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Loan R van Hoeven,1,2 Aukje L Kreuger,3,4 Kit CB Roes,1 Peter F Kemper,2,4 Hendrik Koffijberg,5 Floris J Kranenburg,3,4,6 Jan MM Rondeel,7 Mart P Janssen1,2 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; 2Transfusion Technology Assessment Department, Sanquin Research, Amsterdam, the Netherlands; 3Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; 4Center for Clinical Transfusion Research, Sanquin Research, Leiden, the Netherlands; 5Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands; 6Department of Intensive Care, Leiden University Medical Center, Leiden, the Netherlands; 7Department of Clinical Chemistry, Isala, Zwolle, the Netherlands Background: To enhance the utility of transfusion data for research, ideally every transfusion should be linked to a primary clinical indication. In electronic patient records, many diagnostic and procedural codes are registered, but unfortunately, it is usually not specified which one is the reason for transfusion. Therefore, a method is needed to determine the most likely indication for transfusion in an automated way.Study design and methods: An algorithm to identify the most likely transfusion indication was developed and evaluated against a gold standard based on the review of medical records for 234 cases by 2 experts. In a second step, information on misclassification was used to fine-tune the initial algorithm. The adapted algorithm predicts, out of all data available, the most likely indication for transfusion using information on medical specialism, surgical procedures, and diagnosis and procedure dates relative to the transfusion date.Results: The adapted algorithm was able to predict 74.4% of indications in the sample correctly (extrapolated to the full data set 75.5%). A kappa score, which corrects for the number of options to choose from, was found of 0.63. This indicates that the algorithm performs substantially better than chance level.Conclusion: It is possible to use an automated algorithm to predict the indication for transfusion in terms of procedures and/or diagnoses. Before implementation of the algorithm in other data sets, the obtained results should be externally validated in an independent hospital data set. Keywords: indication for transfusion, selection algorithm, electronic health record data

Artificial intelligence-powered tools, such as ASReview, could reduce the burden of title and abstract screening. This study aimed to assess the accuracy and efficiency of using ASReview in a health economic context. A sample from a previous systematic literature review containing 4,994 articles was used. Previous manual screening resulted in 134 articles included for full-text screening (FT) and 50 for data extraction (DE). Here, accuracy and efficiency was evaluated by comparing the number of identified relevant articles with ASReview versus manual screening. Pre-defined stopping rules using sampling criteria and heuristic criteria were tested. Robustness of the AI-tool's performance was determined using 1,000 simulations. Considering included stopping rules, median accuracy for FT articles remained below 85%, but reached 100% for DE articles. To identify all relevant articles, a median of 89.9% of FT articles needed to be screened, compared to 7.7% for DE articles. Potential time savings between 49 and 59 hours could be achieved, depending on the stopping rule. In our case study, all DE articles were identified after screening 7.7% of the sample, allowing for substantial time savings. ASReview likely has the potential to substantially reduce screening time in systematic reviews of health economic articles. [ABSTRACT FROM AUTHOR]

Introduction: Metamodels, also known as meta-models, surrogate models, or emulators, are used in several fields of research to negate runtime issues with analyzing computational demanding simulation models. This study introduces metamodeling and presents results of a review on metamodeling applications in health economics.Areas Covered: A scoping review was performed to identify studies that applied metamodeling methods in a health economic context. After search and selection, 13 publications were found to employ metamodeling methods in health economics. Metamodels were used to perform value of information analysis (n = 5, 38%), deterministic sensitivity analysis (n = 4, 31%), model calibration (n = 1, 8%), probabilistic sensitivity analysis (n = 1), or optimization (n = 1, 8%). One study was found to extrapolate a simulation model to other countries (n = 1, 8%). Applied metamodeling techniques varied considerably between studies, with linear regression being most frequently applied (n = 7, 54%).Expert Commentary: Although it has great potential to enable computational demanding analyses of health economic models, metamodeling in health economics is still in its infancy, as illustrated by the limited number of applications and the relatively simple metamodeling methods applied. Comprehensive guidance specific to health economics is needed to provide modelers with the information and tools needed to utilize the full potential of metamodels. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment recommendations in clinical practice. Method: The exact definition of and the event types included in the predicted outcomes of four widely used CVD risk prediction models (ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE) and SCORE) was determined according to ICD-10 codes. The models were applied to a Dutch population cohort (n = 18,137) to predict the 10-year CVD risks. Finally, treatment recommendations, based on predicted risks and the treatment threshold associated with each model, were investigated and compared across models. Results: Due to the different definitions of predicted outcomes, the predicted risks varied widely, with an average 10-year CVD risk of 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE). Given the variation in predicted risks and recommended treatment thresholds, preventive drugs would be prescribed for 0.2%, 14.9%, 4.4%, and 2.0% of all individuals when using ATP, FRS, PCE and SCORE, respectively. Conclusion: Widely used CVD prediction models vary substantially regarding their outcomes and associated absolute risk estimates. Consequently, absolute predicted 10-year risks from different prediction models cannot be compared directly. Furthermore, treatment decisions often depend on which prediction model is applied and its recommended risk threshold, introducing unwanted practice variation into risk-based preventive strategies for CVD. [ABSTRACT FROM AUTHOR]

Aims: Electronic prescribing systems may improve medication safety, but only when used appropriately. The effects of task analysis‐based training on clinical, learning and behavioural outcomes were evaluated in the outpatient setting, compared with the usual educational approach. Methods: This was a multicentre, cluster randomized trial [EDUCATional intervention for IT‐mediated MEDication management (MEDUCATE trial)], with physicians as the unit of analysis. It took place in the outpatient clinics of two academic hospitals. Participants comprised specialists and residents (specialty trainees, in the UK) and their patients. Training took the form of a small‐group session and an e‐learning. The primary outcome was the proportion of medication discrepancies per physician, measured as discrepancies between medications registered by physicians in the electronic prescribing system and those reported by patients. Clinical consequences were estimated by the proportion of patients per physician with at least one missed drug–drug interaction with the potential for causing adverse drug events. A questionnaire assessed physicians' knowledge and skills. Results: Among 124 participating physicians, primary outcome data for 115 (93%) were available. A total of 1094 patients were included. A mean of 48% of registered medications per physician were discrepant with the medications that their patients reported in both groups (P = 0.14). Due to registration omissions, a mean of 4% of patients per physician had one or more missed drug–drug interactions with the potential to cause a clinically relevant adverse drug event in the intervention group, and 7% in controls (P = 0.11). The percentages of correct answers on the knowledge and skills test were higher in the intervention group (57%) compared with controls (51%; P = 0.01). Conclusion: The training equipped outpatient physicians with the knowledge and skills for appropriate use of electronic prescribing systems, but had no effect on medication discrepancies. [ABSTRACT FROM AUTHOR]

Background Rhesus D (RhD)-negative women pregnant with a RhD-positive child receive prophylactic injections to prevent haemolytic disease of the newborn. Because of the success of the prophylaxis, the number of naturally immunized women has decreased, thereby also decreasing the number of potential donors who provide the plasma from which the prophylaxis is made. As the current donor pool is ageing, the availability of the prophylaxis is threatened. Objectives Objectives are to investigate whether the anti-D population and the changes therein can be described by a relatively simple model, to determine the impact of ageing of the anti-D donors on the decline of the population and how many new donors should be recruited to meet future supply demand. Methods Data on Dutch anti-D donors in 1994-2013 were used to simulate the donor population size and age composition for various donor recruitment scenarios. Results With a continuous influx of 27 new donors per year and a donor stopping rate of 10% per year, the population size will stabilize at 195 donors, with 2·3% of donors stopping annually due to reaching the donor age limit. A formula is derived to estimate which donor recruitment and retention efforts are required to maintain a prespecified donor pool. Conclusion A relatively simple model can already describe and predict the size of the anti-D donor population and the impact of ageing accurately. [ABSTRACT FROM AUTHOR]

Background: Using information technology for medication management is an opportunity to help physicians to improve the quality of their documentation and communication and ultimately to improve patient care and patient safety. Physician education is necessary to take full advantage of information technology systems. In this trial, we seek to determine the effectiveness of an intensive educational intervention compared with the standard approach in improving information technology-mediated medication management and in reducing potential adverse drug events in the outpatient clinic. Methods/Design: We are conducting a multicenter, cluster randomized controlled trial. The participants are specialists and residents working in the outpatient clinic of internal medicine, cardiology, pulmonology, geriatrics, gastroenterology and rheumatology. The intensive educational intervention is composed of a small-group session and e-learning. The primary outcome is discrepancies between registered medication (by physicians) and actually used medication (by patients). The key secondary outcomes are potential adverse events caused by missed drug-drug interactions. The primary and key secondary endpoints are being assessed shortly after the educational intervention is completed. Sample size will be calculated to ensure sufficient power. A sample size of 40 physicians per group and 20 patients per physician will ensure a power of >90%, which means we will need a total of 80 physicians and 1,600 patients. Discussion: We performed an exploratory trial wherein we tested the recruitment process, e-learning, time schedule, and methods for data collection, data management and data analysis. Accordingly, we refined the processes and content: the recruitment strategy was intensified, extra measures were taken to facilitate smooth conductance of the e-learning and parts were made optional. First versions of the procedures for data collection were determined. Data entry and analysis was further standardized by using the G-standard database in the telephone questionnaire. Trial registration: ISRCTN registry: ISRCTN50890124. Registered 10 June 2013. [ABSTRACT FROM AUTHOR]

Background: The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it. Methods: In a large Dutch population cohort (n = 21,992) we classified individuals to low (<5%) and high (≥5%) fatal cardiovascular disease risk by the Framingham risk score (FRS) and reclassified them based on the systematic coronary risk evaluation (SCORE). Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE. Results: Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%]) within the events, 0.06% (95% CI [-0.08%; 0.22%]) within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]). Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3%) improved the NRI to 5.32% (95% CI [-0.13%; 12.06%]) within the events, 0.24% (95% CI [0.10%; 0.36%]) within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]). Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate. Discussion: In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly added biomarkers or imaging tests are costly or burdensome such a tailored implementation strategy may save resources and improve (cost-)effectiveness. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The aim of this work was to investigate whether measurement of the mean common carotid intima-media thickness (CIMT) improves cardiovascular risk prediction in individuals with diabetes. Methods: We performed a subanalysis among 4,220 individuals with diabetes in a large ongoing individual participant data meta-analysis involving 56,194 subjects from 17 population-based cohorts worldwide. We first refitted the risk factors of the Framingham heart risk score on the individuals without previous cardiovascular disease (baseline model) and then expanded this model with the mean common CIMT (CIMT model). The absolute 10 year risk for developing a myocardial infarction or stroke was estimated from both models. In individuals with diabetes we compared discrimination and calibration of the two models. Reclassification of individuals with diabetes was based on allocation to another cardiovascular risk category when mean common CIMT was added. Results: During a median follow-up of 8.7 years, 684 first-time cardiovascular events occurred among the population with diabetes. The C statistic was 0.67 for the Framingham model and 0.68 for the CIMT model. The absolute 10 year risk for developing a myocardial infarction or stroke was 16% in both models. There was no net reclassification improvement with the addition of mean common CIMT (1.7%; 95% CI −1.8, 3.8). There were no differences in the results between men and women. Conclusions/interpretation: There is no improvement in risk prediction in individuals with diabetes when measurement of the mean common CIMT is added to the Framingham risk score. Therefore, this measurement is not recommended for improving individual cardiovascular risk stratification in individuals with diabetes. [ABSTRACT FROM AUTHOR]

Background:It is important to regularly update survival estimates of patients with malignant mesothelioma as prognosis may vary according to epidemiologic factors and diagnostic and therapeutic management.Methods:We assessed overall (baseline) survival as well as related prognostic variables in a large cohort of 1353 patients with a confirmed diagnosis of malignant mesothelioma between 2005 and 2008.Results:About 50% of the patients were 70 years or older at diagnosis and the median latency time since start of asbestos exposure was 49 years. One year after diagnosis, 47% of the patients were alive, 20% after 2 years and 15% after 3 years. Prognostic variables independently associated with worse survival were: older age (HR=1.04 per year 95% CI (1.03-1.06)), sarcomatoid subtype (HR=2.45 95% CI (2.06-2.90)) and non-pleural localisation (HR=1.67 95% CI (1.26-2.22)).Conclusion:Survival of patients with malignant mesothelioma is still limited and depends highly on patient age, mesothelioma subtype and localisation. In addition, a substantial part of the patients had a long latency time between asbestos exposure and diagnosis. [ABSTRACT FROM AUTHOR]

Background: Numerous markers have been evaluated to facilitate the non-invasive diagnostic work-up of mesothelioma. The purpose of this study was to conduct a structured review of the diagnostic performance of non-invasive marker tests for the detection of mesothelioma in patients with suspected mesothelioma.Methods: Studies on the diagnostic accuracy of serum and cytological markers published till 31 December 2009, available in either PUBMED or Embase, to detect or exclude the presence of mesothelioma were extracted. Study quality was assessed with use of the Quadas criteria.Results: In total, 82 articles were included in this systemic review. Overall, quality of the incorporated studies to address our objective was poor. The most frequently studied immunohistochemical markers for cytological analysis were EMA, Ber-Ep4, CEA, and calretinin. The most frequently investigated serum marker was soluble mesothelin-related protein (SMRP). The markers CEA, Ber-EP4, and calretinin were most valuable in discriminating mesothelioma from other malignant diseases. Markers EMA and SMRP were most valuable in discriminating mesothelioma from non-malignant diseases. No marker performed well in discriminating between mesothelioma and all other diseases.Conclusion: Currently, there is only limited evidence to properly assess the value of non-invasive marker tests in the diagnosis of mesothelioma. Studies were of limited value to address our objective and results showed considerable unexplained study heterogeneity. [ABSTRACT FROM AUTHOR]