Missed Diagnosis of Acute Myocardial Infarction in the Emergency Department (Text Version)

On September 28, 2010, Ernest Moy made this presentation at the 2010 Annual Conference. Select to access the PowerPoint® presentation (1.4 MB). 

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Slide 1

Missed Diagnoses of Acute Myocardial Infarction in the Emergency Department: An Exploration Using HCUP Data

AHRQ Annual Meeting
September 28, 2010

Slide 2

Team

• Agency for Healthcare Research and Quality
  • Ernest Moy, MD, MPH
• Thomson Reuters
  • Cheryl Kassed, PhD, MSPH
  • Marguerite Barrett, MS
  • Rosanna Coffey, PhD
  • Anika Hines, PhD, MPH

Slide 3

Outline

• Background
• Specific Aims
• Methods
• Results
• Conclusion
• Implications

Slide 4

Background

• Some patients with acute myocardial infarction (AMI) are mistakenly released from the emergency department (2-5%).
• Such patients may have increased mortality.
• Failure to hospitalize may be related to race, gender, and the absence of typical cardiac symptoms.
• Little work comparing rates across institutions.

Slide 5

Specific Aims

• To explore the use of administrative data to identify missed diagnoses of AMI:
  • How do HCUP estimates compare to the literature?
  • How do rates of missed diagnosis of AMI vary across subgroups?
  • How do rates of missed diagnosis of AMI vary across hospitals?

Slide 6

Data: HCUP

• Healthcare Cost and Utilization Project (HCUP) is a family of health care databases developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ):
  • SID: State Inpatient Databases = universe of inpatient discharge records from 42 states.
  • SEDD: State Emergency Department Databases = hospital-affiliated emergency departments visits that do not result in hospitalizations.

Slide 7

Methods

• Sample: HCUP data for 9 states with reliable person linkages and race/ethnicity data— AZ, FL, MA, MO, NH, NY, SC, TN, UT.
• Design: Cross-sectional analysis of adults:
  • 18 years or older.
  • First AMI admission between Feb and Dec 2007.
• Analysis: Subgroup estimates compared using t-tests (p-value<0.05).

Slide 8

Methods

• Key Measure:
  • Percentage of patients with an AMI admission who were seen in the ED within the prior 2 to 7 days for a cardiac-related issue:
    • Cardiac diagnosis/symptom.
    • Abdominal pain.

Slide 9

Percent of patients with an ED visit with likely missed AMI—patient attributes

Bar chart showing attributes

Female (ref): 1.6%
Male: 1.7%
Age 18-24 (ref): 3.7%
Age 30-44: 3.1%
Age 45-64: 2.1%
*Age 64-74: 1.5%
*Age 74-85: 1.5%
*Age 85+: 0.7%

White, Non-Hispanic (ref): 1.5%
*African-American: 1.9%
*Hispanic: 1.4%
Asian/Pacific Islander: 1.5%

High income (ref): 1.3%
Moderate income: 1.4%
*Low income: 1.5%
*Very low income: 2.2%

Private insurance (ref): 2.1%
*Medicare: 1.3%
Medicaid: 2.0%
*Uninsured: 2.7%

*p <0.05

Slide 10

Percent of patients with an ED visit with likely missed AMI—hospital attributes

Large metropolitan (ref): 1.1%
*Small metropolitan: 1.3%
*Micropolitan: 5.4%
*Non-core rural: 14.7%

*<100 beds: 9.6%
100-299 beds (ref): 2.1%
*300-499 beds: 0.7%
*500 or more beds: 0.5%

Private, not-for-profit (ref): 1.4%
*Private, for-profit: 1.9
*Public hospitals: 2.2%

Non-teaching (ref): 2.4%
*Teaching: 0.7%

Low occupancy (ref): 9.1%
*Moderate occupancy: 1.7%
*High occupancy: 1%

*p <0.05

Slide 11

Percent of patients with an ED visit with likely missed AMI—other attributes

Weekday (ref): 1.6%
*Weekend: 1.9%

*Jan-Feb: 1.3%
*Mar-Apr: 1.4%
May-Jun (ref): 1.8%
Jul-Aug: 1.7%
Sept-Oct: 1.8%
Nov-Dec: 1.8%

Hospital with cardiac cath (ref): 0.7%
*Hospital without cardiac cath: 6.1%

Slow ED day (ref): 3.6%
*Moderate ED day: 1.7%
*Busy ED day: 1.3%
*Crowded ED day: 1.3%

*p <0.05

Slide 12

Conclusions

• Study rate of AMI missed diagnosis=1.85%
  • Pope et al, study found 2.1%.
• Administrative data are a reasonable source for estimating missed AMI diagnoses.

Slide 13

Conclusions

• Unsurprising results:
  • Vulnerable populations have higher rates of missed diagnoses for AMI—minorities, the uninsured, those with low-income, and those visiting hospitals in rural areas.
• Surprising results:
  • Busy hospitals have lower rates of AMI missed diagnoses (i.e. hospitals with higher occupancy rates, higher bed volume, and residency programs).
  • Weekend visits and slow ED days have higher rates of AMI missed diagnoses.

Slide 14

Limitations

• Administrative data → lower estimates of missed diagnoses.
• Data not representative—9 states.

Slide 15

Implications

• Administrative data may be useful for studying other types of missed diagnoses.
• Reporting on variation in missed diagnoses could lead to better quality of care.