The Effectiveness of Exercise Interventions for the Management of Frailty: A Systematic Review Systematic Reviews of Diagnostic Test Accuracy Screening for frailty among older patients with cancer that qualify for abdominal surgery. Protocol for a systematic review of the diagnostic and prognostic utility of tests currently available for the detection of aspirin resistance in patients with established cardiovascular or cerebrovascular disease Effectiveness of exercise interventions on physical function in community-dwelling frail older people: an umbrella review of systematic reviews PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration Interrater reliability in assessing quality of diagnostic accuracy studies using the QUADAS tool. A preliminary assessment.
This systematic review examines the effectiveness of current exercise interventions for the management of frailty . Eight electronic data bases were search ed for r and omized controlled trials that identified their participants as “ frail ” either in the title , abstract , and /or text and included exercise as an independent component of the intervention . Three of the 47 included studies utilized a vali date d definition of frailty to categorize participants . Emerging evidence suggests that exercise  has a positive impact on some physical determinants and on all functional ability outcomes reported in this systematic review . Exercise programs that optimize the health of frail older adults seem to be different from those recommended for healthy older adults . There was a paucity of evidence to characterize the most beneficial exercise program for this population . However , multicomponent training interventions , of long duration ( ≥5 months ) , performed three times per week , for 30–45 minutes per session , generally had superior outcomes than other exercise programs . In conclusion , structured exercise training seems to have a positive impact on frail older adults and may be used for the management of frailty Diagnosis is a critical component of health care , and clinicians , policymakers , and patients routinely face a range of questions regarding diagnostic tests . They want to know whether testing improves outcome ; what test to use , purchase , or recommend in practice guidelines ; and how to interpret test results . Well- design ed  diagnostic test accuracy studies can help in making these decisions , provided that they transparently and fully report their participants , tests , methods , and results as facilitated , for example , by the STARD ( St and ards for Reporting of Diagnostic Accuracy ) statement ( 1 ) . That 25-item checklist was published in many journals and is now adopted by more than 200 scientific journals worldwide . As in other areas of science , systematic review s and meta- analysis of accuracy studies can be used to obtain more precise estimates when small studies addressing the same test and patients in the same setting are available . Review s can also be useful to establish whether and how scientific findings vary by particular subgroups , and may provide summary estimates with a stronger generalizability than estimates from a single study . Systematic review s may help identify the risk for bias that may be present in the original studies and can be used to address questions that were not directly considered in the primary studies , such as comparisons between tests . The Cochrane Collaboration is the largest international organization preparing , maintaining , and promoting systematic review s to help people make well-informed decisions about health care ( 2 ) . The Collaboration decided in 2003 to make preparations for including systematic review s of diagnostic test accuracy in their Cochrane Data base of Systematic Review s. To enable this , a working group ( Appendix ) . was formed to develop methodology , software , and a h and book The first diagnostic test accuracy review was published in the Cochrane Data base in October 2008 . In this paper , we review recent method ological developments concerning problem formulation , location of literature , quality assessment , and meta- analysis of diagnostic accuracy studies by using our experience from the work on the Cochrane H and book . The information presented here is based on the recent literature and up date s previously published guidelines by Irwig and colleagues ( 3 ) . Definition of the Objectives of the Review Diagnostic test accuracy refers to the ability of a test to distinguish between patients with disease ( or more generally , a specified target condition ) and those without . In a study of test accuracy , the results of the test under evaluation , the index test , are compared with those of the reference st and ard determined in the same patients . The reference st and ard is an agreed-on and accurate method for identifying patients who have the target condition . Test results are typically categorized as positive or negative for the target condition . By using such binary test outcomes , the accuracy is most often expressed as the test 's sensitivity ( the proportion of patients with positive results on the reference st and ard that are also positive on the index test ) and specificity ( the proportion of patients with negative results on the reference st and ard that are also negative on the index test ) . Other measures have been proposed and are in use ( 46 ) . It has long been recognized that test accuracy is not a fixed property of a test . It can vary between patient subgroups , with their spectrum of disease , with the clinical setting , or with the test interpreters and may depend on the results of previous testing . For this reason , inclusion of these elements in the study question is essential . In order to make a policy decision to promote use of a new index test , evidence is required that using the new test increases test accuracy over other testing options , including current practice , or that the new test has equivalent accuracy but offers other advantages ( 79 ) . As with the evaluation of interventions , systematic review s need to include comparative analyses between alternative testing strategies and should not focus solely on evaluating the performance of a test in isolation . In relation to the existing situation , 3 possible roles for a new test can be defined : replacement , triage , and add-on ( 7 ) . If a new test is to replace an existing test , then comparing the accuracy of both tests on the same population and with the same reference st and ard provides the most direct evidence . In triage , the new test is used before the existing test or testing pathway , and only patients with a particular result on the triage test continue the testing pathway . When a test is needed to rule out disease in patients who then need no further testing , a test that gives a minimal proportion of falsenegative results and thus a relatively high sensitivity should be used . Triage tests may be less accurate than existing ones , but they have other advantages , such as simplicity or low cost . A third possible role of a new test is add-on . The new test is then positioned after the existing testing pathway to identify false-positive or false-negative results after the existing pathway . The review should provide data to assess the incremental change in accuracy made by adding the new test . An example of a replacement question can be found in a systematic review of the diagnostic accuracy of urinary markers for primary bladder cancer ( 10 ) . Clinicians may use cytology to triage patients before they undergo invasive cystoscopy  , the reference st and ard for bladder cancer . Because cytology combines high specificity with low sensitivity ( 11 ) , the goal of the review was to identify a tumor marker with sufficient accuracy to either replace cytology or be used in addition to cytology . For a marker to replace cytology , it has to achieve equally high specificity with improved sensitivity . New markers that are sensitive but not specific may have roles as adjuncts to conventional testing . The review included studies in which the test under evaluation ( several different tumor markers and cytology ) was evaluated against cystoscopy or histopathology . Included studies compared 1 or more of the markers , cytology only , or a combination of markers and cytology . Although information on accuracy can help clinicians make decisions about tests , good diagnostic accuracy is a desirable but not sufficient condition for the effectiveness of a test ( 8) . To demonstrate that using a new test does more good than harm to patients tested , r and omized trials of test- and -treatment strategies and review s of such trials may be necessary . However , with the possible exception of screening , in most cases , such r and omized trials are not available and systematic review s of test accuracy may provide the most useful evidence available to guide clinical and health policy decision making and use as input for decision and cost-effectiveness analysis ( 12 ) . Identification and Selection of Studies Identifying test accuracy studies is more difficult than search ing for r and omized trials ( 13 ) . There is not a clear , unequivocal keyword or indexing term for an accuracy study in literature data bases comparable with the term r and omized , controlled trial . The Medical Subject Heading sensitivity and specificity may look suitable but is inconsistently applied in most electronic bibliographic data bases . Furthermore , data on diagnostic test accuracy may be hidden in studies that did not have test accuracy estimation as their primary objective . This complicates the efficient identification of diagnostic test accuracy studies in electronic data bases , such as MEDLINE . Until indexing systems properly code studies of test accuracy , search ing for them will remain challenging and may require additional manual search es , such as screening reference lists . In the development of a comprehensive search strategy , review authors can use search strings that refer to the test(s ) under evaluation , the target condition , and the patient description or a subset of these . For tests with a clear name that are used for a single purpose , search ing for publications in which those tests are mentioned may suffice . For other review s , adding the patient description may be necessary , although this is also often poorly indexed . A search strategy in MEDLINE should contain both Medical Subject Headings and free text words . A search strategy for articles about tests for bladder cancer , for example , should include as many synonyms for bladder cancer as possible in the search strategy , including neoplasm , carcinoma , transitional cell , and hematuria . Several method ological electronic search filters for diagnostic test accuracy studies have been developed , each attempting to restrict the search to articles that are most likely to be test accuracy studies ( 1316 ) . These filters rely on indexing terms for research methodology and text words used in reporting results , but they often miss relevant studies and are unlikely to decrease the number of articles one needs to screen . Therefore , they are not recommended for systematic review s ( 17 , 18 ) . The incremental value of search ing in language s other than English and in the gray literature has not yet been fully investigated . In systematic review s of intervention studies , publication bias is an important and well-studied form of bias in which the decision to report and publish studies is linked to their findings . For clinical trials , the magnitude and determinants of publication bias have been identified by tracing the publication history of cohorts of trials review ed by ethics committees and research boards ( 19 ) . A consistent observation has been that studies with significant results are more likely to be published than studies with nonsignificant findings ( 19 ) . Investigating publication bias for diagnostic tests is problematic , because many studies are done without ethical review or study registration ; therefore , identification of cohorts of studies from registration to final publication status OBJECTIVE The Geriatric Assessment ( GA ) is an established method for evaluating and optimizing diagnostic and treatment plans . However , it requires experience and is time-consuming . Therefore , a variety of screening methods have been developed . The aim of this study was to compare their accuracy for predicting frailty among older patients with cancer qualified for abdominal surgery based on comparison to the GA . MATERIAL AND METHODS One hundred and thirty five consecutive patients ≥65years of age were prospect ively enrolled . The diagnostic performance of eight screening tests was evaluated : The Vulnerable Elderly Survey ( VES-13 ) , Triage Risk Screening Tool ( TRST ) , Geriatric 8 ( G8 ) , Groningen Frailty Index ( GFI ) , abbreviated Comprehensive Geriatric Assessment ( aCGA ) , Rockwood , Balducci and Fried score . RESULTS The prevalence of frailty as diagnosed by the GA was 73 % . Screening methods identified frail patients in 40 - 75.5 % of cases . The sensitivity and specificity of these tests in predicting frailty were 52%-97 % ( Fried score-G8 ) and 44 - 100 % ( G8-Rockwood score ) , respectively . The positive and negative predictive values were 82 - 100 % ( Balducci-Rockwood ) and 43 - 84 % ( TRST-G8 ) , respectively . Age significantly influenced the predictive value of the screening tests whereas gender and type of cancer did not . CONCLUSION At present , there is no universal screening test that adequately identifies frailty in at risk older patients . The results of this study showed that the aCGA and G8 were the best screens for older patients with cancer that qualified for elective abdominal surgery ; the G8 had the highest sensitivity and negative predictive value and the aCGA was a good overall assessment tool Background The benefits of aspirin as an anti-platelet agent are well established ; however , there has been much debate about the lack of uniformity in the efficacy of aspirin to inhibit platelet function . In some patients , aspirin fails to inhibit platelets even where compliance has been verified , a phenomenon which has been termed “ aspirin resistance ” . These patients may in turn be at a higher risk of future vascular events . The proportion of “ resistant ” patients identified depends on the type of platelet function test . Therefore , the aim of this systematic review is to determine which , if any , platelet function test has utility in terms of identifying patients with a high risk of vascular events . The review has been registered with PROSPERO ( CRD42012002151 ) . Methods Relevant studies will be sought from bibliographic data bases . Trials registers will be search ed for ongoing studies . Reference lists will be checked and subject experts contacted . There will be no date or language restrictions . St and ard review ing methodology to minimise bias will be employed . Any prospect i ve studies in patients on aspirin therapy and assessing platelet function in relation to relevant clinical outcomes will be included , as will studies reporting prognostic models . Risk of bias assessment will be based on the Quality Assessment of Diagnostic Accuracy Studies guidelines , and suitable criteria for assessing quality of prognostic studies . Data on test accuracy measures , relative risks , odds or hazard ratios will be extracted and meta-analysed , where possible , using a r and om-effects model to account for between- study heterogeneity . Where appropriate , the causes of heterogeneity will be explored through meta-regression and sub-group or sensitivity analyses . If platelet function testing is demonstrated to have diagnostic/predictive utility in a specific population , the potential for a cost-effectiveness analysis will be considered and , if possible , an economic model constructed . This will be supported by a systematic review of existing economic evaluation studies . Discussion The results of the review could indicate if platelet function test(s ) could lead to a reliable prediction of the risk of clinical ly important events in a defined population , and thus support investigations into adjustments to therapy in order to compensate for a predicted poor response to st and ard aspirin Objectives : This umbrella review aim ed to determine the effectiveness of exercise interventions , alone or in combination with other interventions , in improving physical function in community-dwelling older people identified as pre-frail or frail . Introduction : Exercise is said to have a positive impact on muscle mass and strength which improves physical function and hence is beneficial for the treatment of frailty . Several systematic review s discuss the effects of exercise interventions on physical function parameters , such as strength , mobility , gait , balance and physical performance , and indicate that multi-component exercise , including resistance , aerobic , balance and flexibility training , appears to be the best way in which to improve physical function parameters in frail older people . However , there is still uncertainty as to which exercise characteristics ( type , frequency , intensity , duration and combinations ) are the most effective and sustainable over the long-term . Inclusion criteria : Participants were adults , 60 years or over , living in the community and identified as pre-frail or frail . Quantitative systematic review s , with or without meta- analysis that examined the effectiveness of exercise interventions of any form , duration , frequency and intensity , alone or in combination with other interventions design ed to alter physical function parameters in frail older people , were considered . The quantitative outcome measures were physical function , including muscular strength , gait , balance , mobility and physical performance . Methods : An iterative search strategy for ten bibliometric data bases and gray literature was developed . Critical appraisal of seven systematic review s was conducted independently by two review ers using a st and ard Joanna Briggs Institute tool . Data was extracted independently by two review ers using a st and ard Joanna Briggs Institute data extraction tool and summarized using a narrative synthesis approach . Results : Seven systematic review s were included in this umbrella review , with a total of 58 relevant r and omized controlled trials and 6927 participants . Five systematic review s examined the effects of exercise only , while two systematic review s reported on exercise in combination with a nutritional approach , including protein supplementations , as well as fruit and dairy products . The average  exercise frequency was 2–3 times per week ( mean 3.0 ± 1.5 times per week ; range 1–7 weekly ) for 10–90 minutes per session ( mean of 52.0 ± 16.5 mins ) and a total duration of 5–72 weeks with the majority lasting a minimum of 2.5 months ( mean 22.7 ± 17.7 weeks ) . Multi-component exercise interventions can currently be recommended for pre-frail and frail older adults to improve muscular strength , gait speed , balance and physical performance , including resistance , aerobic , balance and flexibility tasks . Resistance training alone also appeared to be beneficial , in particular for improving muscular strength , gait speed and physical performance . Other types of exercise were not sufficiently studied and their effectiveness is yet to be established . Conclusions : Interventions for pre-frail and frail older adults should include multi-component exercises , including in particular resistance training , as well as aerobic , balance and flexibility tasks . Future research should adopt a consistent definition of frailty and investigate the effects of other types of exercise alone or in combination with nutritional interventions so that more specific recommendations can be made Prediction relates to estimating the probability of something currently unknown . In the context of medical research , prediction typically concerns either diagnosis ( probability of a certain condition being present but not yet detected ) or prognosis ( probability of an outcome developing in the future ) ( 13 ) . Prognosis applies not only to sick persons or those with an established diagnosis but also to , for example , pregnant women at risk for diabetes ( 4 ) . Prediction research includes predictor finding studies , prediction model studies ( development , validation , and extending or updating ) , and prediction model impact studies ( 1 ) . Predictor finding studies ( also known as risk factor or prognostic factor studies ) aim to identify which predictors ( such as age , disease stage , or biomarkers ) independently contribute to the prediction of a diagnostic or prognostic outcome ( 1 , 5 ) . Prediction model studies aim to develop , vali date , or up date ( for example , extend ) a multivariable prediction model . A prediction model uses multiple predictors in combination to estimate probabilities to inform and often guide individual care ( 2 , 6 , 7 ) . These models can predict an individual 's probability of either currently having a particular outcome or disease ( diagnostic prediction model ) or having a particular outcome in the future ( prognostic prediction model ) . Both types of model are widely used in various medical domains and setting s ( 810 ) , as evidence d by the large number of models developed in cancer ( 11 , 12 ) , neurology ( 13 , 14 ) , and cardiovascular disease ( 15 ) . Prediction models are sometimes described as  risk prediction models , predictive models , prediction indices or rules , or risk scores ( 2 , 7 ) . An example is QRISK2 for predicting cardiovascular risk ( 16 ) . Prediction model impact studies evaluate the effect of using a model to guide patient care compared with not using such a model . They use a comparative design , such as a r and omized trial , to study the model 's effect on clinical decision making , patient outcomes , or costs of care ( 1 ) . Systematic review s have a key role in evidence -based medicine and the development of clinical guidelines ( 1719 ) . They are considered to provide the most reliable form of evidence for the effects of an intervention or diagnostic test ( 20 , 21 ) . Systematic review s of prediction models are a relatively new and evolving area but are increasingly undertaken to systematic ally identify , appraise , and summarize evidence on the performance of prediction models ( 1 , 6 , 22 ) . Assessing the quality of included studies is a crucial step in any systematic review ( 20 , 21 ) . The QUIPS ( Quality In Prognosis Studies ) tool has been developed to assess risk of bias ( ROB ) in predictor finding ( prognostic factor ) studies ( 23 ) . Research ers can use the revised Cochrane ROB Tool ( ROB 2.0 ) ( 24 ) to investigate the method ological quality of prediction model impact studies that use a r and omized comparative design , or ROBINS-I ( Risk Of Bias In Nonr and omized Studies of Interventions ) for those that use a nonr and omized comparative design ( 25 ) . As more prediction model studies and systematic review s of such studies are used as evidence for clinical guidance , a tool facilitating quality assessment for individual prediction model studies is urgently needed . We present PROBAST ( Prediction model Risk Of Bias ASsessment Tool ) , a tool to assess the ROB and concerns regarding the applicability of diagnostic and prognostic prediction model studies . PROBAST can be used to assess studies of model development and model validation , including those updating a prediction model ( Box 1 [ 26 ] ) . We refer to the accompanying explanation and elaboration document ( 27 ) , for detailed explanations of how to use PROBAST and how to judge ROB and applicability . Box 1 . Types of diagnostic and prognostic modeling studies or reports addressed by PROBAST . Adopted from the TRIPOD ( Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis ) and CHARMS ( CHecklist for critical Appraisal and data extraction for systematic Review s of prediction Modelling Studies ) guidance ( 7 , 26 ) . PROBAST = Prediction model Risk Of Bias ASsessment Tool . Methods : Development of PROBAST Development of PROBAST was based on a 4-stage approach for developing health research reporting guidelines : define the scope , review the evidence base , use a Web-based Delphi procedure , and refine the tool through piloting ( 28 ) . Guidelines explicitly aim ed at the development of quality assessment tools were not available at the time ( 29 ) . Development Stage 1 : Scope and Definitions A steering group of 9 experts in prediction model studies and development of quality assessment tools agreed on key features of the desired scope of PROBAST . A panel of 38 experts with different background s further refined the scope during the Web-based Delphi procedure . PROBAST was design ed mainly to assess primary studies included in a systematic review . The group agreed that PROBAST would assess both risk of bias and concerns regarding applicability of a study evaluating a multivariable prediction model to be used for individualized diagnosis or prognosis . A domain-based structure was adopted , similar to that used in other ROB tools , such as ROB 2.0 ( 24 ) , ROBINS-I ( 25 ) , QUADAS-2 ( Quality Assessment of Diagnostic Accuracy Studies 2 ) ( 30 ) , and ROBIS ( 31 ) . We agreed that PROBAST should cover primary studies that develop , vali date , or up date multivariable prediction models aim ing to make individualized predictions of a diagnostic or prognostic outcome ( Box 1 ) . Studies that use multivariable modeling techniques to identify predictors ( such as risk or prognostic factors ) associated with an outcome but do not attempt to develop , vali date , or up date a model for making individualized predictions are not covered by PROBAST ( 5 ) . Therefore , PROBAST is not intended for predictor finding studies or prediction model impact studies . Studies of diagnostic and prognostic models often use different terms for predictors and outcomes ( Box 2 ) . A multivariable prediction model is defined as any combination or equation of 2 or more predictors for estimating probability or risk for an individual ( 6 , 7 , 3234 ) . Box 2 . Differences between diagnostic and prognostic prediction model studies . PROBAST = Prediction model Risk Of Bias ASsessment Tool . Development Stage 2 : Review of Evidence We used the following 3 approaches to build an evidence base to inform the development of PROBAST : identifying relevant method ological review s in the area of prediction model research ( November 2012 to January 2013 ) , asking members of the steering group to identify relevant method ological studies ( January 2013 to March 2013 ) , and using the Delphi procedure to ask members of the wider group to identify additional evidence ( February 2012 to July 2014 ) . Identified literature was used to guide the scope and produce an initial list of signaling questions to consider for inclusion in PROBAST ( 1 , 2 , 57 , 26 , 3340 ) . We grouped signaling questions into common themes to identify possible domains . Additional literature provided as part of the Web-based surveys informed development of the explanation and elaboration document . Development Stage 3 : Web-Based Delphi Procedure We used a modified Delphi process to gain feedback and agreement on the scope , structure , and content of PROBAST . Web-based surveys were developed to gather structured feedback for each round . The 38-member Delphi group comprised method ological experts in prediction model research and development of quality assessment tools , experienced systematic review ers , commissioners , and representatives of reimbursement agencies . We included various stakeholders to ensure that the views of end users , method ological experts , and decision makers were represented . The Delphi process consisted of 7 rounds . Round 1 asked about the scope of the tool , and participants agreed to focus on prediction model studies and follow a domain-based structure . Round 2 aim ed to identify relevant domains and agree on which to include . The signaling questions for domains were refined in rounds 3 to 5 . Respondents used a 1-to-5 Likert scale to rate each proposed signaling question for inclusion . They could also suggest rephrasing , provide supporting evidence ( such as references to relevant studies ) , and suggest missing signaling questions . Round 6 refined the domains and introduced further optional guidance for using PROBAST . In the last round , participants received the agreed draft version of PROBAST and had the opportunity to provide any final feedback . Development Stage 4 : Piloting and Refining the Tool We held 6 workshops on PROBAST at consecutive annual Cochrane Colloquia ( Quebec , Canada , 2013 ; Hyderabad , India , 2014 ; Vienna , Austria , 2015 ; Seoul , South Korea , 2016 ; Cape Town , South Africa , 2017 ; and Edinburgh , United Kingdom , 2018 ) . We also held numerous consecutive workshops with MSc and PhD students ( for example , the master 's program in epidemiology at Utrecht University [ Utrecht , the Netherl and s ] and the Evidence -Based Health Care program at Oxford University [ Oxford , United Kingdom ] ) . In these workshops , we piloted the then-current version of PROBAST to gather feedback on practical issues associated with using the tool so that we could further refine and subsequently vali date it . Finally , more than 50 review groups have already piloted PROBAST versions , including the final version , in their review s. Topics included cancer , cardiology , endocrinology , pulmonology , and orthopedics . All feedback received from these initiatives was used to further inform the content and structure of PROBAST , wording of the signaling questions , and content of the guidance documents ( 27 ) . Results : The PROBAST Tool What Does PROBAST Assess ? PROBAST assesses both risk of bias and concerns regarding applicability of primary studies that developed or vali date d multivariable prediction models for diagnosis or prognosis ( Boxes 1 and 2 ) Prediction models in health care aim to predict for an individual whether a particular outcome , such as disease , is present ( diagnostic models ) or whether it will occur in the future ( prognostic models ) ( 16 ) . Diagnostic models can be used to refer patients for further testing , to initiate treatment , or to inform patients . Prognostic models can be used to aid decisions about preventive lifestyle changes , therapeutic interventions , or monitoring strategies or to stratify risk in r and omized trial design and analysis ( 7 , 8) . Potential users of prediction models include health care professionals , policymakers , guideline developers , patients , and the general public . The medical literature contains thous and s of studies developing and validating prediction models and often has numerous prediction models for the same target population and outcomes . For example , more than 60 models address breast cancer prognosis ( 9 ) , more than 250 exist in obstetrics ( 10 ) , and nearly 800 predict outcomes in patients with cardiovascular disease ( 11 ) . This proliferation of prediction models will increase further with the growth of personalized or precision medicine . Systematic review s are considered the most reliable form of evidence when addressing r and omized therapeutic studies and studies of diagnostic test accuracy ( 12 ) . In the era of personalized and precision medicine , interest in systematic review s of prediction model studies is rapidly growing , as exemplified by the formation of the Cochrane Prognosis Methods Group to support systematic review s of prognosis , including prediction model studies ( 13 , 14 ) . Guidance to facilitate systematic review s of prediction models has been developed ( Table 1 ) , including for search strategies ( 15 , 4143 ) , formulation of the review question ( 16 , 17 ) , data extraction ( 16 ) , and meta- analysis ( 17 , 2225 , 40 , 44 , 45 ) . Table 1 . Guidance on Conducting Systematic Review s of Prediction Model Studies  Assessment of risk of bias ( ROB ) is an essential step in any systematic review . Shortcomings in study design , conduct , and analysis can result in study estimates being at ROBthat is , at risk of results being flawed or distorted . When interpreting results from a systematic review , readers can draw stronger conclusions from a review based on primary studies at low ROB than from one based on studies at high or unclear ROB ( 46 ) . Identifying the studies most relevant to the setting s and population s targeted in the review ( based on the applicability of primary studies to the review question ) is also important . We therefore developed PROBAST ( Prediction model Risk Of Bias ASsessment Tool ) to address the lack of suitable tools design ed specifically to assess ROB and applicability of primary prediction model studies . PROBAST consists of 4 domains containing 20 signaling questions to facilitate ROB assessment ( 39 ) . The structure and rating system are similar to those in tools design ed to assess ROB in r and omized trials ( revised Cochrane ROB Tool [ ROB 2.0 ] ) , diagnostic accuracy studies ( QUADAS-2 [ Quality Assessment of Diagnostic Accuracy Studies 2 ] ) , and systematic review s ( ROBIS ) ( 37 , 47 , 48 ) . Although PROBAST was design ed for use in systematic review s of prediction model studies , it can also be used as a general tool for critical appraisal of ( primary ) prediction model studies . Here , we describe the rationale behind the domains and signaling questions , how to use them , and how to reach domain-level and overall judgments about ROB and applicability of primary studies to a review question . At the Web site ( www.probast.org ) , 5 filled-in examples from across the medical field illustrate these processes . Because this is an area of active research , the tool , examples , and accompanying guidance will be up date d when needed , and the latest version of PROBAST should always be downloaded from the Web site . Focus of PROBAST PROBAST is design ed to assess primary studies that develop , vali date , or up date ( for example , extend ) multivariable prediction models for diagnosis or prognosis ( Boxes 1 and 2 ) . A multivariable prediction model is defined as any combination or equation of 2 or more predictors ( such as age , sex , symptoms , signs , disease stage , or biomarkers ) for estimating for an individual the probability or risk of having ( diagnosis ) or developing ( prognosis ) a particular outcome ( 1 , 4 , 68 , 49 , 50 ) . Other names for prediction model include risk prediction model , predictive model , prediction index or rule , and risk score ( 1 , 38 , 4951 ) . Box 1 . Types of diagnostic and prognostic modeling studies or reports addressed by PROBAST . Adopted from the TRIPOD ( Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis ) and CHARMS ( CHecklist for critical Appraisal and data extraction for systematic Review s of prediction Modelling Studies ) guidance ( 8 , 16 ) . Box 2 . Differences between diagnostic and prognostic prediction model studies . PROBAST = Prediction model Risk Of Bias ASsessment Tool . Diagnostic and Prognostic Models Diagnostic prediction models estimate the probability that a certain outcome , the target condition , is currently present . Diagnostic prediction model studies typically include individuals who are suspectedbut not yet knownto have the target condition . Prognostic prediction models estimate the probability that a future outcome or event will occur , such as death , disease recurrence , disease complication , or therapy response . The time period of prediction can vary from hours ( for example , preoperatively predicting postoperative nausea and vomiting ) to years ( for example , predicting lifelong risk for a coronary event ) . Although many prognostic models enroll patients with an established diagnosis , this does not have to be the starting point , as seen in models for predicting development of diabetes in pregnant women ( 52 ) or of osteoporotic fractures in the general population ( 53 ) . Consistent with the TRIPOD ( Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis ) statement ( 7 , 8) , PROBAST thus broadly defines prognostic models as those predicting a future outcome in persons at risk for that outcome . Diagnostic and prognostic model studies often use different terms for predictors and outcomes ( Box 2 ) . The cancer literature frequently distinguishes between prognostic and predictive models , such that predictive models identify individuals with differential treatment effects ( 54 ) . These types of ( predictive ) models are outside the scope of this article . Types of Predictors , Outcomes , and Modeling Techniques PROBAST can be used to assess any type of diagnostic or prognostic prediction model aim ed at individualized predictions regardless of the predictors used ; outcomes being predicted ; or methods used to develop , vali date , or up date ( for example , extend ) the model . Predictors range from demographic characteristics , medical history , and physical examination results ; to imaging results , electrophysiology , blood , urine , or tissue measurements , and disease stages or characteristics ; to results from omics and other new biological measurements . Predictors are also called covariates , risk indicators , prognostic factors , determinants , index test results , or independent variables ( 4 , 68 , 49 , 50 , 55 , 56 , 57 ) . PROBAST distinguishes between c and i date predictors and predictors included in the final model ( 57 ) . C and i date predictors are variables considered potentially predictive of the outcome presence ( diagnosis ) or occurrence (prognosis)that is , all those evaluated in the study regardless of whether they are included in the final multivariable model . PROBAST primarily addresses prediction models for binary and time-to-event outcomes because these are the most common in medicine . However , the tool can also be used to assess models predicting nonbinary outcomes , such as continuous scores ( for example , pain scores or cholesterol levels ) or categorical outcomes ( for example , the Glasgow Coma Scale ) . Almost all PROBAST signaling questions apply equally to continuous and categorical outcomes , except questions addressing number of outcome events per predictor and certain measures of model performance ( such as the c-statistic ) , which are not relevant to continuous outcomes . Prediction models usually involve regression modeling techniques , such as logistic regression or survival models . Prediction models may also be developed or vali date d using nonregression techniques , such as neural networks , r and om forests , or support vector machines . As the use of routine care ( and big ) data increases , additional modeling techniques are becoming more common , including machine and artificial learning models . The main differences between studies using regression and other types of prediction modeling include the methods of data analysis ; nonregression development models can often have greater risks of overfitting when data are sparse , and the potential lack of transparency can affect the applicability and usability of the models . In the section on tailoring PROBAST with additional signaling questions , we provide guidance about how PROBAST can be adapted to address other types of outcomes and modeling techniques . Types of Review Questions PROBAST can be used to assess different types of systematic review questions . For some review questions , all prediction model studies are relevant ( including both development and validation ) , but for other questions only validation studies are relevant . Box 3 gives examples of potential review questions for both prognostic and diagnostic prediction models where PROBAST is applicable . CHARMS ( CHecklist for critical Appraisal and data extraction for systematic Review s of prediction Modelling Studies ) and Table 2 provide explicit guidance on how to frame a focused question for review s of prediction model studies ( 16 , 17 ) . Box 3 . Examples of systematic review questions for which PROBAST is suitable . There are various different questions that systematic review s RATIONALE AND OBJECTIVES Quality Assessment of Diagnostic Accuracy Studies ( QUADAS ) is a new tool to measure the method ological quality of diagnostic accuracy studies in systematic review s. We used data from a systematic review of magnetic resonance spectroscopy ( MRS ) in the characterization of suspected brain tumors to provide a preliminary evaluation of the inter-rater reliability of QUADAS . MATERIAL S AND METHODS A structured literature search identified 19 diagnostic accuracy studies . These publications were distributed r and omly to primary and secondary review ers for dual independent assessment . Review ers recorded method ological quality by using QUADAS on a custom- design ed spreadsheet . We calculated correlation , percentage of agreement , and kappa statistic to assess inter-rater reliability . RESULTS Most studies in our review were judged to have used an accurate reference st and ard . Conversely , the MRS literature frequently failed to specify the length of time between index and reference tests or that the clinicians were unaware of the index test findings when reporting the reference st and ard . There was good correlation ( rho = 0.78 ) between review ers in assessment of the overall number of quality criteria met . However , mean agreement for individual QUADAS questions was only fair ( kappa = 0.22 ) and ranged from no agreement beyond chance ( kappa < 0 ) to moderate agreement ( kappa = 0.58 ) . CONCLUSION Inter-rater reliability in our study was relatively low . Nevertheless , we believe that QUADAS potentially is a useful tool for highlighting the strengths and weaknesses of existing diagnostic accuracy studies . Low reliability suggests that different review ers will reach different conclusions if QUADAS is used to exclude " low- quality " articles from meta-analyses . We discuss methods for improving the validity and reliability of QUADAS
The Frailty Index was examined in almost all these dimensions , with the exception of reliability , and its diagnostic and predictive characteristics were shown to be satisfactory .
Gait speed showed high sensitivity , but only moderate specificity , and excellent predictive ability for future disability in activities of daily living .
The Tilburg Frailty Indicator was shown to be a reliable and valid measure for frailty screening , but its diagnostic accuracy was not evaluated .
Screening Letter , Timed-up- and -go test and PRISMA 7 ( Preferred Reporting Items for Systematic Review s and Meta-Analyses ) demonstrated high sensitivity and moderate specificity for identifying frailty .
In general , low physical activity , variously measured , was one of the most powerful predictors of future decline in activities of daily living .
Conclusion Only a few frailty measures seem to be demonstrably valid , reliable and diagnostically accurate , and have good predictive ability .
Nevertheless , it was clear that no suitable tool for assessing frailty appropriately in emergency departments was identified
Background A scoping search identified systematic review s on diagnostic accuracy and predictive ability of frailty measures in older adults .
In most cases , research was confined to specific assessment measures related to a specific clinical model .
Objectives To summarize the best available evidence from systematic review s in relation to reliability , validity , diagnostic accuracy and predictive ability of frailty measures in older adults .