Likelihood Of Acute Myocardial Infarction Research Articles

Acute Myocardial Infarction-Related Hospitalizations in Non-elderly Patients with Pneumonia: a Population-Based Study.

The objective is to study factors that increase the likelihood of acute myocardial infarction (AMI) in hospitalized adult non-elderly patients with pneumonia compared to other medical inpatients and to understand the utilization rate of percutaneous coronary intervention (PCI) for AMI in inpatients with pneumonia and its related impact on hospitalization stay and cost. A population-based study was conducted using the Nationwide Inpatient Sample (NIS, 2019) with adult non-elderly inpatients (age 18-65years) with a medical condition as their primary diagnosis and a co-diagnosis of pneumonia during hospitalization stay. This study sample was divided by the primary diagnosis of AMI versus other medical conditions (non-AMI). A logistic regression model was used to evaluate the odds ratio (OR) of predictors associated with AMI in patients with pneumonia. The results showed a direct relationship between increasing age and the likelihood of AMI in pneumonia inpatients with three times higher odds seen in 51-65years of age (OR 2.95, 95% CI 2.82-3.09). The comorbidities included complicated hypertension (OR 2.84, 95% CI 2.78-2.89), diabetes with complications (OR 1.27, 95% CI 1.24-1.29), and drug abuse (OR 1.27, 95% CI 1.22-1.31) that increased the likelihood of AMI-related hospitalization. The utilization rate of surgical treatment (PCI) was 14.37% for the management of AMI in inpatients with pneumonia. Inpatients co-diagnosed with pneumonia and comorbidities such as hypertension and diabetes were more likely to be hospitalized for AMI. These at-risk patients should be considered for early risk stratification. Utilization of PCI was associated with a lower in-hospital mortality rate.

Likelihood of myocardial infarction, revascularization and death following catheterization laboratory activation in patients with vs. without both chest pain and ST elevation.

BackgroundEmergent cardiac catheterization laboratory activation (CCLA) for patients with suspected ST-elevation myocardial infarction (STEMI) is employed to expedite acute revascularization (AR). The incidence of false-positive CCLA, in which AR is not performed, remains high. The combination of chest pain (CP) and electrocardiographic ST elevation (STE) are the hallmarks of STEMI. However, CCLA is sometimes initiated for patients lacking this combination. The study objective was to quantify the difference in likelihood of AR and mortality in patients with vs. without both CP and STE.MethodsRetrospective analysis of 1621 consecutive patients for whom CCLA was initiated in a six-hospital network. We assessed the likelihood of acute myocardial infarction (AMI), presence of a culprit lesion (CL), performance of AR, and hospital mortality among patients with both CP and STE (+CP/+STE) compared with patients lacking one or both [non(CP/STE)].Results87.0% of patients presented with CP, 82.4% with STE, and 73.7% with both. Among +CP/+STE patients, AMI was confirmed in 90.4%, a CL in 88.9%, and AR performed in 83.1%. The corresponding values among non(CP/STE) patients were 35.8, 31.9, and 28.1%, respectively (P < 0.0001 for each). Nevertheless, mortality among non(CP/STE) patients was three-fold higher than in +CP/+STE patients (13.3% vs. 4.5%; P < 0.0001), with non-coronary deaths 24-fold more likely.ConclusionPatients lacking the combination of CP and STE have a markedly lower likelihood of AMI and AR than +CP/+STE patients, but significantly higher mortality. Protocols aimed at rapid, focused evaluation of non(CP/STE) patients prior to CCLA are needed.

Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.

Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5-88.8%]; specificity, 92.2% [90.7-93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4-91.6%]); specificity, 89.3% [88.6-90.0%]). Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. URL: https://www.anzctr.org.au. Unique identifier: ACTRN12616001441404.

Diurnal, weekly and seasonal variations of chest pain in patients transported by emergency medical services

ObjectivesChest pain is among the leading causes for emergency medical services (EMS) activation. Acute myocardial infarction (MI) is not only one of the most critical aetiologies of chest pain, but...

Acute myocardial infarction or acute myocarditis? Discharge registry-based study of likelihood and associated features in hospitalised patients

ObjectiveTo evaluate the likelihood of and patient features associated with acute myocardial infarction (AMI) versus acute myocarditis in different population segments.DesignNationwide, multihospital observational retrospective registry study of 9.6 years in...

MY APPROACH to low-level troponin elevations

Cardiac troponin (cTn) I and T complement clinical assessment and the ECG in the early diagnosis of acute myocardial infarction (AMI). cTns are structural proteins unique to the heart. cTn levels in peripheral blood are quantitative markers of cardiomyocyte damage. Thereby, cTns are organ-specific, but not disease-specific, markers. Although AMI is a very important cause of cardiomyocyte damage, and clearly the dominant one in patients presenting with acute chest pain and substantial cTn elevations (eg, 50 times the 99th percentile), multiple other acute and chronic disorders seem to lead to cardiomyocyte damage that can be detected using modern cTn assays. Clinical evaluations of high-sensitivity cTn assays have taught us that detectable (but not elevated) cTn levels are normal and likely reflect cell turnover, and are observed in the majority of healthy individuals using state-of-the art cTn assays. The higher the cTn level at presentation and the higher the absolute change in cTn within the first hour, the higher is the likelihood of AMI. Low-level cTn elevations are those just above the 99th percentile (eg, up to three times the 99th percentile). In patients presenting with acute chest pain, the positive predictive value of low-level cTn elevations for AMI is only about 50%. In patients in whom low-level cTn elevations are detected for other presenting symptoms or possibly during screening, the positive predictive value of low-level cTn elevations for AMI is even lower. I try to answer three questions in order to rapidly identify the cause of low-level cTn elevations.

Relationship Between Pain Severity and Outcomes in Patients Presenting With Potential Acute Coronary Syndromes

Although lay people often assume that severe pain is more commonly associated with worse outcomes, the relationship between pain severity and outcome for patients presenting with potential acute coronary syndrome has not been well described. We hypothesize that pain severity will not be associated with acute myocardial infarction or 30-day cardiovascular complications. We conducted a secondary analysis of a prospective cohort study of patients presenting to the emergency department (ED) with potential acute coronary syndrome. Trained research assistants collected data, including demographics, medical history, symptoms, hospital course, and 30-day outcomes (record review and telephone). Pain score on arrival (0 to 10) was abstracted from nurses' triage documentation in the electronic record. Severe pain was defined as 9 or 10. The main outcomes were the prevalence of acute myocardial infarction during index visit and composite of death, acute myocardial infarction, revascularization including percutaneous coronary intervention, or coronary bypass artery grafting at 30 days. Multivariable modeling was prespecified to adjust for age, race, sex, pain duration, thrombolysis in myocardial infarction (TIMI) score, and mode of arrival. Data are presented as relative risk (RR) with 95% confidence intervals (CI). Patients (3,306) had pain documented (mean age 51.0 years; SD 12.6 years; 57% women; 66% black). Follow-up was 93%. By 30 days, 34 patients had died, 105 patients underwent revascularization (94 percutaneous coronary intervention, 14 coronary bypass artery grafting), and 111 patients experienced acute myocardial infarction. There was not a relationship between severe pain and acute myocardial infarction (RR 1.28; 95% CI 0.93 to 1.76) or 30-day composite outcome (1.19; 95% CI 0.91 to 1.56). After adjusting for potential confounding variables, we found that the prevalence of inhospital acute myocardial infarction was related to TIMI score (adjusted relative risk [aRR] 2.00; 95% CI 1.05 to 3.80), male sex (aRR 1.48; 95% CI 1.00 to 2.18), and arrival by emergency medical services (EMS) (aRR 1.73; 95% CI 1.13 to 2.63) but not age (aRR 1.42; 95% CI 0.68 to 2.95), white race (aRR 1.25; 95% CI 0.85 to 1.86), pain duration greater than 1 hour (aRR 1.36; 95% CI 0.89 to 2.07), or severe pain (aRR 1.43; 95% CI 0.91 to 2.22). Thirty-day composite outcome was related to male sex (aRR 1.53; 95% CI 1.16 to 2.01), white race (aRR 1.43; 95% CI 1.09 to 1.87), and higher TIMI score (aRR 2.24; 95% CI 1.39 to 3.60) but was not related to age (aRR 1.26; 95% CI 0.75 to 2.11), pain duration greater than 1 hour (aRR 0.8; 95% CI 0.60 to 1.06), EMS arrival (aRR 1.23; 95% CI 0.96 to 1.60), or severe pain (aRR 1.39; 95% CI 0.95 to 1.97). For patients who present to the ED with potential acute coronary syndrome, severe pain is not related to likelihood of acute myocardial infarction at presentation or death, acute myocardial infarction or revascularization within 30 days.

A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department

BackgroundSeveral models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED.MethodsMultivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included.ResultsBesides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%.ConclusionThe present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.