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

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

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.

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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).

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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.

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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

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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

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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

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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.

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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.

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Limitations

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

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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.