Person:

Cho, Kelly

The growing understanding of the use of biomarkers in Alzheimer's disease (AD) may enable physicians to make more accurate and timely diagnoses. Florbetaben, a beta-amyloid tracer used with positron emission tomography (PET), is one of these diagnostic biomarkers. This analysis was undertaken to explore the potential value of florbetaben PET in the diagnosis of AD among patients with suspected dementia and to identify key data that are needed to further substantiate its value. A discrete event simulation was developed to conduct exploratory analyses from both US payer and societal perspectives. The model simulates the lifetime course of disease progression for individuals, evaluating the impact of their patient management from initial diagnostic work-up to final diagnosis. Model inputs were obtained from specific analyses of a large longitudinal dataset from the New England Veterans Healthcare System and supplemented with data from public data sources and assumptions. The analyses indicate that florbetaben PET has the potential to improve patient outcomes and reduce costs under certain scenarios. Key data on the use of florbetaben PET, such as its influence on time to confirmation of final diagnosis, treatment uptake, and treatment persistency, are unavailable and would be required to confirm its value.

Background: Patterns of methicillin-resistant S. aureus (MRSA) nasal carriage over time and across the continuum of care settings are poorly characterized. Knowledge of prevalence rates and outcomes associated with MRSA nasal carriage patterns could help direct infection prevention strategies. The VA integrated health-care system and active surveillance program provides an opportunity to delineate nasal carriage patterns and associated outcomes of death, infection, and conversion in carriage. Methods/Findings: We conducted a retrospective cohort study including all patients admitted to 5 acute care VA hospitals between 2008–2010 who had nasal MRSA PCR testing within 48 hours of admission and repeat testing within 30 days. The PCR results were used to define a baseline nasal carriage pattern of never, intermittently, or always colonized at 30 days from admission. Follow-up was up to two years and included acute, long-term, and outpatient care visits. Among 18,038 patients, 91.1%, 4.4%, and 4.6% were never, intermittently, or always colonized at the 30-day baseline. Compared to non-colonized patients, those who were persistently colonized had an increased risk of death (HR 2.58; 95% CI 2.18;3.05) and MRSA infection (HR 10.89; 95% CI 8.6;13.7). Being in the non-colonized group at 30 days had a predictive value of 87% for being non-colonized at 1 year. Conversion to MRSA colonized at 6 months occurred in 11.8% of initially non-colonized patients. Age >70 years, long-term care, antibiotic exposure, and diabetes identified >95% of converters. Conclusions: The vast majority of patients are not nasally colonized with MRSA at 30 days from acute hospital admission. Conversion from non-carriage is infrequent and can be risk-stratified. A positive carriage pattern is strongly associated with infection and death. Active surveillance programs in the year following carriage pattern designation could be tailored to focus on non-colonized patients who are at high risk for conversion, reducing universal screening burden.

Growing evidence suggests that Alzheimer's disease and other types of dementia are underdiagnosed and poorly documented. In our study, we describe patterns of dementia coding and treatment in the Veteran's Administration New England Healthcare System. We conducted a retrospective cohort study with new outpatient ICD-9 codes for several types of dementia between 2002 and 2009. We examined healthcare utilization, medication use, initial dementia diagnoses, and changes in diagnoses over time by provider type. 8,999 veterans received new dementia diagnoses during the study period. Only 18.3% received a code for cognitive impairment other than dementia, most often “memory loss” (65.2%) prior to dementia diagnosis. Two-thirds of patients received their initial code from a PCP. The etiology of dementia was often never specified by ICD-9 code, even by specialists. Patients followed up exclusively by PCPs had lower rates of neuroimaging and were less likely to receive dementia medication. Emergency room visits and hospitalizations were frequent in all patients but highest in those seen by dementia specialists. Dementia medications are commonly used off-label. Our results suggest that, for the majority the patients, no prodrome of the dementia syndrome is documented with diagnostic code, and patients who do not see dementia specialists have less extensive diagnostic assessment and treatment.

Language impairment (LI) and reading disability (RD) are common pediatric neurobehavioral disorders that frequently co-occur, suggesting they share etiological determinants. Recently, our group identified prenatal nicotine exposure as a factor for RD and poor reading performance. Using smoking questionnaire and language data from the Avon Longitudinal Study of Parents and Children, we first determined if this risk could be expanded to other communication disorders by evaluating whether prenatal nicotine exposure increases risk for LI and poor performance on language tasks. Prenatal nicotine exposure increased LI risk (OR = 1.60; p = 0.0305) in a dose-response fashion with low (OR = 1.25; p = 0.1202) and high (OR = 3.84; p = 0.0002) exposures. Next, hypothesizing that the effects of prenatal nicotine may also implicate genes that function in nicotine related pathways, we determined whether known nicotine dependence (ND) genes associate with performance on language tasks. We assessed the association of 33 variants previously implicated in ND with LI and language abilities, finding association between ANKK1/DRD2 and performance on language tasks (p≤0.0003). The associations of markers within ANKK1 were replicated in a separate LI case-control cohort (p<0.05). Our results show that smoking during pregnancy increases the risk for LI and poor performance on language tasks and that ANKK1/DRD2 contributes to language performance. More precisely, these findings suggest that prenatal environmental factors influence in utero development of neural circuits vital to language. Our association of ANKK1/DRD2 further implicates the role of nicotine-related pathways and dopamine signaling in language processing, particularly in comprehension and phonological memory.

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping using EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures reducing the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 days if all data are available; however, the timing is largely dependent on the chart review step which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes/no).