Triglyceride and lipoprotein (a) are markers of coronary artery disease severity among postmenopausal women

OBJECTIVE

After menopause, some women manifest coronary artery disease (CAD) with highly variable angiographic severity. For these women, postmenopausal appearing of some CAD risk factors may have differently influenced the CAD risk and severity. In this study, we attempt to unravel differences in the frequency or intensity of CAD risk factors among postmenopausal women with different angiographic severity.

METHODS

We studied 182 postmenopausal women (64+/-6 years) who underwent coronary angiography to investigate thoracic pain. Subjects with no detectable coronary lesions at angiography were recruited to the non-obstructive group and patients with CAD were grouped in one-vessel or multi-vessel groups. We compared clinical variables as the body mass index (BMI), age at menopause, age, hypertension, diabetes and cigarette smoking, and lipid measurements as plasma levels of total cholesterol, triglyceride, low-density lipoprotein cholesterol, very low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, apolipoprotein (apo) A1, apo B and lipoprotein(a) (Lp(a)).

RESULTS

Comparing to the non-obstructive group, Lp(a) was twofold higher in the one-vessel group and threefold higher in the multi-vessel group and triglycerides were 34% higher in the one-vessel group and 50% higher in the multi-vessel group. No further difference was found among the three groups. After multivariate logistic regression analysis, triglyceride (odds ratio: 1.01; P=0.0013) and Lp(a) (odds ratio: 1.006; P<0.0001) were independently indicative of the presence of obstructive CAD.

CONCLUSIONS

We found that both Lp(a) and triglycerides constitute useful markers of CAD severity among postmenopausal women.

Comparing models of delivery for cancer genetics services among patients receiving primary care who meet criteria for genetic evaluation in two healthcare systems: BRIDGE randomized controlled trial

BACKGROUND

Advances in genetics and sequencing technologies are enabling the identification of more individuals with inherited cancer susceptibility who could benefit from tailored screening and prevention recommendations. While cancer family history information is used in primary care settings to identify unaffected patients who could benefit from a cancer genetics evaluation, this information is underutilized. System-level population health management strategies are needed to assist health care systems in identifying patients who may benefit from genetic services. In addition, because of the limited number of trained genetics specialists and increasing patient volume, the development of innovative and sustainable approaches to delivering cancer genetic services is essential.

METHODS

We are conducting a randomized controlled trial, entitled Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE), to address these needs. The trial is comparing uptake of genetic counseling, uptake of genetic testing, and patient adherence to management recommendations for automated, patient-directed versus enhanced standard of care cancer genetics services delivery models. An algorithm-based system that utilizes structured cancer family history data available in the electronic health record (EHR) is used to identify unaffected patients who receive primary care at the study sites and meet current guidelines for cancer genetic testing. We are enrolling eligible patients at two healthcare systems (University of Utah Health and New York University Langone Health) through outreach to a randomly selected sample of 2780 eligible patients in the two sites, with 1:1 randomization to the genetic services delivery arms within sites. Study outcomes are assessed through genetics clinic records, EHR, and two follow-up questionnaires at 4 weeks and 12 months after last genetic counseling contactpre-test genetic counseling.

DISCUSSION

BRIDGE is being conducted in two healthcare systems with different clinical structures and patient populations. Innovative aspects of the trial include a randomized comparison of a chatbot-based genetic services delivery model to standard of care, as well as identification of at-risk individuals through a sustainable EHR-based system. The findings from the BRIDGE trial will advance the state of the science in identification of unaffected patients with inherited cancer susceptibility and delivery of genetic services to those patients.

TRIAL REGISTRATION

BRIDGE is registered as NCT03985852 . The trial was registered on June 6, 2019 at clinicaltrials.gov .

DMP3: A DYNAMIC MULTILAYER PERCEPTRON CONSTRUCTION ALGORITHM

This paper presents DMP3 (Dynamic Multilayer Perceptron 3), a multilayer perceptron (MLP) constructive training method that constructs MLPs by incrementally adding network elements of varying complexity to the network. DMP3 differs from other MLP construction techniques in several important ways, and the motivation for these differences are given. Information gain rather than error minimization is used to guide the growth of the network, which increases the utility of newly added network elements and decreases the likelihood that a premature dead end in the growth of the network will occur. The generalization performance of DMP3 is compared with that of several other well-known machine learning and neural network learning algorithms on nine real world data sets. Simulation results show that DMP3 performs better (on average) than any of the other algorithms on the data sets tested. The main reasons for this result are discussed in detail.

Self-reported adherence and reasons for nonadherence among patients with low proportion of days covered for antihypertension medications

BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.

A Behavioral Economics-Electronic Health Record Module to Promote Appropriate Diabetes Management in Older Adults: Protocol for a Pragmatic Cluster Randomized Controlled Trial

Background

The integration of behavioral economics (BE) principles and electronic health records (EHRs) using clinical decision support (CDS) tools is a novel approach to improving health outcomes. Meanwhile, the American Geriatrics Society has created the Choosing Wisely (CW) initiative to promote less aggressive glycemic targets and reduction in pharmacologic therapy in older adults with type 2 diabetes mellitus. To date, few studies have shown the effectiveness of combined BE and EHR approaches for managing chronic conditions, and none have addressed guideline-driven deprescribing specifically in type 2 diabetes. We previously conducted a pilot study aimed at promoting appropriate CW guideline adherence using BE nudges and EHRs embedded within CDS tools at 5 clinics within the New York University Langone Health (NYULH) system. The BE-EHR module intervention was tested for usability, adoption, and early effectiveness. Preliminary results suggested a modest improvement of 5.1% in CW compliance.

Objective

This paper presents the protocol for a study that will investigate the effectiveness of a BE-EHR module intervention that leverages BE nudges with EHR technology and CDS tools to reduce overtreatment of type 2 diabetes in adults aged 76 years and older, per the CW guideline.

Methods

A pragmatic, investigator-blind, cluster randomized controlled trial was designed to evaluate the BE-EHR module. A total of 66 NYULH clinics will be randomized 1:1 to receive for 18 months either (1) a 6-component BE-EHR module intervention + standard care within the NYULH EHR, or (2) standard care only. The intervention will be administered to clinicians during any patient encounter (eg, in person, telemedicine, medication refill, etc). The primary outcome will be patient-level CW compliance. Secondary outcomes will measure the frequency of intervention component firings within the NYULH EHR, and provider utilization and interaction with the BE-EHR module components.

Results

Study recruitment commenced on December 7, 2020, with the activation of all 6 BE-EHR components in the NYULH EHR.

Conclusions

This study will test the effectiveness of a previously developed, iteratively refined, user-tested, and pilot-tested BE-EHR module aimed at providing appropriate diabetes care to elderly adults, compared to usual care via a cluster randomized controlled trial. This innovative research will be the first pragmatic randomized controlled trial to use BE principles embedded within the EHR and delivered using CDS tools to specifically promote CW guideline adherence in type 2 diabetes. The study will also collect valuable information on clinician workflow and interaction with the BE-EHR module, guiding future research in optimizing the timely delivery of BE nudges within CDS tools. This work will address the effectiveness of BE-inspired interventions in diabetes and chronic disease management.

Trial Registration

ClinicalTrials.gov NCT04181307; https://clinicaltrials.gov/ct2/show/NCT04181307

International Registered Report Identifier (IRRID)

DERR1-10.2196/28723

LIA: a location-independent transformation for ASOCS adaptive algorithm 2

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of short-comings as a result. ANNs that use dynamic topologies have shown the ability to overcome many of these problems. Adaptive Self-Organizing Concurrent Systems (ASOCS) are a class of learning models with inherently dynamic topologies. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing learning models that use dynamic topologies efficiently in parallel hardware. An LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents the Location-Independent ASOCS (LIA) model as an LIT for ASOCS Adaptive Algorithm 2. The description of LIA gives formal definitions for LIA algorithms. Because LIA implements basic ASOCS mechanisms, these definitions provide a formal description of basic ASOCS mechanisms in general, in addition to LIA.