In an era of rising healthcare costs and increasing emphasis on value-based healthcare spending, laboratory testing that is unnecessary, redundant, or otherwise wasteful is of increasing concern. Such testing may occur for myriad reasons including diagnostic uncertainty, defensive medicine, patient reassurance, or knowledge gaps regarding best practices. Whereas some studies have found the incidence of inappropriate laboratory testing to be as high as 95% (1 ), the true impact of laboratory overtesting is not known and likely varies between institutions and practice settings. The costs-both direct and indirect-of these unwarranted tests are subsequently passed on to the patient, third-party payer, or hospital depending on the reimbursement model.Multiple investigators have studied strategies to reduce unnecessary testing that include provider education, audit and feedback, incentives or penalties, and system-based interventions such as electronic order entry and clinical decision support systems (2 ). These interventions have met with varying success, with reported changes in use ranging from a 98% reduction to a 28% increase in testing (2 ). Techniques that use multimodality strategies to reduce laboratory overutilization seem to be most effective (3 ). With the widespread adoption of the electronic health record (EHR) 2 in recent years, computerized provider order entry (CPOE) systems have emerged as attractive targets for intervention, with the potential for interrupting unwarranted services before implementation. However, studies have shown mixed outcomes and cost savings with this approach (4 -6 ).Cardiac troponin testing has drawn particular scrutiny for potential overuse because of the high proportion of hospitalized patients in whom this test is performed, as well as the frequent ordering of serial tests. In a recent study by Makam and Nguyen (7 ), cardiac biomarkers were measured in 16.9% of all emergency department visits in the US over a 2-year period, despite the absence of acute coronary syndrome (ACS) symptoms in nearly one-third of those tested. Although cardiac troponins are recognized for their sensitivity and specificity in detecting myocardial injury, they are not specific for ACS (8 ). Thus, the advantage of a high negative predictive value in ruling out myocardial infarction (MI) is offset by a high clinical false-positive rate due to non-ACS troponin increases, a balance that is heavily influenced by the pretest probability of ACS in the tested population (7-9 ). The implications of unnecessary cardiac troponin testing therefore include not only the cost of the assay itself, but also the costs (financial and otherwise) of downstream cardiac testing and patient care to follow up on any falsepositive results (7 ). Already a source of major concern and frustration for practicing cardiologists, the "troponin consult" for non-ACS troponin increases is likely to become even more common with increasing adoption of high-sensitivity troponin assays (9 ).In this context, we read with great interest t...
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 P2Y 12 -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.
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