human
***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 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND Implantable cardioverter defibrillator ( ICD ) implantation is a common approach in patients at high risk of sudden cardiac death . To check for normal function , it is necessary to test the ICD . For this purpose , repetitive induction and termination of ventricular fibrillation by direct current shocks is required . This may lead to minor myocardial damage . Cardiac troponin T ( cTnT ) and I ( cTnI ) are specific markers for the detection of myocardial injury . Because these proteins usually are undetectable in healthy individuals , they are excellent markers for detecting minimal myocardial damage . The objective of this study was to evaluate the effect of defibrillation of induced ventricular fibrillation on markers of myocardial damage . METHODS This study included 14 patients who underwent ICD implantation and intraoperative testing .  We measured cTnT , cTnI , creatine kinase MB ( CK-MB ) mass , CK activity , and myoglobin before and at definite times after intraoperative shock application . RESULTS Depending on the effectiveness of shocks and the energy applied , the cardiac-specific markers cTnT and cTnI , as well as CK-MB mass , showed a significant increase compared with the baseline value before testing and peaked for the most part 4 h after shock application . In contrast , the increases in CK activity and myoglobin were predominantly detectable in patients who received additional external shocks . CONCLUSIONS ICD implantation and testing leads to a short release of cardiac markers into the circulation . This release seems to be of cytoplasmic origin and depends on the number and effectiveness of the shocks applied BACKGROUND The implantable cardioverter-defibrillator ( ICD ) is highly effective in reducing mortality among patients at risk for fatal arrhythmias , but inappropriate ICD activations are frequent , with potential adverse effects . METHODS We r and omly assigned 1500 patients with a primary -prevention indication to receive an ICD with one of three programming configurations . The primary objective was to determine whether programmed high-rate therapy ( with a 2.5-second delay before the initiation of therapy at a heart rate of ≥200 beats per minute ) or delayed therapy ( with a 60-second delay at 170 to 199 beats per minute , a 12-second delay at 200 to 249 beats per minute , and a 2.5-second delay at ≥250 beats per minute ) was associated with a decrease in the number of patients with a first occurrence of inappropriate antitachycardia pacing or shocks , as compared with conventional programming ( with a 2.5-second delay at 170 to 199 beats per minute and a 1.0-second delay at ≥200 beats per minute ) . RESULTS During an average follow-up of 1.4 years , high-rate therapy and delayed ICD therapy , as compared with conventional device programming , were associated with reductions in a first occurrence of inappropriate therapy ( hazard ratio with high-rate therapy vs. conventional therapy , 0.21 ; 95 % confidence interval [ CI ] , 0.13 to 0.34 ; P<0.001 ; hazard ratio with delayed therapy vs. conventional therapy , 0.24 ; 95 % CI , 0.15 to 0.40 ; P<0.001 ) and reductions in all-cause mortality ( hazard ratio with high-rate therapy vs. conventional therapy , 0.45 ; 95 % CI , 0.24 to 0.85 ; P=0.01 ; hazard ratio with delayed therapy vs. conventional therapy , 0.56 ; 95 % CI , 0.30 to 1.02 ; P=0.06 ) . There were no significant differences in procedure-related adverse events among the three treatment groups . CONCLUSIONS Programming of ICD therapies for tachyarrhythmias of 200 beats per minute or higher or with a prolonged delay in therapy at 170 beats per minute or higher , as compared with conventional programming , was associated with reductions in inappropriate therapy and all-cause mortality during long-term follow-up . ( Funded by Boston Scientific ; MADIT-RIT Clinical Trials.gov number , NCT00947310 . ) OBJECTIVES The purpose of this study was to prospect ively examine the role of clinical , laboratory , echocardiographic , and electrophysiological variables as predictors of appropriate initial implantable cardioverter-defibrillator ( ICD ) therapy for ventricular tachycardia ( VT ) or ventricular fibrillation ( VF ) or death in the Multicenter Automatic Defibrillator Implantation Trial II ( MADIT-II ) population . BACKGROUND There is limited information regarding the determinants of appropriate ICD therapy in patients with reduced ventricular function after a myocardial infa rct ion . METHODS We used secondary analysis in one arm of a multicenter r and omized clinical trial in patients with a previous myocardial infa rct ion and reduced left ventricular function . RESULTS We analyzed baseline and follow-up data on 719 patients enrolled in the ICD arm of the MADIT-II study . Appropriate ICD therapy was observed in 169 subjects . Clinical , laboratory , echocardiographic , and electrophysiological variables , along with measures of clinical instability such as interim hospitalization for congestive heart failure ( IH-CHF ) and interim hospitalization for coronary events ( IH-CE ) , were examined with proportional hazards models and Kaplan-Meier time-to-event curves before and after first interim hospitalization . Interim hospitalization-CHF , IH-CE , no beta-blockers , digitalis use , blood urea nitrogen ( BUN ) > 25 , body mass index ( BMI ) > or = 30 kg/m2 , and New York Heart Association functional class > II were associated with increased risk for appropriate ICD therapy for VT , VF , or death . In a multivariate ( stepwise selection ) analysis , IH-CHF was associated with an increased risk for the end point of either VT or VF ( hazard ratio [ HR ] 2.52 , 95 % confidence interval [ CI ] 1.69 to 3.74 , p < 0.001 ) and for the combined end point of VT , VF , or death ( HR 2.97 , 95 % CI 2.15 to 4.09 , p < 0.001 ) . Interim hospitalization-CE was associated with an increased risk for VT , VF , or death ( HR 1.66 , 95 % CI 1.09 to 2.52 , p = 0.02 ) . CONCLUSIONS These results provide important mechanistic information , suggesting that worsening clinical condition and cardiac instability , as reflected by an IH-CHF or IH-CE , are subsequently associated with a significant increase in the risk for appropriate ICD therapy ( for VT/VF ) and death AIMS AND OBJECTIVE Outcome data of patients with implantable cardioverter defibrillators ( ICD ) and atrial fibrillation ( AF ) are conflicting . The German DEVICE registry aims to add further information on this particular cohort . METHODS AND RESULTS The German DEVICE registry is a nationwide prospect i ve multicenter data base of ICD implantations . 3261 patients are included ( 81 % males , 2701 ( 82.8 % ) first ICD implantations , 560 ( 17.2 % ) ICD replacements ) .  Cardiac resynchronization therapy ( CRT-D ) was performed in 882 patients ( 27.0 % ) . Sinus rhythm ( SR ) was present in 2654 ( 81.4 % ) and atrial fibrillation ( AF ) in 607 ( 18.6 % ) . Left ventricular ejection fraction ( LVEF ) did not differ between groups ( SR 32.3 % , AF 30.4 % ; p = 0.09 ) . AF patients were older ( AF 70.9 versus SR 63.9 years ; p < 0.0001 ) , presented with more co-morbidities ( diabetes , hypertension , chronic kidney disease ; all p < 0.001 ) . In-hospital complications were not significantly different between groups ( p = 0.58 ) . Follow-up information after one year was available in 2967 patients ( 91 % ) . One-year overall mortality after first ICD implantation was 4.9 % for SR and 11.2 % for AF patients ( p < 0.0001 ) ; mortality one year after ICD replacement was 8.4 % for SR and 12.0 % for AF ( p = 0.34 ) . No statistically significant difference between SR and AF patients receiving a CRT device was observed ( SR 6.9 % , AF 10.7 % , p = 0.16 ) in terms of one-year mortality . CONCLUSION The German DEVICE registry demonstrates that patients with AF who receive ICD devices are older , have more co-morbidity and more severe heart failure . AF carries an independent 1.39 fold risk ( 95 % CI 1.02 - 1.89 ) of death after one year in patients only with first ICD implantation A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results 
    Output:
    
agent
Atrial fibrillation history is statistically significantly associated with adverse major clinical outcomes in ICD-implanted HF patients .
Patients with AF have a higher risk of all-cause mortality , appropriate , and inappropriate ICD interventions compared with patients with no AF history .
***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 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND Implantable cardioverter defibrillator ( ICD ) implantation is a common approach in patients at high risk of sudden cardiac death . To check for normal function , it is necessary to test the ICD . For this purpose , repetitive induction and termination of ventricular fibrillation by direct current shocks is required . This may lead to minor myocardial damage . Cardiac troponin T ( cTnT ) and I ( cTnI ) are specific markers for the detection of myocardial injury . Because these proteins usually are undetectable in healthy individuals , they are excellent markers for detecting minimal myocardial damage . The objective of this study was to evaluate the effect of defibrillation of induced ventricular fibrillation on markers of myocardial damage . METHODS This study included 14 patients who underwent ICD implantation and intraoperative testing .  We measured cTnT , cTnI , creatine kinase MB ( CK-MB ) mass , CK activity , and myoglobin before and at definite times after intraoperative shock application . RESULTS Depending on the effectiveness of shocks and the energy applied , the cardiac-specific markers cTnT and cTnI , as well as CK-MB mass , showed a significant increase compared with the baseline value before testing and peaked for the most part 4 h after shock application . In contrast , the increases in CK activity and myoglobin were predominantly detectable in patients who received additional external shocks . CONCLUSIONS ICD implantation and testing leads to a short release of cardiac markers into the circulation . This release seems to be of cytoplasmic origin and depends on the number and effectiveness of the shocks applied BACKGROUND The implantable cardioverter-defibrillator ( ICD ) is highly effective in reducing mortality among patients at risk for fatal arrhythmias , but inappropriate ICD activations are frequent , with potential adverse effects . METHODS We r and omly assigned 1500 patients with a primary -prevention indication to receive an ICD with one of three programming configurations . The primary objective was to determine whether programmed high-rate therapy ( with a 2.5-second delay before the initiation of therapy at a heart rate of ≥200 beats per minute ) or delayed therapy ( with a 60-second delay at 170 to 199 beats per minute , a 12-second delay at 200 to 249 beats per minute , and a 2.5-second delay at ≥250 beats per minute ) was associated with a decrease in the number of patients with a first occurrence of inappropriate antitachycardia pacing or shocks , as compared with conventional programming ( with a 2.5-second delay at 170 to 199 beats per minute and a 1.0-second delay at ≥200 beats per minute ) . RESULTS During an average follow-up of 1.4 years , high-rate therapy and delayed ICD therapy , as compared with conventional device programming , were associated with reductions in a first occurrence of inappropriate therapy ( hazard ratio with high-rate therapy vs. conventional therapy , 0.21 ; 95 % confidence interval [ CI ] , 0.13 to 0.34 ; P<0.001 ; hazard ratio with delayed therapy vs. conventional therapy , 0.24 ; 95 % CI , 0.15 to 0.40 ; P<0.001 ) and reductions in all-cause mortality ( hazard ratio with high-rate therapy vs. conventional therapy , 0.45 ; 95 % CI , 0.24 to 0.85 ; P=0.01 ; hazard ratio with delayed therapy vs. conventional therapy , 0.56 ; 95 % CI , 0.30 to 1.02 ; P=0.06 ) . There were no significant differences in procedure-related adverse events among the three treatment groups . CONCLUSIONS Programming of ICD therapies for tachyarrhythmias of 200 beats per minute or higher or with a prolonged delay in therapy at 170 beats per minute or higher , as compared with conventional programming , was associated with reductions in inappropriate therapy and all-cause mortality during long-term follow-up . ( Funded by Boston Scientific ; MADIT-RIT Clinical Trials.gov number , NCT00947310 . ) OBJECTIVES The purpose of this study was to prospect ively examine the role of clinical , laboratory , echocardiographic , and electrophysiological variables as predictors of appropriate initial implantable cardioverter-defibrillator ( ICD ) therapy for ventricular tachycardia ( VT ) or ventricular fibrillation ( VF ) or death in the Multicenter Automatic Defibrillator Implantation Trial II ( MADIT-II ) population . BACKGROUND There is limited information regarding the determinants of appropriate ICD therapy in patients with reduced ventricular function after a myocardial infa rct ion . METHODS We used secondary analysis in one arm of a multicenter r and omized clinical trial in patients with a previous myocardial infa rct ion and reduced left ventricular function . RESULTS We analyzed baseline and follow-up data on 719 patients enrolled in the ICD arm of the MADIT-II study . Appropriate ICD therapy was observed in 169 subjects . Clinical , laboratory , echocardiographic , and electrophysiological variables , along with measures of clinical instability such as interim hospitalization for congestive heart failure ( IH-CHF ) and interim hospitalization for coronary events ( IH-CE ) , were examined with proportional hazards models and Kaplan-Meier time-to-event curves before and after first interim hospitalization . Interim hospitalization-CHF , IH-CE , no beta-blockers , digitalis use , blood urea nitrogen ( BUN ) > 25 , body mass index ( BMI ) > or = 30 kg/m2 , and New York Heart Association functional class > II were associated with increased risk for appropriate ICD therapy for VT , VF , or death . In a multivariate ( stepwise selection ) analysis , IH-CHF was associated with an increased risk for the end point of either VT or VF ( hazard ratio [ HR ] 2.52 , 95 % confidence interval [ CI ] 1.69 to 3.74 , p < 0.001 ) and for the combined end point of VT , VF , or death ( HR 2.97 , 95 % CI 2.15 to 4.09 , p < 0.001 ) . Interim hospitalization-CE was associated with an increased risk for VT , VF , or death ( HR 1.66 , 95 % CI 1.09 to 2.52 , p = 0.02 ) . CONCLUSIONS These results provide important mechanistic information , suggesting that worsening clinical condition and cardiac instability , as reflected by an IH-CHF or IH-CE , are subsequently associated with a significant increase in the risk for appropriate ICD therapy ( for VT/VF ) and death AIMS AND OBJECTIVE Outcome data of patients with implantable cardioverter defibrillators ( ICD ) and atrial fibrillation ( AF ) are conflicting . The German DEVICE registry aims to add further information on this particular cohort . METHODS AND RESULTS The German DEVICE registry is a nationwide prospect i ve multicenter data base of ICD implantations . 3261 patients are included ( 81 % males , 2701 ( 82.8 % ) first ICD implantations , 560 ( 17.2 % ) ICD replacements ) .  Cardiac resynchronization therapy ( CRT-D ) was performed in 882 patients ( 27.0 % ) . Sinus rhythm ( SR ) was present in 2654 ( 81.4 % ) and atrial fibrillation ( AF ) in 607 ( 18.6 % ) . Left ventricular ejection fraction ( LVEF ) did not differ between groups ( SR 32.3 % , AF 30.4 % ; p = 0.09 ) . AF patients were older ( AF 70.9 versus SR 63.9 years ; p < 0.0001 ) , presented with more co-morbidities ( diabetes , hypertension , chronic kidney disease ; all p < 0.001 ) . In-hospital complications were not significantly different between groups ( p = 0.58 ) . Follow-up information after one year was available in 2967 patients ( 91 % ) . One-year overall mortality after first ICD implantation was 4.9 % for SR and 11.2 % for AF patients ( p < 0.0001 ) ; mortality one year after ICD replacement was 8.4 % for SR and 12.0 % for AF ( p = 0.34 ) . No statistically significant difference between SR and AF patients receiving a CRT device was observed ( SR 6.9 % , AF 10.7 % , p = 0.16 ) in terms of one-year mortality . CONCLUSION The German DEVICE registry demonstrates that patients with AF who receive ICD devices are older , have more co-morbidity and more severe heart failure . AF carries an independent 1.39 fold risk ( 95 % CI 1.02 - 1.89 ) of death after one year in patients only with first ICD implantation A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results 
    Output:
    