***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: Prognosis studies are investigations of future events or the evaluation of associations between risk factors and health outcomes in population s of patients ( 1 ) . The results of such studies improve our underst and ing of the clinical course of a disease and assist clinicians in making informed decisions about how best to manage patients . Prognostic research also informs the design of intervention studies by helping define subgroups of patients who may benefit from a new treatment and by providing necessary information about the natural history of a disorder ( 2 ) . There has recently been a rapid increase in the use of systematic review methods to synthesize the evidence on research questions related to prognosis . It is essential that investigators conducting systematic review s thoroughly appraise the method ologic quality of included studies to be confident that a study 's design , conduct , analysis , and interpretation have adequately reduced the opportunity for bias ( 3 , 4 ) . Caution is warranted , however , because inclusion of method ologically weak studies can threaten the internal validity of a systematic review ( 4 ) . This follows abundant empirical evidence that inadequate attention to biases can cause invalid results and inferences ( 5 - 9 ) . However , there is limited consensus on how to appraise the quality of prognosis studies ( 1 ) . A useful framework to assess bias in such studies follows the basic principles of epidemiologic research ( 10 , 11 ) . We focus on 6 areas of potential bias : study participation , study attrition , prognostic factor measurement , confounding measurement and account , outcome measurement , and analysis . The main objectives of our review of review s are to describe methods used to assess the quality of prognosis studies and to describe how well current practice s assess potential biases . Our secondary objective is to develop recommendations to guide future quality appraisal , both within single studies of prognostic factors and within systematic review s of the evidence . We hope this work facilitates future discussion and research on biases in prognosis studies and systematic review s. Methods Literature Search and Study Selection We identified systematic review s of prognosis studies by search ing MEDLINE ( 1966 to October 2005 ) using the search strategy recommended by McKibbon and colleagues ( 12 ) . This strategy combines broad search terms for systematic review s ( systematic review .mp ; meta- analysis .mp ) and a sensitive search strategy for prognosis studies ( cohort , incidence , mortality , follow-up studies , prognos * , predict * , or course ) . We also search ed the reference lists of included review s and method ologic papers to identify other relevant publications . We restricted our search to English- language publications . One review er conducted the search and selected the studies . Systematic review s , defined as review s of published studies with a comprehensive search and systematic selection , were included if they assessed the method ologic quality of the included studies by using 1 or more explicit criteria . We excluded studies if they were meta-analyses of independent patient data only , if their primary goal was to investigate the effectiveness of an intervention or specific diagnostic or screening tests , or if they included studies that were not done on humans . Data Extraction and Synthesis Individual items included in the quality assessment of the systematic review s were recorded as they were reported in the publication ( that is , the information that would be available to readers and future review ers ) . We review ed journal Web sites and contacted the authors of the systematic review s for additional information when authors made such an offer in their original papers . When review s assessed different study design s by using different sets of quality items , we extracted only those items used to assess cohort studies . We constructed a comprehensive list of distinct items that the review s used to assess the quality of their included studies . The full text of each review was screened . All items used by the review authors to assess the quality of studies were extracted into a computerized spreadsheet by 1 review er . Two experienced review ers , a clinical epidemiologist and an epidemiologist , independently synthesized the quality items extracted from the prognosis review s to determine how well the systematic review s assessed potential biases . We did this in 3 steps : 1 ) identified distinct concepts or domains addressed by the quality items ; 2 ) grouped each extracted quality item into the appropriate domain or domains ; and 3 ) identified the domains necessary to assess potential biases in prognosis studies . We then used this information to assess how well the review s ' quality assessment included items from the domains necessary to assess potential biases . After completing each of the first 3 steps , the review ers met to attempt to reach a consensus . The consensus process involved each review er presenting his or her observations and results , followed by discussion and debate . A third review er was available in cases of persistent disagreement or uncertainty . In the first step , all domains addressed by the quality items were identified . The first review er iteratively and progressively defined the domains as items were extracted from the included review s. The second review er defined domains from a r and om list of all extracted quality items . Limited guidance was provided to the review ers so that their assessment s and definitions of domains would be independent . The review ers agreed on a final set of domains that adequately and completely defined all of the extracted items . In the second step , review ers independently grouped each extracted item into the appropriate domains . Review ers considered each extracted item by asking , What is each particular quality item addressing ? or What are the review 's authors getting at with the particular quality assessment item ? . Items were grouped into the domain or domains that best represented the concepts being addressed . For example , the extracted items at least 80 % of the group originally identified was located for follow-up and follow-up was sufficiently complete or does n't jeopardize validity were each independently classified by both review ers as assessing the domain completeness of follow-up adequate , whereas the extracted item quantification and description of all subjects lost to follow-up was classified as assessing the domain completeness of follow-up described . In the third step , we identified the domains necessary to assess potential biases . Each review er considered the ability of the identified domains to adequately address , at least in part , 1 of the following 6 potential biases : 1 ) study participation , 2 ) study attrition , 3 ) prognostic factor measurement , 4 ) confounding measurement and account , 5 ) outcome measurement , and 6 ) analysis . Domains were considered to adequately address part of the framework if information garnered from that domain would inform the assessment of potential bias . For example , both review ers judged that the identified domain study population represents source population or population of interest assessed potential bias in a prognosis study , whereas the domain research question definition did not , although the latter is an important consideration in assessing the inclusion of studies in a systematic review . Finally , on the basis of our previous ratings , we looked at whether each review included items from the domains necessary to assess the 6 potential biases . We calculated the frequency of systematic review s by assessing each potential bias and the number of review s that adequately assessed bias overall . From this systematic synthesis , we developed recommendations for improving quality appraisal in future systematic review s of prognosis studies . We used Microsoft Access and Excel 2002 ( Microsoft Corp. , Redmond , Washington ) for data management and SAS for Windows , version 9.1 ( SAS Institute , Inc. , Cary , North Carolina ) for descriptive statistics . Role of the Funding Sources The funding sources , the Canadian Institutes of Health Research , the Canadian Chiropractic Research Foundation , the Ontario Chiropractic Association , and the Ontario Ministry of Health and Long Term Care , did not have a role in the collection , analysis , or interpretation of the data or in the decision to su bmi t the manuscript for publication . Results We identified 1384 potentially relevant articles . Figure 1 shows a flow chart of studies that were included and excluded . Figure 2 shows the number of review s identified by year of publication . We excluded 131 systematic review s of prognosis studies that did not seem to include any quality assessment of the included studies ; this represented 44 % of prognosis review s. We included 163 review s of prognosis studies in our analysis ( 13 - 175 ) . The most common topics were cancer ( 15 % ) , musculoskeletal disorders and rheumatology ( 13 % ) , cardiovascular ( 10 % ) , neurology ( 10 % ) , and obstetrics ( 10 % ) . Other review s included a wide range of health and health care topics . Sixty-three percent of the review s investigated the association between a specific prognostic factor and a particular outcome ; the remainder investigated multiple prognostic factors or models . The number of primary studies included in each systematic review ranged from 3 to 167 ( median , 18 [ interquartile range , 12 to 31 ] ) . A complete description of the included review s is available from the authors on request . Figure 1 . Flow diagram of inclusion and exclusion criteria of systematic review s. Figure 2 . Number of systematic review s of prognosis studies identified over time . Quality Items One hundred fifty-three review s provided adequate detail to allow extraction of quality items . Eight hundred eighty-two distinct quality items were extracted from the review s. Most review s developed their own set of quality items , with only a few applying criteria from previous review s. Most quality items Background Studies about the influence of various factors on clinical therapy and course in acute coronary syndromes have shown that the outcome is related to admission time to the hospital , with an impaired prognosis in patients admitted out of regular working hours . However little is known about the impact of admission on weekend in hospitals with catheterisation laboratories . Methods We analyzed data of the prospect i ve MITRA-PLUS registry of 11,516 patients with ST-elevation myocardial infa rct ion ( STEMI ) admitted to hospitals with catheterization facilities for differences of in-hospital mortality between patients admitted during regular working hours , at night and on weekends . Results The prehospital delay and “ door-to-balloon”-time were significantly longer on weekends and at nights than at regular working hours ( median 196 Vs . 240 Vs . 155 min ; P < 0.0001 ; 60 Vs . 84 min at weekends , resp . 75 min at nights ; P < 0.0001 ) .  Reperfusion therapy  was performed in 72.8 % ( 8,248/11,332 ) patients , and there were less patients treated on weekend versus “ on”-hours ( 69.7 Vs . 77 % , P < 0.0001 ) . On weekends we found a significant higher in-hospital mortality ( 11.1 Vs . 9.4 % , P = 0.01 ) and at night there was a trend to higher in-hospital mortality when compared with regular working hours ( 10.6 Vs . 9.4 % , P = 0.07 ) . Conclusion In patients with STEMI admitted to hospitals with catheterization facilities , admission during the “ off”-hours is associated with higher in-hospital mortality . This may be due to lower rates of revascularization therapy and longer prehospital and in-hospital delays as compared to “ on”-hours Objective To evaluate the association between door-to-balloon time and mortality in hospital in patients undergoing primary percutaneous coronary intervention for ST elevation myocardial infa rct ion to assess the incremental mortality benefit of reductions in door-to-balloon times of less than 90 minutes . Design Prospect i ve cohort study of patients enrolled in the American College of Cardiology National Cardiovascular Data Registry , 2005 - 6 . Setting Acute care hospitals . Participants 43 801 patients with ST elevation myocardial infa rct ion undergoing primary percutaneous coronary intervention .  Main outcome measure Mortality in hospital . Results Median door-to-balloon time was 83 minutes ( interquartile range 6 - 109 , 57.9 % treated within 90 minutes ) . Overall mortality in hospital was 4.6 % . Multivariable logistic regression models with fractional polynomial models indicated that longer door-to-balloon times were associated with a higher adjusted risk of mortality in hospital in a continuous non-linear fashion ( 30 minutes=3.0 % , 60 minutes=3.5 % , 90 minutes=4.3 % , 120 minutes=5.6 % , 150 minutes=7.0 % , 180 minutes=8.4 % , P<0.001 ) . A reduction in door-to-balloon time from 90 minutes to 60 minutes was associated with 0.8 % lower mortality , and a reduction from 60 minutes to 30 minutes with a 0.5 % lower mortality . Conclusion Any delay in primary percutaneous coronary intervention after a patient arrives at hospital is associated with higher mortality in hospital in those admitted with ST elevation myocardial infa rct ion . Time to treatment should be as short as possible , even in centres currently providing primary percutaneous coronary intervention within 90 minutes Background : The use of the prehospital electrocardiogram ( ECG ) to identify patients with ST-segment elevation myocardial infa rct ion ( STEMI ) , coupled with a central ised system to alert the cardiac catheterisation team in preparation for prompt intervention , has been shown to reduce door-to-balloon times ( DBT ) effectively . A confounding variable in prolonging the recommended 90 min DBT is the time of day or day of the week of patient presentation . We postulated that use of the prehospital ECG , coupled with an emergency department initiated “ Cath Alert ” system , could neutralise DBT delays related to time of day or day of week . Methods : A prospect i ve study was conducted on 167 consecutive patients presenting to our emergency department with acute STEMI . All patients were treated with primary percutaneous coronary intervention .  Patients were grouped according to time of presentation : during regular hours ( Monday to Friday 08:00 to 17:00 ) vs off hours ( after 17:00 on weekdays and all hours on weekends ) . Baseline recorded variables included mode of presentation , transmission of prehospital ECG , and activation of Cath Alert system .  Results : Overall , the mean ( SD ) DBT was 69 ( 35 ) mins , with the majority of patients ( n = 131 , 78 % ) achieving the recommended DBT of 90 mins . The shortest DBT occurred in patients who arrived by emergency medical services with use of the prehospital ECG and Cath Alert system ( 53 ( 21 ) min ) , while those 
    Output:
    
A number of patient characteristics , hospital characteristics , physician characteristics , care processes and " other " factors were associated with door to balloon time .
Prognostic factors for longer times include : pre-hospital delay in presentation , cerebrovascular disease , absence of chest pain , lower PCI volume and specialization hospital , lower sum ST elevation , absence of Q waves and left bundle branch block .
Underst and ing prognostic factors for door to balloon time can likely lead to improved quality of care for STEMI