Dataset Information

The Manchester Acute Coronary Syndromes (MACS) decision rule for suspected cardiac chest pain: derivation and external validation.

ABSTRACT: We aimed to derive and validate a clinical decision rule (CDR) for suspected cardiac chest pain in the emergency department (ED). Incorporating information available at the time of first presentation, this CDR would effectively risk-stratify patients and immediately identify: (A) patients for whom hospitalisation may be safely avoided; and (B) high-risk patients, facilitating judicious use of resources.In two sequential prospective observational cohort studies at heterogeneous centres, we included ED patients with suspected cardiac chest pain. We recorded clinical features and drew blood on arrival. The primary outcome was major adverse cardiac events (MACE) (death, prevalent or incident acute myocardial infarction, coronary revascularisation or new coronary stenosis >50%) within 30 days. The CDR was derived by logistic regression, considering reliable (?>0.6) univariate predictors (p<0.05) for inclusion.In the derivation study (n=698) we derived a CDR including eight variables (high sensitivity troponin T; heart-type fatty acid binding protein; ECG ischaemia; diaphoresis observed; vomiting; pain radiation to right arm/shoulder; worsening angina; hypotension), which had a C-statistic of 0.95 (95% CI 0.93 to 0.97) implying near perfect diagnostic performance. On external validation (n=463) the CDR identified 27.0% of patients as 'very low risk' and potentially suitable for discharge from the ED. 0.0% of these patients had prevalent acute myocardial infarction and 1.6% developed MACE (n=2; both coronary stenoses without revascularisation). 9.9% of patients were classified as 'high-risk', 95.7% of whom developed MACE.The Manchester Acute Coronary Syndromes (MACS) rule has the potential to safely reduce unnecessary hospital admissions and facilitate judicious use of high dependency resources.

SUBMITTER: Body R

PROVIDER: S-EPMC4174131 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Manchester Acute Coronary Syndromes (MACS) decision rule for suspected cardiac chest pain: derivation and external validation.

Body Richard R Carley Simon S McDowell Garry G Pemberton Philip P Burrows Gillian G Cook Gary G Lewis Philip S PS Smith Alexander A Mackway-Jones Kevin K

Heart (British Cardiac Society) 20140429 18

<h4>Objective</h4>We aimed to derive and validate a clinical decision rule (CDR) for suspected cardiac chest pain in the emergency department (ED). Incorporating information available at the time of first presentation, this CDR would effectively risk-stratify patients and immediately identify: (A) patients for whom hospitalisation may be safely avoided; and (B) high-risk patients, facilitating judicious use of resources.<h4>Methods</h4>In two sequential prospective observational cohort studies a ...[more]

PMID: 24780911

Similar Datasets

Project description:ObjectivesThe Manchester Acute Coronary Syndromes ECG (MACS-ECG) prediction model calculates a score based on objective ECG measurements to give the probability of a non-ST elevation myocardial infarction (NSTEMI). The model showed good performance in the emergency department (ED), but its accuracy in the pre-hospital setting is unknown. We aimed to externally validate MACS-ECG in the pre-hospital environment.MethodsWe undertook a secondary analysis from the Pre-hospital Evaluation of Sensitive Troponin (PRESTO) study, a multi-centre prospective study to validate decision aids in the pre-hospital setting (26 February 2019 to 23 March 2020). Patients with chest pain where the treating paramedic suspected acute coronary syndrome were included. Paramedics collected demographic and historical data and interpreted ECGs contemporaneously (as 'normal' or 'abnormal'). After completing recruitment, we analysed ECGs to calculate the MACS-ECG score, using both a pre-defined threshold and a novel threshold that optimises sensitivity to differentiate AMI from non-AMI. This was compared with subjective ECG interpretation by paramedics. The diagnosis of AMI was adjudicated by two investigators based on serial troponin testing in hospital.ResultsOf 691 participants, 87 had type 1 AMI and 687 had complete data for paramedic ECG interpretation. The MACS-ECG model had a C-index of 0.68 (95% CI: 0.61 to 0.75). At the pre-determined cut-off, MACS-ECG had 2.3% (95% CI: 0.3% to 8.1%) sensitivity, 99.5% (95% CI: 98.6% to 99.9%) specificity, 40.0% (95% CI: 10.2% to 79.3%) positive predictive value (PPV) and 87.6% (87.3% to 88.0%) negative predictive value (NPV). At the optimal threshold for sensitivity, MACS-ECG had 50.6% sensitivity (39.6% to 61.5%), 83.1% specificity (79.9% to 86.0%), 30.1% PPV (24.7% to 36.2%) and 92.1% NPV (90.4% to 93.5%). In comparison, paramedics had a sensitivity of 71.3% (95% CI: 60.8% to 80.5%) with 53.8% (95% CI: 53.8% to 61.8%) specificity, 19.7% (17.2% to 22.45%) PPV and 93.3% (90.8% to 95.1%) NPV.ConclusionNeither MACS-ECG nor paramedic ECG interpretation had a sufficiently high PPV or NPV to 'rule in' or 'rule out' NSTEMI alone.

Project description:BackgroundHigh-sensitivity cardiac troponin (hs-cTn) assays have different analytic characteristics.ObjectivesThe goal of this study was to quantify differences between assays for common analytical benchmarks and to determine whether they may result in differences in the management of patients with suspected acute coronary syndrome (ACS).MethodsThe authors included patients with suspected ACS enrolled in the ROMICAT (Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography) I and II trials, with blood samples taken at emergency department presentation (ROMICAT-I and -II) or at 2 and 4 h thereafter (ROMICAT-II). hs-cTn concentrations were measured using 3 assays (Roche Diagnostics, Elecsys 2010 platform; Abbott Diagnostics, ARCHITECT i2000SR; Siemens Diagnostics, HsVista). Per blood sample, we determined concordance across analytic benchmarks (99th percentile). Per-patient, the authors determined concordance of management recommendations (rule-out/observe/rule-in) per the 0/2-h algorithm, and their association with diagnostic test findings (coronary artery stenosis >50% on coronary computed tomography angiography or inducible ischemia on perfusion imaging) and ACS.ResultsAmong 1,027 samples from 624 patients (52.8 ± 10.0 years; 39.4% women), samples were classified as <LOD (56.3% vs. 10.4% vs. 41.2%; p < 0.001), LOD-99th percentile (36.5% vs. 83.5% vs. 52.6; p < 0.001), >99th percentile (7.2% vs. 6.0% vs. 6.2%) by Roche, Abbott, and Siemens, respectively. A total of 37.4% (n = 384 of 1,027) of blood samples were classified into the same analytical benchmark category, with low concordance across benchmarks (<LOD 11.1%; LOD-99th percentile 29.3%; >99th percentile 43.6%). Serial samples were available in 242 patients (40.1% women; mean age: 52.8 ± 8.0 years). The concordance of management recommendations across assays was 74.8% (n = 181 of 242) considering serial hs-cTn measurements. Of patients who were recommended to discharge, 19.6% to 21.1% had positive diagnostic test findings and 2.8% to 4.3% had ACS at presentation.ConclusionsCaregivers should be aware that there are significant differences between hs-cTn assays in stratifying individual samples and patients with intermediate likelihood of ACS according to analytical benchmarks that may result in different management recommendations. (Rule Out Myocardial Infarction by Computer Assisted Tomography [ROMICAT]; NCT00990262) (Multicenter Study to Rule Out Myocardial Infarction by Cardiac Computed Tomography [ROMICAT-II]; NCT01084239).

Project description:BackgroundUnnecessary diagnostic imaging leads to higher costs, longer emergency department stays, and increased patient exposure to ionizing radiation. We sought to prospectively derive and validate two decision instruments (DIs) for selective chest computed tomography (CT) in adult blunt trauma patients.Methods and findingsFrom September 2011 to May 2014, we prospectively enrolled blunt trauma patients over 14 y of age presenting to eight US, urban level 1 trauma centers in this observational study. During the derivation phase, physicians recorded the presence or absence of 14 clinical criteria before viewing chest imaging results. We determined injury outcomes by CT radiology readings and categorized injuries as major or minor according to an expert-panel-derived clinical classification scheme. We then employed recursive partitioning to derive two DIs: Chest CT-All maximized sensitivity for all injuries, and Chest CT-Major maximized sensitivity for only major thoracic injuries (while increasing specificity). In the validation phase, we employed similar methodology to prospectively test the performance of both DIs. We enrolled 11,477 patients-6,002 patients in the derivation phase and 5,475 patients in the validation phase. The derived Chest CT-All DI consisted of (1) abnormal chest X-ray, (2) rapid deceleration mechanism, (3) distracting injury, (4) chest wall tenderness, (5) sternal tenderness, (6) thoracic spine tenderness, and (7) scapular tenderness. The Chest CT-Major DI had the same criteria without rapid deceleration mechanism. In the validation phase, Chest CT-All had a sensitivity of 99.2% (95% CI 95.4%-100%), a specificity of 20.8% (95% CI 19.2%-22.4%), and a negative predictive value (NPV) of 99.8% (95% CI 98.9%-100%) for major injury, and a sensitivity of 95.4% (95% CI 93.6%-96.9%), a specificity of 25.5% (95% CI 23.5%-27.5%), and a NPV of 93.9% (95% CI 91.5%-95.8%) for either major or minor injury. Chest CT-Major had a sensitivity of 99.2% (95% CI 95.4%-100%), a specificity of 31.7% (95% CI 29.9%-33.5%), and a NPV of 99.9% (95% CI 99.3%-100%) for major injury and a sensitivity of 90.7% (95% CI 88.3%-92.8%), a specificity of 37.9% (95% CI 35.8%-40.1%), and a NPV of 91.8% (95% CI 89.7%-93.6%) for either major or minor injury. Regarding the limitations of our work, some clinicians may disagree with our injury classification and sensitivity thresholds for injury detection.ConclusionsWe prospectively derived and validated two DIs (Chest CT-All and Chest CT-Major) that identify blunt trauma patients with clinically significant thoracic injuries with high sensitivity, allowing for a safe reduction of approximately 25%-37% of unnecessary chest CTs. Trauma evaluation protocols that incorporate these DIs may decrease unnecessary costs and radiation exposure in the disproportionately young trauma population.

Dataset Information

The Manchester Acute Coronary Syndromes (MACS) decision rule for suspected cardiac chest pain: derivation and external validation.

Publications

The Manchester Acute Coronary Syndromes (MACS) decision rule for suspected cardiac chest pain: derivation and external validation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets