Risk Factors for Alzheimer's Disease and Related Dementia Diagnoses in American Indians.

The burden of Alzheimer's disease and related dementias (ADRD) has increased substantially in the United States, particularly in health disparity populations. Little is known about the epidemiology of ADRD in American Indian (AI) adults, although they have a high prevalence of ADRD risk factors including hypertension, diabetes, obesity, and smoking. Using electronic health records from a large health care organization during 2016-18, we describe characteristics of AI patients aged ≥55 years with and without an ADRD diagnosis, assess ADRD risk factors and contrast findings with results from age- and sex-matched non-Hispanic White (NHW) patients. To identify factors associated with ADRD diagnoses, we estimated population-averaged prevalence rate ratios to approximate relative risk (RR) using generalized estimating equations models adjusted for age, sex, and marital and rural residency status. The age-adjusted prevalence of ADRD diagnosis was 6.6% of AI patients, compared with 4.4% in NHW patients. Patient age and diagnosis of hypertension, depression, hyperlipidemia, or diabetes were significantly associated with higher risk of ADRD diagnosis in AIs (RR range: 1.1-2.8) whereas female sex or being married/having a partner were associated with lower risk of ADRD diagnosis (each RR=.7). ADRD risk factors were generally similar between AI and NHW patients, except for sex and marital status. However, the adjusted risk of ADRD was approximately 49% higher in AI patients. To our knowledge, our study is the first to examine ADRD diagnoses and comorbidities in AIs across a large geographical region in southwest United States. Future efforts to confirm our findings in diverse AI communities are warranted.

Adoption of a clinical pharmacogenomics implementation program during outpatient care--initial results of the University of Chicago "1,200 Patients Project".

Pharmacogenomic testing is viewed as an integral part of precision medicine. To achieve this, we originated The 1,200 Patients Project which offers broad, preemptive pharmacogenomic testing to patients at our institution. We analyzed enrollment, genotype, and encounter-level data from the first year of implementation to assess utility of providing pharmacogenomic results. Results were delivered via a genomic prescribing system (GPS) in the form of traffic lights: green (favorable), yellow (caution), and red (high risk). Additional supporting information was provided as a virtual pharmacogenomic consult, including citation to relevant publications. Currently, 812 patients have participated, representing 90% of those approached; 608 have been successfully genotyped across a custom array. A total of 268 clinic encounters have occurred at which results were accessible via the GPS. At 86% of visits, physicians accessed the GPS, receiving 367 result signals for medications patients were taking: 57% green lights, 41% yellow lights, and 1.4% red lights. Physician click frequencies to obtain clinical details about alerts varied according to color severity (100% of red were clicked, 72% yellow, 20% green). For 85% of visits, clinical pharmacogenomic information was available for at least one drug the patient was taking, suggesting relevance of the delivered information. We successfully implemented an individualized health care model of preemptive pharmacogenomic testing, delivering results along with pharmacogenomic decision support. Patient interest was robust, physician adoption of information was high, and results were routinely utilized. Ongoing examination of a larger number of clinic encounters and inclusion of more physicians and patients is warranted.

COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling.

Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10(-7). Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p<0.004). Therefore, we suggest that network topology is a scalable and generalizable method predictive of hospitalization.