Objective

To investigate whether the beneficial and harmful effects of platelet glycoprotein IIb/IIIa receptor blockers in non‐ST elevation acute coronary syndromes (NSTE‐ACS) depend on age.

Methods

A meta‐analysis of six trials of platelet glycoprotein IIb/IIIa receptor blockers in patients with NSTE‐ACS (PRISM, PRISM‐PLUS, PARAGON‐A, PURSUIT, PARAGON‐B, GUSTO IV‐ACS; n = 31 402) was performed. We applied multivariable logistic regression analyses to evaluate the drug effects on death or non‐fatal myocardial infarction at 30 days, and on major bleeding, by age subgroups (<60, 60–69, 70–79, ⩾80 years). We quantified the reduction of death or myocardial infarction as the number needed to treat (NNT), and the increase of major bleeding as the number needed to harm (NNH).

Results

Subgroups had 11 155 (35%), 9727 (31%), 8468 (27%) and 2049 (7%) patients, respectively. The relative benefit of platelet glycoprotein IIb/IIIa receptor blockers did not differ significantly (p = 0.5) between age subgroups (OR (95% CI) for death or myocardial infarction: 0.86 (0.74 to 0.99), 0.90 (0.80 to 1.02), 0.97 (0.86 to 1.10), 0.90 (0.73 to 1.16); overall 0.91 (0.86 to 0.99). ORs for major bleeding were 1.9 (1.3 to 2.8), 1.9 (1.4 to 2.7), 1.6 (1.2 to 2.1) and 2.5 (1.5–4.1). Overall NNT was 105, and overall NNH was 90. The oldest patients had larger absolute increases in major bleeding, but also had the largest absolute reductions of death or myocardial infarction. Patients ⩾80 years had half of the NNT and a third of the NNH of patients <60 years.

Conclusions

In patients with NSTE‐ACS, the relative reduction of death or non‐fatal myocardial infarction with platelet glycoprotein IIb/IIIa receptor blockers was independent of patient age. Larger absolute outcome reductions were seen in older patients, but with a higher risk of major bleeding. Close monitoring of these patients is warranted.

New markers may improve prediction of diagnostic and prognostic outcomes. We review various measures to quantify the incremental value of markers over standard, readily available characteristics. Widely used traditional measures include the improvement in model fit or in the area under the receiver operating characteristic (ROC) curve (AUC). New measures include the net reclassification index (NRI) and decision–analytic measures, such as the fraction of true positive classifications penalized for false positive classifications (‘net benefit’, NB).

For illustration we discuss a case study on the presence of residual tumor versus benign tissue in 544 patients with testicular cancer. We assessed 3 tumor markers (AFP, HCG, and LDH) for their incremental value over currently standard clinical predictors. AUC and R2 values suggested adding continuous LDH and AFP whereas NB only favored HCG as a potentially promising marker at a clinically defendable decision threshold of 20% risk. Results based on the NRI fell in the middle, suggesting reclassification potential of all three markers.

We conclude that improvement in standard discrimination measures, which focus on finding variables that might be promising across all decision thresholds, may not detect the most informative markers at a specific threshold of particular clinical relevance. When a marker is intended to support decision making, calculation of the improvement in a decision–analytic measure, such as NB, is preferable over an overall judgment as obtained from the AUC in ROC analysis.

The performance of prediction models can be assessed using a variety of different methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration.

Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision–analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.

We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n=544 for model development, n=273 for external validation).

We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for making clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.

Background

Peritoneal dialysis is an effective treatment for end-stage renal disease. Key to successful peritoneal dialysis is a well-functioning catheter. The different insertion techniques may be of great importance. Mostly, the standard operative approach is the open technique; however, laparoscopic insertion is increasingly popular. Catheter malfunction is reported up to 35% for the open technique and up to 13% for the laparoscopic technique. However, evidence is lacking to definitely conclude that the laparoscopic approach is to be preferred. This review and meta-analysis was carried out to investigate if one of the techniques is superior to the other.

Methods

Comprehensive searches were conducted in MEDLINE, Embase and CENTRAL (the Cochrane Library 2012, issue 10). Reference lists were searched manually. The methodology was in accordance with the Cochrane Handbook for interventional systematic reviews, and written based on the PRISMA-statement.

Results

Three randomized controlled trials and eight cohort studies were identified. Nine postoperative outcome measures were meta-analyzed; of these, seven were not different between operation techniques. Based on the meta-analysis, the proportion of migrating catheters was lower (odds ratio (OR) 0.21, confidence interval (CI) 0.07 to 0.63; P = 0.006), and the one-year catheter survival was higher in the laparoscopic group (OR 3.93, CI 1.80 to 8.57; P = 0.0006).

Conclusions

Based on these results there is some evidence in favour of the laparoscopic insertion technique for having a higher one-year catheter survival and less migration, which would be clinically relevant.

In this article, the third in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues review how prognostic models are developed and validated, and then address how prognostic models are assessed for their impact on practice and patient outcomes, illustrating these ideas with examples.

In the second article in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues discuss the role of prognostic factors in current clinical practice, randomised trials, and developing new interventions, and explain why and how prognostic factor research should be improved.

Background

Lynch syndrome is caused by germline mismatch repair (MMR) gene mutations. The PREMM1,2,6 model predicts the likelihood of a MMR gene mutation based on personal and family cancer history.

Objective

To compare strategies using PREMM1,2,6 and tumour testing (microsatellite instability (MSI) and/or immunohistochemistry (IHC) staining) to identify mutation carriers.

Design

Data from population-based or clinic-based patients with colorectal cancers enrolled through the Colon Cancer Family Registry were analysed. Evaluation included MSI, IHC and germline mutation analysis for MLH1, MSH2, MSH6 and PMS2. Personal and family cancer histories were used to calculate PREMM1,2,6 predictions. Discriminative ability to identify carriers from non-carriers using the area under the receiver operating characteristic curve (AUC) was assessed. Predictions were based on logistic regression models for (1) cancer assessment using PREMM1,2,6, (2) MSI, (3) IHC for loss of any MMR protein expression, (4) MSI + IHC, (5) PREMM1,2,6 + MSI, (6) PREMM1,2,6 + IHC, (7) PREMM1,2,6 + IHC + MSI.

Results

Among 1651 subjects, 239 (14%) had mutations (90 MLH1, 125 MSH2, 24 MSH6). PREMM1,2,6 discriminated well with AUC 0.90 (95% CI 0.88 to 0.92). MSI alone, IHC alone, or MSI + IHC each had lower AUCs: 0.77, 0.82 and 0.82, respectively. The added value of IHC + PREMM1,2,6 was slightly greater than PREMM1,2,6 + MSI (AUC 0.94 vs 0.93). Adding MSI to PREMM1,2,6 + IHC did not improve discrimination.

Conclusion

PREMM1,2,6 and IHC showed excellent performance in distinguishing mutation carriers from non-carriers and performed best when combined. MSI may have a greater role in distinguishing Lynch syndrome from other familial colorectal cancer subtypes among cases with high PREMM1,2,6 scores where genetic evaluation does not disclose a MMR mutation.

Background

The Prevention and Reactivation Care Program (PReCaP) entails an innovative multidisciplinary, integrated and goal oriented approach aimed at reducing hospital related functional decline among elderly patients. Despite calls for process evaluation as an essential component of clinical trials in the geriatric care field, studies assessing fidelity lag behind the number of effect studies. The threefold purpose of this study was (1) to systematically assess intervention fidelity of the hospital phase of the PReCaP in the first year of the intervention delivery; (2) to improve our understanding of the moderating factors and modifications affecting intervention fidelity; and (3) to explore the feasibility of the PReCaP fidelity assessment in view of the modifications.

Methods

Based on the PReCaP description we developed a fidelity instrument incorporating nineteen (n=19) intervention components. A combination of data collection methods was utilized, i.e. data collection from patient records and individual Goal Attainment Scaling care plans, in-depth interviews with stakeholders, and non-participant observations. Descriptive analysis was performed to obtain levels of fidelity of each of the nineteen PReCaP components. Moderating factors were identified by using the Conceptual Framework for Implementation Fidelity.

Results

Ten of the nineteen intervention components were always or often delivered to the group of twenty elderly patients. Moderating factors, such as facilitating strategies and context were useful in explaining the non- or low-adherence of particular intervention components.

Conclusions

Fidelity assessment was carried out to evaluate the adherence to the PReCaP in the Vlietland Ziekenhuis in the Netherlands. Given that the fidelity was assessed in the first year of PReCaP implementation it was commendable that ten of the nineteen intervention components were performed always or often. The adequate delivery of the intervention components strongly depended on various moderating factors. Since the intervention is still developing and undergoing continuous modifications, it has been concluded that the fidelity criteria should evolve with the modified intervention. Furthermore, repeated intervention fidelity assessments will be necessary to ensure a valid and reliable fidelity assessment of the PReCaP.

Trial registration

The Netherlands National Trial Register: NTR2317

Abstract

The Glasgow Outcome Scale (GOS) is firmly established as the primary outcome measure for use in Phase III trials of interventions in traumatic brain injury (TBI). However, the GOS has been criticized for its lack of sensitivity to detect small but clinically relevant changes in outcome. The Glasgow Outcome Scale-Extended (GOSE) potentially addresses this criticism, and in this study we estimate the efficiency gain associated with using the GOSE in place of the GOS in ordinal analysis of 6-month outcome. The study uses both simulation and the reanalysis of existing data from two completed TBI studies, one an observational cohort study and the other a randomized controlled trial. As expected, the results show that using an ordinal technique to analyze the GOS gives a substantial gain in efficiency relative to the conventional analysis, which collapses the GOS onto a binary scale (favorable versus unfavorable outcome). We also found that using the GOSE gave a modest but consistent increase in efficiency relative to the GOS in both studies, corresponding to a reduction in the required sample size of the order of 3–5%. We recommend that the GOSE be used in place of the GOS as the primary outcome measure in trials of TBI, with an appropriate ordinal approach being taken to the statistical analysis.

Background

We developed a Monte Carlo Markov model designed to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD. Internal, predictive, and external validity of the model have not yet been established.

Methods

The Rotterdam Ischemic Heart Disease and Stroke Computer Simulation (RISC) model was developed using data covering 5 years of follow-up from the Rotterdam Study. To prove 1) internal and 2) predictive validity, the incidences of coronary heart disease (CHD), stroke, CVD death, and non-CVD death simulated by the model over a 13-year period were compared with those recorded for 3,478 participants in the Rotterdam Study with at least 13 years of follow-up. 3) External validity was verified using 10 years of follow-up data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study of 25,492 participants, for whom CVD and non-CVD mortality was compared.

Results

At year 5, the observed incidences (with simulated incidences in brackets) of CHD, stroke, and CVD and non-CVD mortality for the 3,478 Rotterdam Study participants were 5.30% (4.68%), 3.60% (3.23%), 4.70% (4.80%), and 7.50% (7.96%), respectively. At year 13, these percentages were 10.60% (10.91%), 9.90% (9.13%), 14.20% (15.12%), and 24.30% (23.42%). After recalibrating the model for the EPIC-Norfolk population, the 10-year observed (simulated) incidences of CVD and non-CVD mortality were 3.70% (4.95%) and 6.50% (6.29%). All observed incidences fell well within the 95% credibility intervals of the simulated incidences.

Conclusions

We have confirmed the internal, predictive, and external validity of the RISC model. These findings provide a basis for analyzing the effects of modifying cardiovascular disease risk factors on the burden of CVD with the RISC model.

Background

In well-child care it is difficult to determine whether preschool children with asthma symptoms actually have or will develop asthma at school age. The PIAMA (Prevention and Incidence of Asthma and Mite Allergy) Risk Score has been proposed as an instrument that predicts asthma at school age, using eight easy obtainable parameters, assessed at the time of first asthma symptoms at preschool age. The aim of this study is to present the rationale and design of a study 1) to externally validate and update the PIAMA Risk Score, 2) to develop an Asthma Risk Appraisal Tool to predict asthma at school age in (specific subgroups of) preschool children with asthma symptoms and 3) to test implementation of the Asthma Risk Appraisal Tool in well-child care.

Methods and design

The study will be performed within the framework of Generation R, a prospective multi-ethnic cohort study. In total, consent for postnatal follow-up was obtained from 7893 children, born between 2002 and 2006. At preschool age the PIAMA Risk Score will be assessed and used to predict asthma at school age. Discrimination (C-index) and calibration will be assessed for the external validation. We will study whether the predictive ability of the PIAMA Risk Score can be improved by removing or adding predictors (e.g. preterm birth). The (updated) PIAMA Risk Score will be converted to the Asthma Risk Appraisal Tool- to predict asthma at school age in preschool children with asthma symptoms. Additionally, we will conduct a pilot study to test implementation of the Asthma Risk Appraisal Tool in well-child care.

Discussion

Application of the Asthma Risk Appraisal Tool in well-child care will help to distinguish preschool children at high- and low-risk of developing asthma at school age when asthma symptoms appear.

This study will increase knowledge about the validity of the PIAMA risk score and might improve risk assessment of developing asthma at school age in (specific subgroups of) preschool children, who present with asthma symptoms at well-child care.

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease.

Background

Individuals with a family history of colorectal cancer (CRC) are at increased risk for CRC. Current screening recommendations for these individuals are based on expert opinion. We investigated optimal screening strategies for individuals with a varying degree of family history of CRC based on a cost-effectiveness analysis.

Methods

We used the MISCAN-Colon micro-simulation model to estimate costs and effects of CRC screening strategies, varying by age to start and stop screening and screening interval. We defined four risk groups, characterized by the number of affected first degree relatives (FDR) and their age at CRC diagnosis. For all risk groups, the optimal screening strategy had an incremental cost effectiveness ratio (ICER) of approximately $50,000 per life-year gained.

Results

The optimal screening strategy for individuals with 1 FDR diagnosed after age 50 was 6 colonoscopies every 5 years starting at age 50, compared to 4 colonoscopies every 7 years starting at age 50 for average risk individuals. The optimal strategy had 10 colonoscopies every 4 years for individuals with 1 FDR diagnosed before age 50, 13 colonoscopies every 3 years for individuals with 2 or more FDRs diagnosed after age 50, and 15 colonoscopies every 3 years for individuals with two or more FDRs of which at least 1 is diagnosed before age 50.

Conclusions

The optimal screening strategy varies considerably with the number of affected FDRs and their age of diagnosis. Shorter screening intervals than the currently recommended 5 years may be appropriate for the highest risk individuals.

Background

Diagnostic and prognostic literature is overwhelmed with studies reporting univariable predictor-outcome associations. Currently, methods to incorporate such information in the construction of a prediction model are underdeveloped and unfamiliar to many researchers.

Methods

This article aims to improve upon an adaptation method originally proposed by Greenland (1987) and Steyerberg (2000) to incorporate previously published univariable associations in the construction of a novel prediction model. The proposed method improves upon the variance estimation component by reconfiguring the adaptation process in established theory and making it more robust. Different variants of the proposed method were tested in a simulation study, where performance was measured by comparing estimated associations with their predefined values according to the Mean Squared Error and coverage of the 90% confidence intervals.

Results

Results demonstrate that performance of estimated multivariable associations considerably improves for small datasets where external evidence is included. Although the error of estimated associations decreases with increasing amount of individual participant data, it does not disappear completely, even in very large datasets.

Conclusions

The proposed method to aggregate previously published univariable associations with individual participant data in the construction of a novel prediction models outperforms established approaches and is especially worthwhile when relatively limited individual participant data are available.

Background

When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model.

Methods

An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition.

Results

Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition.

Conclusions

The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.

Background

The purpose of this article is to report on the quality of the existing evidence base regarding the effectiveness of clinical pathway (CPW) research in the hospital setting. The analysis is based on a recently published Cochrane review of the effectiveness of CPWs.

Methods

An integral component of the review process was a rigorous appraisal of the methodological quality of published CPW evaluations. This allowed the identification of strengths and limitations of the evidence base for CPW effectiveness. We followed the validated Cochrane Effective Practice and Organisation of Care Group (EPOC) criteria for randomized and non-randomized clinical pathway evaluations. In addition, we tested the hypotheses that simple pre-post studies tend to overestimate CPW effects reported.

Results

Out of the 260 primary studies meeting CPW content criteria, only 27 studies met the EPOC study design criteria, with the majority of CPW studies (more than 70 %) excluded from the review on the basis that they were simple pre-post evaluations, mostly comparing two or more annual patient cohorts. Methodologically poor study designs are often used to evaluate CPWs and this compromises the quality of the existing evidence base.

Conclusions

Cochrane EPOC methodological criteria, including the selection of rigorous study designs along with detailed descriptions of CPW development and implementation processes, are recommended for quantitative evaluations to improve the evidence base for the use of CPWs in hospitals.

Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations.

Background

We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures.

Methods and Findings

We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively.

Conclusions

The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.

Please see later in the article for the Editors' Summary

Editors' Summary

Background

There are often times in our lives when we would like to be able to predict the future. Is the stock market going to go up, for example, or will it rain tomorrow? Being able predict future health is also important, both to patients and to physicians, and there is an increasing body of published clinical “prediction research.” Diagnostic prediction research investigates the ability of variables or test results to predict the presence or absence of a specific diagnosis. So, for example, one recent study compared the ability of two imaging techniques to diagnose pulmonary embolism (a blood clot in the lungs). Prognostic prediction research investigates the ability of various markers to predict future outcomes such as the risk of a heart attack. Both types of prediction research can investigate the predictive properties of patient characteristics, single variables, tests, or markers, or combinations of variables, tests, or markers (multivariable studies). Both types of prediction research can include also studies that build multivariable prediction models to guide patient management (model development), or that test the performance of models (validation), or that quantify the effect of using a prediction model on patient and physician behaviors and outcomes (impact assessment).

Why Was This Study Done?

With the increase in prediction research, there is an increased interest in the methodology of this type of research because poorly done or poorly reported prediction research is likely to have limited reliability and applicability and will, therefore, be of little use in patient management. In this systematic review, the researchers investigate the reporting and methods of prediction studies by examining the aims, design, participant selection, definition and measurement of outcomes and candidate predictors, statistical power and analyses, and performance measures included in multivariable prediction research articles published in 2008 in several general medical journals. In a systematic review, researchers identify all the studies undertaken on a given topic using a predefined set of criteria and systematically analyze the reported methods and results of these studies.

What Did the Researchers Do and Find?

The researchers identified all the multivariable prediction studies meeting their predefined criteria that were published in 2008 in six high impact general medical journals by browsing through all the issues of the journals (a hand search). They then scored the methods and reporting of each study using a comprehensive item list based on recent recommendations for the conduct of prediction research (for example, the reporting recommendations for tumor marker prognostic studies—the REMARK guidelines). Of 71 retrieved studies, 51 were predictor finding studies, 14 were prediction model development studies, three externally validated an existing model, and three reported on a model's impact on participant outcome. Study design, participant selection, definitions of outcomes and predictors, and predictor selection were generally well reported, but other methodological and reporting aspects of the studies were suboptimal. For example, despite many recommendations, continuous predictors were often dichotomized. That is, rather than using the measured value of a variable in a prediction model (for example, blood pressure in a cardiovascular disease prediction model), measurements were frequently assigned to two broad categories. Similarly, many of the studies failed to adequately estimate the sample size needed to minimize bias in predictor effects, and few of the model development papers quantified and validated the proposed model's predictive performance.

What Do These Findings Mean?

These findings indicate that, in 2008, most of the prediction research published in high impact general medical journals failed to follow current guidelines for the conduct and reporting of clinical prediction studies. Because the studies examined here were published in high impact medical journals, they are likely to be representative of the higher quality studies published in 2008. However, reporting standards may have improved since 2008, and the conduct of prediction research may actually be better than this analysis suggests because the length restrictions that are often applied to journal articles may account for some of reporting omissions. Nevertheless, despite some encouraging findings, the researchers conclude that the poor reporting and poor methods they found in many published prediction studies is a cause for concern and is likely to limit the reliability and applicability of this type of clinical research.

Additional Information

Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001221.

The EQUATOR Network is an international initiative that seeks to improve the reliability and value of medical research literature by promoting transparent and accurate reporting of research studies; its website includes information on a wide range of reporting guidelines including the REMARK recommendations (in English and Spanish)

A video of a presentation by Doug Altman, one of the researchers of this study, on improving the reporting standards of the medical evidence base, is available

The Cochrane Prognosis Methods Group provides additional information on the methodology of prognostic research

Purpose

Early detection of hepatocellular carcinoma (HCC) is essential for improved prognosis and long-term survival. To date, screening for HCC depends on serological testing (alpha-fetoprotein, AFP) and imaging (ultrasonography), both of which are not highly sensitive. A meta-analysis was performed to discuss recent developments in biomarkers that may be effective in screening for HCC.

Methods

A systematic search of PubMed, Embase, and Web of Science was performed for articles published between January 2005 and October 2010, and focusing on biomarkers for HCC in urine, serum, or saliva. Data on sensitivity and specificity of tests were extracted from each included article and displayed with a summary ROC. A meta-analysis was carried out in which the area under the curve for each biomarker was used to compare the accuracy of different tests.

Results

In seven well-defined studies, three biomarkers were identified for potential use, namely, Golgi protein 73 (GP73), interleukin-6 (IL-6), and squamous cell carcinoma antigen (SCCA). Comparison with AFP showed that GP73 was superior (p = 0.006; 95 % CL −0.23, −0.12), IL-6 was similar (p = 0.66; 95 % CL −0.31, 0.25), and SCCA was inferior to AFP (p = 0.001; 95 % CL 0.12, 0.23).

Conclusion

GP73 is a valuable serum marker that seems to be superior to AFP and can be useful in the diagnosis and screening of HCC. Although GP73 may improve the detection and treatment of one of the most common malignancies worldwide, additional research is required.

Summary

Appropriate quantification of added usefulness offered by new markers included in risk prediction algorithms is a problem of active research and debate. Standard methods, including statistical significance and c statistic are useful but not sufficient. Net reclassification improvement (NRI) offers a simple intuitive way of quantifying improvement offered by new markers and has been gaining popularity among researchers. However, several aspects of the NRI have not been studied in sufficient detail.

In this paper we propose a prospective formulation for the NRI which offers immediate application to survival and competing risk data as well as allows for easy weighting with observed or perceived costs. We address the issue of the number and choice of categories and their impact on NRI. We contrast category-based NRI with one which is category-free and conclude that NRIs cannot be compared across studies unless they are defined in the same manner. We discuss the impact of differing event rates when models are applied to different samples or definitions of events and durations of follow-up vary between studies. We also show how NRI can be applied to case-control data. The concepts presented in the paper are illustrated in a Framingham Heart Study example.

In conclusion, NRI can be readily calculated for survival, competing risk, and case-control data, is more objective and comparable across studies using the category-free version, and can include relative costs for classifications. We recommend that researchers clearly define and justify the choices they make when choosing NRI for their application.

Background

Hospital related functional decline in older patients is an underestimated problem. Thirty-five procent of 70-year old patients experience functional decline during hospital admission in comparison with pre-illness baseline. This percentage increases considerably with age.

Methods/design

To address this issue, the Vlietland Ziekenhuis in The Netherlands has implemented an innovative program (PReCaP), aimed at reducing hospital related functional decline among elderly patients by offering interventions that are multidisciplinary, integrated and goal-oriented at the physical, social, and psychological domains of functional decline.

Discussion

This paper presents a detailed description of the intervention, which incorporates five distinctive elements: (1) Early identification of elderly patients with a high risk of functional decline, and if necessary followed by the start of the reactivation treatment within 48 h after hospital admission; (2) Intensive follow-up treatment for a selected patient group at the Prevention and Reactivation Centre (PRC); (3) Availability of multidisciplinary geriatric expertise; (4) Provision of support and consultation of relevant professionals to informal caregivers; (5) Intensive follow-up throughout the entire chain of care by a casemanager with geriatric expertise. Outcome and process evaluations are ongoing and results will be published in a series of future papers.

Trial registration

The Netherlands National Trial Register: NTR2317

Background

The new 7th edition of the Union for International Cancer Control–American Joint Committee on Cancer (UICC-AJCC) tumor, node, metastasis (TNM) staging system is the ratification of data-driven recommendations from the Worldwide Esophageal Cancer Collaboration database. Generalizability remains questionable for single institutions. The present study serves as a validation of the 7th edition of the TNM system in a prospective cohort of patients with predominantly adenocarcinomas from a single institution.

Methods

Included were patients who underwent transhiatal esophagectomy with curative intent between 1991 and 2008 for invasive carcinoma of the esophagus or gastroesophageal junction. Excluded were patients who had received neoadjuvant chemo(radio)therapy, patients after a noncurative resection and patients who died in the hospital. Tumors were staged according to both the 6th and the 7th editions of the UICC-AJCC staging systems. Survival was calculated by the Kaplan–Meier method, and multivariate analysis was performed with a Cox regression model. The likelihood ratio chi-square test related to the Cox regression model and the Akaike information criterion were used for measuring goodness of fit.

Results

A study population of 358 patients was identified. All patients underwent transhiatal esophagectomy for adenocarcinoma. Overall 5-year survival rate was 38%. Univariate analysis revealed that pT stage, pN stage, and pM stage significantly predicted overall survival. Prediction was best for the 7th edition, stratifying for all substages.

Conclusions

The application of the 7th UICC-AJCC staging system results in a better prognostic stratification of overall survival compared to the 6th edition. The fact that the 7th edition performs better predominantly in patients with adenocarcinomas who underwent a transhiatal surgical approach, in addition to findings from earlier research in other cohorts, supports its generalizability for different esophageal cancer practices.

BACKGROUND & AIMS

We developed and validated a model to estimate the risks for mutations in the mismatch repair (MMR) genes MLH1, MSH2, and MSH6 based on personal and family history of cancer.

METHODS

Data were analyzed from 4539 probands tested for mutations in MLH1, MSH2, and MSH6. A multivariable polytomous logistic regression model (PREMM1,2,6) was developed to predict the overall risk of MMR gene mutations and the risk of mutation in each of the 3 genes. The model’s discriminative ability was validated in 1827 population-based CRC cases.

RESULTS

Twelve percent of the original cohort carried pathogenic mutations (204 in MLH1, 250 in MSH2, and 71 in MSH6). The PREMM1,2,6 model incorporated the following factors from the probands and first- and second-degree relatives (odds ratio; 95% confidence intervals [CI]): male sex (1.9; 1.5–2.4), a CRC (4.3; 3.3–5.6), multiple CRCs (13.7; 8.5–22), endometrial cancer (6.1; 4.6–8.2), and extracolonic cancers (3.3; 2.4–4.6). The areas under the receiver operating characteristic curves were 0.86 (95% CI: 0.82–0.91) for MLH1 mutation carriers, 0.87 (95% CI: 0.83–0.92) for MSH2, and 0.81 (95% CI: 0.69– 0.93) for MSH6; in validation, they were 0.88 for the overall cohort (95% CI: 0.86–0.90) and the population-based cases (95% CI: 0.83–0.92).

CONCLUSIONS

We developed the PREMM1,2,6 model that incorporates information on cancer history from probands and their relatives to estimate an individual’s risk for mutations in the MMR genes MLH1, MSH2, and MSH6. This web-based decision making tool can be used to assess risk for hereditary CRC and guide clinical management.

Background

Given the fact that prostate cancer incidence will increase in the coming years, new prognostic biomarkers are needed with regard to the biological aggressiveness of the prostate cancer diagnosed. Since cytokines have been associated with the biology of cancer and its prognosis, we determined whether transforming growth factor beta 1 (TGFβ1), interleukin-7 (IL-7) receptor and IL-7 levels add additional prognostic information with regard to prostate cancer-specific survival.

Materials and methods

Retrospective survival analysis of forty-four prostate cancer patients, that underwent radical prostatectomy, was performed (1989–2001). Age, Gleason score and pre-treatment PSA levels were collected. IL-7, IL-7 receptor and TGFβ1 levels in prostate cancer tissue were determined by quantitative real-time RT-PCR and their additional prognostic value analyzed with regard to prostate cancer survival. Hazard ratios and their confidence intervals were estimated, and Akaike’s information criterion was calculated for model comparison.

Results

The predictive ability of a model for prostate cancer survival more than doubled when TGFβ1 and IL-7 were added to a model containing only the Gleason score and pre-treatment PSA (AIC: 18.1 and AIC: 6.5, respectively).

Conclusion

IL-7 and TGFβ1 are promising markers to indicate those at risk for poor prostate cancer survival. This additional information may be of interest with regard to the biological aggressiveness of the diagnosed prostate cancer, especially for those patients screened for prostate cancer and their considered therapy.

Background

Prediction rules for intracranial traumatic findings in patients with minor head injury are designed to reduce the use of computed tomography (CT) without missing patients at risk for complications. This study investigates whether alternative modelling techniques might improve the applicability and simplicity of such prediction rules.

Methods

We included 3181 patients with minor head injury who had received CT scans between February 2002 and August 2004. Of these patients 243 (7.6%) had intracranial traumatic findings and 17 (0.5%) underwent neurosurgical intervention. We analyzed sensitivity, specificity and area under the ROC curve (AUC-value) to compare the performance of various modelling techniques by 10 × 10 cross-validation. The techniques included logistic regression, Bayes network, Chi-squared Automatic Interaction Detection (CHAID), neural net, support vector machines, Classification And Regression Trees (CART) and "decision list" models.

Results

The cross-validated performance was best for the logistic regression model (AUC 0.78), followed by the Bayes network model and the neural net model (both AUC 0.74). The other models performed poorly (AUC < 0.70). The advantage of the Bayes network model was that it provided a graphical representation of the relationships between the predictors and the outcome.

Conclusions

No alternative modelling technique outperformed the logistic regression model. However, the Bayes network model had a presentation format which provided more detailed insights into the structure of the prediction problem. The search for methods with good predictive performance and an attractive presentation format should continue.