Nicole R. Fowler

Genetic Risk Score Predicts Late-Life Cognitive Impairment.

Introduction. A family history of Alzheimers disease is a significant risk factor for its onset, but the genetic risk associated with possessing multiple risk alleles is still poorly understood. Methods. In a sample of 95 older adults (Mean age = 75.1, 64.2% female), we constructed a genetic risk score based on the accumulation of risk alleles in BDNF, COMT, and APOE. A neuropsychological evaluation and consensus determined cognitive status (44 nonimpaired, 51 impaired). Logistic regression was performed to determine whether the genetic risk score predicted cognitive impairment above and beyond that associated with each gene. Results. An increased genetic risk score was associated with a nearly 4-fold increased risk of cognitive impairment (OR = 3.824, P = .013) when including the individual gene polymorphisms as covariates in the model. Discussion. A risk score combining multiple genetic influences may be more useful in predicting late-life cognitive impairment than individual polymorphisms.

Adherence and Tolerability of Alzheimer's Disease Medications: A Pragmatic Randomized Trial

Post‐marketing comparative trials describe medication use patterns in diverse, real‐world populations. Our objective was to determine if differences in rates of adherence and tolerability exist among new users to acetylcholinesterase inhibitors (AChEIs).

Racial and Ethnic Differences in Initiation and Discontinuation of Antidementia Drugs by Medicare Beneficiaries.

To examine racial and ethnic differences in initiation and time to discontinuation of antidementia medication in Medicare beneficiaries.

Assessing patient risk of central line-associated bacteremia via machine learning

HighlightsMachine learning is being increasingly used in healthcare to predict risk.Its models offer avoidance of bias, personalization, and a nonlinear approach.We describe the development of a model to predict CLABSI, yielding an AUROC of 0.82.Infection preventionists use this model to target interventions to high‐risk patients to save time. Background: Central line‐associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. Methods: A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. Results: Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best‐performing model was 0.82 in production. Discussion: This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost‐effective but otherwise time‐limited interventions. Conclusions: Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection.

Social Network Size and Cranial Magnetic Resonance Imaging Findings in Older Adults: The Cardiovascular Health Study

onance imaging with voxel-based morphometry); apparent cardiac, gastrointestinal, or prostatic disease; and taking drugs that might affect the DAT scan or MIBG test. Head-up tilt, bowel, urodynamic, polysomnographic, and cognitive tests were performed to the extent possible. The smell test was not performed. All participants and their families provided informed consent before participation in the study. Of 1,500 outpatients, only four fulfilled the above criteria. Most were referred from local general physicians to explore neurological etiologies of syncope, constipation, RBD, and memory disorder. They were uniformly elderly (mean age 78, range 66–82); three were male, and one was female. All could walk independently. Their neurological diagnoses were pure autonomic failure (n = 1), constipation with RBD (n = 1), RBD (n = 1), and mild cognitive impairment (n = 1) according to clinical and laboratory manifestations (Table 1). All had an abnormal MIBG test, but none had an abnormal DAT scan except for case one (mild decrease in the left striatum; no tremor, rigidity, or akinesia on the right side of the body). It is not well known which neuroimaging marker is useful to identify the premotor phase of PD. A DAT scan can detect hyposmia, postural hypotension, RBD, and possibly constipation in situ in premotor PD. The MIBG test can detect mild memory disorder, constipation, postural hypotension, and RBD in situ. There is no large report to compare sensitivity and specificity of the DAT scan and MIBG test in individuals with premotor PD. Pathologically, it has been shown that degeneration and Lewy neurites can appear earlier in the myenteric and cardiac plexus than in the brain. Considering the present study results, the MIBG test can identify premotor PD during a negative DAT scan. Common nonmotor PD features such as postural hypotension, constipation, RBD, and mild memory impairment, together with the MIBG test, may provide a window of opportunity to identify cases of PD in the very early phase, but because this was a pilot study with a small number of participants, confirmatory studies are needed. In conclusion, MIBG myocardial scintigraphy identified premotor PD during a negative DAT scan.

One-Year Effect of the Medicare Annual Wellness Visit on Detection of Cognitive Impairment: A Cohort Study: Medicare Detection of Cognitive Impairment

To examine the effect of the Medicare Annual Wellness Visit (AWV) on the detection of cognitive impairment and on follow‐up cognitive care for older adults.

Improving Nursing Facility Care Through an Innovative Payment Demonstration Project: Optimizing Patient Transfers, Impacting Medical Quality, and Improving Symptoms: Transforming Institutional Care Phase 2: Implementation of nursing facility payment model

Optimizing Patient Transfers, Impacting Medical Quality, and Improving Symptoms: Transforming Institutional Care (OPTIMISTIC) is a 2‐phase Center for Medicare and Medicaid Innovations demonstration project now testing a novel Medicare Part B payment model for nursing facilities and practitioners in 40 Indiana nursing facilities. The new payment codes are intended to promote high‐quality care in place for acutely ill long‐stay residents. The focus of the initiative is to reduce hospitalizations through the diagnosis and on‐site management of 6 common acute clinical conditions (linked to a majority of potentially avoidable hospitalizations of nursing facility residents ): pneumonia, urinary tract infection, skin infection, heart failure, chronic obstructive pulmonary disease or asthma, and dehydration. This article describes the OPTIMISTIC Phase 2 model design, nursing facility and practitioner recruitment and training, and early experiences implementing new Medicare payment codes for nursing facilities and practitioners. Lessons learned from the OPTIMISTIC experience may be useful to others engaged in multicomponent quality improvement initiatives.

Patient characteristics associated with screening positive for Alzheimer’s disease and related dementia

Introduction Screening all older adults for Alzheimer’s disease and related dementias (ADRD) in primary care may not be acceptable or feasible. The goal of this study was to identify factors that could optimize screening in primary care and enhance its feasibility. Methods This is a cross-sectional study in rural, suburban, and urban primary care practices in Indiana. A total of 1,723 patients ≥65 years of age were screened for ADRD using the Memory Impairment Screen. Logistic regression was used to identify patient-specific factors associated with screening positive for ADRD. Results The positive screening rate was 4.9%. Rates varied significantly across the three study sites. The rural site had the lowest rate (2.8%), which was significantly lower than the rates at the suburban (5.6%) and urban (6.6%) sites (P<0.01). Patient age, sex, and education were significantly (P<0.05) associated with screening positive for ADRD. Conclusion Targeted screening of patients at risk for ADRD may represent a more optimal and feasible screening alternative to population screening.

I'm Not Sure We Had A Choice?: Decision Quality and The Use of Cardiac Implantable Electronic Devices In Older Adults With Cognitive Impairment

Background The decision to implant a cardiac device in a person with Alzheimer’s disease or related dementia requires considering the possible trade-offs of quality of life (QOL) and quantity of life. This study measured the decision-making experience of patients with and without cognitive impairment (CI) who received a cardiac device and their family members who were involved in the decision. Methods and Results Semi-structured interviews and questionnaires were administered with 15 patient-family member dyads. Interviews revealed few conversations between physicians, patients and family members about the patient’s cognitive status or about the benefits, risks, and long-term implications of the device for someone with CI. Participants largely stated that the decision to get the device was based on the patient’s functional status at the time of the implant, and not on expectations about future functioning. Patients with CI had more regret, measured with the Decision Regret Scale (DRS), (p=0.037) and family members of patients without CI reported more decisional conflict, measured with the Decisional Conflict Scale (p=0.057). Conclusions Although CI impacts life expectancy and QOL, cognitive status was largely not discussed prior to device implant. Few differences were found between the experiences of dyads that included patients with or without CI.

Supporting Family Decision Makers for Nursing Home Residents: A Promising Approach

Acting as a surrogate decision maker for a family member with dementia is one of the most difficult aspects of being a caregiver.1 In this issue, Hanson et al2 present the results of a randomized trial testing a decision aid that aims to both improve the quality of communication for goals of care for surrogate decision makers and to increase the delivery of palliative care for residents with advanced dementia. This investigation involved over 300 dyads of long-stay residents with advanced dementia and their family decision makers in 22 diverse nursing homes. The intervention was conducted at the nursing home level; it involved enrolled family decision makers viewing an 18-minute goals of care video decision aid and the nursing staff at that facility receiving a 1-hour training on the aid and principles for family communication. Following the delivery of the goals of care video and staff training, the intervention facilities were prompted to meet with family decision makers. The authors separately report that 69% of structured communications between staff and family occurred within 3 months of family viewing the video.3 Family decision makers and staff in the control arm received an informational video on interactions with people with dementia and a 45-minute training session on the study procedures. The results from this important study suggest that the decision aid intervention enhanced family decision makers’ quality of communication at both 3 months and 9 months postintervention—primarily driven by better communication about end-of-life care. In addition, the intervention group demonstrated increased goal concordance between family decision makers and providers at 9 months postintervention. While the authors describe this low-intensity study as an efficacy trial of a decision support tool, it’s important to note that the effects of the intervention appear to remain over time. Aside from the results of the primary analysis, this study has important implications for overall quality of care for nursing home residents. Key quality indicators, such as readmission rates, measured by regulatory bodies and payers, such as State Departments of Health and Centers for Medicare and Medicaid Services are necessary targets for interventions that are implemented in this setting. Hanson and colleagues2 selected relevant secondary outcomes for their trial, including the rate of hospital transfer. The authors found that hospital transfer rates of patients in the intervention arm were reduced by half. A considerable number of transfers of long stay nursing facility patients are thought to be potentially avoidable and put residents at risk of adverse events.4 Reduction of unnecessary hospital transfers among nursing homes residents is an important patient-centered outcome—such transfers represent real burden and stress for these frail patients and for their families. In addition to publicly reported quality metrics, nursing homes will soon begin facing financial penalties for readmissions, similar to the hospital readmission penalties.5 Moving the needle of reducing avoidable hospital transfers is of interest to clinicians, policymakers, and nursing facility administrators. Use of a structured tool to frame and record care preferences is key. Hanson et al2 found that residents in the intervention group were more likely to have a completed POLST. The POLST (http://www.polst.org), has been shown to reduce hospitalizations and increase the likelihood of receiving care that is consistent with resident preferences for care.6 While Hanson and colleagues’2 decision support tool was primarily designed to improve communication, and not explicitly aimed at reducing utilization, the results are consistent with previous research that families of people with advanced dementia choose care focused on function and comfort when given the opportunity for proactive care planning. “Care consistency with documented preferences” is one of the 10 hospice and palliative care quality measures identified by the Measuring What Matters project.7 There are, however, a dearth of validated tools and strategies available for adoption by healthsystemsandhealthcareproviderstocapturethiskeyqualitymetric.Forthisgoalsofcarevideotrial, theresearchteamused an Advance Care Planning Problem Score8 to measure whether care was concordant with patient wishes. Based on the use of this tool, the authors report that “discussion of residents’ wishes to guide treatment was relatively infrequent.” The ability of people with moderate to advanced dementia to actively participate in care planning around serious medical decisions and express clear preferences for future treatment is limited by their disease. Thus, family surrogate decision makers must rely on their knowledge of the resident’s previous wishes or decisions and their belief of whatis inthebestinterestoftheresident.Toolsthatmeasurecare consistencywithdocumentedpreferences,expressedbypatients or their surrogates, are needed to reflect quality of care for this population.9 The authors specifically state that the goals of care intervention was designed for practical implementation in the nursing home setting. Nursing homes provide care for patients with increasingly complex medical conditions as well as high numbers of patients near the end of life. It is a setting of care, however, that is plagued by inconsistent quality and high turnover of both leadership and front line staff.10 Inadequate physician and other medical provider presence has also been cited as a contributor to reduced quality of care and increased hospital transfers. It is disappointing, although not surprising, that the authors found that only 1 in 4 of all family deRelated article page 24 Research Original Investigation The Goals of Care Intervention for Advanced Dementia