Penn Medicine

1 http://www.uphs.upenn.edu/cep

Penn Medicine

The Impact of an Early Warning System for Sepsis

CHOP Healthcare InformaticsApril 25 , 2014

Joel Betesh, MD

Gordon Tait, BS

Asaf Hanish, MPH

Benjamin French, PhD

Neil Fishman MD

Barry Fuchs, MD

Christine Vanzandbergen, PA, MPH

Craig A Umscheid, MD, MS

2 http://www.uphs.upenn.edu/cep http://www.uphs.upenn.edu/cep

The University of Pennsylvania Health System

Three acute care hospitals:• Hospital of the University of Pennsylvania• Penn Presbyterian Medical Center• Pennsylvania Hospital

• 1,540 acute care beds• 80,020 acute care admissions in 2012• 73 ACGME accredited training programs


Sepsis Mortality Index (SMI) Definition of Sepsis Mortality Index includes

patients with:• Discharge status of ‘Expired’• Principal and/or secondary diagnosis/diagnoses

related to sepsis

Expected rate based upon MS-DRG-based algorithm

ExpectedObservedSMI


Penn Medicine SMI for FY 2011Goal: Lower the high Sepsis Mortality Index

(SMI) at the University of Pennsylvania as determined by University HealthSystem Consortium (UHC)

UHC SMI data for FY11• SMI: 1.50 (UPHS), 1.54 (HUP)• UHC Median: 1.19

– Top 5 Performers: 0.53, 0.65, 0.77, 0.78, 0.78• UHC Rank for Penn: 67/113• Target (Best Quartile): 0.98


The Opportunity

• Since the fall of 2011, we have vital sign, covering provider and covering nurse data in our inpatient EHR, Sunrise Clinical Manager.

• Other hospitals have begun to use automated alerts for Sepsis based on data in their EHRs.


The SIRS Criteria

2 or more of the following criteria:

• Temperature > 100.4°F or < 96.8

• HR > 90

• RR > 20 or PaCO2 < 32

• WBC > 12000 < 4000, or > 10% immature (band) forms


Penn Medicine Criteria for Presumed Sepsis

Variable Point

Temperature <36°C or >38°C 1

Heart Rate >90 beats per minute 1

RR >20 breaths/min; or PACO2 <32 mmHg 1

WBC count <4000 or >12,000 or >10% bands1

Lactate >2.2 1

Systolic blood pressure <100 1

RR: respiratory Rate; WBC: white blood cell

Criterion for presumed sepsis: >4 points


4,700 patients. Criterion for presumed sepsis: >4 points. 193 patients scored 4 or more. A score of 4 or more had a positive predictive value of about 23% for death, transfer to ICU or RRT. On average these events occurred over 48 hours after the score of 4 was first met. We felt there were an additional 20% of patients who had sepsis and would be identified by this trigger and could benefit from early diagnosis and treatment. This early warning system was likely to also identify patients who had new episodes of bleeding, cardiac ischemia and pulmonary emboli.

Results of Retrospective Analysis (4 week period Oct 2011)


EWS Data Gathering and Goals

• Decrease total and sepsis related inpatient mortality

• Decrease time to antibiotics

• Decrease ICU transfers (ICU transfers could also increase)

• Decrease RRTs (RRTs could also increase)


Decisions Made Score of 4+ had an acceptable Positive Predictive Value

• 23% for hard outcomes of death, ICU transfer or RRT

• Anticipated additional 20-30% for sepsis

Exclude ICUs, Maternity, PACU, ER, Hospice, and patients <18 years

Fire ONCE per visit to start

Alert to bring to bedside within 30 minutes patient’s intern and nurse manager (notified by pager) and patient’s nurse (notified by pop up alert in EHR)

Limit to patients admitted at go-live or after (9/5/2012)– To gradually ramp up and not overwhelm staff


EWS System Architecture


Alarm Process (I)

Will retrieve offending criteria Will fetch and display:

Last 24 hours of Vital Signs

Last 48 hours of relevant Labs

Task Form-based

1) The Alarm Process will: Auto-enter the “Early Warning System Assessment” order

• Will generate a primary task for the Covering Nurse (see below):


Alarm Process (II) 2) Primary task completion will create a follow-up task (aka Survey)

– See below:


Primary Process Evaluation

NOTIFICATION• Coordinator page sent - 99%

• Small, well defined group with limited devices

• Nursing pop-up notification viewed - 72%• Relies on accurate nursing assignment in SCM

• Covering provider notification sent - 83%• Relies on accurate assignment in SCM• Large number of devices and carriers

SCM Task completion• Primary RN task - 95%• Follow up coordinator task - 95%

Team gathered at bedside within 30 minutes - 90%


Preliminary results of EWS alert Silent/control period:

• Admitted between Jun 06, 2012 and Sep 04, 2012

• Discharged by Oct 04, 2012

• 15,570 admissions

• 595 patients triggered the alert


Preliminary results of EWS alert

Loud/intervention period: • Admitted between Sep 12, 2012 and Dec 11, 2012

• Discharged by Jan 10, 2013

• 16,103 admissions

• 731 patients triggered the alert.

Chi square compared proportions and Wilcoxon Rank Sum to compare medians before and after the alert went live

Results adjusted for age, gender, admitting service and Charlson index


EWS Process and Outcome Measures


OUTCOME MEASURES

Trend downward but did not achieve significance.

HUP PAH PMC UPHS0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

2.0%

4.0%

1.1%

2.2%

0.8%1.6%

0.8% 1.0%

% RRT < 6hrs

Pre/Silent Post/Loud

Facility

% In

patie

nts

Post-period at HUP restricted to Nov/Dec.


Subset AnalysisWe looked at subsets of patients who might show more of a benefit for the alert such as:

Elective admissionsEmergency admissionsMedicine admissionsSurgery admissionsPatients with a discharge diagnosis of sepsis


Subgroup AnalysisPatients with a Discharge Diagnosis of Sepsis

Mortality

Entity StudyperiodSeptic patients Observed Expected O-E

HUP Pre/Silent 179 27 23.7 1.14

HUP Post/Loud 214 35 28.0 1.25

PAH Pre/Silent 21 4 2.2 1.82

PAH Post/Loud 30 2 3.1 0.65

PMC Pre/Silent 28 8 2.4 3.36

PMC Post/Loud 41 3 4.2 0.71

UPHS Pre/Silent 228 39 28.3 1.38

UPHS Post/Loud 285 40 35.4 1.13


Since go live in 2012

Done: Alert added in the ER Added alert in Acute Rehab and LTach Units Added second alert after 10 day buffer at Rehab and LTACH

Being Discussed: Allowing a second trigger during a single hospital stay Heavy analysis of data with a view to decrease false positives

and increasing sensitivity of alert Using change in systolic BP rather than absolute value Machine learning approach to identify new/better parameters

for the alert


Penn Medicine Sepsis Mortality Index for FY 2013


Acknowledgments Katherine Clark

Jessica Guidi

Mark Upton

Hilary Faust

Denise Feeley

Meghan Lane-Fall

Mark Mikkelsen

William Schweickert

Patrick Donnelly

Jean Romano

Kirsten Smith

Barry Fuchs

And many others


Contact:

[email protected] Joanne. [email protected] [email protected]

[email protected]

Questions? Suggestions?

mailto:[email protected]




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