ICU Strain: Causes and

ImpactSean M Bagshaw

Department of Critical Care Medicine

University of Alberta

Edmonton, Canada

CCCF 2017

Toronto, Canada

Wednesday, October 4, 2017 – 9:10-9:30

2017 Disclosures

•Salary support: Canada government

•Grant support: Canada/Alberta government

•Speaking/consulting: Baxter Healthcare Corp.

•Steering Committee: Spectral Medical, Inc.

•Data Safety Monitoring Committee: CytoPherx, Inc.

What is “Strained ICU Capacity”?

• Strain can be conceptually defined as:

“a dynamic discrepancy between the availability of

finite ICU resources (i.e., beds, ventilators, healthcare

providers) (supply) and requests to admit and provide

high-quality care for patients with life-limiting or life-

threatening critical illness (demand)”

Bagshaw et al Crit Care Med 2016

PatientCharacteristics

Admission APACHE II

Score

Admission time

Bedside Workload

Demographic Features

Co-existing Disease Burden

Case-mix (Primary Diagnosis)

Surgical Status

ICU Capacity Strain

❶ ❷

Support Intensity

❸

Bed Availability

Outcome

Conceptual Framework for “Strain”

“Structural”

factors

“Patient-specific”

factors

“Provider-specific”

factors

0 20 40 60 80 100 120

Equipment shortages

Inadequate # ancillary services

Inadequate distribution ICU resources

Inadequate # allied health – outside ICU

Inadequate # allied health – inside ICU

Preferential priority to ER for ward beds

Time consuming paper/electronic charting

Variation in care plans between physicians

Reduced resources after hours

Unrealistic patient/family expectations

More inexperienced/fewer experienced nurses

Inadequate goals of care discussions

Inadequate # bedside nurses

Inappropriate admissions

Increase in patient acuity/complexity

Increased in patient volume

Unable to discharge – lack of ward beds

Agree Strongly Agree

Opgenorth et al PLoS One (Under Review)

Bagshaw et al Crit Care Med 2016

Perceived Impact of Strain by Healthcare Providers

Patient/Family

Reduced quality and safety of care

Delay patient access to critical care services

Precipitate premature or unplanned ICU discharges

Reduced opportunities to support patients’ families

Providers

Increased workload

Negative impact on workplace environment and relationships between ICU workers

Moral distress, burnout and reduced job satisfaction

Increase in staff absenteeism, sick days and turnover

Health System

Unnecessary, excessive and inefficient resource utilization throughout the healthcare

system

Inappropriate admissions/discharges

Added expenses for overtime and training new staff in response to attrition and

turnover

Kerlin et al AATS 2014

6-month prospective cohort study in 24 bed medical ICU

Survey of providers (charge RN + physician) on perception of strain

Concomitant assessment of operational and strain measures

226 (89%) surveys (18 nurses; 17 physicians)

ICU census

perceived as

an important

measure of

strain for

providers

Gabler et al AJRCCM 2013

ICU census on the

day of admission was

associated with

mortality (adj OR 1.02;

95% CI, 1.00-1.03)

Strained ICU

Capacity

Patient factors

(i.e., APACHE II)

Outcomes

(i.e., patient, ICU

performance)

e2 ~ N(E2, S2)

e1 ~ N(E2, S2)

e3 ~ N(E2, S2)

Schematic of Hypothesized Relationship between Strain

and Outcome

Strain VariableICU Mortality

OR (95% CI) p-value

Direct Effect

AVAILABLE BEDS ≤ 1 1.120 (0.999, 1.256) 0.0525

AVAILABLE BEDS ≤ 2 1.029 (0.931, 1.138) 0.5695

AVAILABLE BEDS ≤ 3 0.948 (0.856, 1.051) 0.3109

OCCUPANCY ≥ 90% 1.046 (0.940, 1.165) 0.4087

OCCUPANCY ≥ 95% 1.098 (0.964, 1.250) 0.1593

Indirect Effect

AVAILABLE BEDS ≤ 1 1.043 (1.018, 1.069) 0.0007

AVAILABLE BEDS ≤ 2 1.037 (1.016, 1.059) 0.0005

AVAILABLE BEDS ≤ 3 1.028 (1.007, 1.050) 0.0102

OCCUPANCY ≥ 90% 1.046 (1.023, 1.070) 0.0001

OCCUPANCY ≥ 95% 1.046 (1.017, 1.076) 0.0015

Integrated (Total) Effect

AVAILABLE BEDS ≤ 1 1.168 (1.039, 1.313) 0.0094

AVAILABLE BEDS ≤ 2 1.067 (0.964, 1.182) 0.2105

AVAILABLE BEDS ≤ 3 0.975 (0.878, 1.082) 0.6353

OCCUPANCY ≥ 90% 1.094 (0.980, 1.222) 0.1087

OCCUPANCY ≥ 95% 1.148 (1.006, 1.311) 0.0408

Strain and ICU Mortality

Strain, defined by bed

availability at ICU

admission, was

associated with a 17%

incremental risk of

ICU mortality

Bagshaw et al JCRC 2017

Wagner et al Ann Internal Med 2013

↑ Strain translated into shortened ICU stay (~6 hr) and small ↑ risk of ICU readmission (1.0%)

Goldfrad et al Lancet 2000

Nighttime vs. daytime discharge and hospital death:

Crude OR 1.46 (95% CI, 1.18-1.80)

Case-mix-adjusted OR 1.33 (95% CI, 1.06-1.65)

Premature Discharge-adjusted OR 1.17 (95% CI, 0.95-1.87)

>50% night time discharges were “premature”

Consequences of Discharges from ICU at Night

After Hours Discharge from ICU (ANZ)

Santamaria et al AJRCCM 2015

Design/Setting/Population: Prospective observational cohort of 10,211 patients

discharge alive from 40 ICUs in ANZ to evaluate predictors of hospital mortality

• Age 63 (49-74) years; 56% received MV;

APA-III risk of death 9%

• 16.4% discharged after hours (18:00-06:00)

• Crude mortality 7.4% vs. 4.8%, p<0.001

In adjusted analysis, accounting for limitation of medical treatments, no association

between after hours discharge and mortality was evident (OR 1.16; CI 0.89-1.53)

After Hours Discharge (1900 – 0800) (Alberta)

After hours discharge associated with incremental 19% risk of hospital death

(OR 1.19; 95% CI, 1.01-1.39)

Bagshaw et al Under Review

0 5 10 15 20

05

1015

Hourly Death Rate

Hours

Dea

th R

ate(

%)

After hours discharge occurred in 23.3% (27.4% dead vs. 23.0% alive, p<0.001)

Associated with ↑ occupancy; ↑ admissions; ↑ SOFA and TISS score 72 h prior

Stelfox et al Arch IM 2012

If there are no ICU

beds available, care

processes appear to

be modified

Town et al CCM 2014

Observational cohort study at a single academic/tertiary hospital with

5 ICUs (n=63 beds) examining association between ICU bed

availability and ICU readmission/ward cardiac arrest rate

If there are no

ICU beds

available,

there may be

impact on

adverse events

and outcomes

Cardoso et al Crit Care 2011

Queuing: longer

waiting time

increases risk of

mortality

HR (per hr) 1.015;

(95%CI, 1.01-1.02)

Daud-Gallotti et al PLoS One 2014

Increased RN

workload, defined by

NAS, associated with

↑ risk of nosocomial

infection

Bedside Workload and Mortality

Bagshaw et al Under Review

Weissman et al JCRC 2015

Retrospective cohort study utilizing Project IMPACT database

n=776,905 evaluable patient-days for VTE prophylaxis (68%)

2 strain

measures

associated with

reduced odds of

receiving VTE

prophylaxis

Hua et al ICM 2016

Teno et al JAMA IM 2016

As ICU beds

increased, advanced

dementia patients

were more likely to

receive MV

~ OR 1.06

95%CI, 1.05-1.07)

Avoidable Days:

• Range 4.8-11.3%

per month (173-402

days/month)

• Cost ICU day

(~$3000 CDN) ~

range in avoidable

costs ~$6-14 million

CDN per year!

• ↑ 1-year trend

Avoidable Days: The proportion of total ICU patient-days accounted for by avoidable delay in ICU discharge. Source: eCritical Alberta

Final Thoughts

• Measuring strain on ICU capacity is complex (importance may vary

contingent on whether you are “on-call” or are medical leadership)

• Accumulating evidence imply strained capacity can exert direct and indirect

effects on patient care processes and outcomes

• We likely need a suite of QIs to understand “strain” at the ICU-level (beyond

the ICU KPIs we routinely capture):

– Structure: occupancy/bed availability; nurse:patient ratio

– Process: queuing; admission rate; acuity; workload; avoidable bed-days; after hours

discharge

– Outcome: patient (mortality; length of stay; family satisfaction); healthcare provider

(turnover; sick days); ICU performance (SMR)

Thank You for Your Attention!

• Acknowledgements:

– Dawn Opgenorth (Project Manager)

– Xiaoming Wang (Statistician, AHS)

– Malik Agyemang (eCritical Alberta)

– Guanmin Chen and Peter Faris (Statistician, AHS)

• Co-Investigators/collaborators:

– Tom Stelfox, Damon Scales, David Zygun, David McKinlay, Armann Ingolfsson, Peter

Dodek, Dan Zuege, Elaine Gilfoyle, Barbara Sonnenberg, Brian Rowe, Paul Boucher,

Michael Meier, Luc Berthiuame, Noel Gibney, Chip Doig

E: bagshaw@ualberta.ca Tw: @drseanbagshaw