Algorithm can help quickly rule in, rule out AMI patients--may be costly
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Assays that detect the presence of cardiac troponin have been used in EDs to diagnose AMI in patients who present with chest pain, but they sometimes required multiple blood samples over six or more hours to provide results that allowed physicians to rule out or rule in patients with confidence. High sensitivity cardiac troponin (hs-cTn) assays have been developed that show promise in the early diagnosis of AMIs and potentially could speed up rule-in or rule-out decision making. But other conditions also elevate troponin levels, decreasing the positive predictive value (PPV) of the tests.
Tobias Reichlin, MD, of the cardiology department at University Hospital Basel in Switzerland, and colleagues wrote that at present, physicians are unsure how hs-cTn tests can be leveraged in clinical practice or what time interval between the baseline and second test is safe and feasible. They sought to develop and validate an algorithm that incorporates baseline high sensitivity cardiac troponin T (hs-cTnT) and absolute hs-cTnT changes within an hour for rapid rule-in or rule-out.
“Simple ‘how-to-use’ instructions for clinical decision making are critically needed to take clinical advantage of the new assays and to shorten the time to rule-in and rule-out AMI,” they wrote.
They used the international, multicenter APACE (Advantageous Predictors of Acute Coronary Syndrome Evaluation) database to identify 872 patients with acute chest pain who presented at EDs. Blood samples were collected to determine hs-cTnT (Roche Diagnostics) at presentation and then at one, two, three and six hours.
The researchers randomly assigned patients to two groups: one to develop (436 patients) and the other to validate (436 patients) the algorithm. The primary endpoint was death during 30-days follow-up.
Two independent cardiologists adjudicated the final diagnosis. Seventeen percent of the patients had a final diagnosis of AMI. Among the validation cohort—the 436 patients analyzed by the algorithm within an hour—60 percent were classified as rule-outs, 17 percent as rule-ins and 23 percent as in an observational zone. The sensitivity and negative predictive value (NPV) in the rule-out group was 100 percent. The specificity and PPV in the rule-in group was 97 percent and 84 percent, respectively. The number of patients falsely ruled in for AMI based on the algorithm was 12, while eight patients in the observational zone were finally classified as having AMI.
The cumulative 30-day survival for the rule-out, rule-in and observational groups was 99.8 percent, 98.6 percent and 95.3 percent, respectively. The 30-day mortality was 0.2 percent in the rule-out group, which the authors said reinforced that early discharge was appropriate for those patients.
“With the use of the algorithm, a safe rule-out as well as an accurate rule-in of AMI can be performed within an hour,” they wrote. “[U]sing this algorithm significantly shortens the time needed for rule-out and rule-in of AMI and may obviate the need for prolonged monitoring and serial blood sampling in three of four consecutive patients with acute chest pain.”
Reichlin and colleagues noted that 12-lead echocardiograms, patient history and physical examinations also are used in clinical practice and that the algorithm in conjunction with these resources might provide higher accuracy.
The proportion of patients with AMI was higher in their study than other chest pain studies, the researchers wrote, and consequently the algorithm should be validated in a lower-risk cohort and through additional studies.
L. Kristin Newby, MD, a cardiologist at Duke Clinical Research Institute in Durham, N.C., wrote in an editorial that prospective studies needed to assess not only the sensitivity, NPV and PPV but also clinical outcomes and the cost of implementation.
“It is unlikely that the observed 100 percent sensitivity and NPV and very high specificity and PPV will hold up in general practice in which patient populations almost certainly will be less selected, the prevalence of MI may vary widely and the prevalence of confounding comorbidities like heart failure and renal insufficiency will be higher,” Newby wrote. She recommended integrating the algorithm, once validated, into clinical decision support tools and in EMRs.
She described the study as “a major advance in understanding the application of hsTn testing that with continued development could substantially improve evaluation of ED patients with suspected MI.”
The study was supported in part by Abbott, Roche and Siemens Healthcare.