Background: Quality and performance measures for acute MI are based on evidence for treatment of type 1 MI as defined by the Universal Definition of MI. Patients with other MI subtypes still receive a coded diagnosis of MI, and are subject to the same quality metrics as those with type 1 MI, despite different pathophysiology and a lack of evidence-based treatment standards. In order to assess the potential health system impact, we examined the correlation between coded diagnosis of MI and clinical MI subtype, as well as rates of adherence with MI guideline-based targets across MI subtypes.
Methods: We retrospectively examined every inpatient encounter during calendar year 2013 with a final primary coded diagnosis of acute MI at our two affiliated academic medical centers, one county safety-net hospital serving the urban poor and one private hospital. Using all available medical records, each case was adjudicated by two independent investigators and the clinical MI type was determined based on the Universal Definition of MI. Adherence with MI performance metrics was also extracted from the medical record.
Results: Out of 289 encounters at one hospital and 139 at the other, 224 (77.5%) and 105 (75.5%) cases were adjudicated as type 1 MI, respectively. Type 2 MI, or myocardial oxygen supply-demand mismatch, was the next most common diagnosis, occurring in 15.2% (44 of 289) and 14.4% (20 of 139) of cases at the two hospitals. Compared to type 1 MI, encounters not adjudicated as type 1 MI were significantly less likely to have received guideline-recommended MI therapies, with marked differences in use of P2Y12-inhibitors and revascularization, modest differences in use of aspirin and statins, and no difference in beta-blockers (Table).
Conclusions: Approximately 25% of patient encounters with a primary coded diagnosis of acute MI did not represent type 1 MI events. Patients without type 1 MI were significantly less likely to receive guideline-recommended MI therapies. These findings highlight an important disconnect between clinical and coding diagnoses, with potential important implications for patient care, billing practices, and quality and outcome reporting.