Better COVID Risk Prediction with Respiratory Scoring

Better Clinical Decision Support for COVID-19:
Identifying Patients at Highest-Risk
Kathleen Merkley, DNP, APRN, FNP
Senior VP of Professional Services
Leah Evans
Analytics Director, Sr., Outsourcing Services

© 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Better Clinical Decision Support for COVID-19
The clinical spectrum of COVID-19 ranges
from asymptomatic to critical illness,
making identifying patients at risk for
respiratory deterioration a vital part of
disease management.
However, with so many aspects of the
SARS-CoV-2 infection unprecedented on
clinical and social levels, it’s not surprising
that healthcare organizations don’t have an
established, reliable tool to predict clinical
deterioration in patients with the virus.

Better Clinical Decision Support for COVID-19
With in-hospital mortality for patients with
COVID-19 estimated between 15.3 and
24.5 percent in 2021, a clear opportunity
exists to improve outcomes for individuals
at a high risk of deterioration with better
clinical decision support tools.
Identifying these patients early, however,
requires risk scoring tools that specifically
identify the onset of respiratory failure, the
primary indicator of COVID-19 severity,
rather than commonly used scoring tools
for other acute conditions.

An Opportunity for Better Clinical Decision Support Tools:
Existing Risk Scoring May Be Misleading for Patient with COVID-19
Risk-scoring tools exist for common acute
conditions, such as septic shock, hypovolemic
shock, or cardiogenic shock.
But these methods, including the Modified Early
Warning System (MEWS), Sequential Organ
Failure Assessment (SOFA) scores, and
National Early Warning Score 2 (NEWS2),
developed in the U.K. by the National Health
Service (NHS), may be misleading for patients
with COVID-19. COVID-19 primarily manifests
with hypoxemia (a below-normal blood oxygen
level) without associated systemic disturbance.

As such, using these risk scores, a
patient with the virus may not appear
critically ill—according to vital signs,
such as a heart rate and blood
pressure—while still in significant
respiratory compromise.

For instance, Dr. Guy Glover, a critical
care physician at Guy’s and St Thomas’
NHS Foundation Trust (GSTT) in the
U.K., shared a case typical of how
COVID-19’s unique pattern of
physiology has confounded clinicians
and conventional escalation pathways.

In Dr. Glover’s example, a 36-
year-old male presented to the
emergency department with
respiratory symptoms.
The patient had tested positive
for SARS-CoV-2 four days earlier.
The hospital admitted him on
conventional low flow oxygen
and monitored, per the hospital
protocol, with four-to-six-hour
physiological observations and
NEWS2 assessments.

Overnight the patient’s condition deteriorated
but didn’t trigger the conventional hospital
escalation protocol of oxygen saturations
(SpO2) 92 percent, respiratory rate 20bpm,
HR 90bpm, SBP 120mHg, alert, temperature
37.3 degrees Celsius (99.14 Fahrenheit).
The care team had escalated his oxygen
to a high flow non-rebreather mask (a
device to deliver oxygen in emergency
situations) to maintain his SpO2.

Despite the patient’s deterioration, with a
lack of systemic physiologic disturbance
and because the NEWS2 score only grades
supplemental oxygen in a binary (yes or no)
way, without increasing weighting for rising
fraction of inspired oxygen (FiO2), his
NEWS2 value remained at 3—well below
the recommended threshold for escalation
for critical care review. However, he was
clearly in imminent danger.

A Better Way: Data Points to Respiratory Scoring to
More Accurately Predict COVID-19 Severity
In a study conducted at GSTT and led by
Dr. Glover in the U.K., researchers
assessed the widely used NEWS2 score
versus a simple respiratory assessment,
the Respiratory rate—Oxygenation (ROX)
index, as a more sensitive predictor of
deterioration among hospitalized patients
with COVID-19.

The U.K. study leveraged the from a
healthcare-specific data platform (the
Health Catalyst Data Operating System
(DOSTM)) and used an acute deterioration
analytic accelerator dataset, developed for
systemwide quality improvement work.

Employing a nimble and iterative approach,
the U.K. clinicians, analysts, and data
architects quickly adapted the existing
population dataset to flag patients who had
tested positive for COVID-19 and identify
key outcomes, such as the need for
advanced respiratory support alongside
existing measures including:
 cardiac arrest
 critical care admission
 death

The U.K. team then extracted additional data
from the data platform, including co-morbidity
(Deyo-Charlson and Elixhauser-van Walraven
Comorbidity Indexes) and a novel frailty
assessment score to rapidly characterize the
patients they were studying.

Reporting on 708 cases, the research
demonstrated the earliest singular marker of
clinical deterioration was a rising FiO2.
Additionally, tachypnea (abnormally rapid
breathing) was a late sign of deterioration,
while the cardiovascular parameters
generally didn’t indicate an impending
adverse event.

Scoring supplemental oxygen as a
binary variable had poor predictive
power for deterioration, therefore
demonstrating mechanistically why
the NEWS2 score was relatively
insensitive to deterioration in
COVID-19.

The study confirmed that the predictive validity
of the ROX index for COVID-19 deterioration
(a composite outcome of cardiac arrest,
critical care admission, or death) was
significantly greater than the NEWS2.

This understanding of the ROX index’s
predictive capability confirms the index’s
potential to translate into an earlier
detection of an adverse event by up to
four hours—necessary time to escalate
the patient for assessment by the critical
care outreach team promptly and safely
before it’s too late.

While the ROX index appears to be
important, the NEWS2 system retains
many advantages for the U.K. NHS due
to its widespread adoption and
standardized approach.
The research group next aims to
incorporate its initial findings into a
modified NEWS-FiO2 model.

The scope of the data platform and the
scalable nature of the datasets will allow
researchers to test the model in larger
patient cohorts (e.g., other respiratory
viral infections) to explore generalizability
of the findings.

What Health Systems Need to Create
COVID-19 Deterioration Scoring
To improve risk prediction among patients with
COVID-19 and adopt a respiratory deterioration
scoring process along the lines of the above U.K.
efforts, health systems need key features and
practices in place:
1. Aggregated historical patient data
2. Optimizing response in the deterioration pathway
3. Collaboration between clinicians and data
scientists

1-Aggregated historical patient data
The first step in understanding and optimizing
the deterioration pathway is to aggregate
historical patient data on baseline
characteristics and diagnosis as well as
longitudinal laboratory and physiological data,
then link these values to predictor variables to
patient outcomes.
By characterizing typical patterns of physio-
logical deterioration, these clinicians can
explore these signals can then be explored and
iteratively optimize the warning scores.

2-Optimizing response in the deterioration pathway
Subsequently, care teams must optimize
response in the deterioration pathway—for
example, by measuring and improving the critical
care outreach team’s response times and
escalation to critical care.
In the U.K. study described above, the research
group has further used healthcare vendor
(Health Catalyst) data to report for the first time
that onset of physiological deterioration to critical
care admission associates with mortality.

2-Optimizing response in the deterioration pathway
Furthermore, metrics in the vendor’s acute
deterioration analytic accelerator allow the health
system to track and improve their responsive-
ness to deterioration, with the potential to
improve both clinical and operational outcomes.

3-Collaboration between clinicians and data scientists
Work of this nature requires collaboration
between clinicians and data scientists,
which provides the ability to identify clinical
problems, pose key questions and
hypotheses, and develop data-driven
answers and solutions.

Harnessing the Potential for Improved
COVID-19 Outcomes Globally
More robust insight into COVID-19 deterioration
scoring can contribute to improving patient care
and the overall pandemic by saving lives and
decreasing intensive care time for patients.
Applied globally, health systems would
recognize potentially very sick patients
earlier and intervene sooner, improving
the chance for positive outcomes.

For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

More about this topic
Link to original article for a more in-depth discussion.
Better Clinical Decision Support for COVID-19: Identifying Patients at Highest-Risk
Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions
Health Catalyst Editors
Why Data-Driven Healthcare Is the Best Defense Against COVID-19
The Right Way to Build Predictive Models for the Most Vulnerable Patient Populations
Healthcare Data Quality: Five Lessons Learned from COVID-19
Taylor Larsen, DOS Marts Data Quality, Director
A Sustainable Healthcare Emergency Management Framework: COVID-19 and Beyond
Holly Rimmasch, Chief Clinical Officer; Ed Corbett, MD, Medical Officer; Anne Marie Bickmore, Chief Product Officer

Kathleen Merkley, DNP, APRN joined Health Catalyst in March 2013, as an Engagement
Executive. Prior to coming to HC, she worked for Intermountain Healthcare as the corporate
clinical IT implementation manager. Kathleen is a registered nurse/nurse practitioner and just
received her doctorate in nursing practice from the University of Utah in May 2013.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Kathleen Merkley, DNP, APRN, FNP

Leah Evans joined Health Catalyst in November 2016 as a Senior Data Architect and
currently serves as an Analytics Director within the Outsourcing Services team. She has
worked in the healthcare data space since 2005. Leah came to Health Catalyst from
Perficient, where she worked as a Solutions Architect. Prior to Perficient, she spent nine
years at SCP Health, working with the Business Intelligence team as the Data Architect and BI
Manager. Leah holds a bachelor's degree in Computer Science from the University of Louisiana at
Lafayette.
Leah Evans

Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement
company that helps healthcare organizations of all sizes improve clinical, financial, and operational
outcomes needed to improve population health and accountable care. Our proven enterprise data
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Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”

Better COVID Risk Prediction with Respiratory Scoring

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