***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
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    Input: STUDY OBJECTIVE Current risk stratification tools do not identify very-low-risk patients who can be safely discharged without prolonged emergency department ( ED ) observation , expensive rule-out protocol s , or provocative testing . We seek to develop a clinical prediction rule applicable within 2 hours of ED arrival that would miss fewer than 2 % of acute coronary syndrome patients and allow discharge within 2 to 3 hours for at least 30 % of patients without acute coronary syndrome . METHODS This prospect i ve , cohort study enrolled consenting eligible subjects at least 25 years old at a single site . At 30 days , investigators assigned a diagnosis of acute coronary syndrome or no acute coronary syndrome according to predefined explicit definitions . A recursive partitioning model included risk factors , pain characteristics , physical and ECG findings , and cardiac marker results . RESULTS Of 769 patients studied , 77 ( 10.0 % ) had acute myocardial infa rct ion and 88 ( 11.4 % ) definite unstable angina . We derived a clinical prediction rule that was 98.8 % sensitive and 32.5 % specific . Patients have very low risk of acute coronary syndrome if they have a normal initial ECG , no previous ischemic chest pain , and age younger than 40 years . In addition , patients at least 40 years old and with a normal ECG result , no previous ischemic chest pain , and low-risk pain characteristics have very low risk if they have an initial creatine kinase-MB ( CK-MB ) less than 3.0 microg/L or an initial CK-MB greater than or equal to 3.0 microg/L but no ECG or serum-marker increase at 2 hours . CONCLUSION The Vancouver Chest Pain Rule for early discharge defines a group of patients who can be safely discharged after a brief evaluation in the ED . Prospect i ve validation is needed INTRODUCTION We use clinical , ECG , and biochemical data to stratify risk in patients with chest pain without ST segment elevation . However , the prognostic performance of these studies in relation to time from onset of symptoms is unknown . PATIENTS AND METHOD In a single-center , prospect i ve study , 321 consecutive patients who had been admitted in the emergency room with a suspected acute coronary syndrome without ST segment elevation were included in the study . Blood sample s were collected for CK , CK-MB mass , myoglobin , and cardiac troponin T analysis 6 , 12 and 18 hours after the onset of pain and other clinical and ECG data were recorded . Univariate and multivariate analysis was used to identify independent prognostic predictors 6 and 12 hours after the onset of chest pain . RESULTS Five variables were independent predictors of the recurrence of ischemia . The model correctly classified 82 % of the patients . Age , history of coronary artery disease , prolonged chest pain at rest in the preceding 15 days , pain , ST-segment changes with pain , and cardiac troponin T in excess of 0.1 ng/m 12 hours after the onset of chest pain were identified by logistic regression . A similar model was analyzed at 6 hours , after changing the cutoff point for cardiac troponin T. Cardiac troponin T was considered positive with values of 0.04 ng/ml 6 hours after the onset of chest pain . CONCLUSIONS More than 80 % of the patients admitted to the emergency room with chest pain without ST segment elevation can be correctly classified for new ischemic recurrences using clinical , ECG , and biochemical parameters 6 hours after the onset of pain STUDY OBJECTIVE Accurate identification of the presence of acute myocardial infa rct ion in adult patients who present to the emergency department with anterior chest pain remains elusive . The artificial neural network is a powerful nonlinear statistical paradigm for the recognition of complex patterns , with the ability to maintain accuracy when some data required for network function are missing . Earlier studies revealed that the artificial neural network is able to accurately identify acute myocardial infa rct ion in patients experiencing chest pain . However , these studies did not measure network performance in real time , when a significant amount of data required for network function may not be available . They also did not use chemical cardiac marker data . METHODS Two thous and two hundred four adult patients presenting to the ED with anterior chest pain were used to train an artificial neural network to recognize the presence of acute myocardial infa rct ion . Only data available at the time of initial patient evaluation were used to replicate the conditions of real-time patient evaluation . Forty variables from patient histories , physical examinations , ECG results , and chemical cardiac marker determinations were used to train and then test the network . RESULTS The network correctly identified 121 of the 128 patients ( sensitivity 94.5 % ; 95 % confidence interval 90.6 % to 97.9 % ) with myocardial infa rct ion at a specificity of 95.9 % ( 95 % confidence interval 93.0 % to 98.5 % ) , despite the fact that an average of 5 % ( individual range 0 % to 35 % ) of the input data required by the network were missing on all patients . CONCLUSION Network accuracy and the maintenance of that accuracy when some data required for function are unavailable suggest that the artificial neural network may be a potential real time aid to the diagnosis of acute myocardial infa rct ion during initial patient evaluation BACKGROUND Appropriate treatment policies should include an accurate estimate of a patient 's baseline risk . Risk modeling to date has been underutilized in patients with acute coronary syndromes without persistent ST-segment elevation . METHODS AND RESULTS We analyzed the relation between baseline characteristics and the 30-day incidence of death and the composite of death or myocardial (re)infa rct ion in 9461 patients with acute coronary syndromes without persistent ST-segment elevation enrolled in the PURSUIT trial [ Platelet glycoprotein IIb/IIIa in Unstable angina : Receptor Suppression Using Integrilin ( eptifibatide ) Therapy ] . Variables examined included demographics , history , hemodynamic condition , and symptom duration . Risk models were created with multivariable logistic regression and vali date d by bootstrapping techniques . There was a 3.6 % mortality rate and 11.4 % infa rct ion rate by 30 days . More than 20 significant predictors for mortality and for the composite end point were identified . The most important baseline determinants of death were age ( adjusted chi(2)=95 ) , heart rate ( chi(2)=32 ) , systolic blood pressure ( chi(2)=20 ) , ST-segment depression ( chi(2)=20 ) , signs of heart failure ( chi(2)=18 ) , and cardiac enzymes ( chi(2)=15 ) . Determinants of mortality were generally also predictive of death or myocardial (re)infa rct ion . Differences were observed , however , in the relative prognostic importance of predictive variables for mortality alone or the composite end point ; for example , sex was a more important determinant of the composite end point ( chi(2)=21 ) than of death alone ( chi(2)=10 ) . The accuracy of the prediction of the composite end point was less than that of mortality ( C-index 0.67 versus 0.81 ) . CONCLUSIONS The occurrence of adverse events after presentation with acute coronary syndromes is affected by multiple factors . These factors should be considered in the clinical decision-making process Background : Most Canadian emergency departments use an unstructured , individualized approach to patients with chest pain , without data to support the safety and efficiency of this practice . We sought to determine the proportions of patients with chest discomfort in emergency departments who either had acute coronary syndrome ( ACS ) and were inappropriately discharged from the emergency department or did not have ACS and were held for investigation . Methods : Consecutive consenting patients aged 25 years or older presenting with chest discomfort to 2 urban tertiary care emergency departments between June 2000 and April 2001 were prospect ively enrolled unless they had a terminal illness , an obvious traumatic cause , a radiographically identifiable cause , severe communication problems or no fixed address in British Columbia or they would not be available for follow-up by telephone . At 30 days we assigned predefined explicit outcome diagnoses : definite ACS ( acute myocardial infa rct ion [ AMI ] or definite unstable angina ) or no ACS . Results : Of 1819 patients , 241 ( 13.2 % ) were assigned a 30-day diagnosis of AMI and 157 ( 8.6 % ) , definite unstable angina . Of these 398 patients , 21 ( 5.3 % ) were discharged from the emergency department without a diagnosis of ACS and without plans for further investigation . The clinical sensitivity for detecting ACS was 94.7 % ( 95 % confidence interval [ CI ] 92.5%– 96.9 % ) and the specificity 73.8 % ( 95 % CI 71.5%– 76.0 % ) . Of the patients without ACS or an adverse event , 71.1 % were admitted to hospital or held in the emergency department for more than 3 hours . Interpretation : The current individualized approach to evaluation and disposition of patients with chest discomfort in 2 Canadian tertiary care emergency departments misses 5.3 % of cases of ACS while consuming considerable health care re sources for patients without coronary disease . Opportunities exist to improve both safety and efficiency Identifying acute coronary syndrome is a difficult task in the emergency department because symptoms may be atypical and the electrocardiogram has low sensitivity . In this prospect i ve cohort study done in a tertiary community emergency hospital , we developed and tested a neural diagnostic tree in 566 consecutive patients with chest pain and no ST-segment elevation for the diagnosis of acute coronary syndrome .  Multivariate regression and recursive partitioning analysis allowed the construction of decision rules and of a neural tree for the diagnosis of acute myocardial infa rct ion and acute coronary syndrome . Predictive variables of acute coronary syndrome were : age > or = 60 years ( odds ratio [ OR ] = 2.3 ; P = 0.0016 ) , previous history of coronary artery disease ( OR = 2.9 ; P = 0.0008 ) , diabetes ( OR = 2.8 ; P = 0.0240 ) , definite/probable angina-type chest pain ( OR = 17.3 ; P = 0.0000 ) and ischemic electrocardiogram ( ECG ) changes on admission ( OR = 3.5 ; P = 0.0002 ) . The receiver operating characteristic curve of possible diagnostic decision rules of the regression model disclosed a C-index of 0.904 ( 95 % confidence interval = 0.878 to 0.930 ) for acute coronary syndrome and 0.803 ( 95 % confidence interval 0.757 to 0.849 ) for acute myocardial infa rct ion . For both disorders , sensitivities of the neural tree were 99 % and 93 % , respectively , and negative predictive values were both 98 % . Negative likelihood ratios were 0.02 and 0.1 , respectively . It is concluded that this simple and easy-to-use neural diagnostic tree was very accurate in the identification of non-ST segment elevation chest pain patients without acute coronary syndrome . Patients identified as low probability of disease could receive immediate stress testing and be discharged if the test is negative STUDY OBJECTIVE The Thrombolysis in Myocardial Infa rct ion ( TIMI ) risk score is a 7-item tool derived from trials of patients with unstable angina/non-ST segment elevation myocardial infa rct ion for risk stratification with respect to outcomes . It has been retrospectively evaluated in emergency department ( ED ) patients with potential acute coronary syndrome but has not been prospect ively vali date d in this patient population . To vali date the use of the TIMI risk score in the ED , we prospect ively assess its potential utility in a broad ED chest pain patient population . METHODS This was a prospect i ve observational cohort study of consecutive ED chest pain patients enrolled from July 2003 until October 2004 . Data included demographics , medical and cardiac history , and components of the TIMI risk score . Investigators followed the hospital course daily for admitted patients , and 30-day follow-up was performed on hospitalized and discharged patients . The main outcome was death , acute myocardial infa rct ion , or revascularization as stratified by TIMI risk score at 30 days . RESULTS There were 1,481 eligible patient visits ; 30-day follow-up was completed on 1,458 ( 98.4 % ) patients . Patients had mean age of 53.2+/-14 years and were 40 % men , 66 % black , and 30 % white .  Myocardial infa rct ion occurred in 95 patients . The incidence of each TIMI risk factor was age greater than 65 years 21 % , known coronary stenosis 18 % , 3 or more risk factors 26 % , ST-segment deviation 6 % , 2 or more anginal events in the previous 24 hours 33 % , aspirin use in the previous 7 days 35 % , and elevated markers 6 % . The incidence of 30-day death , acute myocardial infa rct ion , and revascularization according to TIMI score is as follows : TIMI 0 , 1.7 % ( 95 % confidence interval [ CI ] 0.42 to 2.95 ) ; TIMI 1 , 8.2 % ( 95 % CI 5.27 to 11.04 ) ; TIMI 2 , 8.6 % ( 95 % CI 5.02 to 12.0 
    Output:
    
Current prediction rules for ACS have substantial method ological limitations and have not been successfully implemented in the clinical setting .