Adjusting for partial verification or workup bias in meta-analyses of diagnostic accuracy studies.

A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can bias the accuracy estimates. Numerous methods have been described for correcting this partial verification bias or workup bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial verification or workup bias is present in a subset of the primary studies. The method corrects for verification bias without having to exclude primary studies with verification bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with verification bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients.

Early mobilization in clinical practice: the reliability and feasibility of the 'Start To Move' Protocol.

PURPOSE: The properties of a local Intensive Care Unit early mobilization protocol ('Start To Move As Soon As Possible') in critically ill patients, consisting of an objective diagnostic assessment linked to six treatment levels were evaluated. This study aimed to investigate whether the protocol can be reliably applied by different health-care providers (reliability), to examine the associations between prescribed and delivered treatments (feasibility) and to explore safety and patient satisfaction with the protocol. METHODS: Cross-sectional observational study evaluating the reliability of the protocol between physiotherapist was evaluated with Cohen's kappa, percentage of agreement, and intraclass correlation coefficients in 61 patients. Feasibility was analyzed as agreement between prescribed and delivered treatments with Spearman's rank correlation coefficients in 60 patients. A satisfaction survey was used to evaluate patient satisfaction with the protocol. RESULTS: Excellent agreement was observed between physiotherapists for diagnostic level assignment (Kappa = 0.92), while the majority of the treatment proposals per level showed moderate to substantial agreement between the physiotherapists (Kappa range: 0.40-0.89). Three hundred and thirteen treatments were prescribed. Perfect agreement was observed between prescribed and delivered treatments in level 0 (Spearman's rho 1.00) and excellent associations for levels 1-5 (0.941, 0.995, 0.951, 0.998, and 0.999), respectively. Unwanted safety events rate was 3%. Most patients (92%) were very satisfied with physiotherapy. CONCLUSION: Excellent inter-rater agreement for diagnostic level assignment and moderate to substantial agreement for proposed treatments support the reliability of the protocol. Perfect to excellent associations between prescribed and delivered treatments supports its feasibility. Complications were rare, and most patients were very positive regarding the care provided by physiotherapists during their stay in the ICU.

Adjusting for differential-verification bias in diagnostic-accuracy studies: a Bayesian approach.

In studies of diagnostic accuracy, the performance of an index test is assessed by verifying its results against those of a reference standard. If verification of index-test results by the preferred reference standard can be performed only in a subset of subjects, an alternative reference test could be given to the remainder. The drawback of this so-called differential-verification design is that the second reference test is often of lesser quality, or defines the target condition in a different way. Incorrectly treating results of the 2 reference standards as equivalent will lead to differential-verification bias. The Bayesian methods presented in this paper use a single model to (1) acknowledge the different nature of the 2 reference standards and (2) make simultaneous inferences about the population prevalence and the sensitivity, specificity, and predictive values of the index test with respect to both reference tests, in relation to latent disease status. We illustrate this approach using data from a study on the accuracy of the elbow extension test for diagnosis of elbow fractures in patients with elbow injury, using either radiography or follow-up as reference standards.

Diagnostic accuracy and user-friendliness of 5 point-of-care D-dimer tests for the exclusion of deep vein thrombosis.

BACKGROUND: Point-of-care D-dimer tests have recently been introduced to enable rapid exclusion of deep venous thrombosis (DVT) without the need to refer a patient for conventional laboratory-based D-dimer testing. Before implementation in practice, however, the diagnostic accuracy of each test should be validated. METHODS: We analyzed data of 577 prospectively identified consecutive primary care patients suspected to have DVT, who underwent 5 point-of-care D-dimer tests-4 quantitative (Vidas®, Pathfast™, Cardiac®, and Triage®) and 1 qualitative (Clearview Simplify®)-and ultrasonography as the reference method. We evaluated the tests for the accuracy of their measurements and submitted a questionnaire to 20 users to assess the user-friendliness of each test. RESULTS: All D-dimer tests showed negative predictive values higher than 98%. Sensitivity was high for all point-of-care tests, with a range of 0.91 (Clearview Simplify) to 0.99 (Vidas). Specificity varied between 0.39 (Pathfast) and 0.64 (Clearview Simplify). The quantitative point-of-care tests showed similar and high discriminative power for DVT, according to calculated areas under the ROC curves (range 0.88-0.89). The quantitative Vidas and Pathfast devices showed limited user-friendliness for primary care, owing to a laborious calibration process and long analyzer warm-up time compared to the Cardiac and Triage. For the qualitative Clearview Simplify assay, no analyzer or calibration was needed, but interpretation of a test result was sometimes difficult because of poor color contrast. CONCLUSIONS: Point-of-care D-dimer assays show good and similar diagnostic accuracy. The quantitative Cardiac and Triage and the qualitative Clearview Simplify D-dimer seem most user-friendly for excluding DVT in the doctor's office.

Dealing with missing predictor values when applying clinical prediction models.

BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with such missing values. We evaluated 6 strategies to deal with missing values. METHODS: We developed and validated (in 1295 and 532 primary care patients, respectively) a prediction model to predict the risk of deep venous thrombosis. In an application set (259 patients), we mimicked 3 situations in which (1) an important predictor (D-dimer test), (2) a weaker predictor (difference in calf circumference), and (3) both predictors simultaneously were missing. The 6 strategies to deal with missing values were (1) ignoring the predictor, (2) overall mean imputation, (3) subgroup mean imputation, (4) multiple imputation, (5) applying a submodel including only the observed predictors as derived from the development set, or (6) the "one-step-sweep" method. We compared the model's discriminative ability (expressed by the ROC area) with the true ROC area (no missing values) and the model's estimated calibration slope and intercept with the ideal values of 1 and 0, respectively. RESULTS: Ignoring the predictor led to the worst and multiple imputation to the best discrimination. Multiple imputation led to calibration intercepts closest to the true value. The effect of the strategies on the slope differed between the 3 scenarios. CONCLUSIONS: Multiple imputation is preferred if a predictor value is missing.

Prediction rule for cardiovascular events and mortality in peripheral arterial disease patients: data from the prospective Second Manifestations of ARTerial disease (SMART) cohort study.

BACKGROUND: Patients with peripheral arterial disease (PAD) are at high risk of secondary cardiovascular death and events such as myocardial infarction or stroke. To minimize this elevated risk, cardiovascular risk factors should be treated in all PAD patients. Secondary risk management may benefit from a prediction tool to identify PAD patients at the highest risk who could be referred for an additional extensive workup. Stratifying PAD patients according to their risk of secondary events could aid in achieving optimal therapy compliance. To this end we developed a prediction model for secondary cardiovascular events in PAD patients. METHODS: The model was developed using data from 800 PAD patients who participated in the Second Manifestations of ARTerial disease (SMART) cohort study. From the baseline characteristics, 13 candidate predictors were selected for the model development. Missing values were imputed by means of single regression imputation. Continuous predictors were truncated and transformed where necessary, followed by model reduction by means of backward stepwise selection. To correct for over-fitting, a bootstrapping technique was applied. Finally, a score chart was created that divides patients in four risk categories that have been linked to the risk of a cardiovascular event during 1- and 5-year follow-up. RESULTS: During a mean follow-up of 4.7 years, 120 events occurred (27% nonfatal myocardial infarction, 21% nonfatal stroke, and 52% mortality from vascular causes), corresponding to a 1- and 5-year cumulative incidence of 3.1% and 13.2%, respectively. Important predictors for the secondary risk of a cardiovascular event are age, history of symptomatic cardiovascular disease, systolic blood pressure, high-density lipoprotein cholesterol, smoking behavior, ankle-brachial pressure index, and creatinine level. The risk of a cardiovascular event in a patient as predicted by the model was 0% to 10% and 1% to 28% for the four risk categories at 1- and 5-year follow-up, respectively. The discriminating capacity of the prediction model, indicated by the c statistic, was 0.76 (95% confidence interval, 0.71-0.80). CONCLUSION: A prediction model can be used to predict secondary cardiovascular risk in PAD patients. We propose such a prediction model to allow for the identification of PAD patients at the highest risk of a cardiovascular event or cardiovascular death, which may be a viable tool in vascular secondary health care practice.

The risk of severe postoperative pain: modification and validation of a clinical prediction rule.

BACKGROUND: Recently, a prediction rule was developed to preoperatively predict the risk of severe pain in the first postoperative hour in surgical inpatients. We aimed to modify the rule to enhance its use in both surgical inpatients and outpatients (ambulatory patients). Subsequently, we prospectively tested the modified rule in patients who underwent surgery later in time and in another hospital (external validation). METHODS: The rule was originally developed from the data of 1395 adult inpatients. We modified the rule with the data of 549 outpatients who underwent surgery between 1997 and 1999 in the same center (Academic Medical Center Amsterdam, The Netherlands). Furthermore, we tested the performance of the modified rule in 1035 in- and outpatients who underwent surgery in 2004, in the University Medical Center Utrecht, The Netherlands (external validation). Performance was quantified by the rule's calibration (agreement between observed frequencies and predicted risks) and discrimination (ability to distinguish between patients at high and low risk). RESULTS: Modification of the original rule to enhance prediction in outpatients included reclassification of the predictor "type of surgery," addition of the predictor "surgical setting" (ambulatory surgery: yes/no) and addition of interaction terms between surgical setting and the other predictors. One-third of the patients in the Utrecht cohort reported severe postoperative pain (36%), compared to 62% of the patients in the Amsterdam cohort. The distribution of most predictors was similar in the two cohorts, although the patients in the Utrecht cohort were slightly older, more often underwent ambulatory surgery and had large expected incision sizes less often than patients in the Amsterdam cohort. The modified prediction rule showed good calibration, when an adjusted intercept was used for the lower incidence in the Utrecht cohort. The discrimination was reasonable (area under the Receiver Operating Characteristic curve 0.65 [95% confidence interval 0.57-0.73]). CONCLUSIONS: A previously developed prediction rule to predict severe postoperative pain was modified to allow use in both inpatients and outpatients. By validating the rule in patients who underwent surgery several years later in another hospital, it was shown that the rule could be generalized in time and place. We demonstrated that, instead of deriving new prediction rules for new populations, a simple adjustment may be enough to recalibrate prediction rules for new populations. This is in line with the perception that external validation and updating of prediction rules is a continuing and multistage process.

Cost-effectiveness Analysis for Apixaban in the Acute Treatment and Prevention of Venous Thromboembolism in the Netherlands.

PURPOSE: Low-molecular weight heparin (LMWH) followed by vitamin K antagonists (VKAs) are the current standard treatment of acute venous thromboembolism (VTE) and prevention of recurrent VTE. The direct oral anticoagulant apixaban was recently found noninferior in efficacy and superior in preventing major bleeding compared with LMWH/VKAs in the AMPLIFY (Apixaban for the Initial Management of Pulmonary Embolism and Deep-Vein Thrombosis as First-Line Therapy) trial. The objective of this study was to calculate the cost-effectiveness of apixaban compared with LMWH/VKA in the treatment of acute VTE and prevention of recurrent VTE in the Netherlands. METHODS: A Markov model was designed to simulate a cohort of 1,000 VTE patients receiving either apixaban or LMWH/VKA. Transition probabilities, costs, and utilities were obtained from the AMPLIFY trial and other literature. The incremental cost-effectiveness ratio (ICER) was calculated from the societal perspective; therefore, the model included both direct (inside and outside the health care sector) and indirect costs. In the univariate and probabilistic sensitivity analyses (PSAs) the robustness of the results was tested, and various additional scenario analyses were conducted. FINDINGS: In the base-case analysis, apixaban saved €236 and 0.044 quality-adjusted life years (QALYs) and 0.039 LYs were gained compared with LMWH/VKA. In the univariate sensitivity analysis the model appeared to be robust. The results of 2,000 iterations in the PSA found that the probability of apixaban being cost-effective at a willingness-to-pay threshold of €20,000/QALY was 100% and cost-saving was 94%. The scenario of 18-month treatment duration was the only scenario not indicating cost-savings with an ICER of €425/QALY. IMPLICATIONS: In acute anticoagulation use apixaban was found to be cost-saving. A longer anticoagulation period (18 months) resulted in a higher difference in drug costs, indicating a higher ICER. The cost-effectiveness of long-term or life-long use should be examined in future research.

Reporting of Bayesian analysis in epidemiologic research should become more transparent.

OBJECTIVES: The objective of this systematic review is to investigate the use of Bayesian data analysis in epidemiology in the past decade and particularly to evaluate the quality of research papers reporting the results of these analyses. STUDY DESIGN AND SETTING: Complete volumes of five major epidemiological journals in the period 2005-2015 were searched via PubMed. In addition, we performed an extensive within-manuscript search using a specialized Java application. Details of reporting on Bayesian statistics were examined in the original research papers with primary Bayesian data analyses. RESULTS: The number of studies in which Bayesian techniques were used for primary data analysis remains constant over the years. Though many authors presented thorough descriptions of the analyses they performed and the results they obtained, several reports presented incomplete method sections and even some incomplete result sections. Especially, information on the process of prior elicitation, specification, and evaluation was often lacking. CONCLUSION: Though available guidance papers concerned with reporting of Bayesian analyses emphasize the importance of transparent prior specification, the results obtained in this systematic review show that these guidance papers are often not used. Additional efforts should be made to increase the awareness of the existence and importance of these checklists to overcome the controversy with respect to the use of Bayesian techniques. The reporting quality in epidemiological literature could be improved by updating existing guidelines on the reporting of frequentist analyses to address issues that are important for Bayesian data analyses.

PP081. Predictors of pregnancy induced hypertension in an urban low resource setting.

INTRODUCTION: Hypertensive disorders in pregnancy, including Pregnancy Induced Hypertension (PIH), are important causes of morbidity and mortality in pregnancy. Identifying women at high risk will allow for early management to reduce complications of PIH. OBJECTIVES: The objectives were to determine the incidence of PIH among pregnant women and develop risk prediction models for early detection of women at increased risk of PIH. METHODS: A longitudinal cohort study involving 2539 pregnant women attending antenatal clinic in the Greater Accra region of Ghana was conducted between February and May 2010. The outcome, PIH, was defined as systolic or diastolic blood pressure BP of 140mmHg or 90mmHg respectively. Logistic regression was used to derive the prediction models and bootstrapping technique was used to internally validate them. A score chart was used to classify pregnant women into low, moderate and high risk of developing PIH. RESULTS: The incidence of PIH was 8.0% (95% C.I: 7.98-8.02%) and 10.9% (95% C.I:10.89-10.91%) in nulliparous and multiparous women respectively. Systolic blood pressure, diastolic blood pressure, history of hypertension in parents, family history of diabetes, proteinuria, body mass index (BMI) were among independent predictors in early pregnancy of subsequent PIH. The prognostic performance, estimated by the area under the Receiver Operating Characteristic (ROC) curve, ranged between 0.64 and 0.84 for the models for nulliparous and multiparous. CONCLUSION: Using a limited set of maternal characteristics, pregnant women at increased risk of developing PIH can be identified. Categorizing women by risk of PIH and providing tailored antenatal care will minimize complications of PIH.

Diagnostic accuracy of conventional or age adjusted D-dimer cut-off values in older patients with suspected venous thromboembolism: systematic review and meta-analysis.

OBJECTIVE: To review the diagnostic accuracy of D-dimer testing in older patients (>50 years) with suspected venous thromboembolism, using conventional or age adjusted D-dimer cut-off values. DESIGN: Systematic review and bivariate random effects meta-analysis. DATA SOURCES: We searched Medline and Embase for studies published before 21 June 2012 and we contacted the authors of primary studies. STUDY SELECTION: Primary studies that enrolled older patients with suspected venous thromboembolism in whom D-dimer testing, using both conventional (500 µg/L) and age adjusted (age × 10 µg/L) cut-off values, and reference testing were performed. For patients with a non-high clinical probability, 2 × 2 tables were reconstructed and stratified by age category and applied D-dimer cut-off level. RESULTS: 13 cohorts including 12,497 patients with a non-high clinical probability were included in the meta-analysis. The specificity of the conventional cut-off value decreased with increasing age, from 57.6% (95% confidence interval 51.4% to 63.6%) in patients aged 51-60 years to 39.4% (33.5% to 45.6%) in those aged 61-70, 24.5% (20.0% to 29.7% in those aged 71-80, and 14.7% (11.3% to 18.6%) in those aged >80. Age adjusted cut-off values revealed higher specificities over all age categories: 62.3% (56.2% to 68.0%), 49.5% (43.2% to 55.8%), 44.2% (38.0% to 50.5%), and 35.2% (29.4% to 41.5%), respectively. Sensitivities of the age adjusted cut-off remained above 97% in all age categories. CONCLUSIONS: The application of age adjusted cut-off values for D-dimer tests substantially increases specificity without modifying sensitivity, thereby improving the clinical utility of D-dimer testing in patients aged 50 or more with a non-high clinical probability.

Prediction model with metabolic syndrome to predict recurrent vascular events in patients with clinically manifest vascular diseases.

BACKGROUND: Although the overall average 10-year cardiovascular risk for patients with manifest atherosclerosis is considered to be more than 20%, actual risk for individual patients ranges from much lower to much higher. We investigated whether information on metabolic syndrome (MetS) or its individual components improves cardiovascular risk stratification in these patients. DESIGN AND METHODS: We conducted a prospective cohort study in 3679 patients with clinical manifest atherosclerosis from the Secondary Manifestations of ARTerial disease (SMART) study. Primary outcome was defined as any cardiovascular event (cardiovascular death, ischemic stroke or myocardial infarction). Three pre-specified prediction models were derived, all including information on established MetS components. The association between outcome and predictors was quantified using a Cox proportional hazard analysis. Model performance was assessed using global goodness-of-fit fit (χ(2)), discrimination (C-index) and ability to improve risk stratification. RESULTS: A total of 417 cardiovascular events occurred among 3679 patients with 15,102 person-years of follow-up (median follow-up 3.7 years, range 1.6-6.4 years). Compared to a model with age and gender only, all MetS-based models performed slightly better in terms of global model fit (χ(2)) but not C-index. The Net Reclassification Index associated with the addition of MetS (yes/no), the dichotomous MetS-components or the continuous MetS-components on top of age and gender was 2.1% (p = 0.29), 2.3% (p = 0.31) and 7.5% (p = 0.01), respectively. CONCLUSIONS: Prediction models incorporating age, gender and MetS can discriminate between patients with clinical manifest atherosclerosis at the highest vascular risk and those at lower risk. The addition of MetS components to a model with age and gender correctly reclassifies only a small proportion of patients into higher- and lower-risk categories. The clinical utility of a prediction model with MetS is therefore limited.

A simple adaptation method improved the interpretability of prediction models for composite end points.

OBJECTIVE: The pros and cons of composite end points in prognostic research are discussed, and an adaptation method, designed to accurately adjust absolute risks for a composite end point to risks for the individual component outcomes, is presented. STUDY DESIGN AND SETTING: An example prediction model for recurrent cardiovascular events (composite end point) was used to evaluate the performance regarding the individual component outcomes (cardiovascular death, myocardial infarction, and stroke) before and after the adaptation method. RESULTS: Discrimination for the individual component outcomes (concordance index for myocardial infarction, 0.68; concordance index for stroke, 0.70) was very similar to discrimination for the original composite end point (concordance index, 0.70). For cardiovascular death, it even increased substantially (concordance index, 0.78). After adaptation, calibration plots for the component outcomes also improved, with visible convergence of the predicted risks and the observed incidences. CONCLUSION: In sum, these findings show that the adaptation method is useful when validating or applying a composite end point prediction model to the individual component outcomes. Following from this, recommendations concerning reporting of composite end points in future research are also included. Without the need for extra data, composite end point prediction models can easily be directly expanded to allow for the estimation of risk for each individual component outcome, improving the interpretability for clinicians and patients.

Validation of two age dependent D-dimer cut-off values for exclusion of deep vein thrombosis in suspected elderly patients in primary care: retrospective, cross sectional, diagnostic analysis.

OBJECTIVE: To determine whether the use of age adapted D-dimer cut-off values can be translated to primary care patients who are suspected of deep vein thrombosis. DESIGN: Retrospective, cross sectional diagnostic study. SETTING: 110 primary care doctors affiliated with three hospitals in the Netherlands. PARTICIPANTS: 1374 consecutive patients (936 (68.1%) aged >50 years) with clinically suspected deep vein thrombosis. MAIN OUTCOME MEASURES: Proportion of patients with D-dimer values below two proposed age adapted cut-off levels (age in years × 10 µg/L in patients aged >50 years, or 750 µg/L in patients aged ≥ 60 years), in whom deep vein thrombosis could be excluded; and the number of false negative results. RESULTS: Using the Wells score, 647 patients had an unlikely clinical probability of deep vein thrombosis. In these patients (at all ages), deep vein thrombosis could be excluded in 309 (47.8%) using the age dependent cut-off value compared with 272 (42.0%) using the conventional cut-off value of 500 µg/L (increase 5.7%, 95% confidence interval 4.1% to 7.8%). This exclusion rate resulted in 0.5% and 0.3% false negative cases, respectively (increase 0.2%, 0.004% to 8.6%).The increase in exclusion rate by using the age dependent cut-off value was highest in the oldest patients. In patients older than 80 years, deep vein thrombosis could be safely excluded in 22 (35.5%) patients using the age dependent cut-off value compared with 13 (21.0%) using the conventional cut-off value (increase 14.5%, 6.8% to 25.8%). Compared with the age dependent cut-off value, the cut-off value of 750 µg/L had a similar exclusion rate (307 (47.4%) patients) and false negative rate (0.3%). CONCLUSIONS: Combined with a low clinical probability of deep vein thrombosis, use of the age dependent D-dimer cut-off value for patients older than 50 years or the cut-off value of 750 µg/L for patients aged 60 years and older resulted in a considerable increase in the proportion of patients in primary care in whom deep vein thrombosis could be safely excluded, compared with the conventional cut-off value of 500 µg/L.

Development and validation of clinical prediction models: marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming.

OBJECTIVE: Many prediction models are developed by multivariable logistic regression. However, there are several alternative methods to develop prediction models. We compared the accuracy of a model that predicts the presence of deep venous thrombosis (DVT) when developed by four different methods. STUDY DESIGN AND SETTING: We used the data of 2,086 primary care patients suspected of DVT, which included 21 candidate predictors. The cohort was split into a derivation set (1,668 patients, 329 with DVT) and a validation set (418 patients, 86 with DVT). Also, 100 cross-validations were conducted in the full cohort. The models were developed by logistic regression, logistic regression with shrinkage by bootstrapping techniques, logistic regression with shrinkage by penalized maximum likelihood estimation, and genetic programming. The accuracy of the models was tested by assessing discrimination and calibration. RESULTS: There were only marginal differences in the discrimination and calibration of the models in the validation set and cross-validations. CONCLUSION: The accuracy measures of the models developed by the four different methods were only slightly different, and the 95% confidence intervals were mostly overlapped. We have shown that models with good predictive accuracy are most likely developed by sensible modeling strategies rather than by complex development methods.

Accuracy of symptoms, signs, and C-reactive protein for early chronic obstructive pulmonary disease.

BACKGROUND: Guidelines recommend detection of early chronic obstructive pulmonary disease (COPD), but evidence on the diagnostic work-up for COPD only concerns advanced and established COPD. AIM: To quantify the accuracy of symptoms and signs for early COPD, and the added value of C-reactive protein (CRP), in primary care patients presenting with cough. DESIGN AND SETTING: Cross-sectional diagnostic study of 73 primary care practices in the Netherlands. METHOD: Four hundred primary care patients (182 males, mean age 63 years) older than 50 years, presenting with persistent cough (>14 days) without established COPD participated, of whom 382 completed the study. They underwent a systematic diagnostic work-up of symptoms, signs, conventional laboratory CRP level, and hospital lung functions tests, including body plethysmography, and an expert panel decided whether COPD was present (reference test). The independent value of all items was estimated by multivariable logistic regression analysis. RESULTS: According to the expert panel, 118 patients had COPD (30%). Symptoms and signs with independent diagnostic value were age, sex, current smoking, smoking more than 20 pack-years, cardiovascular comorbidity, wheezing complaints, diminished breath sounds, and wheezing on auscultation. Combining these items resulted in an area under the receiver operating characteristic curve (ROC area) of 0.79 (95% confidence interval = 0.74 to 0.83) after internal validation. The proportion of subjects with elevated CRP was higher in those with early COPD, but CRP added no relevant diagnostic information above symptoms and signs. CONCLUSION: In subjects presenting with persistent cough, the CRP level has no added value for detection of early COPD.

Incorporation of historical data in the analysis of randomized therapeutic trials.

Historical studies provide a valuable source of information for the motivation and design of later trials. Bayesian techniques offer possibilities for the quantitative inclusion of prior knowledge within the analysis of current trial data. Combining information from previous studies into an informative prior distribution is, however, a delicate case. The power prior distribution is a tool to estimate the effect of an intervention in a current study sample, while accounting for the information provided by previous research. In this study we evaluate the use of the power prior distribution, illustrated with data from a large randomized clinical trial on the effect of ST-wave analysis in intrapartum fetal monitoring. We advocate the use of a power prior distribution with pre-specified fixed study weights based on differences in study characteristics. We propose obtaining a ranking of the historical studies via expert elicitation, based on relevance for the current study, and specify study weights accordingly.

Does a decision aid help physicians to detect chronic obstructive pulmonary disease?

BACKGROUND: Numerous decision aids have been developed recently, but the value they add above that of the initial clinical assessment is not well known. AIM: To quantify whether a formal decision aid for chronic obstructive pulmonary disease (COPD) adds diagnostic information, above the physician's clinical assessment. DESIGN AND SETTING: Subanalysis of a diagnostic study in Dutch primary care. METHOD: Sixty-five primary care physicians included 357 patients who attended for persistent cough and were not known to have COPD. The physicians estimated the probability of COPD after short history taking and physical examination. After this, the presence or absence of COPD was determined using results of extensive diagnostic work-up. The extent to which an 8-item decision aid for COPD, which included only symptoms and signs, added diagnostic value above the physician's estimation was quantified by the increase of the area under the receiver operating characteristic curve (ROC area), and the improvement in diagnostic risk classification across two classes: 'low probability of COPD' (<20%) and 'possible COPD' (≥20%). RESULTS: One hundred and four patients (29%) had COPD. Adding the decision aid to the clinical assessment increased the ROC area from 0.75 (95% confidence interval [CI] = 0.70 to 0.81) to 0.84 (95% CI = 0.80 to 0.89) (P<0.005), and improved the diagnostic risk classification of the patients, such that 35 fewer patients needed spirometry testing and eight fewer COPD cases were missed. CONCLUSION: A short decision aid for COPD added diagnostic value to the physician's clinical assessment.

Correcting for partial verification bias: a comparison of methods.

PURPOSE: A common problem in diagnostic research is that the reference standard has not been carried out in all patients. This partial verification may lead to biased accuracy measures of the test under study. The authors studied the performance of multiple imputation and the conventional correction method proposed by Begg and Greenes under a range of different situations of partial verification. METHODS: In a series of simulations, using a previously published deep venous thrombosis data set (n = 1292), the authors set the outcome of the reference standard to missing based on various underlying mechanisms and by varying the total number of missing values. They then compared the performance of the different correction methods. RESULTS: The results of the study show that when the mechanism of missing reference data is known, accuracy measures can easily be correctly adjusted using either the Begg and Greenes method, or multiple imputation. In situations where the mechanism of missing reference data is complex or unknown, we recommend using multiple imputation methods to correct. CONCLUSIONS: These methods can easily apply for both continuous and categorical variables, are readily available in statistical software and give reliable estimates of the missing reference data.