Keywords
dashboard - cardiac amyloidosis - electronic healthcare record - African American
veterans
Background and Significance
Background and Significance
It is important to identify the underlying disease etiologies that cause the syndrome
of heart failure (HF), which impacts 6 million Americans.[1] Cardiac amyloidosis (CA) is a cardiomyopathy caused by the accumulation of transthyretin
amyloid fibrils within the myocardium, or less often, by the accumulation of serum
light chains. It was previously thought to be rare[2] but is now an increasingly recognized etiology of HF.[3] Although there is geographic variability, approximately 1% of unselected patients
and 12% of HF patients with preserved ejection fraction have transthyretin (ATTR)
CA.[4] In comparison to the general population, African American (AA) men of older age
are disproportionately affected by CA because 3 to 4% of AA's are carriers of the
V122I ATTR mutation, which causes hereditary ATTR.[5] In a study comparing the incidence of CA in 2012, AA men and women were twice as
likely to have CA than Whites of the same sex.[6] This is particularly important for veterans because 16% of patients in the Department
of Veterans Affairs (VA) health care system are AA, of which 85% are men.[7] There is an urgent need for more effective disease detection[8] because disease-modifying therapies have become available as of 2019. Disease detection
has become easier due to the development of noninvasive diagnostic tests and the availability
of CA screening recommendations in the 2022 HF guidelines.[9]
However, CA remains underdiagnosed due to lack of standardized screening, the siloed
nature of clinical information,[10] low clinician awareness, and a historical dependence on invasive myocardial biopsy.
Screening for CA is comprised of bone scintigraphy and laboratory tests to exclude
light chain amyloidosis. Patients additionally require cardiac imaging, biomarker
tests, assessment for amyloid-associated comorbidities, and tissue biopsies.[11] Further, diagnosis of CA requires multidisciplinary coordination beyond cardiology.
Hence, an electronic health record (EHR) dashboard that compiles risk factors for
CA that can easily be reviewed by a clinician will enable a comprehensive view of
patient presentation and facilitate identification of those patients who may benefit
from further CA screening. Clinical dashboards are useful due to their ability to
collect, summarize, and effectively present timely data for improving patient care.[12] Clinical dashboards have been used for management of various conditions such as
hospital-acquired infections,[13] sickle cell disease,[14] predictive modeling of cystic fibrosis deterioration,[15] and trending of childhood obesity.[16]
This paper describes a clinical dashboard used to identify AA veterans with risk factors
for CA based on current guidelines[11] as part of a quality improvement initiative. We describe dashboard features that
optimized its use in clinical practice, including a write-back feature and social
determinants of health (SDoH) screening. The patients identified using this dashboard
are at high risk for CA and will be proactively considered for disease-altering treatment
earlier than they would have if we relied on individual patient-clinician decision
making.
Objectives
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Describe the development and key characteristics of a dashboard to facilitate a cardiac
amyloid early diagnosis program.
-
Identify barriers and opportunities in dashboard development.
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Identify future clinical informatics opportunities for cardiac amyloidosis.
Methods
Setting and Dashboard Users
Three VA health systems participated in this project: Tucson VA (TUC), Greater Los
Angeles VA (GLA), and Tampa VA (TPA). The dashboard was used by physicians (n = 6), nurse practitioners (n = 8), and residents/fellows (n = 2) among the three sites.
Dashboard Software Platform, Data Query, and Storage
The data source was the VA Corporate Data Warehouse (CDW), a central repository of
all patient care-related data. Each VA facility has a local installation of the EHR—Veterans
Health Information Systems and Technology Architecture (VistA)—which is then aggregated
to the national VA CDW. The dataset is automatically refreshed daily through stored
procedures using Structured Query Language (SQL) Server Integration Services and the
SQL Server Job Agent. The dashboard was created using Microsoft SQL Report Builder
(paginated report) for SQL Server Report Services. Several dashboard best practices
were followed, including continuous clinician feedback regarding User Experience (UX)
and User Interface (UI) to target visual appearance for optimized clinical use.[17]
[18]
[Figs. 1] and [2] depict a system architecture of our design with unified modeling language of data
flow.
Fig. 1 Diagram showing system architecture of our dashboard solution. CA, cardiac amyloidosis;
echo, echocardiogram; HF, heart failure.
Fig. 2 Unified modeling language diagram showing information flow in the various system
components of our dashboard solution. echo, echocardiogram; CA, cardiac amyloidosis;
CHF, congestive heart failure; GLA, Greater Los Angeles; sp, stored procedure; TPA,
Tampa; TUC, Tucson. Solid line, movement of data; dashed line, reference to/source
of.
Incorporation of Clinical Guidelines
To identify the patient characteristics needed in the dashboard, we utilized the American
College of Cardiology/American Heart Association (ACC/AHA) consensus statements[11]
[19] and clinical guidelines[9] on diagnosis and treatment of CA (see [Supplementary Table S1]). The clinical team reviewed the guidelines and applied their own clinical experience
to specify the patient characteristics needed in the dashboard ([Fig. 3]; for complete list: [Supplementary Table S2], available in the online version) to facilitate identification of at-risk patients.
Fig. 3 Summary of dashboard parameters. IVSd, interventricular septum thickness, diastolic;
LVIDd, left ventricular internal diameter, end diastolic; LVPWd, left ventricular
posterior wall thickness, diastolic; MRI, magnetic resonance imaging; RWT, relative
wall thickness.
Dashboard Development
Inclusion and Exclusion Criteria
The target population included veterans that self-identified as Black/AA between the
ages of 18 to 90 with a diagnosis of HF. HF was defined using International Classification
of Diseases (ICD-10) codes and required a diagnosis listed in the patient problem
list, ≥1 inpatient encounter for HF, or ≥2 outpatient encounters for HF within the
past 2 years. Patients were excluded, if they had any of the following: existing amyloid
(any type), relocated, current hospice, acute medical issues precluding further evaluation,
history of medical noncompliance, or patient previously declining evaluation.
Demographics
The dashboard displays several aspects of patient information, including the patient's
respective VA facility, name, age, assigned gender, body mass index, primary care
provider, and cardiology provider ([Fig. 4A]).
Fig. 4 Sample screenshot of the dashboard. (A–C) Each row corresponds to an individual patient which displays horizontally across
the dashboard screen. For presentation, we have partitioned the view of the dashboard
into three separate panels (A–C).
Amyloid Risk Assessment Scores
There are two risk scores included in the dashboard. One is a published score comprised
of age, sex, absence of hypertension, left ventricular (LV) posterior wall thickness,
and LV relative wall thickness.[20] This score was developed based on retrospective analysis of patients at a tertiary
care center. A score of ≥6 was associated with a 25% positive predictive value for
ATTR CA. We used this best-available risk stratification score while being aware of
its limitation that it lacked external validation. Further, the score was dependent
on echocardiogram features and lacked assessment for amyloid-related diagnoses as
recommended by practice guidelines.[19] To mimic optimal clinical practice, we developed a second risk score that consists
of the sum of amyloid-related comorbidities ([Supplementary Table S2], available in the online version) derived from the practice guidelines.[21] A score of ≥2 high-risk comorbidities was considered high risk based on team consensus.
Dashboard Output Parameters
Based on practice guidelines,[19] we included amyloid and nonamyloid-related comorbidities, laboratory tests, imaging
studies, and medications that would be relevant to identify HF patients with possible
amyloidosis ([Supplementary Table S2], available in the online version). These output parameters were defined by ICD-10
codes ([Supplementary Table S3], available in the online version).
Patients with known amyloid were excluded from additional review. Nonamyloid-related
comorbidities that are commonly assessed by cardiologists were included. In particular,
history of myocardial infarction was relevant, because recent infarction may lead
to false-positive bone scintigraphy results.[11] History of cocaine and methamphetamine use was included as this might impact eligibility
and adherence to treatment. The laboratory tests shown are routinely ordered in the
evaluation of cardiac amyloid and HF. From echocardiogram semistructured text reports,
we extracted key features using natural language processing (NLP; [Supplementary Table S4], available in the online version). We included the date of the last technetium-99m
(PYP) scan or cardiac MRI for ease of finding the report in the EHR. Unfortunately,
because electrocardiography images are not available in the CDW, we were not able
to include these results. Amyloid-specific therapies were included as these may help
clinicians identify patients already diagnosed with CA and exclude them from further
review. We also included hydroxychloroquine, which can lead to false-positive bone
scintigraphy results.[22]
Dashboard Risk Assessment and Tracking of Diagnostic Testing
The clinical teams were instructed to review patient characteristics in the dashboard
and the EHR in a standardized workflow manner ([Fig. 5]) to decide if patients had sufficient risk to warrant additional diagnostic testing.
To assist with tracking, we created a “write-back” feature to document clinical decision-making
and facilitate downstream tracking of patient flow through the screening process ([Fig. 6]).
Fig. 5 Workflow of screening patients for cardiac amyloidosis.
Fig. 6 Screenshot of the writeback feature.
Social Determinants of Health Screening
For patients who underwent amyloid testing and subsequent evaluation in clinic, clinicians
were asked to conduct screening for SDoH using an 11-question VA-specific questionnaire
regarding social needs that may impact health, termed Assessing Circumstances and
Offering Resources for Needs (ACORN) survey.[23] If this screening tool was completed, the results were available to the clinician
in the dashboard.
Summary Results and Outcome Visualization Function
We created an outcome visualization function page to allow for review of progress
through the stages of diagnosis at each facility ([Fig. 7]). Additional summary pages displayed averages or counts of demographics and comorbidities
by facility.
Fig. 7 An outcome visualization function depicting the number of patients at each VA facility
and their progress through the screening process by clinician. VA, Veteran Affairs.
The overall patient sample size in this initiative was 1,732 patients. There were
858 AA HF patients identified in GLA, 690 in TPA, and 184 in TUC, of which 949 (55%)
were identified as high-risk for CA. A separate report will describe the outcomes
and results of these patients identified by the dashboard.
Data Validation
To ensure validity of the dashboard, clinicians manually cross checked the patient-specific
dashboard information against the patient's EHR. Any data fields that were inconsistent
or incomplete were reported. Errors were remedied by data analysts and additional
features were developed based on clinical team consensus. This process was repeated
over 12 weeks until the dashboard was deemed accurate and representative of the patient's
EHR.
Lessons Learned
This article describes the development of an interactive dashboard to facilitate early
detection of CA in AA veterans with HF. This dashboard consolidates a myriad of features
including risk scores, comorbidities, biomarkers, and imaging to facilitate a diagnosis
of this complex disease. Aggregation of multiple data types in a simple user interface
aimed to facilitate clinicians' ability to make rapid inferences as experienced by
other authors.[24] The dashboard serves as an informatics tool that allows us to operationalize a proactive
population health approach in a high-risk population.
Our incorporation of novel risk scores is an example of both the benefits and challenges
of implementing research into clinical practice. We incorporated a validated risk
score that was recently published[20] to guide clinicians in identifying HF patients who would likely benefit from further
diagnostic testing. In the original publication, the score was validated in two external
population samples, and therefore, we felt it was appropriate for use in our project.
However, we did not validate its performance at our three sites prior to embarking
on this project; this would have been impractical. It is important to note that our
intent was to accelerate the translation of the risk scores into practice. Nonetheless,
we will report on the effectiveness of this screening initiative which will help us
to validate our approach. This project evaluation is concordant with the concept of
the learning health system in which health systems utilize, as well as generate, new
evidence in the process of patient care.
The use of NLP methods was critical in this project and highlights the need to incorporate
multiple technical approaches to dashboard development. We extracted echocardiogram
measurements to calculate CA risk score and targeted keywords from the echocardiogram
reports that indicated higher risk of CA ([Supplementary Table S4], available in the online version). Because the echocardiogram data was semistructured,
we implemented NLP/regular expressions to extract these parameters. Other studies
have employed NLP in research settings to identify combinations of cardiac/noncardiac
phenotypes to predict transthyretin cardiomyopathy with good performance.[25] Given the amount of unstructured data in the EHR, we found that integrating NLP
technology into the diagnostic framework for HF patients with less common etiologies
is a promising direction.
Moreover, the challenges encountered during dashboard development are important to
understand. Dashboard development was a lengthy process prior to project launch, with
refinements occurring over the next 6 months. Team members had limited experience
in specifying dashboard requirements that were translatable to data analysts. Another
challenge was that data formats were not standardized across VA sites, and diagnostic
test naming was frequently different. With the collaboration of data analysts and
clinicians across the three sites, we were able to harmonize this after several dashboard
revisions. However, as we scale this dashboard solution across 120+ VA medical centers
we anticipate further challenges of data harmonization that will constrain the goal
of data monetization.
There are strengths to our project as well. We had a clear purpose, understood the
user, and knew what was being evaluated and how. These factors were critical for a
successful design as has also been noted by Hysong et al.[26] The dashboard was functional and adequate to support clinician review after several
months of development. It was also comprehensive and included most elements needed
for risk-stratifying patients for CA. The write-back feature allowed users to capture
decision-making and record risk assessments. The goal of automatic data collection
with a clinician-familiar display of data to facilitate quick clinical decision-making
was achieved. Nonetheless, further refinement and implementation efforts are likely
needed to optimize usability and sustainment.
To our knowledge, this report is one of the earliest descriptions of a dashboard for
CA. A study by Willis et al[27] sought to develop EHR alerts and other tools to identify HF patients at risk of
CA by implementing combinations of phenotypes associated with confirmed cases of disease.
However, this was never implemented into the EHR. In contrast, our dashboard was created
to support immediate implementation of a clinical program for population screening
based on current evidence and guidelines. We acknowledge that our dashboard-driven
screening project could lead to unintended consequences, including unnecessary testing
for CA-negative patients, technology overload leading to clinician burnout, and automation
bias with overreliance on technology. We would like to acknowledge that clinicians
are inherently limited by available knowledge of the risk factors of the disease.
In our forthcoming evaluation of this project, we will report the effectiveness of
screening for CA including false-positive tests.
There are several areas for future work. For our self-developed risk score, we would
like to go beyond clinician guidelines and create a predictive model that utilizes
unstructured and structured data features to identify ATTR CA in our patient population.[28]
[29] We plan to create EHR-embedded clinical decision support alerts, which would improve
clinician workflow and increase the reach of this initiative. Because amyloidosis
involves a multidisciplinary evaluation, this dashboard could be tailored to support
workflows in related disciplines, such as hematology and neurology, to identify patients
with light chain amyloidosis and transthyretin polyneuropathy, respectively. Finally,
we would also like to incorporate additional data sources such as electrocardiography,
echocardiogram, genetic testing, and other records, which will require additional
data integration and processing techniques. Our dissemination efforts will initially
focus on VA facilities, but this work can be replicated at non-VA health systems as
well. However, as this project is a pilot initiative, we don't have a formal implementation
plan yet.
Conclusion
In this project, we created a dashboard to facilitate early diagnosis of CA among
AA veterans with HF. This dashboard supports cardiologists and related disciplines
by bringing together disparate clinical information from the EHR and provides patient-level
risk stratification to facilitate early diagnosis. We anticipate that this tool will
facilitate efforts to reduce underdiagnosis of CA and reduce unnecessary variation
in care that may adversely affect patients including those from at-risk and underserved
communities.
Clinical Relevance Statement
Clinical Relevance Statement
Diagnosis and treatment of CA is an increasingly important area of cardiology practice
with implications for several other specialties involved in care of these patients.
This project demonstrates the value of a patient-level dashboard to facilitate early
identification of cases and downstream care tracking. Given the numerous cardiac and
noncardiac features of CA and complexity of diagnosis, this dashboard may serve as
a template for other health systems that seek to employ a population approach to management
of this underrecognized condition.
Multiple-Choice Questions
Multiple-Choice Questions
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Which of the following challenges is mentioned in the article regarding screening
for cardiac amyloidosis?
-
Lack of diagnostic tools to diagnose the disease
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Low prevalence to raise awareness
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Complex disease process requiring a multidisciplinary approach
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Irrelevant to diagnose given lack of medical therapy
Correct Answer: The correct answer is option c. Because amyloid deposition affects multiple organs
of the body, not just the heart, it is often missed unless a clinician, or group of
clinicians, make the connection between several comorbidities. It is also a complex
disease process that requires further research and understanding, making it a challenge
to effectively screen and diagnose.
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What types of patients does the dashboard specifically target for risk assessment?
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Women under 75 years of age
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All veterans regardless of race
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Patients with confirmed heart failure, regardless of subtype
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African American veterans with heart failure
Correct Answer: The correct answer is option d. Cardiac amyloidosis often disproportionately affects
African American men of older age. In a study comparing the incidence of CA in 2012,
African American men and women were 2× as likely to have CA than their racial counterparts.
Targeting veterans is especially important, as approximately 16% of the Veteran Health
Administration is of African American descent.
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What technology was used to extract echocardiogram report data for the dashboard?
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Natural language processing
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Machine-based learning
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Manual entry by clinicians
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Data queries from the Corporate Data Warehouse
Correct Answer: The correct answer is option a. This was especially critical to the successful creation
of this dashboard because echocardiogram measurements are essential to calculating
a patient's ATTR risk score. Echocardiogram data are semistructured, meaning we had
to utilize natural language processing and regular expressions to successfully extract
these parameters.
