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ABSTRACT: Background/Aims Over 50% of depressed patients fail to remit after an adequate antidepressant (AD) treatment course, and 35% remain symptomatic after two adequate treatment courses. Patients with treatment-resistant depression (TRD) have higher risks of morbidity and mortality, and substantially higher healthcare expenditures. This study aims to develop and validate algorithms to identify patients with TRD in claims databases. Methods We first identified Harvard Pilgrim Health Care members aged 18 years or older who had a diagnosis of depression and new use of selective serotonin reuptake inhibitors or serotonin- norepinephrine reuptake inhibitors (after at least 365 days of no AD use) in 2000-2009. Among these patients, we identified those who received adequate treatment, defined as treatment initiated at or greater than the recommended starting dose based on practice guidelines and taken for at least 8 weeks. We will further identify patients with TRD, i.e., patients who are treated adequately but fail to remit. Although multiple definitions for TRD exist in the guidelines and literature, we will use the emerging consensus of failure to remit after two adequate treatment courses as our definition of TRD. We will consider various markers of treatment resistance such as switching ADs (particularly to those reserved for second-line), adding atypical antipsychotics or other non-AD medications commonly used for depression, and invasive nonpharmacologic intervention. We will validate our algorithms via chart review. Results In preliminary results, 114,002 patients meeting inclusion criteria initiated an AD and 63,882 (56.0%) completed an adequate treatment course. Among these patients, 35,547 (55.6%) continued that treatment, 10,255 (16.1%) stopped treatment, and 5,753 (9.0%) switched to another AD. Among switchers, 3,625 (63.0%) achieved adequate treatment with the second AD. After examining treatment patterns and markers of treatment resistance, we will select the most promising algorithm and validate 300 randomly selected potential cases identified by the algorithm beginning in December. Discussion Claims databases have potential to identify TRD, but algorithms to identify such patients must be developed. Once such an algorithm is validated, these databases can be assessed to answer important questions about the safety and effectiveness of treatments for TRD patients. Primary: Mental Health.
Clinical Medicine & Research 08/2012; 10(3):181.
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ABSTRACT: Conventional computed tomography is an emerging modality in forensic identification but is not sufficiently accurate for use in dental identification primarily because of problems with metallic dental restoration-induced streak artifact. In this study, the accuracy and reliability of recording forensic information from cone beam computed tomography (CBCT) scans of the jaws when compared to conventional panoramic radiographs has been analyzed under experimental conditions. Information could be recorded with near-perfect repeatability and reproducibility. Information could also be recorded accurately, the sensitivity being 96.6% (95% CI, 95.1-98.1) and specificity being 98.4% (95% CI, 96.2-100). The metal dental restoration-induced streak artifact was at a level that permitted, in most cases, accurate observations. This is considered an important step in validating CBCT as a tool in comparative dental identification of bodies. It may have a role in mass fatalities and in chemical, biological, radiological, and nuclear incidents, but further studies are required to assess the feasibility of this.
Journal of Forensic Sciences 03/2012; 57(4):964-8. · 1.23 Impact Factor
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Archives of dermatology 02/2012; 148(2):260-2; author reply 262. · 4.76 Impact Factor
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ABSTRACT: Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information' and 'causal density'. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.
PLoS ONE 01/2012; 7(1):e29072. · 4.09 Impact Factor
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Dean R Freestone,
Levin Kuhlmann,
David B Grayden,
Anthony N Burkitt,
Alan Lai,
Timothy S Nelson,
Simon Vogrin, Michael Murphy,
Wendyl D'Souza,
Radwa Badawy,
Dragan Nesic,
Mark J Cook
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ABSTRACT: Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
Epilepsy & Behavior 12/2011; 22 Suppl 1:S110-8. · 2.34 Impact Factor
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ABSTRACT: A recent investigation of the British General Household Survey (GHS) found substantial over-reporting of childlessness in recent years, particularly at older ages. We examine the phenomenon in further detail and find that the principal cause was change in survey procedures. To some extent the bias can be corrected for by using information on own children in the household. Revised fertility histories give period estimates of total fertility that are in close agreement with national vital registration statistics, unlike those based on original fertility histories of recent years. Misreporting in fertility histories dates primarily from administrative changes in the GHS in the years 1998-2000, and particularly from 2003, when the option of laptop self-completion (CASI) was introduced for reporting demographic histories.
Population Studies 11/2011; 65(3):305-18. · 1.08 Impact Factor
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Dean R Freestone,
Anthony N Burkitt,
Alan Lai,
Timothy S Nelson,
David B Grayden,
Simon Vogrin, Michael Murphy,
Wendyl D'Souza,
Radwa Badawy,
Levin Kuhlmann,
Mark J Cook
[show abstract]
[hide abstract]
ABSTRACT: This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus via intracranial electroencephalography (iEEG). Stimuli consisted of 100 single bi-phasic pulses, delivered every 10 minutes. Neural excitability is estimated by extracting a feature from the iEEG responses to the stimuli, which we dub the mean phase variance (PV). We show that the mean PV increases with the rate of inter-ictal discharges in one patient. In another patient, we show that the mean PV changes with sleep and an epileptic seizure. The results demonstrate a proof-of-principal for the method to be applied in a seizure anticipation framework.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:1644-7.
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ABSTRACT: The potential advantages of gecko-inspired robots have been discussed and related to the particular problems of robotic systems in space. Different approaches to climbing robots in general have been introduced and, in particular, differing approaches to gecko-inspired systems have been discussed. The phenomenon of dry adhesion in nature has been introduced, along with methods for its recreation in engineered materials. Different designs for robots intended to take advantage of gecko-like dry adhesion have been conceived and prototyped, showing potential for further development. In particular, one design has been focused on the realisation of a robust and reliable system, while the other, using novel materials and actuators, has potential for miniaturisation. Potential future development work has been identified.
09/2007; , ISBN: 978-3-902613-15-8
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Dean R Freestone,
Anthony N Burkitt,
Alan Lai,
Timothy S Nelson,
David B Grayden,
Simon Vogrin, Michael Murphy,
Wendyl d'Souza,
Radwa Badawy,
Levin Kuhlmann,
Mark J Cook
[show abstract]
[hide abstract]
ABSTRACT: This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus via intracranial electroencephalography (iEEG). Stimuli consisted of 100 single bi-phasic pulses, delivered every 10 minutes. Neural excitability is estimated by extracting a feature from the iEEG responses to the stimuli, which we dub the mean phase variance (PV). We show that the mean PV increases with the rate of inter-ictal discharges in one patient. In another patient, we show that the mean PV changes with sleep and an epileptic seizure. The results demonstrate a proof-of-principal for the method to be applied in a seizure anticipation framework.
2011:1644-1647.
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Dean R Freestone,
Levin Kuhlmann,
David B Grayden,
Anthony N Burkitt,
Alan Lai,
Timothy S Nelson,
Simon Vogrin, Michael Murphy,
Wendyl d'Souza,
Radwa Badawy,
Dragan Nesic,
Mark J Cook
[show abstract]
[hide abstract]
ABSTRACT: Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
22 Suppl 1:S110-8.
