Download PDF. The management of low-risk patients presenting to emergency departments is a common and challenging clinical problem entailing 8 million emergency department visits annually. Inadvertent discharge of patients with acute coronary syndrome from the emergency department is associated with increased mortality and liability, whereas inappropriate admission of patients without serious disease is neither indicated nor cost-effective. Clinical judgment and basic clinical tools history, physical examination, and electrocardiogram remain primary in meeting this challenge and affording early identification of low-risk patients with chest pain. Additionally, established and newer diagnostic methods have extended clinicians' diagnostic capacity in this setting.
Crossref Medline Google Scholar 8. Although an Libby ellis sex ppv cardiac troponin level is indicative of myocardial necrosis, it does not specify the mechanism of injury. Do patients' Libby ellis sex ppv risk factor reports predict acute cardiac ischemia in the emergency department? Ann Intern Med. The markers selected in our panel were a mixture of markers that may be significant for NSCLC, Non-Smokers healthySmokers high-risk, no cancerand Asthma sufferers. Prediction of the need for intensive care in patients who come to the emergency departments with acute chest pain. The safety and utility of outpatient ETT are predicated on performance of the test within 72 hours 24 hours is Teflon spray lubereliability of the patient to follow up for the test, and close communication between the CPU physician and the patient's personal physician. A frequent concern pertaining to patients with repeated negative CPU visits is the interval over which negative testing remains valid. PLoS Med.
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Patients and Methods: A total of 1, human plasma samples were processed using a multiplex immunoassay platform.
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Patients and Methods: A total of 1, human plasma samples were processed using a multiplex immunoassay platform. Conclusion: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings. According to the American Cancer Society, on a global scale, lung cancer is the leading cause of cancer-related incidence and death at 2.
By , in the US, an estimated , living individuals were diagnosed with lung and bronchus cancer 2. An additional , new cases with an estimated , deaths are expected in 3.
Lung cancer originates in the lungs, but can metastasize to other organs in the body. Symptoms and detection. Current methods of detecting lung cancer include a chest x-ray CXR , computed tomography CT scan, magnetic resonance imaging MRI , positron emission tomography PET scan, sputum analysis, and lung biopsy.
It is evident that the sooner lung cancer is diagnosed, the better the prognosis for the patient. Only NLST clinical trial. We had a few caveats concerning the NLST study. First, due to the substantial pack history of the NLST participants, it was not known whether these findings were relevant to individuals who were non-smokers or those who smoked less. Second, screening using LDCT and radiography showed a high rate of false positives which can lead to unnecessary lung biopsy, surgery, other follow-up diagnostic tests, and stress.
Additional factors associated with LDCT and radiography screening that warrants further exploration include: i the economic implications of over diagnosis, ii the radiation exposure from multiple scans may lead to development of other cancers, and iii the access to high-quality screening in certain settings.
Study population. This study consisted of 1, subjects 2, samples distributed in the following cohorts: i asthma sufferers, ii non-smokers, iii smokers, iv NSCLC, and v other cancers. Asthma also includes other respiratory diseases, e. Other cancers e. Table I summarizes the main criteria used to select samples for each cohort. All sample groups consisted of a mixture of Caucasian, African-American and Hispanic. The Asthma group included individuals diagnosed with asthma and with other respiratory diseases, e.
COPD but no lung cancer, regardless of age and smoking status. Other cancers included patients diagnosed with breast, ovarian, prostate, pancreatic, or colon-rectal cancer. Training set. The training set consisted of Subjects 1, samples ran in duplicates to evaluate 82 biomarkers and 6 multivariate analysis methods and to train the selected algorithm.
There were: i NSCLC which served as a positive control, ii healthy non-smokers which served as our negative control, iii 33 asthma sufferers, iv high-risk smokers, and v 90 other cancers.
Protein biomarker concentrations, gender, and race were included in the analysis. Subjects ranged from years old and were distributed between female and male cohorts as shown in Table II. Patient samples consisted predominantly of Caucasians and some African-Americans and Hispanics. All samples were randomized and cohorts were distributed evenly across the total plates of the study using R and Python.
Validation set. To verify the performance of the selected biomarkers and the final algorithm, a blind and independent sample set of subjects 1, samples was processed. Sample collection and handling. Selection of biomarkers. Eighty-two commercially available protein analytes were used for the initial screening. Multiplexed immunoassay procedure. This study used a custom-made multiplexed immunoassay developed by Millipore Billerica, MA, USA to measure the concentration of selected biomarkers in human plasma samples.
The reagent kits were designed on magnetic beads using a capture sandwich immunoassay format. The Bio-Plex Manager 6. Multivariate classification and analysis. These algorithms considered two independent measurements of 33 biomarkers from a single subject, their gender and smoking status, and classified each measurement as positive or negative for NSCLC. The penalized logistic regression model.
When developing predictive models based on many variables, such as the concentration of numerous biomarkers for NSCLC, logistic regression may fail due to non-convergence, 11 or it may be that regression coefficients for the parameter estimates have a large variance. By reducing the number of parameters, such as the number of biomarkers in the model, one can reduce the variance of the regression coefficients. A form of penalized regression that allows such constraints is the least absolute shrinkage and selection operator LASSO model.
All classifiers of this type seek to find the probability P that an observation belongs to a class A or B given the data for that observation. The class with the highest probability is the one to which each new observation is assigned. Theoretically, Bayes classifiers have the lowest error rates amongst the set of classifiers.
In practice, however, this does not always occur due to violations of the assumptions made about the data when applying a Bayes classifier. It simplifies the calculations of the probabilities used in classification by assuming that each class is independent of the other classes given the data. Neural nets. One way to think of a neural net is as a weighted directed graph where the edges and their weights represent the influence each vertex has on the others to which it is connected.
There are two parts to a neural net, the input layer formed by the data and the output layer the values, which in this case are the classes to be predicted. Between the input layer and the output layer is a network of hidden vertices. There may be, depending on the way the neural net is designed, several vertices between the input layer and the output layer.
Neural nets are widely used in artificial intelligence and data mining, but there is the danger that the models the neural nets produce will over-fit the data i. Tools for implementing neural nets as discussed herein are available for the statistical software computing language and environment, R.
The nearest neighbor classifiers are a subset of memory-based classifiers. Nearest neighbor classifiers do not require a model to be fit. To create a k-nearest neighbor knn classifier, the following steps are taken:. The distance can be calculated using any valid metric, though Euclidian and Mahalanobis distances are often used.
The group that has the highest count is the group to which the new observation is assigned. Nearest neighbor algorithms have problems dealing with categorical data due to the requirement that a distance needs to be calculated between two points but that can be overcome by defining a distance arbitrarily between any two groups. This class of algorithm is also sensitive to changes in scale and metric. With these issues in mind, nearest neighbor algorithms can be very powerful, especially in large data sets.
Tools for implementing k-nearest neighbor classifiers as discussed herein are available for the statistical software computing language and environment, R. Random forests. Classification trees are typically noisy. Random forests attempt to reduce this noise by taking the average of many trees. The result is a classifier whose error has reduced variance compared to a classification tree. This set is the random forest. To classify a new observation using the random forest, classify the new observation using each classification tree in the random forest.
Random forests reduce many of the problems found in classification trees but at the price of interpretability. Tools for implementing random forests as discussed herein are available for the statistical software computing language and environment, R. Biomarker characteristics. The median plasma concentrations for all biomarkers were used to represent the central tendency of the plasma concentrations to provide resistance to bias due to skewed distributions and outliers as shown on Table IV and Figure 1.
The p- Values using the T-test, unadjusted for multiple comparisons, are statistically significant at a 0. From a univariate perspective, this indicates there are many biomarkers that discriminated NSCLC from other pathologies to a degree. Thirty-three plasma proteins were selected based on statistical and biological significance. We conclude that gender is marginally significant, as otherwise shown in previous studies done by Izbicka et al.
Multivariate analysis. To calculate the performance of the model, we used fold cross validation. The data was divided into 10 partitions, using nine partitions to train and evaluate the model on the remaining partition. The process is repeated until all partitions are used to evaluate the model. This model has an accuracy of This indicates potential for clinical use. Validation performance. The cohorts consisted of 74 asthma sufferers, healthy non-smokers, NSCLC, high-risk smokers, and other cancer patients.
All cancers include breast, ovarian, prostate, pancreatic, colonrectal cancer, and non-small cell lung cancer. The performance of the algorithm improved when the asthma and non-NSCLC cancers were removed from the data set. Significant markers. The markers selected in our panel were a mixture of markers that may be significant for NSCLC, Non-Smokers healthy , Smokers high-risk, no cancer , and Asthma sufferers.
Assay precision. Inter-assay precision was determined using a low and high-quality control processed in duplicates over 24 plates, performed over 4 days by 4 different operators using 4 Luminex platforms. Biomarker inter-assay precision was between 2. No cross-reactivity between analytes in each panel was observed. Multiplex assays have been evaluated as a potential diagnostic tool for lung cancer in numerous studies 13 - Independent lung tumor studies by Okamura et al.
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Download PDF. The management of low-risk patients presenting to emergency departments is a common and challenging clinical problem entailing 8 million emergency department visits annually. Inadvertent discharge of patients with acute coronary syndrome from the emergency department is associated with increased mortality and liability, whereas inappropriate admission of patients without serious disease is neither indicated nor cost-effective.
Clinical judgment and basic clinical tools history, physical examination, and electrocardiogram remain primary in meeting this challenge and affording early identification of low-risk patients with chest pain. Additionally, established and newer diagnostic methods have extended clinicians' diagnostic capacity in this setting. Low-risk patients presenting with chest pain are increasingly managed in chest pain units in which accelerated diagnostic protocols are performed, comprising serial electrocardiograms and cardiac injury markers to exclude acute coronary syndrome.
Patients with negative findings usually complete the accelerated diagnostic protocol with a confirmatory test to exclude ischemia. This is typically an exercise treadmill test or a cardiac imaging study if the exercise treadmill test is not applicable. Rest myocardial perfusion imaging has assumed an important role in this setting.
Computed tomography coronary angiography has also shown promise in this setting. A negative accelerated diagnostic protocol evaluation allows discharge, whereas patients with positive findings are admitted.
This approach has been found to be safe, accurate, and cost-effective in low-risk patients presenting with chest pain. Therefore, the challenge to clinicians is rapid identification of those who require admission for urgent management and those with a benign cause who can be discharged directly from the ED. To meet this challenge, an increasing array of diagnostic strategies and modalities have been applied, including chest pain units CPUs , new cardiac biomarkers, risk scores, accelerated diagnostic protocols ADPs , and noninvasive imaging of the myocardium and coronary arteries.
In this regard, it is emphasized that the primary goal of evaluation of these patients in the acute setting is accurate risk stratification and identification by exclusion of ACS and other serious conditions rather than detection of coronary artery disease CAD.
However, with all of these methods, clinical judgment is essential for optimal interpretation and application. The purpose of the present document is to provide current information on the appropriate application of these diagnostic tools in management of low-risk patients presenting with acute chest pain. This is a narrative review developed from evidence obtained by a comprehensive search of the English language medical literature and current guidelines during the past 3 decades.
Specific areas targeted include clinical assessment, testing methods, special populations, and outcomes. Literature citations were limited to articles listed in Index Medicus. The few smaller studies were included for either their historical or methodological importance.
This document was evaluated by 6 outside reviewers selected by the American Heart Association. The writing group represents a broad spectrum of investigators with experience and expertise pertaining to the multiple aspects of evaluation of low-risk patients presenting to the ED with chest pain.
Management adheres to current guidelines where applicable and includes relevant information regarding the specific issues in low-risk patients. Download figure Download PowerPoint Figure. Evaluation of patients presenting with symptoms suggestive of ACS. Copyright , American Heart Association.
Risk stratification begins with the initial approach to the patient, which provides important insight into the hazard for cardiovascular complications. Table 1. Modified with permission from Anderson et al. On the basis of the initial clinical presentation, patients are considered for 1 of the following strategies: 1 Those with objective evidence of ACS or hemodynamic or electric instability are admitted for urgent therapy; 2 stable patients with no objective evidence of ischemia normal or near-normal ECG and negative baseline cardiac injury markers are considered low risk and can be admitted to an observation unit CPU for further evaluation by an ADP.
The latter comprises serial ECGs and cardiac injury markers. In those in whom these studies are negative, a confirmatory test is performed by any of several methods, from exercise treadmill testing ETT to cardiac imaging, depending on the specific features of each patient. Those with positive ECGs or cardiac injury markers are diagnosed with ACS and admitted; patients with a positive confirmatory test are also admitted with an increased likelihood of ACS Figure. CPUs are predicated on the understanding that low risk is not no risk.
These units provide an integrated approach to further stratification of low-risk patients by short-term observation with repeat ECGs and serial cardiac injury markers. A recent report indicated that units certified by the Society of Chest Pain Centers had better adherence to Medicare and Medicaid core measures for treatment of acute MI than institutions without these certified units.
As previously described, this process uses serial ECGs and cardiac injury markers, usually obtained over a 6- to hour period. Historical information in patients with chest pain is crucial in determining the cause of symptoms and risk stratification. Because myriad conditions can cause chest pain, a systematic approach to assessment of symptoms should be pursued. Table 2 lists the important differential diagnoses in patients with chest pain. Table 2.
Common Causes of Acute Chest Pain. Myocardial ischemia usually produces chest discomfort that is diffuse and often radiates to the arm, neck, or jaw. Ischemic cardiac pain manifests by chest heaviness, pressure, tightness, squeezing, or burning and is often provoked by exertion, emotional stress, or temperature extremes.
Because chest discomfort due to myocardial ischemia tends to be similar in location and quality during recurrent episodes, it is helpful diagnostically if the symptoms are consistent with prior episodes. These symptoms include jaw, neck, or arm discomfort; dyspnea; nausea; vomiting; diaphoresis; and unexplained fatigue. Sharp, stabbing, or reproducible pain reduces but does not exclude the likelihood of ACS.
Pleuritic chest pain is consistent with a pulmonary condition, musculoskeletal disease, or pericarditis. The pain of angina pectoris often occurs episodically, lasts from 2 to 10 minutes during physical exertion, and is relieved by rest.
Pain that is abrupt in onset and worst at onset is often associated with pneumothorax, aortic dissection, or PE. Nontraumatic musculoskeletal pain usually manifests vaguely, and the circumstances that led to its onset may not be recalled. However, like MI, all these conditions can present atypically, which necessitates a high index of suspicion when patients with chest pain are evaluated. Chest pain due to myocardial ischemia may be relieved by rest or sublingual nitroglycerin; however, relief with nitroglycerin should not be used as a diagnostic test for determining chest pain origin in the ED because it does not predict myocardial ischemia.
Other symptoms that support an ischemic origin of chest pain include dyspnea, particularly in the elderly, because dyspnea may predominate over chest pain, 30 in which case a pulmonary source should also be strongly considered.
Rarely, palpitations may be the presenting symptom of cardiac arrhythmias in the setting of ACS or PE. Patients with PE, pneumonia, and severe bronchitis occasionally present with hemoptysis.
In addition, fever and chills usually point to an infectious or inflammatory process such as pneumonia, pleurisy, or pericarditis. Signs of heart failure reflect left or right ventricular dysfunction. Bruits usually indicate peripheral arterial disease and increase the risk of concomitant CAD. The examination should also target potential noncardiac causes for the patient's symptoms, such as unequal extremity pulses aortic dissection , prominent murmurs endocarditis , friction rub pericarditis , fever and abnormal lung sounds pneumonia , or reproduction of chest pain with palpation of the chest wall musculoskeletal disorders.
A normal physical examination is present in the majority of uncomplicated cases of ACS and contributes to the initial impression of low clinical risk. Its utility in indicating subclinical ischemia has been demonstrated during observation of high-risk patients presenting with chest pain, but the yield of this method in low-risk patients is minimal. ST elevation is closely associated with total or near-total coronary occlusion and indicates that the patient is a potential candidate for acute coronary reperfusion therapy.
The presence or absence of ischemic ST-segment and T-wave changes during chest pain in ACS patients has been shown to increase both the positive and negative predictive value of the ECG for adverse events compared with the ECG recording in the absence of pain 39 ; however, this has not been a consistent finding, particularly in patients with undifferentiated chest pain, such as those managed in a CPU.
Other relevant entities evident on the chest roentgenogram include pneumonia, pneumothorax, and pneumomediastinum. Current guidelines recommend measurement of a cardiac injury marker which should include a highly sensitive and specific cardiac troponin assay in all patients with suspected myocardial ischemia. Contemporary troponin assays have improved sensitivity, specificity, and precision at lower levels. Although an elevated cardiac troponin level is indicative of myocardial necrosis, it does not specify the mechanism of injury.
An abnormal value alone may not indicate MI, because there are numerous nonischemic causes of elevated cardiac troponin. Criteria include a rise or fall of cardiac troponin with at least 1 value above the 99th percentile of the upper reference limit and at least 1 of the following: Symptoms of cardiac ischemia, characteristic ECG alterations, or imaging evidence of a new regional wall-motion abnormality RWMA.
If this cannot be achieved, point-of-care methods should be considered; however, the latter are limited by lower accuracy than contemporary troponin assays.
A variety of other biomarkers have been found to have independent value for predicting subsequent ischemic events, particularly mortality, in patients with ACS 45 , 57 ; however, few of these newer biomarkers are commercially available, nor have they been validated in an undifferentiated population, such as patients presenting to the ED with chest pain.
Currently, only 2 of these biomarkers, B-type natriuretic peptide or N-terminal prohormone B-type natriuretic peptide and high-sensitivity C-reactive protein, are available for routine use, and only B-type natriuretic peptide is typically used in the ED. The utility of B-type natriuretic peptide has been demonstrated in a number of investigations, and elevations of this marker provide powerful risk stratification across a broad spectrum of ACS patients.
A useful approach to risk stratification has been the development of a variety of risk-scoring systems based on the history and initial clinical presentation. Favorable low risk scores allow consideration for CPU management. Criteria for this test are the patient's ability to exercise and a normal baseline ECG that allows interpretation of exercise-induced ST-segment alterations. If these conditions are not fulfilled, an imaging test eg, myocardial perfusion imaging [MPI] or echocardiogram is considered, with or without stress pharmacological or exercise.
In addition, coronary angiography invasive or CTCA has also been used. Again, the primary purpose of confirmatory testing as part of an ADP during CPU observation is to further minimize the likelihood of ACS to a level so low that discharge is safe. Of the multiple studies performed on test methods in low-risk patients, 5 were prospective, randomized, controlled trials.
The latter assessed treadmill testing, 69 treadmill testing or myocardial perfusion scintigraphy, 70 myocardial perfusion scintigraphy, 71 conventional coronary angiography, 72 and CTCA. The safety of the test is based on strict adherence to these criteria. Table 3. MPHR indicates maximum predicted heart rate. No adverse effects of early ETT were reported.
Another study of patients who received ETT or stress imaging tests after a negative ADP showed similarly excellent prognostic findings. The low positive predictive value for an ACS and its variability among studies Table 4 is likely related to the differences in the study cohorts. Although the positive predictive value is low, the number of unnecessary admissions is reduced.
Table 4. Adapted from Amsterdam et al. The exercise protocol used has usually been the modified Bruce method; however, the standard Bruce protocol is reasonable in patients in whom adequate functional capacity is anticipated. In addition to the objective data afforded by the ETT, failure of the test to reproduce the chest pain that prompted the ED visit is important in markedly lowering the likelihood of ACS.
Risk stratification can be enhanced by the integration of multiple ETT variables into scores such as the Duke Treadmill Score. These findings are additional end points for the ETT Table 3. To optimize the safety of exercise testing in this patient population, the test is stopped at the onset of minimal criteria of ischemia 0.
Although guidelines exist for supervision of ETT by noncardiologists and nonphysicians, 88 these guidelines do not cover the topic of exercise testing in a CPU. Although there are numerous studies that support the safety of ETT performed by cardiologists in the ED 15 Table 4 , other reports describe the safety and accuracy of specially trained healthcare professionals in performing ETT in low-risk patients presenting with chest pain.