Citations

Background: The decline in cardiovascular disease (CVD) mortality in the United States noted since the mid 1970s has recently leveled off. This has generated concern and a need for further analyses to understand geographic variation, cause-specific deaths, location of death, and age-specific trends. Methods: Death certificate data obtained from the Minnesota Department of Health were used for analysis. The American Heart Association (AHA) classification was applied to the underlying cause of death to generate the following CVD categories: coronary heart disease (CHD), non-CHD diseases of the heart, and non-cardiac circulatory diseases. Temporal trends in death rates were explored by negative binomial regression, which enables estimates of age- and sex-adjusted average annual percentage changes (AAPCs). Differences in AAPCs between 2000-2009 and 2010-2017 were tested overall and across demographic, CVD, and location of death categories. Results: CVD mortality rates, which were declining in 2000-2009, have leveled off in 2010-2017 (Table). These trends were observed across all subgroups. After adjusting for sex, the age group that drove the decline in rates from 2000-2009 was the 65-84 year old group, which experienced no change in rates from 2010-2017. Across CVD categories, after adjusting for age and sex, the decline in rates from 2000-2009 was largely attributable to steep reductions in CHD and non-cardiac circulatory diseases, with lesser reduction in non-CHD diseases of the heart. In 2010-2017, CHD and non-cardiac circulatory diseases rates showed no change, whereas the rate of non-CHD diseases of the heart increased. The rates for in- and out-of-hospital deaths improved in 2000-2009 with no further improvement observed in 2010-2017. Conclusions: CVD mortality rates are no longer declining in Minnesota, paralleling national trends. These trends are particularly concerning for non-CHD diseases of the heart.

Background: Accurate identification of stroke cases from electronic health record (EHR) is needed for efficient and valid clinical and epidemiology research. A number of studies have evaluated the validity of using ICD-9 and ICD-10 codes for stroke identification. Previous research results show that sensitivity and positive predictive value (PPV) for stroke identification is the lowest among all other cerebrovascular diseases, with notable differences observed by stroke pathological subtypes. While most prevalent cerebrovascular disease cases can be detected using 430-438/I60-I69 collectively, more accurate and comprehensive stroke phenotyping algorithm is needed to identify incident stroke cases from EHR. In our work, we compared case identification results using ICD-codes exclusively with ICD plus a natural language processing (NLP) algorithm. Methods: We developed the NLP part of our stroke algorithm using a list of expert provided stroke-related keywords, which covers transient ischemic attack (TIA), ischemic stroke, and hemorrhagic stroke. We validated the hybrid (ICD+NLP) algorithm and compared it with ICD-exclusive algorithm in a previously established atrial fibrillation cohort (n=5,062). Clinical notes and ICD codes of all patients after the incident AF event were reviewed by two nurse abstracters to confirm new stroke incidences. All past clinical notes and ICD codes of a subset of patients (n=402) were reviewed to confirm lifetime (prior and current) stroke cases. Manual abstraction results were considered the gold standard for evaluation of ICD only and ICD+NLP automatic extraction. Sensitivity and positive predictive values (PPV) of both algorithms were calculated. Results: Among 5,062 patients, 593 patients were confirmed to have suffered a stroke after atrial fibrillation while 4,469 patients were confirmed with no EHR evidence of stroke after AF. The ICD-exclusive algorithm had a sensitivity of 47.2% and a PPV of 47.8% for detecting new stroke incidences. The hybrid stroke algorithm achieved a sensitivity of 92.4% and a PPV of 60.6% for extraction of incident stroke. For extraction of lifetime stroke cases, the hybrid approach achieved a sensitivity of 93.9% and a PPV of 88.7%. Performance of new stroke incidence extraction is limited because past/current stroke mentions in clinical notes are difficult to distinguish by our NLP algorithm (may be a future direction). Conclusions: We developed and validated a stroke algorithm that performed well for identifying incident and lifetime stroke cases. The addition of NLP into the stroke algorithm improved the sensitivity and PPV of compared to an ICD-exclusive algorithm.

Objective To demonstrate DNA sequencing analysis (DNAsa) of sinus cultures in patients with CRS is a reliable method of detecting pathogens in polymicrobial CRS infections. Methods After obtaining Institutional Review Board approval for this prospective cohort study, we selected a random sample of 50 patients with CRS at Medstar Georgetown University Hospital between September 2016 and March 2017. We defined CRS as a history of rhinosinusitis refractory to maximal medical therapy and prior endoscopic sinus surgery. Patients demonstrating active purulence in a sinus cavity were prospectively selected to undergo standard hospital cultures (SHC) and DNAsa cultures. Organisms identified in both methods were compared for each patient. Results Specimens were obtained from 29 female and 16 male patients with a mean age of 50 years. A total of 45 cultures were included in our final analysis; five cultures were excluded after inappropriate laboratory processing. Results from these patients were compared and analyzed. Cohen's weighted kappa analysis showed agreement between the two testing methods in identifying predominant microorganisms. DNAsa detected 31.9% more microorganisms compared to SHC ( P < 0.05). When multiple microorganisms were detected, DNAsa yielded more positive results compared to SHC ( P < 0.05). Conclusions DNAsa detects all microorganisms identified by SHC as well as predominant microorganisms not detected by SHC. Thus molecular pathogen identification may be more reliable for identifying multiple microorganisms as compared to standard culture techniques that identify only one or two microorganisms. In recalcitrant cases of CRS, DNAsa may provide better guidance in selection of appropriate antimicrobial treatment.

We investigated the effect of candidate variants in AS3MT (arsenic (III) methyltransferase) with urinary arsenic metabolites and their principal components in a subset of 264 participants in the Multi-Ethnic Study of Atherosclerosis (MESA). Urinary arsenic species, including inorganic arsenic (iAs), monomethylarsonate (MMA), dimethylarsinate (DMA), and arsenobetaine (Ab), were measured using high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICPMS) and corrected for organic sources from seafood consumption by regressing Ab on arsenic species using a validated method. Principal components of arsenic metabolism were also used as independent phenotypes. We conducted linear regression of arsenic traits with allelic dosage of candidate single nucleotide polymorphisms (SNPs) rs12768205 (G > A), rs3740394 (A > G), and rs3740393 (G > C) measured using Illumina MetaboChip. Models were stratified by non-Hispanic white vs. all other race/ethnicity and adjusted for age, sex, arsenic exposure, study site, and population stratification. Consistent with previous studies, rs12768205 showed evidence for strongest association (non-Hispanic white: iAs% −0.14 (P 0.83), MMA% −0.66 (0.49), DMA% 0.81(0.49); other race/ethnicity: 0.13 (0.71), −1.21 (0.09), 1.08 (0.20)). No association, however, passed the strict Bonferroni p-value. This was a novel study among an ethnically diverse population exposed to low arsenic levels.

Objective To determine if hepatocyte growth factor (HGF), a promising biomarker of coronary heart disease (CHD) given its release into circulation in response to endothelial damage, is associated with subclinical and clinical CHD in a racial/ethnic diverse population. Methods HGF was measured in 6738 participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Highest mean HGF values (pg/mL) were observed in Hispanic, followed by African, non-Hispanic white, then Chinese Americans. Results In all races/ethnicities, HGF levels were associated with older age, higher systolic blood pressure (SBP) and body mass index, lower high-density lipoprotein, diabetes and current smoking. In fully adjusted models, each SD higher HGF was associated with an average increase in coronary artery calcium (CAC) of 55 Agatston units for non-Hispanic whites (p<0.001) and 51 Agatston units for African-Americans (p=0.007) but was not in the other race/ethnic groups (interaction p=0.02). There were 529 incident CHD events, and CHD risk was 41% higher in African (p<0.001), 17% in non-Hispanic white (p=0.026) and Chinese (p=0.36), and 6% in Hispanic Americans (p=0.56) per SD increase in HGF. Conclusion In a large and diverse population-based cohort, we report that HGF is associated with subclinical and incident CHD. We demonstrate evidence of racial/ethnic heterogeneity within these associations, as the results are most compelling in African-Americans and non-Hispanic white Americans. We provide evidence that HGF is a biomarker of atherosclerotic disease that is independent of traditional risk factors.

Advances in pharmacogenomics (PGx) have the potential to transform healthcare by allowing precision medicine to become a reality. However, PGx knowledge is new, complex, and evolving, and relying on the cognition of clinicians alone is insufficient for clinical implementation. Integrating clinical decision support (CDS) tools in the electronic health record (EHR) is critical for translating PGx into clinical practice. Herein, we review current strategies to implement PGx using EHR‐CDS functionalities.