Predicting, detecting, and responding to
clinical deterioration on the wards:
Is there room for improvement?
Chris Bonafide, MD, MSCE
Division of General Pediatrics
bonafide@email.chop.edu
CCEB
CENTER FOR PEDIATRIC CLINICAL EFFECTIVENESS
Case
Case
•
•
•
•
•
•
•
•
High-risk patient
Worsening vital signs
New oxygen requirement
Worsening labs
Concerned staff
Urgent interventions
Delayed transfer to ICU
Poor outcome
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
What is clinical deterioration?
Trajectories of Ward Hospitalization
Death
Cardiopulmonary Arrest
Acute Respiratory Compromise
D
C
Increased
Care Needs
Clinical Deterioration
Vital
Signworsening
Changes
•Acute
of clinical status
•On a trajectory toward arrest
BC
Routine Care Needs
B
A
A
Adapted from: Duncan H, Hutchison J, Parshuram CS. The Pediatric Early Warning System score: a severity of illness score to predict urgent medical need in hospitalized children. J Crit Care. Sep
2006;21(3):271-278.
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
What are rapid response systems?
• Hospital-wide systems designed to prevent cardiac
arrest and death in ward patients by:
1. Facilitating the identification of patients at risk
2. Deploying an expert team to the bedside of patients
exhibiting signs of deterioration
•
Due to strong support from safety organizations
2005-2010, most US hospitals have some form of
rapid response system
–
–
CHOP
HUP
What are rapid response systems?
Rapid Response System
Afferent Arm
(identification)
Prediction
of deterioration
risk over time
Prognostication
tools
Efferent Arm
(response)
Detection
of active
deterioration
Standardized
calling criteria
Early warning
scores
Code blue
team
Medical
emergency
team
Tools to supplement the clinical skills of
nurses and physicians at the bedside
Rapid response systems: mixed results
Mortality rate
better
Cardiac arrest rate
worse
better
Adults
No significant
reduction
worse
Adults
34% reduction
Children
Children
21% reduction
38% reduction
Pooled
Pooled
Chan PS, Jain R, Nallmothu BK, Berg RA, Sasson C. Rapid Response Teams: A Systematic Review and Meta-analysis. Arch Intern Med. Jan 11 2010;170(1):18-26.
Opportunities for
rapid response system improvement
1. IDENTIFY a clinical profile of children at high risk of
deterioration, and consider monitoring them more
closely
2. DETECT deterioration more accurately using
evidence-based tools
3. INTEGRATE detection into continuous physiologic
monitoring systems
4. ELIMINATE barriers to calling for urgent assistance
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
Who deteriorates?
Rapid Response System
Afferent Arm
(identification)
Prediction
of deterioration
risk over time
Prognostication
tools
Efferent Arm
(response)
Detection
of active
deterioration
Standardized
calling criteria
Early warning
scores
Code blue
team
Medical
emergency
team
Tools to supplement the clinical skills of
nurses and physicians at the bedside
25
25
5
10
15
20
Hours after admission
0
5
10
Percent
15
20
Age
0
Percent
CHOP deterioration data
0
2
4
6
8
10
Age
12
14
16
18
0
24
48
72
96
120
144
168
hours after hospital admission
192
216
240
Development of a predictive score to identify pediatric
inpatients at risk of clinical deterioration
•
Objective: To develop a predictive score for deterioration using non-vital sign risk
factors
– Intended use: identifying high-risk children who should be intensively monitored
•
•
•
Design: Case-control study
Setting: The Children’s Hospital of Philadelphia
Patients:
– Cases (n=141) were children who deteriorated while receiving care on a non-ICU
inpatient unit
– Controls (n=423) were randomly selected
•
•
•
Exposures: Complex chronic conditions, other patient factors, and laboratory
studies in the 72h before deterioration
Outcome: Clinical deterioration, defined as cardiopulmonary arrest, acute
respiratory compromise, or urgent ICU transfer
Analysis: Multivariable conditional logistic regression
Predictive score
Final multivariable conditional logistic regression model for clinical deterioration.
Predictor
Adjusted OR (95% CI) p-value
Regression Coefficient (95% CI)
Scorea
Complex Chronic Conditions
Epilepsy
4.36 (1.94-9.78)
<0.001
1.47 (0.66-2.28)
2
Congenital/genetic defects
2.13 (0.93-4.89)
0.075
0.76 (-0.07-1.59)
1
History of any transplant
3.01 (1.31-6.92)
0.010
1.10 (0.27-1.93)
2
Percutaneous or naso-enteral tube in preceding
24 hours
2.14 (1.29-3.55)
0.003
0.76 (0.25-1.27)
1
Age <1 year
1.86 (1.03-3.35)
0.038
0.62 (0.03-1.21)
1
Blood culture sent to lab in preceding 72 hours
5.81 (3.29-10.28)
<0.001
1.76 (1.19-2.33)
3
Hemoglobin <10g/dL in preceding 72 hours
3.01 (1.79-5.06)
<0.001
1.10 (0.58-1.62)
2
Other Patient Factors
Laboratory Studies
Abbreviations: CI, confidence interval; OR, odds ratio.
aScore
derived by dividing regression coefficients for each covariate by the smallest coefficient (age<1 year, 0.62) and then rounding to the
nearest integer. Score ranges from 0 to 12.
Results
Risk strata and estimated probabilities of deterioration.
Risk stratum
Very low
Low
Intermediate
High
Scores
0-2
3-4
5-6
7-12
SSLR (95% CI)
0.39 (0.29-0.51)
1.18 (0.85-1.64)
2.63 (1.74-3.96)
96.00 (13.24-696.17)
Abbreviations: CI, confidence interval; SSLR, stratum-specific likelihood ratio.
aCalculated
bSome
using an incidence (pre-test probability) of deterioration of 0.15%.
individual scores above 7 include only cases.
Probability of deteriorationa
0.06%
0.18%
0.39%
12.60%
Conclusions
• Identified a group of risk factors that may be useful
to assess on admission and periodically during the
hospitalization to identify patients who deserve more
intensive monitoring for signs of deterioration
Next steps
25
• Domain validation and updating of score parameters
using patients at the time of admission from the
emergency department to predict deterioration in
the first 12 hours
15
10
5
0
Percent
20
Hours after admission
0
24
48
72
96
120
144
168
hours after hospital admission
192
216
240
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
Do vital sign abnormalities
precede deterioration?
Rapid Response System
Afferent Arm
(identification)
Prediction
of deterioration
risk over time
Prognostication
tools
Efferent Arm
(response)
Detection
of active
deterioration
Standardized
calling criteria
Early warning
scores
Code blue
team
Medical
emergency
team
Tools to supplement the clinical skills of
nurses and physicians at the bedside
Pediatric Early Warning Scores
• Combine intermittent vital sign values into a
manually-calculated composite score
•
•
•
•
Monaghan’s Paediatric Early Warning Score
Haines’ Paediatric Early Warning Tool
Parshuram’s Bedside Paediatric Early Warning System Score
Edwards’ Cardiff and Vale Paediatric Early Warning System
– Abnormal parameters based on expert opinion
– Not adequately validated
– Variations of the scores above used widely
What is abnormal for hospitalized children?
• Age-based reference ranges for HR and RR
– not evidence-based
– vary widely between sources
• Better evidence exists for normal blood pressure in
healthy children, but these ranges have not been
evaluated in-hospital
Development of “expected” vital sign curves
•
Objective: To develop expected HR, RR, SBP, and DBP curves using data from
hospitalized children, to serve as the basis for:
– In-hospital reference ranges
– Vital sign-based early warning score development
•
Design: Retrospective cohort study
•
Setting: Cincinnati Children’s Hospital
•
Data Source: Manually documented vital signs in EHR
•
Patients:
– Admissions to non-ICU inpatient units in 2008 (n=11,789)
– Excluded age >=18, DNR or death during admission, LOS>1 year
– Excluded vital sign observations that were physiologically implausible
• HR 0-300 = plausible
•
Analysis: generalized additive models for location scale and shape (GAMLSS) using BoxCox power exponential distribution
Vital sign data: HR
n=542,766 obs
First set of curves
Vital sign data: HR
n=542,766 obs
79 high HR values from
one patient hospitalized
for 56 days
16 low HR
values from
one patient
within a 4hour window
HR
57
56
10
33
28
46
RR
173
133
49
119
115
132
Single observations in patients
who survived to discharge and
were not DNR
150
150
100
100
50
50
hr, numeric
hr, numeric
100
80
60
40
0
200
400
time difference admission to observation as numeric
50
100
time difference admission to observation as numeric
5
10
15
20
time difference admission to observation as numeric
0
500
1000
1500
2000
time difference admission to observation as numeric
150
20
40
60
80
time difference admission to observation as numeric
100
0
50
100
time difference admission to observation as numeric
150
0
500
1000
time difference admission to observation as numeric
1500
100
150
100
hr, numeric
100
600
50
0
0
0
50
50
hr, numeric
100
150
150
200
25
200
40
0
50
hr, numeric
100
50
0
0
50
hr, numeric
100
0
150
10
20
30
time difference admission to observation as numeric
0
150
0
40
0
0
20
0
10
20
30
time difference admission to observation as numeric
150
0
2500
Addressing documentation error
• Used RR as a data integrity check
– RR documented simultaneously
– RR<HR
– RR physiologically plausible (5-120)
Addressing Documentation Error
Single patient spikes still problematic
Ascertainment bias issues
• Clustering of extreme values
– In a single patient experiencing an acute event
over a short time
– In a single patient with abnormal baseline values
over the course of a long admission
• Addressed by:
– Randomly selecting one HR from each 6-hour
window of each patient’s admission
– Randomly selecting up to 10 of these values for
each admission
Data for curve generation
Next steps for curve analysis
• Developing second set of curves with data
integrity steps in place
• Validation using CHOP sample
• Will then use the z-scores for these curves to
develop early warning score using vital sign
data from case-control study
Opportunities to integrate detection
tools into physiologic monitoring?
• Most inpatients are connected to physiologic monitors
• Alarm parameters are set manually and adjusted as
needed
• CHOP monitors generate ~20,000 alarms/day
• Nurses are automatically paged with a generic message for
each of these alarms
• Can we identify and filter out false alarms?
• Can physiologic data be combined to generate multiparameter alarms?
• Can alarms be adaptive to recognize important withinsubject changes that may not reach pre-set alarm
parameters?
HUP ICU Smart Alarms Project
• Evaluates HR, RR, SpO2, Skin Temp continuously
• Evaluates BP measured at periodic intervals using a cuff
• Compares monitored values to a model of normality generated
using neural networking methods applied to a training data set
• Variance from data set used to evaluate probability that vital signs
are normal
• Generates a status index ranging from 0 (no abnormalities) to 10
(severe abnormalities in all variables)
• Short-term median filtering for noise removal
• Historic filtering for coping with missing parameters
http://www.obsmedical.com/products/hospital-patient-monitoring/visensia-central-station
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
Qualitative evaluation of the mechanisms by which
rapid response systems impact patient safety
•
Objectives:
•
To qualitatively determine how the identification and response components of rapid response
systems impact nurse decision-making relevant to patient safety
•
To identify barriers to recognizing and responding to clinical deterioration that exist despite rapid
response system implementation
•
Design: Qualitative study using semi-structured interviews
•
Setting: CHOP
•
Subjects: 27 nurses who care for children on non-ICU units
•
Data Collection and Analysis:
•
Audio recorded and transcribed interviews
•
Coded using constant comparative methods
•
Analyzed using a grounded theory approach
Theme: Despite implementation of an open access
medical emergency team, some barriers to calling for
urgent assistance still exist.
•
Some nurses doubted their own ability to recognize
deterioration.
•
Some nurses were hesitant to call for help for fear of being
viewed as inadequate or unable to handle a difficult
situation.
•
While most nurses reported a collaborative working
relationship with physicians, issues of hierarchy were
discussed, with nurses reporting that physicians sometimes
disagreed with their assessment of the need for urgent
assistance. This prevented or delayed some nurses from
calling the medical emergency team.
Barrier examples
•
•
Medical nurse, 5-10 years experience:
We had a child on BiPap who we had tried everything to keep his sats up… and literally
nothing was working. At the 6:00 hour both me and the charge nurse were like, to the
resident, we said, “We need you to do something. Can we just call the CAT team for a second
opinion? Just something, maybe change the CPAP, just something.” We have had issues with
this one particular one who insisted that, “He just needs some chest PT.” I insisted that I was
doing chest PT for five straight hours now and I was doing it hard. I was doing it good. We
just kept meeting resistance…
•
•
Medical nurse, 2-5 years experience:
I felt very uncomfortable with the patient… I was in there doing blood pressures and I don’t
even think I got to write them all down. I was doing them so frequently. She was very sick. I
felt resistance from every member of the team. That made me hesitate to speak up. I did
speak up several times, but then I stopped. I spoke up so many times saying, “This is not okay.
I am extremely concerned.” Multiple times, but I never said, “No, that’s it.” I just didn’t take
that last step…
Next steps for qualitative study
• Stratify analysis by nursing characteristics
• Expansion to physicians to enable direct
comparisons with nursing themes
Outline
• What is clinical deterioration?
• What are rapid response systems?
• Who deteriorates?
• Do vital sign abnormalities precede deterioration?
• Once deterioration has been detected, are there barriers to
calling for help?
• Summary
Summary of opportunities for
rapid response system improvement
1. IDENTIFY a clinical profile of children at high risk of
deterioration, and consider monitoring them more
closely
2. DETECT deterioration more accurately using
evidence-based tools
3. INTEGRATE detection into continuous physiologic
monitoring systems
4. ELIMINATE barriers to calling for urgent assistance
Thank you
• Mentors/Collaborators
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–
–
–
–
–
–
–
–
–
–
–
Ron Keren
John Holmes
Vinay Nadkarni
Russell Localio
Richard Landis
Bob Berg
Kathryn Roberts
Fran Barg
Chris Feudtner
Alex Fiks
Rich Lin
Carrie Daymont
Pat Brady
• Research Assistants
–
–
–
–
–
Emily Huang
Kathleen McLaughlin
Shelby Drayton
Annie Chung
Duy-An Ho