Oanh Kieu Nguyen

Transient and Progressive Electrophysiological Alterations in the Corticostriatal Pathway in a Mouse Model of Huntington's Disease

Alterations in the corticostriatal pathway may precede symptomatology and striatal cell death in Huntingtons disease (HD) patients. Here we examined spontaneous EPSCs in striatal medium-sized spiny neurons in slices from a mouse model of HD (R6/2). Spontaneous EPSC frequency was similar in young (3–4 weeks) transgenics and controls but decreased significantly in transgenics when overt behavioral symptoms began (5–7 weeks) and was most pronounced in severely impaired transgenics (11–15 weeks). These differences were maintained after bicuculline or tetrodotoxin, indicating they were specific to glutamatergic input and likely presynaptic in origin. Decreases in presynaptic and postsynaptic protein markers, synaptophysin and postsynaptic density-95, occurred in 11–15 week R6/2 mice, supporting the electrophysiological results. Furthermore, isolated, large-amplitude synaptic events (>100 pA) occurred more frequently in transgenic animals, particularly at 5–7 weeks, suggesting additional dysregulation of cortical inputs. Large events were blocked by tetrodotoxin, indicating a possible cortical origin. Addition of bicuculline and 4-aminopyridine facilitated the occurrence of large events. Riluzole, a compound that decreases glutamate release, reduced these events. Together, these observations indicate that both progressive and transient alterations occur along the corticostriatal pathway in experimental HD. These alterations are likely to contribute to the selective vulnerability of striatal medium-sized spiny neurons.

Morphological and electrophysiological characterization of abnormal cell types in pediatric cortical dysplasia.

The mechanisms responsible for seizure generation in cortical dysplasia (CD) are unknown, but morphologically abnormal cells could contribute. We examined the passive and active membrane properties of cells from pediatric CD in vitro. Normal‐ and abnormal‐appearing cells were identified morphologically by using infrared videomicroscopy and biocytin in slices from children with mild to severe CD. Electrophysiological properties were assessed with patch clamp recordings. Four groups of abnormal‐appearing cells were observed. The first consisted of large, pyramidal cells probably corresponding to cytomegalic neurons. Under conditions that reduced the contribution of K+ conductances, these cells generated large Ca2+ currents and influx when depolarized. When these cells were acutely dissociated, peak Ca2+ currents and densities were greater in cytomegalic compared with normal‐appearing pyramidal neurons. The second group included large, nonpyramidal cells with atypical somatodendritic morphology that could correspond to “balloon” cells. These cells did not display active voltage‐ or ligand‐gated currents and did not appear to receive synaptic inputs. The third group included misoriented and dysmorphic pyramidal neurons, and the fourth group consisted of immature‐looking pyramidal neurons. Electrophysiologically, neurons in these latter two groups did not display significant abnormalities when compared with normal‐appearing pyramidal neurons. We conclude that there are cells with abnormal intrinsic membrane properties in pediatric CD. Among the four groups of cells, the most abnormal electrophysiological properties were displayed by cytomegalic neurons and large cells with atypical morphology. Cytomegalic neurons could play an important role in the generation of epileptic activity.

Increased GABAergic function in mouse models of Huntington's disease: reversal by BDNF.

Huntingtons disease (HD) is characterized by loss of striatal γ‐aminobutyric acid (GABA)ergic medium‐sized spiny projection neurons (MSSNs), whereas some classes of striatal interneurons are relatively spared. Striatal interneurons provide most of the inhibitory synaptic input to MSSNs and use GABA as their neurotransmitter. We reported previously alterations in glutamatergic synaptic activity in the R6/2 and R6/1 mouse models of HD. In the present study, we used whole‐cell voltage clamp recordings to examine GABAergic synaptic currents in MSSNs from striatal slices in these two mouse models compared to those in age‐matched control littermates. The frequency of spontaneous GABAergic synaptic currents was increased significantly in MSSNs from R6/2 transgenics starting around 5–7 weeks (when the overt behavioral phenotype begins) and continuing in 9–14‐week‐old mice. A similar increase was observed in 12–15‐month‐old R6/1 transgenics. Bath application of brain‐derived neurotrophic factor, which is downregulated in HD, significantly reduced the frequency of spontaneous GABAergic synaptic currents in MSSNs from R6/2 but not control mice at 9–14 weeks. Increased GABA current densities also occurred in acutely isolated MSSNs from R6/2 animals. Immunofluorescence demonstrated increased expression of the ubiquitous α1 subunit of GABAA receptors in MSSNs from R6/2 animals. These results indicate that increases in spontaneous GABAergic synaptic currents and postsynaptic receptor function occur in parallel to progressive decreases in glutamatergic inputs to MSSNs. In conjunction, both changes will severely alter striatal outputs to target areas involved in the control of movement.

Pediatric Cortical Dysplasia: Correlations between Neuroimaging, Electrophysiology and Location of Cytomegalic Neurons and Balloon Cells and Glutamate/GABA Synaptic Circuits

Seizures in cortical dysplasia (CD) could be from cytomegalic neurons and balloon cells acting as epileptic ‘pacemakers’, or abnormal neurotransmission. This study examined these hypotheses using in vitro electrophysiological techniques to determine intrinsic membrane properties and spontaneous glutamatergic and GABAergic synaptic activity for normal-pyramidal neurons, cytomegalic neurons and balloon cells from 67 neocortical sites originating from 43 CD patients (ages 0.2–14 years). Magnetic resonance imaging (MRI), 18fluoro-2-deoxyglucose positron emission tomography (FDG-PET) and electrocorticography graded cortical sample sites from least to worst CD abnormality. Results found that cytomegalic neurons and balloon cells were observed more frequently in areas of severe CD compared with mild or normal CD regions as assessed by FDG-PET/MRI. Cytomegalic neurons (but not balloon cells) correlated with the worst electrocorticography scores. Electrophysiological recordings demonstrated that cytomegalic and normal-pyramidal neurons displayed similar firing properties without intrinsic bursting. By contrast, balloon cells were electrically silent. Normal-pyramidal and cytomegalic neurons displayed decreased spontaneous glutamatergic synaptic activity in areas of severe FDG-PET/MRI abnormalities compared with normal regions, while GABAergic activity was unaltered. In CD, these findings indicate that cytomegalic neurons (but not balloon cells) might contribute to epileptogenesis, but are not likely to be ‘pacemaker’ cells capable of spontaneous paroxysmal depolarizations. Furthermore, there was more GABA relative to glutamate synaptic neurotransmission in areas of severe CD. Thus, in CD tissue alternate mechanisms of epileptogenesis should be considered, and we suggest that GABAergic synaptic circuits interacting with cytomegalic and normal-pyramidal neurons with immature receptor properties might contribute to seizure generation.

Use of cardiac biomarker testing in the emergency department.

IMPORTANCE Cardiac biomarker testing is not routinely indicated in the emergency department (ED) because of low utility and potential downstream harms from false-positive results. However, current rates of testing are unknown. OBJECTIVE To determine the use of cardiac biomarker testing overall, as well as stratified by disposition status and selected characteristics. DESIGN, SETTING, AND PARTICIPANTS Retrospective study of ED visits by adults (≥18 years old) selected from the 2009 and 2010 National Hospital Ambulatory Medical Care Survey, a probability sample of ED visits in the United States. EXPOSURES Selected patient, visit, and ED characteristics. MAIN OUTCOMES AND MEASURES Receipt of cardiac biomarker testing during the ED visit. RESULTS Of 44,448 ED visits, cardiac biomarkers were tested in 16.9% of visits, representing 28.6 million visits. Biomarker testing occurred in 8.2% of visits in the absence of acute coronary syndrome (ACS)-related symptoms, representing 8.5 million visits, almost one-third of all visits with biomarker testing. Among individuals subsequently hospitalized, cardiac biomarkers were tested in 47.0% of all visits. In this group, biomarkers were tested in 35.4% of visits despite the absence of ACS-related symptoms. Among all ED visits, the number of other tests or services performed was the strongest predictor of biomarker testing independent of symptoms of ACS. Compared with 0 to 5 other tests or services performed, more than 10 other tests or services performed was associated with 59.55 (95% CI, 39.23-90.40) times the odds of biomarker testing. The adjusted probabilities of biomarker testing if 0 to 5, 6 to 10, or more than 10 other tests or services performed were 6.3%, 34.3%, and 62.3%, respectively. CONCLUSIONS AND RELEVANCE Cardiac biomarker testing in the ED is common even among those without symptoms suggestive of ACS. Cardiac biomarker testing is also frequently used during visits with a high volume of other tests or services independent of the clinical presentation. More attention is needed to develop strategies for appropriate use of cardiac biomarkers.

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm

BackgroundEffective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date.MethodsThe source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard.ResultsThe electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date.ConclusionsA diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management.

Envisioning a Social-Health Information Exchange as a Platform to Support a Patient-Centered Medical Neighborhood: A Feasibility Study

ABSTRACTBACKGROUNDSocial determinants directly contribute to poorer health, and coordination between healthcare and community-based resources is pivotal to addressing these needs. However, our healthcare system remains poorly equipped to address social determinants of health. The potential of health information technology to bridge this gap across the delivery of healthcare and social services remains unrealized.OBJECTIVE, DESIGN, AND PARTICIPANTSWe conducted in-depth, in-person interviews with 50 healthcare and social service providers to determine the feasibility of a social-health information exchange (S-HIE) in an urban safety-net setting in Dallas County, Texas. After completion of interviews, we conducted a town hall meeting to identify desired functionalities for a S-HIE.APPROACHWe conducted thematic analysis of interview responses using the constant comparative method to explore perceptions about current communication and coordination across sectors, and barriers and enablers to S-HIE implementation. We sought participant confirmation of findings and conducted a forced-rank vote during the town hall to prioritize potential S-HIE functionalities.KEY RESULTSWe found that healthcare and social service providers perceived a need for improved information sharing, communication, and care coordination across sectors and were enthusiastic about the potential of a S-HIE, but shared many technical, legal, and ethical concerns around cross-sector information sharing. Desired technical S-HIE functionalities encompassed fairly simple transactional operations such as the ability to view basic demographic information, visit and referral data, and medical history from both healthcare and social service settings.CONCLUSIONSA S-HIE is an innovative and feasible approach to enabling better linkages between healthcare and social service providers. However, to develop S-HIEs in communities across the country, policy interventions are needed to standardize regulatory requirements, to foster increased IT capability and uptake among social service agencies, and to align healthcare and social service priorities to enable dissemination and broader adoption of this and similar IT initiatives.

Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: A systematic review

BACKGROUND Although timely treatment of sepsis improves outcomes, delays in administering evidence-based therapies are common. PURPOSE To determine whether automated real-time electronic sepsis alerts can: (1) accurately identify sepsis and (2) improve process measures and outcomes. DATA SOURCES We systematically searched MEDLINE, Embase, The Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature from database inception through June 27, 2014. STUDY SELECTION Included studies that empirically evaluated 1 or both of the prespecified objectives. DATA EXTRACTION Two independent reviewers extracted data and assessed the risk of bias. Diagnostic accuracy of sepsis identification was measured by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). Effectiveness was assessed by changes in sepsis care process measures and outcomes. DATA SYNTHESIS Of 1293 citations, 8 studies met inclusion criteria, 5 for the identification of sepsis (n = 35,423) and 5 for the effectiveness of sepsis alerts (n = 6894). Though definition of sepsis alert thresholds varied, most included systemic inflammatory response syndrome criteria ± evidence of shock. Diagnostic accuracy varied greatly, with PPV ranging from 20.5% to 53.8%, NPV 76.5% to 99.7%, LR+ 1.2 to 145.8, and LR- 0.06 to 0.86. There was modest evidence for improvement in process measures (ie, antibiotic escalation), but only among patients in non-critical care settings; there were no corresponding improvements in mortality or length of stay. Minimal data were reported on potential harms due to false positive alerts. CONCLUSIONS Automated sepsis alerts derived from electronic health data may improve care processes but tend to have poor PPV and do not improve mortality or length of stay.

An Evidence-Based Medicine Approach to Antihyperglycemic Therapy in Diabetes Mellitus to Overcome Overtreatment

Overtreatment is pervasive in medicine and leads to potential patient harms and excessive costs in health care. Although evidence-based medicine is often derided as practice by rote algorithmic medicine, the appropriate application of key evidence-based medicine principles in clinical decision making is fundamental to preventing overtreatment and promoting high-value, individualized patient-centered care. Specifically, this article discusses the importance of (1) using absolute rather than relative estimates of benefits to inform treatment decisions; (2) considering the time horizon to benefit of treatments; (3) balancing potential harms and benefits; and (4) using shared decision making by physicians to incorporate the patient’s values and preferences into treatment decisions. Here, we illustrate the application of these principles to considering the decision of whether or not to recommend intensive glycemic control to patients to minimize microvascular and cardiovascular complications in type 2 diabetes mellitus. Through this lens, this example will illustrate how an evidence-based medicine approach can be used to individualize glycemic goals and prevent overtreatment, and can serve as a template for applying evidence-based medicine to inform treatment decisions for other conditions to optimize health and individualize patient care.

Clinical Criteria to Identify Patients With Sepsis

Clinical Criteria to Identify Patients With Sepsis To the Editor Dr Seymour and colleagues1 assessed the predictive validity of various clinical criteria to identify patients with sepsis. However, their conclusion that the Sequential [Sepsisrelated] Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) scores are more clinically useful than the systemic inflammatory response syndrome (SIRS) criteria was primarily based on differences in the area under the receiver operating curve (AUROC), which has several limitations.2 First, differences in the AUROC may be of minimal clinical relevance. For example, although the American College of Cardiology/American Heart Association (ACC/AHA) and the Adult Treatment Panel III cardiovascular risk prediction models have similar AUROCs, the ACC/AHA model recommends statin therapy for more adults and better reclassifies patients according to their true risk (net reclassification index [NRI], 0.332).3,4 To better assess for clinically meaningful differences in the utility of different sepsis diagnostic criteria, the authors should report the overall categorical NRI (for ≥2 vs <2 points), event and nonevent NRIs, and risk reclassification tables. Second, Seymour and colleagues proposed implementing both the SOFA and qSOFA scores in practice by defining patients with scores of 2 points or greater as having “sepsis” and those with less than 2 points as not having sepsis. However, the reported AUROCs were calculated using continuous scores. Assessing the performance of each set of clinical criteria using the dichotomous categorization and reporting the positive and negative predictive values and corresponding likelihood ratios compared with the SIRS criteria would be more clinically relevant than reporting the differences in the AUROC or the relative difference in outcomes across deciles of baseline risk. Most important, it is unclear that the lower AUROC reflects inferior predictive validity of the SIRS criteria. Rather, the lower AUROC (and relative fold difference in outcomes) may reflect the influence of incorporation bias. The current standard of care for clinicians during the period of this study was to initiate early goal-directed therapy for patients with suspected sepsis based on SIRS criteria. Thus, the lower performance of SIRS for predicting mortality may simply be an artifact—patients diagnosed as having sepsis by 2 or more SIRS criteria were more likely to have received early goal-directed therapy and less likely to die as a consequence.5 Before the current diagnostic criteria for sepsis are replaced, we believe that more robust analyses are needed, including a prospective study of the utility of these various clinical criteria that would eliminate the influence of incorporation bias.