Knowing loved ones' end-of-life health care wishes: Attachment security predicts caregivers' accuracy.

Objective: At times, caregivers make life-and-death decisions for loved ones. Yet very little is known about the factors that make caregivers more or less accurate as surrogate decision makers for their loved ones. Previous research suggests that in low stress situations, individuals with high attachment-related anxiety are attentive to their relationship partners' wishes and concerns, but get overwhelmed by stressful situations. Individuals with high attachment-related avoidance are likely to avoid intimacy and stressful situations altogether. We hypothesized that both of these insecure attachment patterns limit surrogates' ability to process distressing information and should therefore be associated with lower accuracy in the stressful task of predicting their loved ones' end-of-life health care wishes. Method: Older patients visiting a medical clinic stated their preferences toward end-of-life health care in different health contexts, and surrogate decision makers independently predicted those preferences. For comparison purposes, surrogates also predicted patients' perceptions of everyday living conditions so that surrogates' accuracy of their loved ones' perceptions in nonstressful situations could be assessed. Results: Surrogates high on either type of insecure attachment dimension were less accurate in predicting their loved ones' end-of-life health care wishes. It is interesting to note that even though surrogates' attachment-related anxiety was associated with lower accuracy of end-of-life health care wishes of their loved ones, it was associated with higher accuracy in the nonstressful task of predicting their loved ones' everyday living conditions. Conclusions: Attachment orientation plays an important role in accuracy about loved ones' end-of-life health care wishes. Interventions may target emotion regulation strategies associated with insecure attachment orientations

Accuracy of ICD-9-CM Codes by Hospital Characteristics and Stroke Severity: Paul Coverdell National Acute Stroke Program

Background—Epidemiological and health services research often use International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes to identify patients with clinical conditions in administrative databases. We determined whether there are systematic variations between stroke patient clinical diagnoses and ICD‐9‐CM codes, stratified by hospital characteristics and stroke severity. Methods and Results—We used the records of patients discharged from hospitals participating in the Paul Coverdell National Acute Stroke Program in 2013. Within this stroke‐enriched cohort, we compared agreement between the attending physician\u27s clinical diagnosis and principal ICD‐9‐CM code and determined whether disagreements varied by hospital characteristics (presence of a stroke unit, stroke team, number of hospital beds, and hospital location). For patients with a documented National Institutes of Health Stroke Scale score at admission, we assessed whether diagnostic agreement varied by stroke severity. Agreement was generally high (\u3e 89%); differences between the physician diagnosis and ICD‐9‐CM codes were primarily attributed to discordance between ischemic stroke and transient ischemic attack (TIA), and subarachnoid and intracerebral hemorrhage. Agreement was higher for patients in metropolitan hospitals with stroke units, stroke teams, and \u3e 200 beds (all P \u3c 0.001). Agreement was lowest (60.3%) for rural hospitals with ≤ 200 beds and without stroke units or teams. Agreement was also lower for milder (94.9%) versus more‐severe (96.4%) ischemic strokes (P \u3c 0.001). Conclusions—We identified disagreements in stroke/TIA coding by hospital characteristics and stroke severity, particularly for milder ischemic strokes. Such systematic variations in ICD‐9‐CM coding practices can affect stroke case identification in epidemiological studies and may have implications for hospital‐level quality metric

A Delphi process to address medication appropriateness for older persons with multiple chronic conditions

BACKGROUND: Frameworks exist to evaluate the appropriateness of medication regimens for older patients with multiple medical conditions (MCCs). Less is known about how to translate the concepts of the frameworks into specific strategies to identify and remediate inappropriate regimens. METHODS: Modified Delphi method involving iterative rounds of input from panel members. Panelists (n = 9) represented the disciplines of nursing, medicine and pharmacy. Included among the physicians were two geriatricians, one general internist, one family practitioner, one cardiologist and two nephrologists. They participated in 3 rounds of web-based anonymous surveys. RESULTS: The panel reached consensus on a set of markers to identify problems with medication regimens, including patient/caregiver report of non-adherence, medication complexity, cognitive impairment, medications identified by expert opinion as inappropriate for older persons, excessively tight blood sugar and blood pressure control among persons with diabetes mellitus, patient/caregiver report of adverse medication effects or medications not achieving desired outcomes, and total number of medications. The panel also reached consensus on approaches to address these problems, including endorsement of strategies to discontinue medications with known benefit if necessary because of problems with feasibility or lack of alignment with patient goals. CONCLUSIONS: The results of the Delphi process provide the basis for an algorithm to improve medication regimens among older persons with MCCs. The algorithm will require assessment not only of medications and diagnoses but also cognition and social support, and it will support discontinuation of medications both when risks outweigh benefits and when regimens are not feasible or do not align with goals

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6–7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160–167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287–294 cM, including D2), chromosome 5 (p-value < 0.001 for 0–7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7–14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage

Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods.

One of the objectives of the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org/) is to establish a national longitudinal cohort of 9 and 10 year olds that will be followed for 10 years in order to prospectively study the risk and protective factors influencing substance use and its consequences, examine the impact of substance use on neurocognitive, health and psychosocial outcomes, and to understand the relationship between substance use and psychopathology. This article provides an overview of the ABCD Study Substance Use Workgroup, provides the goals for the workgroup, rationale for the substance use battery, and includes details on the substance use module methods and measurement tools used during baseline, 6-month and 1-year follow-up assessment time-points. Prospective, longitudinal assessment of these substance use domains over a period of ten years in a nationwide sample of youth presents an unprecedented opportunity to further understand the timing and interactive relationships between substance use and neurocognitive, health, and psychopathology outcomes in youth living in the United States

Choosing a physician depends on how you want to feel: The role of ideal affect in health-related decision making

When given a choice, how do people decide which physician to select? Although significant research has demonstrated that how people actually feel (their “actual affect”) influences their health care preferences, how people ideally want to feel (their “ideal affect”) may play an even greater role. Specifically, we predicted that people trust physicians whose affective characteristics match their ideal affect, which leads people to prefer those physicians more. Consistent with this prediction, the more participants wanted to feel high arousal positive states on average ([ideal HAP]; e.g., excited), the more likely they were to select a HAP-focused physician. Similarly, the more people wanted to feel low arousal positive states on average ([ideal LAP]; e.g., calm), the more likely they were to select a LAP-focused physician. Also as predicted, these links were mediated by perceived physician trustworthiness. Notably, while participants’ ideal affect predicted physician preference, actual affect (how much people actually felt HAP and LAP on average) did not. These findings suggest that people base even serious decisions on how they want to feel and highlight the importance of considering ideal affect in models of decision making, person perception, and patient physician communication

Methods for detecting gene × gene interaction in multiplex extended pedigrees

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem

Methods for detecting gene × gene interaction in multiplex extended pedigrees

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem

Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe