Critical Decisions in the Emergency Department

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Slide 1

Critical Decisions in the Emergency Department

University of Pennsylvania:
Brendan G. Carr, MD MS
Sage Myers, MD MS
Scott Lorch, MD MS
Patrick Reilly, MD
Dylan Small, PhD
Charles C. Branas, PhD

Agency for Healthcare Research & Quality (AHRQ)
Ryan Mutter, PhD

Slide 2

How do we design and measure the emergency care system?
(Trauma as a case study)

University of Pennsylvania:
Brendan G. Carr, MD MS
Sage Myers, MD MS
Scott Lorch, MD MS
Patrick Reilly, MD
Dylan Small, PhD
Charles C. Branas, PhD

Agency for Healthcare Research & Quality
Ryan Mutter, PhD

Slide 3

Disclosures

• Federal research funding:
  • AHRQ, National Institute of Child Health and Human Development (NICHD), Centers for Disease Control and Prevention (CDC), National Institute of Neurological Disorders and Stroke (NINDS).
  • www.traumamaps.org.
  • www.strokemaps.org.
• American Hospital Association (AHA) research funding:
  • National Registry of Cardiopulmonary Resuscitation (NRCPR) / Get With The Guidelines® (GWTG).
• National Quality Forum:
  • RECS Steering Committee.
• HHS/Assistant Secretary for Preparedness and Response (ASPR) Senior Policy Advisor:
  • I am not appearing in this role today.

Slide 4

Conceptual Framework

• Ambulatory Care Sensitive Conditions:
  • Conditions for which good outpatient care can potentially prevent the need for hospitalization, or for which early intervention can prevent complications or more severe disease.
• Emergency Care Sensitive Conditions: [Image: A red arrow points to this text.]
  • Conditions for which rapid diagnosis and early intervention in acute illness or acutely decompensated chronic illness improves patient outcomes.

Slide 5

The "3Ts" Road Map to Transform U.S. Health Care
The "How" of High-Quality Care

Image: The figure depicts the "3Ts" Road Map. A double-headed arrows points between "Basic biomedical science" and T1: "Key T1 activity to test what care works. Clinical efficacy research." A second double-headed arrows points between T1 and "Clinical efficacy knowledge." A third double-headed arrows points between "Clinical efficacy knowledge" and T2: "Key T2 activities to test who benefits from promising care. Outcomes research. Comparative effectiveness research. Health services research." Another double-headed arrows points between T2 and "Clinical effectiveness knowledge." The next double-headed arrows points between "Clinical effectiveness knowledge" and T3: "Key T3 activities to test how to deliver high-quality care reliably and in all settings. Measurement and accountability of high health care quality and costs. Implementation of interventions and health care system redesign. Scaling and spread of effective interventions. Research in above domains." A single headed arrows points from T3 to "Improved health care quality and value and population health." A double-headed arrows points between T3 and "Basic biomedical science."

Slide 6

Background: The Volume-Outcome relationship

Image: A screenshot from the New England Journal of Medicine (NEJM) Web site shows the abstract for a 1979 article titled "Should operations be regionalized? The empirical relation between surgical volume and mortality." The following text is superimposed over the article abstract:

• 12 surgical procedures: CABG (coronary artery bypass grafting), AAA (abdominal aortic aneurysm), TURP (transurethral resection of the prostate), etc.
• 1500 hospitals.

↑ Procedures = ↓ Mortality

Slide 7

Images: The title and introduction section of a 2000 JAMA article by David Magid titled "Relation between hospital primary angioplasty volume and mortality for patients with acute MI treated with primary angioplasty vs thrombolytic therapy" is shown. A line graph is superimposed over the article, showing adjusted odds of death from a retrospective cohort study of 446 acute care hospitals stratified into low, intermediate, and high volume for angioplasty: Low = 1.1, Medium = slight above 0.6, High = 0.6.

Slide 8

Cardiac Arrest Mortality

Image: A graph shows standardized mortality percentage for cardiac arrest patients admitted to ICU per year. A note above a bracketed area of the graph states "Hospitals that treated over 50 patients per year had lower mortality."

Slide 9

More Experience Means Better Results

Images: A photograph shows the masked face of a surgeon at work. The University of Pennsylvania Health System logo is also shown.

Slide 10

Background: The time-outcome relationship

Images: The title of a 2000 JAMA article by Cannon et al. titled "Relationship of symptom-onset-to-balloon time and door-to-balloon time with mortality in patients undergoing angioplasty for acute myocardial infarction" is shown. A graph below the article title shows "Door to Balloon Time" in minutes with multivariate-adjusted odds of In-Hospital Mortality.

Slide 11

Images: The title of a 2000 Neurology article by Marler et al. titled "Early stroke treatment associated with better outcome" is shown. A graph below the article title shows odds ratio of favourable outcome at 3 months compared with minutes from stroke onset to start of treatment. The odds ratio of favourable outcome (1.7 [1.2 -2.6]) is 30% more likely to have minimal or no disability at three months. Mortality at three months was 17 vs. 21% (p = 0.3).

Slide 12

Sepsis

Images: The title of a 2001 NEJM article by Rivers et al. titled "Early goal-directed therapy in the treatment of severe sepsis and septic shock" is shown. A partial table below the article title shows Kaplan-Meier estimates of mortality and causes of in-hospital death. The column showing relative risk is circled in red. Relative risk of in-hospital, 28 day, and 60 day mortality was substantially reduced under early goal-directed therapy.

Slide 13

Images: The titles of two 2001 NEJM articles on induced/therapeutic hypothermia are shown. A line graph below the articles compares cumulative survival percentages for hypothermia and normothermia patients at risk. The hypothermia patients show better survival percentages for up to 200 days.

Slide 14

The "3Ts" Road Map to Transform U.S. Health Care
The "How" of High-Quality Care

Image: The "3Ts" Road Map figure shown on Slide 5 is repeated. A red arrow points to "Clinical efficacy knowledge."

Slide 15

STEMI

Images: The title of the 2000 JAMA article by Cannon et al. and graph shown on Slide 10 are repeated.

Slide 16

STEMI

Images: The title of the 2000 JAMA article by Cannon et al. and graph shown on Slide 10 are repeated; the graph is grayed out and red text is superimposed over it, stating, "30+% of STEMI [ST-segment-elevation myocardial infarction] patients get no reperfusion therapy."

Slide 17

Images: The title of the 2000 Neurology article by Marler et al. and graph shown on Slide 11 are repeated.

Slide 18

Images: The title of the 2000 Neurology article by Marler et al. and graph shown on Slide 11 are repeated; the graph is grayed out and red text is superimposed over it, stating, "3% of ischemic strokes treated at TJC [The Joint Commission] certified centers. 3 - 8.5% receive rt-PA [recombinant tissue plasminogen activator]."

Slide 19

Images: The title of the 2001 NEJM article by Rivers et al. and partial table shown on Slide 12 are repeated.

Slide 20

Images: The title of the 2001 NEJM article by Rivers et al. and partial table shown on Slide 12 are repeated; the table is grayed out and red text is superimposed over it, stating, "5-7% of EDs perform EDGT [early goal-directed therapy]."

Slide 21

Images: The titles of the two 2001 NEJM articles on induced/therapeutic hypothermia and line graph shown on Slide 13 are repeated.

Slide 22

Images: The titles of the two 2001 NEJM articles on induced/therapeutic hypothermia and line graph shown on Slide 13 are repeated; the graph is grayed out and red text is superimposed over it, stating, "26% of physicians have used hypothermia (ever)."

Slide 23

Image: A balance scale is shown; a small weight captioned "Volume" tips the scale and apparently has more weight than a larger weight captioned "Time" on the other side.

Slide 24

The "3Ts" Road Map to Transform U.S. Health Care
The "How" of High-Quality Care

Image: The "3Ts" Road Map figure shown on Slide 5 is repeated. A red arrow points to "Clinical effectiveness knowledge."

Slide 25

What is regionalization?

• The organization of a system for the delivery of health care within a region to avoid costly duplication of services and to ensure availability of essential services.
  —Mosby's medical dictionary

Slide 26

Regionalized Trauma Care

Image: The cover of the American College of Surgeons' Committee on Trauma report, Resources for Optimal Care of the Injured Patient, is shown.

Slide 27

Prehospital triage

Image: The Field Triage Decision Scheme: National Trauma Triage Protocol is shown. The Decision Scheme is available at http://www.cdc.gov/fieldtriage/.

Slide 28

The Trauma Model—Inventory

Image: A table captioned "National Inventory of Hospital Trauma Centers" shows trauma centers by State/Regional certification and American College of Surgeons (ACS) verification.

Slide 29

Access to trauma care

Images: Two maps of the United States are shown. One displays the areas of the United States within 45 minutes' access to Level I or II Trauma Centers by ambulance or helicopter; the other displays the areas of the United States within 60 minutes' access to Level I or II Trauma Centers.

Slide 30

Trauma care outcomes

Images: The title of a 2006 NEJM article by Mackenzie titled "A National Evaluation of the Effect of Trauma-Center Care on Mortality" is shown. A section of a table below the article title shows adjusted case fatality rates and relative risks of death after treatment in a trauma center, compared with treatment in a non-trauma center. The relative risk of death within 365 days after injury is circled in red: 0.75 (%95 CI 0.60 - 0.95).

Slide 31

Trauma Model. All success?

• 27,130,283 injuries treated in U.S. hospitals in 2006:
  • 32% in trauma centers.
  • 68% in non-trauma centers.
• Severely injured patients (ISS>15):
  • More likely to be treated in trauma centers
    (51.3% TC vs. 48.7% nTC, p<0.001).
• Critically injured patients (ISS>25):
  • More likely to be treated in non-trauma centers
    (41.6% TC vs. 58.4% nTC, p<0.001).

Slide 32

Research questions with policy implications

• Have we improved population outcomes for injury?
  1. In a nationally representative analysis—Do trauma centers save lives?
  2. What is the relationship between access to trauma care and injury outcomes? (supply and demand)?

Slide 33

Research questions with policy implications

• (What can understanding population outcomes for trauma teach us about examining other systems created to focus on unplanned critical illness?)
  • Stroke.
  • STEMI.
  • Cardiac arrest...

Slide 34

Q1. In a nationally representative analysis—Do trauma centers save lives?

• Population:
  • All injured patients treated at trauma centers and non-trauma centers in the U.S.
• Data:
  • Nationwide Emergency Department Sample (HCUP).
  • Trauma Center Level (American Trauma Society).
• Geography:
  • Patient location, hospital location (U.S. census, ArcGIS).
• Prehospital transport time estimates:
  • Empirically derived & arcGIS network analyst.

Slide 35

Q1. In a nationally representative analysis—Do trauma centers save lives?

• Analysis:

  Logistic regression:

  • Survey weights.
  • Confounders:
    • Age, injury severity, comorbid conditions, region, insurance, hospital size, teaching status, hospital ownership, (prehospital time).
  • Sub groups:
    • Severely injured, penetrating, blunt, age >55, only patients surviving to admission.

Slide 36

Characteristics of hospitals with ED encounters for injury—2009

Slide 37

Characteristics of injured patients—2009

Slide 38

Relation between treatment at a level 1 or 2 trauma center and death

* P <0.05, ** p <.01

Slide 39

Relation between treatment at a level 1 or 2 trauma center and death

* P <0.05, ** p <.01

Slide 40

Unmeasured confounders?

• Have not fully controlled for case mix?
• Have not fully controlled for injury severity?
  (no physiologic data)
• The system is intentionally (and effectively) regionalized.
  • The sickest and most complex patients are taken to the highest tier centers.
• Ideally, we would conduct a trial in which we randomize to treatment at a trauma center.

Image: The Field Triage Decision Scheme: National Trauma Triage Protocol is shown in the lower-right corner of the slide.

Slide 41

But we can't randomize...so... IV

Image: A figure shows the interrelation of four text boxes. At the center of the figure is a box containing the text "Trauma Center vs. Non-Trauma Center Hospital." An arrow points from this box to a box on the upper right, captioned "Death." In the lower right is a box containing the text "Unmeasured Confounders (e.g., unmeasured injury severity)"; two arrows point from this box to "Trauma Center vs. Non-Trauma Center Hospital" and "Death." In the lower left is a box containing the text "IV (Differential Travel Time)"; an arrow points from this box to "Trauma Center vs. Non-Trauma Center Hospital." A dotted-line arrow with an X on it points from "IV (Differential Travel Time)" to "Unmeasured Confounders." Another dotted-line arrow with an X on it points from "IV (Differential Travel Time)" to "Death."

Above the figure is the following text:

IV assumptions:

1. IV affects the treatment.
2. IV has no direct effect on the outcome.
3. IV is independent of unmeasured confounders.

Slide 42

Relation between treatment at a level 1 or 2 trauma center and death

* P <0.05, ** p <.01

Slide 43

Final model examining impact of trauma center on mortality*

* With IV.

Slide 44

The "3Ts" Road Map to Transform U.S. Health Care
The "How" of High-Quality Care

Image: The "3Ts" Road Map figure shown on Slide 5 is repeated. A red arrow points to "Improved health care quality and value and population health."

Slide 45

Question 2. Population outcomes for trauma

• Data Sources (trauma system—supply):
  • U.S. Census Data:
    • Location of residence at the level of the block group and county.
  • CDC, American Trauma Society, Penn Cartographic Modeling Lab:
    • Trauma center access.
• Data sources (injury death—demand):
  • National Center for Vital Statistics:
    • Multiple Cause of Death (MCOD) Data.

Slide 46

Question 2. Population outcomes for trauma

• Methods:
  • Supply Side—Access to trauma care:
    • Access to level 1/2 trauma center within an hour.
  • Demand Side—Injury Deaths:
    • ICD codes to identify injury death location.
    • Population data to calculate injury death rate.
• Analysis:
  • Examine relation between injury death rates and access to trauma care using poisson distribution.

Slide 47

Question 2. Population outcomes for trauma

• Results:
  • Supply:
    • 60 minute access to trauma care:
      • 84.7% of U.S. residents.
      • 46.4% of U.S. counties.
    • Mean time to care = 43 minutes +/- 22.
  • Demand:
    • 152,766 injury deaths in 2005.
    • 27,964 in counties without access within 60 min.
    • 124,802 in counties with access to care within 60 min.

Slide 48

Image: A map of the United States displays the areas within 60 minutes' access to Trauma Centers.

Slide 49

Image: A map of the United States displays highest injury death rates (greater than 50%).

Slide 50

Image: The information on the two maps shown in Slides 48 and 50 are combined. A block of text is superimposed over the map, stating, "Counties without access to trauma care within 60 minutes had higher rates of injury death when compared to counties with access to trauma care within 60 minutes (OR 1.24, 95% CI 1.18-1.30). The relative risk of death increased at a rate of 3.4% for each 10 minute increase in time to trauma care (95% CI 2.4%-4.4%)."

Slide 51

Q1. Next steps & remaining questions

• We have a dichotomous outcome variable but are using linear regression...
• Hard to estimate the strength of the instrument given survey design of the Nationwide Emergency Department Sample (NEDS).
  Is differential distance unrelated to outcome?
• It would be nice to generate point estimates.
• The direction of effect flips—do you believe it?

Slide 52

Q2. Next steps & remaining questions

• What is the right geographic unit to sum outcomes to?
  (we used counties)
• Should we be targeting counts or rates of death?
• Should we adjust for injury severity and case mix?
  (is there systematic variability in severity by geography?)

Slide 53

Questions?

Image: A photograph shows a road sign saying "Drive Carefully. We have two cemeteries, no hospital."