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Figure3

Algorithm for the investigation of patients with elevated cardiac troponin concentrations in the context of an alternative acute illness. Change in cardiac troponin concentration on serial measurement is used to identify patients with acute and chronic myocardial injury. The definition of change in cardiac troponin will be dependent on the assay and should be consistent with the local pathway for the assessment of patients with an isolated presentation with suspected acute coronary syndrome. CAD, coronary artery disease.

Patients presenting with isolated symptoms or signs of myocardial ischaemia should be assessed using established pathways for patients with suspected acute coronary syndrome ( figure 4 ). Appropriate diagnostic and risk stratification thresholds will differ depending on the high-sensitivity cardiac troponin assay in use.

Figure4

Pathway for the investigation of patients with isolated suspected acute coronary syndrome optimised for the ARCHITECT high-sensitivity cardiac troponin I assay. Reproduced from Shah . 17

Those who develop symptoms and signs of myocardial ischaemia in the context of another acute illness should undergo serial high-sensitivity cardiac troponin testing. Patients should be classified with either acute or chronic myocardial injury based on a change in cardiac troponin concentration, ideally using assay-specific absolute delta criteria. In the absence of these criteria, those with troponin concentrations ≤99th centile at presentation with an increase of >50% of the 99th centile upper reference limit on serial testing (and at least one value >99th centile) are considered to have acute myocardial injury. Where troponin concentrations are >99th centile at presentation, a relative change of >20% is consistent with acute injury. 26 In patients who meet these criteria, careful clinical assessment is required to determine the likelihood of coronary artery disease. There are no dedicated risk assessment tools for use in this setting, and therefore this assessment relies on clinical judgement, review of the presenting symptoms, medical history, cardiovascular risk factors and serial 12-lead electrocardiographic findings. There is an unmet need for novel risk prediction scores or validation of existing tools, such as the GRACE score, to guide clinicians when assessing patients with acute myocardial injury.

Those patients known to have coronary artery disease may not require further investigation if the mechanism of acute myocardial injury is secondary to oxygen supply-demand mismatch. This may occur in a wide range of conditions where there has been a sustained period of hypotension, tachycardia or hypoxaemia. However, where there is no evidence of oxygen supply-demand mismatch, invasive coronary angiography should be considered to determine whether acute myocardial injury is a consequence of plaque rupture or thrombosis. Where type 1 myocardial infarction is confirmed, application of the GRACE score confers important prognostic information. 15 Those without known coronary artery disease should be considered for invasive or CT coronary angiography. Where obstructive coronary artery disease is identified and oxygen supply-demand imbalance has been documented, the diagnosis of type 2 myocardial infarction may be helpful and patients should be considered for secondary prevention. Those patients without obstructive coronary artery disease have acute myocardial injury as a consequence of their presenting illness.

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> 16.3 Cluster-randomized trials > 16.3.2 Assessing risk of bias in cluster-randomized trials

16.3.2 Assessing risk of bias in cluster-randomized trials

In cluster-randomized trials, particular biases to consider include: (i) recruitment bias; (ii) baseline imbalance; (iii) loss of clusters; (iv) incorrect analysis; and (v) comparability with individually randomized trials.

(i) Recruitment bias can occur when individuals are recruited to the trial after the clusters have been randomized, as the knowledge of whether each cluster is an ‘intervention’ or ‘control’ cluster could affect the types of participants recruited.Farrin et al. showed differential participant recruitment in a trial of low back pain randomized by primary care practice; a greater number of less severe participants were recruited to the ‘active management’ practices (Farrin 2005) .Puffer et al. reviewed 36 cluster-randomized trials, and found possible recruitment bias in 14 (39%) (Puffer 2003) .

(ii) Cluster-randomized trials often randomize all clusters at once, so lack of concealment of an allocation sequence should not usually be an issue.However, because small numbers of clusters are randomized, there is a possibility of chance baseline imbalance between the randomized groups, in terms of either the clusters or the individuals.Although not a form of bias as such, the risk of baseline differences can be reduced by using stratified or pair-matched randomization of clusters.Reporting of the baseline comparability of clusters, or statistical adjustment for baseline characteristics, can help reduce concern about the effects of baseline imbalance.

(iii) Occasionally complete clusters are lost from a trial, and have to be omitted from the analysis.Just as for missing outcome data in individually randomized trials, this may lead to bias.In addition, missing outcomes for individuals within clusters may also lead to a risk of bias in cluster-randomized trials.

(iv) Many cluster-randomized trials are analysed by incorrect statistical methods, not taking the clustering into account.For example, Eldridge et al. reviewed 152 cluster-randomized trials in primary care of which 41% did not account for clustering in their analyses (Eldridge 2004) .Such analyses create a ‘unit of analysis error’ and produce over-precise results (the standard error of the estimated intervention effect is too small) and P values that are too small.They do not lead to biased estimates of effect. However, if they remain uncorrected, they will receive too much weight in a meta-analysis.Approximate methods of correcting trial results that do not allow for clustering are suggested in Section 16.3.6 . Some of these can be implemented by review authors.

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