Higher sensitivity and NPV with the ESC 0/1-hour and 0/2-hour algorithms for acute MI
Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction
Introduction and methods
Background
The 0/1-hour and 0/2-hour algorithms are first and second choices for high-sensitivity cardiac troponin (hs-cTn)-based diagnostic protocols for the triage of patients with suspected acute myocardial infarction (AMI) in the 2020 ESC guidelines [1]. These algorithms apply assay-specific thresholds for cardiac troponin lower than the 99th percentile of a normal reference in combination with absolute changes within hour 1 or 2 for the triage of patients [2-9].
The 0/3 hour algorithm, which applies a cardiac troponin threshold at the 99th percentile of a normal reference within 3 hours in combination with clinical criteria, is recommended as an alternative in the 2020 guidelines [1].
In this meta-analysis, the diagnostic accuracies and triage efficacies of the ESC 0/1-hour, 0/2-hour, and 0/3-hour algorithms were examined.
Methods
A literature search through PubMed, Embrase, Cochrane Central Register of Controlled Trials, Web of Science and Scopus was performed for studies published between January 2011 and December 2020. Eligible studies were RCTs, prospective cohort studies or implementation studies that evaluated the diagnostic accuracy of the ESC 0/1-hour, 0/2-hour, or 0/3-hour algorithm in adult patients with suspected NSTEMI or ACS. There was a focus on studies using the Elecsys hs-cTnT (Roche), Architect hs-cTnl (Abbott) and Centaur/Atellica hs-cTnl (Siemens) assays because these are predominantly used by EDs.
A total of 32 studies with 30,066 patients and 4246 cases of index AMI were included.
Outcomes
The primary outcome was diagnostic accuracy (sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) with index admission AMI. Secondary outcomes were 30-day mortality, and proportion of patients triaged toward rule-out, observation or rule-in.
Main results
Rule-out performance
- The 0/1-hour algorithm classified 54% (95%CI:45-62%) of patient to the rule-out category with a pooled sensitivity of 99.1% (95%CI:98.5-99.5%, I²=0%) and NPV of 99.8% (95%CI: 99.6-99.9%).
- The 0/2-hour algorithm classified 61% (95%CI:52-69%) of patients to the rule-out category with a pooled sensitivity of 98.6% (95%CI:97.2-99.3%, I²=0) and NPV of 99.6% (95%CI: 99.4-99.8%).
- For the 0/3-hour algorithm, this was 66% (95%CI:52-79%) of patients with a pooled sensitivity of 93.7% (95%CI:87.4-97.0%, I²=84%) and NPV of 98.7 (95%CI:97.7-99.3%).
- Mortality rates were comparable for patients in the rule-out category for all 3 algorithms.
Rule-in performance
- The 0/1-hour algorithm classified 17% (95%CI:12-23%) of patients to the rule-in category with a pooled specificity of 94.0% (95%CI:90.7-96.2%, I²=98%) and PPV of 65.1% (95%CI:56.3-73.0%).
- The 0/2-hour algorithm classified 15% (95%CI:9-22%) of patients to the rule-in category with a pooled specificity of 96.1% (95%CI: 92.9-97.9%, I²=98%) and PPV of 75.9% (95%CI:66.3-83.5%).
- For the 0/3-hour algorithm this was 19% (95%CI:12-28%) of patients with a pooled specificity of 93.2% (95%CI:86.9-96.6%, I²=98%) and PPV of 64.4% (95%CI: 47.4-78.3%).
Conclusion
This meta-analysis showed that the 0/1-hour and 0/2-hour algorithms have higher sensitivities and NPVs than the 0/3-hour algorithm for the triage of patients with suspected MI.
The pooled sensitivities for the 0/1-hour, 0/2-hour, and 0/3-hour algorithms were 99.1%, 98.6% and 93.7%. With a prevalence of AMI of 12 % across the studies, 120 of 1000 tested patients would receive a diagnosis of MI. Of these 120 patients, no more than 3 may test false-negative according to the 0/1-hour and 0/2-hour algorithms and up to 15 may test false-negative according to the 0/3-hour algorithm.
Pooled specificities for the 0/1-hour, 0/2-hour and 0/3-hour algorithm were 94.0%, 96.1% and 93.2%, implying that of the 880 of 1000 patients without AMI, 18 to 115 patients may test false-positive using the ESC algorithms.
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