land, Oregon, has been deeply involved in bringing
risk scores to point of care, via your pocket. He
helped develop both the ASCVD Risk Estimator and
the newest version of the AnticoagEvaluator.
The ACC’s ASCVD Risk Estimator app helps providers and patients calculate risk and also provides
easy access to recommendations specific to calculated risk estimates. Additionally, the app (developed
jointly with the AHA) includes guideline reference
information for both providers and patients related
to therapy, monitoring, and lifestyle.
“My biggest thing is that risk scores can be very
simple or very complicated, but a big barrier that
we have long recognized is that people are not using
risks scores appropriately and thus there is either an
under-appreciation or an over-appreciation of risk,”
said Dr. Gluckman in an interview with CardioSource
WorldNews. Since we tend “not to do a great of a
job of estimating risk and having that correlate with
what the true risk is, having these aids at the point
of care can better inform decision making.”
But like pretty much anything having to do with
the technological translation of algorithms to apps, it
is a challenge to make the apps optimally usable and
relevant. Dr. Gluckman and the ACC are now working
on how to make the app give feedback about patients
for whom the calculator is not appropriate, such as
someone already taking a statin. They’re also working
on making the tools require minimal inputting of data, possibly
Scan the QR code to
through mining electronic health
download the ASCVD
records (EHRs) for discrete data
Risk Estimator app...
that may exist therein.
The ASCVD Risk Estimator is
far and away the most downloaded app in the ACC library
of mobile clinical apps (TABLE).
With >328,000 downloads and
>4.8 million sessions (meaning
the app was opened and used),
...or ACC’s Anticoagwe’re taking no risk in saying
Evaluator app!
that this score estimator has
achieved widespread acceptance.
The ACC’s AnticoagEvaluator
calculates a patient’s CHA2DS2VASc score for stroke risk, the
HAS-BLED score for major bleeding, and renal function (CockroftGault Equation), if necessary, to assist decision making in patients with nonvalvular atrial fibrillation (AF).
Both apps are available on both iTunes (iPhones,
iPads) and Google Play (Galaxy, Nexus, other Android devices).
PICK A SCORE, ANY SCORE
While there are scores of risk scores out there, precious few are actually recommended in the clinical
practice guidelines or performance measures. Obviously, the ASCVD PCEs have a recommendation, as
does the CHA2DS2-VASc stroke risk score for patients
with atrial fibrillation. Beyond those, the new DAPT
score, derived from the Dual Antiplatelet Therapy
study, is mentioned in the 2016 ACC/AHA duration
of DAPT guideline update as something that “may
be useful for decisions about whether to continue
(prolong or extend) DAPT” in patients who have
received a stent.6 In the ACS arena, the GRACE and
28 CardioSource WorldNews
TABLE
ACC Clinical App Overview
Downloads
...
Sessions
...
Total
Avg/Month
Total
Avg/Month
ASCVD Risk Calculator
328,630
11,332
4,825,083
166,382
Guideline Clinical
73,506
3,675
547,480
27,374
AnticoagEvaluator
43,575
1,176
67,021
8,255
Statin Intolerance
8,698
669
19,917
1,532
TAVR In-Hospital Mortality Risk
2,911
537
8,024
950
Total
457,320
5,467,525
Downloads = the # of times app has been downloaded from the stores (Apple or GooglePlay) onto a de vice (phone or tablet).
Sessions = the # of times the app has been used.
(i.e., A clinician who got the app off iTunes and used it five times would count towards 1 download and 5 sessions.
TIMI scores are both widely used, as is the Seattle
Heart Failure Score in the HF world.
“Of course, when a national guideline comes out
and says you should be using this risk score, most
people pay attention to that,” said Dr. Eagle. “For
virtually everything we do, somebody has published
a multivariable model and established some sort of
a risk score, but the primary prevention, secondary
prevention, and AF stroke risk, these are the big ones.”
Predictably, the most validated risk scores are
the ones included in the guidelines and they are
being used “prevalently,” said Dr. Maddox, but “it’s
difficult to out get your arms around all the various
ways clinicians are accessing risk calculators and
we are definitely undercounting.
“I can do a CHA2DS2-VASc in my head, as can others,
and I may or may not record the actual result,” said Dr.
Maddox. “I may just calculate the score and realize my
patient needs anticoagulation and put him on it.
Also, the apps measure sessions—each time the app
is opened and used—but not multiple uses per session. Other docs are still using paper pads to calculate
risk scores or interactive website-based calculators,
so it’s impossible to truly track how often calculators
and/scores are used in clinical practice. Even when a
score is calculated, Dr. Maddox said, there’s no surety
that it’s documented. A free-text score is difficult to
abstract and may not make it into the medical record.
NOT MUCH NEW
Newer risk factors and biomarkers for heart disease,
including genetic data, can be evaluated in the context
of existing risk estimation approaches—by adding the
new component and then assessing the change in C
statistic. But at the end of the day, clinicians just want
to know whether an added predictor will change risk
such that they should change their treatment.
“The more evidence and validation you have behind
it -- and that’s what going into vetting them for the
guidelines -- the more likely it is to be taken up,” said
Dr. Maddox. “The brand new ones are not validated.”
As for the research on new risk factors and
biomarkers, Dr. Maddox said that, in reality, most
of them add “very little” to the prior way that we
would calculate risk.
Or, to put it more bluntly, “If the new data take
the C statistic from 0.65 to 0.66, so instead of
misclassifying 35% were going to misclassify 34%,
well, so what. And it’s usually a score that’s been
derived in a small cohort and whether or not it applies to the person in front of me, I have no idea.”
He suggested that there is some “naiveté” that
says if the number goes up at all, it matters. “Is it
applicable to other populations? How much lifting
does it take to get that additional information? Just
showing a little bit of improvement is just one step
of 20 before it’s ready for clinical use,” he said.
BIG DATA ≠ GOOD DATA
Information technology has been touted as the
means to support and improve clinical practice.
However, moving from promise to reality has been
a much slower process than imagined. Difficulties
in implementation have dogged many technologies,
including EHRs.
EHR use has increased dramatically in the last
5 years. In 2009, 12% of US hospitals were using
basic EHR systems.7 Fast forward to 2016 and the
technology has reached the point of “near universal
adoption,” according to HIMSS Analytics, a global
health care advisor.8
The fact that EHR use was mandated in the
Affordable Care Act (ACA) and the Health Information Technology for Economic and Clinical Health
(HITECH) Act hasn’t hurt the adoption curve any,
nor have the incentive payments from Medicare and
Medicaid. But adoption is different from satisfaction, and users continue to grumble about EHR usability, reliability, and accuracy: The systems don’t
work properly or integrate efficiently. They are a
time-wasting imposition. The data inputs are notoriously inaccurate or of poor quality and coding is
pretty much a crapshoot.
“Medical records are not necessarily coded consistently from one provider to another, or one institution to another,” explained Dr. Eagle. “If I have a
patient who comes in with pneumonia, A-fib, and a
small MI, what’s my principal diagnosis? One of the
big challenges with using this kind of administrative data to drive clinical decision making revolves
around the inconsistencies with how we code.”
Despite their limitations, EHRs offer exciting opportunities to explore risk prediction using terabytes or even petabytes of qualitative and quantitative data. But having a ton of data and knowing
what to do with it are very different things.
In some cases, EHRs go to the head of the class
by being “smart” about things and will automatically derive a risk score when appropriate and present it to the provider. More often, at this point,
they require some prompting, but are still useful in
assisting in the derivation of a risk prediction.
September 2016