔT), radial error (ΔR), angle error (Δθ) were 1.94 ± 0.73 mm; 1.16 ± 0.69 mm; 2.22 ± 4.24 degrees, respectively. Vector error on each coordinate axis ΔX, ΔY, ΔZ were -0.35 ± 1.02 mm, +0.99 ± 0.82 mm, +0.73 ± 0.99 mm, respectively. There was a statistically significant correlation between subdural air volume, cortical shift, intracranial electrode bending, and accuracy. CONCLUSIONS: The current STN-DBS electrode implantation technique applied in our centers was quite accurate with acceptable error. More clinical trials are necessary to directly compare affecting factors to the accuracy of electrode implantation.

Spectral Topography of the Subthalamic Nucleus to Inform Next-Generation Deep Brain Stimulation.

BACKGROUND: The landscape of neurophysiological symptoms and behavioral biomarkers in basal ganglia signals for movement disorders is expanding. The clinical translation of sensing-based deep brain stimulation (DBS) also requires a thorough understanding of the anatomical organization of spectral biomarkers within the subthalamic nucleus (STN). OBJECTIVES: The aims were to systematically investigate the spectral topography, including a wide range of sub-bands in STN local field potentials (LFP) of Parkinson's disease (PD) patients, and to evaluate its predictive performance for clinical response to DBS. METHODS: STN-LFPs were recorded from 70 PD patients (130 hemispheres) awake and at rest using multicontact DBS electrodes. A comprehensive spatial characterization, including hot spot localization and focality estimation, was performed for multiple sub-bands (delta, theta, alpha, low-beta, high-beta, low-gamma, high-gamma, and fast-gamma (FG) as well as low- and fast high-frequency oscillations [HFO]) and compared to the clinical hot spot for rigidity response to DBS. A spectral biomarker map was established and used to predict the clinical response to DBS. RESULTS: The STN shows a heterogeneous topographic distribution of different spectral biomarkers, with the strongest segregation in the inferior-superior axis. Relative to the superiorly localized beta hot spot, HFOs (FG, slow HFO) were localized up to 2 mm more inferiorly. Beta oscillations are spatially more spread compared to other sub-bands. Both the spatial proximity of contacts to the beta hot spot and the distance to higher-frequency hot spots were predictive for the best rigidity response to DBS. CONCLUSIONS: The spatial segregation and properties of spectral biomarkers within the DBS target structure can additionally be informative for the implementation of next-generation sensing-based DBS. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Deep Brain Stimulation: When to Test Directional?

Background: Directional deep brain stimulation (DBS) allows for steering of the stimulation field, but extensive and time-consuming testing of all segmented contacts is necessary to identify the possible benefit of steering. It is therefore important to determine under which circumstances directional current steering is advantageous. Methods: Fifty two Parkinson's disease patients implanted in the STN with a directional DBS system underwent a standardized monopolar programming session 5 to 9 months after implantation. Individual contacts were tested for a potential advantage of directional stimulation. Results were used to build a prediction model for the selection of ring levels that would benefit from directional stimulation. Results: On average, there was no significant difference in therapeutic window between ring-level contact and best directional contact. However, according to our standardized protocol, 35% of the contacts and 66% of patients had a larger therapeutic window under directional stimulation compared to ring-mode. The segmented contacts warranting directional current steering could be predicted with a sensitivity of 79% and a specificity of 57%. Conclusion: To reduce time required for DBS programming, we recommend additional directional contact testing initially only on ring-level contacts with a therapeutic window of less than 2.0 mA.

XBP1 Activation Reduces Severity of Polycystic Kidney Disease due to a Nontruncating Polycystin-1 Mutation in Mice.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in Pkd1 and Pkd2. They encode the polytopic integral membrane proteins polycystin-1 (PC1) and polycystin-2 (PC2), respectively, which are expressed on primary cilia. Formation of kidney cysts in ADPKD starts when a somatic second hit mechanism inactivates the wild-type Pkd allele. Approximately one quarter of families with ADPDK due to Pkd1 have germline nonsynonymous amino acid substitution (missense) mutations. A subset of these mutations is hypomorphic, retaining some residual PC1 function. Previous studies have shown that the highly conserved Ire1 α -XBP1 pathway of the unfolded protein response can modulate levels of functional PC1 in the presence of mutations in genes required for post-translational maturation of integral membrane proteins. We examine how activity of the endoplasmic reticulum chaperone-inducing transcription factor XBP1 affects ADPKD in a murine model with missense Pkd1 . METHODS: We engineered a Pkd1 REJ domain missense murine model, Pkd1 R2216W , on the basis of the orthologous human hypomorphic allele Pkd1 R2220W , and examined the effects of transgenic activation of XBP1 on ADPKD progression. RESULTS: Expression of active XBP1 in cultured cells bearing PC1 R2216W mutations increased levels and ciliary trafficking of PC1 R2216W . Mice homozygous for Pkd1 R2216W or heterozygous for Pkd1 R2216Win trans with a conditional Pkd1 fl allele exhibit severe ADPKD following inactivation in neonates or adults. Transgenic expression of spliced XBP1 in tubule segments destined to form cysts reduced cell proliferation and improved Pkd progression, according to structural and functional parameters. CONCLUSIONS: Modulating ER chaperone function through XBP1 activity improved Pkd in a murine model of PC1, suggesting therapeutic targeting of hypomorphic mutations.

Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters.

OBJECTIVES: Subthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming. MATERIALS AND METHODS: A total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months. RESULTS: Image-based contact prediction showed high interrater reliability (Cohen kappa 0.851-0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0-83.3) and 65% (95% CI 44.9-81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was -0.5 mA (p < 0.001, Wilcoxon paired signed rank test). CONCLUSIONS: Integration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.

Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease.

BACKGROUND: Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. OBJECTIVE: In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). MATERIALS AND METHODS: STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. RESULTS: Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. CONCLUSION: LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.

Do age and language impairment affect speed of recognition for words with high and low closeness centrality within the phonological network?

Purpose: Speed and accuracy of lexical access change with healthy ageing and neurodegeneration. While a word's immediate phonological neighbourhood density (i.e. words differing by a single phoneme) influences access, connectivity to all words in the phonological network (i.e. closeness centrality) may influence processing. This study aimed to investigate the effect of closeness centrality on speed and accuracy of lexical processing pre- and post- a single word-training session in healthy younger and older adults, and adults with logopenic primary progressive aphasia (lvPPA), which affects phonological processing.Method: Participants included 29 young and 17 older healthy controls, and 10 adults with lvPPA. Participants received one session of word-training on words with high or low closeness centrality, using a picture-word verification task. Changes in lexical decision reaction times (RT) and accuracy were measured.Result: Baseline RT was unaffected by age and accuracy was at ceiling for controls. Post-training, only young adults' RT were significantly faster. Adults with lvPPA were slower and less accurate than controls at baseline, with no training effect. Closeness centrality did not influence performance.Conclusion: Absence of training effect for older adults suggests higher threshold to induce priming, possibly associated with insufficient dosage or fatigue. Implications for word-finding interventions with older adults are discussed.

A smoking quitline integrated with clinician counselling at outpatient health facilities in Vietnam: a single-arm prospective cohort study.

BACKGROUND: Limited evidence is available about the combination of multiple smoking cessation modalities in low- and middle-income countries. The study aimed to assess the feasibility of a smoking cessation intervention that integrates follow-up counselling phone calls and scheduled text messages with brief advice from physicians in Vietnam. METHODS: This was a single-arm intervention study. Smokers were referred to the study Quitline after brief advice by physicians at three rural district hospitals in Hanoi, Vietnam. Following referral, participants received nine counselling phone calls in 12 months and a scheduled text message service that lasted for three months. Participants who reported smoking cessation for at least 30 days at the 12-month follow-up were invited for a urinary cotinine test to confirm cessation. RESULTS: The Quitline centre had 431 referrals from participating hospitals. Among them, 221 (51.3%) were enrolled. After the baseline phone call, 141 (63.8%) participated in all 4 follow-up calls within the first month and 117 (52.9%) participated in all phone calls in 12 months. The median number of successful phone calls was 8 (interquartile range: 6 - 8). At the end of the study, 90 (40.7%) self-reported abstinence from smoking over the previous 30 days. Among them, 22 (24.4%) submitted a sample for cotinine test, of which 13 (59.1% of those tested) returned a negative result. The proportion of biochemically-verified quitters was 5.9%. CONCLUSIONS: The integration of brief advice and referral from healthcare facilities, Quitline counselling phone calls, and scheduled text messaging was feasible in rural health facilities in northern Vietnam. TRIAL REGISTRATION: ACTRN12619000554167 .

Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression.

This study aims to investigate the influence of all the mixture components of high-performance concrete (HPC) on its early compressive strength, ranging from 1 to 14 days. To this purpose, a Gaussian Process Regression (GPR) algorithm was first constructed using a database gathered from the available literature. The database included the contents of cement, blast furnace slag (BFS), fly ash (FA), water, superplasticizer, coarse, fine aggregates, and testing age as input variables to predict the output of the problem, which was the early compressive strength. Several standard statistical criteria, such as the Pearson correlation coefficient, root mean square error and mean absolute error, were used to quantify the performance of the GPR model. To analyze the sensitivity and influence of the HPC mixture components, partial dependence plots analysis was conducted with both one-dimensional and two-dimensional. Firstly, the results showed that the GPR performed well in predicting the early strength of HPC. Second, it was determined that the cement content and testing age of HPC were the most sensitive and significant elements affecting the early strength of HPC, followed by the BFS, water, superplasticizer, FA, fine aggregate, and coarse aggregate contents. To put it simply, this research might assist engineers select the appropriate amount of mixture components in the HPC production process to obtain the necessary early compressive strength.

Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS.

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database. As a result, the optimal selection of the ANN design is carried out and evaluated using conventional statistical metrics. The results demonstrate that utilizing the best architecture [8-14-4-1] among the 240 investigated architectures, and the best ANN model, is a very efficient predictor of the compressive strength of concrete using GGBFS, with a maximum R2 value of 0.968 on the training part and 0.965 on the testing part. Furthermore, a sensitivity analysis is performed over 500 Monte Carlo simulations using the best ANN model to determine the reliability of ANN model in predicting the compressive strength of concrete. The findings of this research may make it easier and more efficient to apply the ANN model to many civil engineering challenges.

Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams.

In this paper, an extensive simulation program is conducted to find out the optimal ANN model to predict the shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and shear reinforcements. For acquiring this purpose, an experimental database containing 125 samples is collected from the literature and used to find the best architecture of ANN. In this database, the input variables consist of 9 inputs, such as the ratio of the beam width, the effective depth, the shear span to the effective depth, the compressive strength of concrete, the longitudinal FRP reinforcement ratio, the modulus of elasticity of longitudinal FRP reinforcement, the FRP shear reinforcement ratio, the tensile strength of FRP shear reinforcement, the modulus of elasticity of FRP shear reinforcement. Thereafter, the selection of the appropriate architecture of ANN model is performed and evaluated by common statistical measurements. The results show that the optimal ANN model is a highly efficient predictor of the shear strength of FRP concrete beams with a maximum R2 value of 0.9634 on the training part and an R2 of 0.9577 on the testing part, using the best architecture. In addition, a sensitivity analysis using the optimal ANN model over 500 Monte Carlo simulations is performed to interpret the influence of reinforcement type on the stability and accuracy of ANN model in predicting shear strength. The results of this investigation could facilitate and enhance the use of ANN model in different real-world problems in the field of civil engineering.

A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.

In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity.

Deep Brain Stimulation for Chronic Cluster Headache: Meta-Analysis of Individual Patient Data.

OBJECTIVE: Deep brain stimulation (DBS) is a treatment option for refractory chronic cluster headache (CCH). Despite several recent prospective case series reporting a good outcome, the effectiveness and the optimal stimulation target of DBS for CCH remain unclear. We aimed to obtain precise estimates and predictors of long-term pain relief in an individual patient data meta-analysis. Furthermore, we aimed to construct a probabilistic stimulation map of effective DBS. METHODS: We invited investigators of published cohorts of patients undergoing DBS for CCH, identified by a systematic review of MEDLINE from inception to Febuary 15, 2019, to provide individual patient data on baseline covariates, pre- and postoperative headache scores at median (12-month) and long-term follow-up, in addition to individual imaging data to obtain individual electrode positions. We calculated a stimulation map using voxel-wise statistical analysis. We used multiple regression analysis to estimate predictors of pain relief. RESULTS: Among 40 patients from four different cohorts representing ~50% of all previously published cases, we found a significant 77% mean reduction in headache attack frequency over a mean follow-up of 44 months, with an overall response rate of 75%. Positive outcome was not associated with baseline covariates. We identified 2 hotspots of stimulation covering the midbrain ventral and retrorubral tegmentum. INTERPRETATION: This study supports the hypothesis that DBS provides long-term pain relief for the majority of CCH patients. Our stimulation map of the region of influence of therapeutic DBS identified an optimal anatomical target site that can help surgeons to guide their surgical planning in the future. ANN NEUROL 2020;88:956-969.

Parametric Investigation of Particle Swarm Optimization to Improve the Performance of the Adaptive Neuro-Fuzzy Inference System in Determining the Buckling Capacity of Circular Opening Steel Beams.

In this paper, the main objectives are to investigate and select the most suitable parameters used in particle swarm optimization (PSO), namely the number of rules (nrule), population size (npop), initial weight (wini), personal learning coefficient (c1), global learning coefficient (c2), and velocity limits (fv), in order to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. This is an important mechanical property in terms of the safety of structures under subjected loads. An available database of 3645 data samples was used for generation of training (70%) and testing (30%) datasets. Monte Carlo simulations, which are natural variability generators, were used in the training phase of the algorithm. Various statistical measurements, such as root mean square error (RMSE), mean absolute error (MAE), Willmott's index of agreement (IA), and Pearson's coefficient of correlation (R), were used to evaluate the performance of the models. The results of the study show that the performance of ANFIS optimized by PSO (ANFIS-PSO) is suitable for determining the buckling capacity of circular opening steel beams, but is very sensitive under different PSO investigation and selection parameters. The findings of this study show that nrule = 10, npop = 50, wini = 0.1 to 0.4, c1 = [1, 1.4], c2 = [1.8, 2], fv = 0.1, which are the most suitable selection values to ensure the best performance for ANFIS-PSO. In short, this study might help in selection of suitable PSO parameters for optimization of the ANFIS model.

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression.

Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (Pu) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict Pu was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of Pu, with a value of R2 of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R2, respectively, compared with simulation using the single FNN. Finally, the performance in predicting the Pu in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.

Topological states and phase transitions in Sb2Te3-GeTe multilayers.

Topological insulators (TIs) are bulk insulators with exotic 'topologically protected' surface conducting modes. It has recently been pointed out that when stacked together, interactions between surface modes can induce diverse phases including the TI, Dirac semimetal, and Weyl semimetal. However, currently a full experimental understanding of the conditions under which topological modes interact is lacking. Here, working with multilayers of the TI Sb2Te3 and the band insulator GeTe, we provide experimental evidence of multiple topological modes in a single Sb2Te3-GeTe-Sb2Te3 structure. Furthermore, we show that reducing the thickness of the GeTe layer induces a phase transition from a Dirac-like phase to a gapped phase. By comparing different multilayer structures we demonstrate that this transition occurs due to the hybridisation of states associated with different TI films. Our results demonstrate that the Sb2Te3-GeTe system offers strong potential towards manipulating topological states as well as towards controlledly inducing various topological phases.

Week-long high-maintenance dose clopidogrel regimen achieves better platelet aggregation inhibition than a standard loading dose before percutaneous coronary intervention: results of a double-blind, randomized clinical trial.

BACKGROUND: Adequate platelet inhibition before percutaneous coronary intervention (PCI) reduces periprocedural and long-term ischemic complications. Reduced response to clopidogrel has been associated with subsequent major adverse cardiovascular events. Strategies to optimize platelet inhibition pre-PCI are under investigation. This study evaluated the effect on platelet aggregation of four different dosing regimens of clopidogrel given before elective PCI in a randomized, prospective, double-blind, and placebo-controlled design. METHODS: One hundred twenty participants were randomized to one of four groups of clopidogrel: (a) 300 mg on the day prior to angiography; (b) 600 mg on the day prior to angiography; (c) 300 mg followed by 75 mg daily started 1 week prior to angiography; and (d) 300 mg followed by 150 mg daily started 1 week prior to angiography. Platelet aggregation was assessed by light transmission aggregometry (LTA) after stimulation with adenosine diphosphate 20 microM at baseline and at the time of diagnostic coronary angiography. The absolute change in platelet aggregation between these two time points was considered the main outcome measure. RESULTS: At the time of diagnostic coronary angiography, the 300-mg/150-mg daily regimen achieved the greatest decrease in platelet aggregation (37 +/- 19%), while the 300 mg regimen provided the smallest (20 +/- 22%), an absolute difference between the two groups of 17.2 +/- 5.1% (P = 0.005). CONCLUSIONS: A 300-mg loading dose of clopidogrel followed by 150 mg daily for 1 week prior to coronary angiography provides more effective platelet inhibition, as defined by LTA, compared to the standard 300-mg loading dose regimen at the time of coronary intervention.

Comparison of four tests to assess inhibition of platelet function by clopidogrel in stable coronary artery disease patients.

AIMS: We investigated the comparability of platelet function tests in quantifying platelet inhibition achieved by clopidogrel. METHODS AND RESULTS: This pre-specified substudy of a randomized, double-blind trial included 116 patients with stable coronary artery disease requiring diagnostic angiography. Patients received clopidogrel for 1 (300 or 600 mg) or 7 days (300 + 75 or 150 mg daily) before the procedure. Blood samples obtained before clopidogrel initiation and before diagnostic coronary angiography were assayed using light transmission aggregometry [adenosine diphosphate (ADP) 5 and 20 microM as the agonist], whole-blood aggregometry (ADP 5 and 20 microM), PFA-100 (Collagen-ADP cartridge), and VerifyNow P2Y12. Although all assays studied were found sensitive to clopidogrel ingestion, none could distinguish categorically between patients who had, or not, ingested clopidogrel. Agreement between assays to identify patients with insufficient inhibition of platelet aggregation by clopidogrel was low. CONCLUSION: The assessment of platelet function inhibition by clopidogrel is highly test-specific. Decision to increase clopidogrel dosage may vary on the basis of the assay used, thus highlighting the need for unambiguous guidelines with respect to assay selection, as platelet function assays are not interchangeable. At present, platelet function testing evaluating clopidogrel efficacy cannot be recommended in routine clinical practice.

Assessment of VerifyNow P2Y12 assay accuracy in evaluating clopidogrel-induced platelet inhibition.

The emergence of point-of-care assays enabling bedside testing such as the VerifyNow P2Y12 system might prove useful in clinical settings. The aim of this study was to evaluate the ability of the VerifyNow P2Y12 assay to estimate the inhibition of platelet aggregation provided by clopidogrel in the absence of baseline off-drug aggregation data. Sixty-eight patients with coronary artery disease scheduled to initiate clopidogrel therapy underwent platelet aggregation testing by VerifyNow P2Y12 at baseline and after clopidogrel administration. The inhibition reported by the VerifyNow assay (relative to thrombin receptor activating peptide-induced platelet aggregation, serving as baseline) was compared with that calculated with the actual adenosine diphosphate-induced baseline obtained with the same methodology. The postclopidogrel thrombin receptor activating peptide-induced aggregation showed a great discordance with that induced by adenosine diphosphate before clopidogrel with a bias of 24 units (95% limits of agreement from -142 to 190 units). Moreover, the inhibition reported by the assay overestimated the standard before-and-after testing data by an average of 8% (95% limits of agreement from -49% to 65%), making its use without a true baseline comparator unsatisfactory. The VerifyNow P2Y12 assay fails to accurately quantify platelet inhibition achieved by clopidogrel compared with before-and-after testing. Further studies are required to establish the clinical usefulness of the VerifyNow P2Y12 assay to accurately predict the occurrence of major adverse cardiovascular events in patients with reduced clopidogrel efficacy before it can be implemented in clinical practice. At present, the use of this assay in clinical care cannot be recommended for monitoring clopidogrel therapy.

Initial surgical treatment for chronic frontal sinusitis: a pilot study.

OBJECTIVES: The initial surgical treatment for chronic frontal sinusitis is not well defined. Our objective was to determine the effectiveness of anterior ethmoidectomy for chronic frontal sinusitis. METHODS: Patients with chronic frontal sinusitis who underwent anterior ethmoidectomy as initial surgical treatment were reviewed. Data were collected from computed tomography scans with use of the Lund-Mackay scale. Data on demographics, comorbidities, management, postoperative recovery, and follow-up were collected. RESULTS: Seventy-seven patients representing 121 diseased frontal sinuses met the inclusion criteria. The respiratory comorbidities were asthma alone (8.3%), asthma and polyps (6.6%), aspirin triad (5.8%), and cystic fibrosis (0.8%). Nineteen of 121 frontal sinuses (15.7%) belonged to smokers. Fourteen of 121 frontal sinuses (11.5%) exhibited postoperative evidence of disease. Of these 14 frontal sinuses, 10 (8.3%) underwent revision surgery. Frontal sinuses of patients with aspirin triad, with both nasal polyposis and asthma, or with inter-frontal sinus septal cells were more likely to fail Draf I surgery (p < .05). CONCLUSIONS: Anterior ethmoidectomy for drainage of frontal sinuses appears to be effective initial surgical treatment for chronic frontal sinusitis. Patients with aspirin triad, both asthma and polyposis, or inter-frontal sinus septal cells are more likely to fail this procedure.