Karel G M Moons

University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

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Publications (389)2086.34 Total impact

  • Journal of Maternal-Fetal and Neonatal Medicine 11/2015; DOI:10.3109/14767058.2015.1124080 · 1.37 Impact Factor

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    PLoS Medicine 10/2015; 13(12):e1001886. DOI:10.1371/journal.pmed.1001886 · 14.43 Impact Factor
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    ABSTRACT: b>Objective To validate all diagnostic prediction models for ruling out pulmonary embolism that are easily applicable in primary care. Design Systematic review followed by independent external validation study to assess transportability of retrieved models to primary care medicine. Setting 300 general practices in the Netherlands. Participants Individual patient dataset of 598 patients with suspected acute pulmonary embolism in primary care. Main outcome measures Discriminative ability of all models retrieved by systematic literature search, assessed by calculation and comparison of C statistics. After stratification into groups with high and low probability of pulmonary embolism according to pre-specified model cut-offs combined with qualitative D-dimer test, sensitivity, specificity, efficiency (overall proportion of patients with low probability of pulmonary embolism), and failure rate (proportion of pulmonary embolism cases in group of patients with low probability) were calculated for all models. Results Ten published prediction models for the diagnosis of pulmonary embolism were found. Five of these models could be validated in the primary care dataset: the original Wells, modified Wells, simplified Wells, revised Geneva, and simplified revised Geneva models. Discriminative ability was comparable for all models (range of C statistic 0.75-0.80). Sensitivity ranged from 88% (simplified revised Geneva) to 96% (simplified Wells) and specificity from 48% (revised Geneva) to 53% (simplified revised Geneva). Efficiency of all models was between 43% and 48%. Differences were observed between failure rates, especially between the simplified Wells and the simplified revised Geneva models (failure rates 1.2% (95% confidence interval 0.2% to 3.3%) and 3.1% (1.4% to 5.9%), respectively; absolute difference −1.98% (−3.33% to −0.74%)). Irrespective of the diagnostic prediction model used, three patients were incorrectly classified as having low probability of pulmonary embolism; pulmonary embolism was diagnosed only after referral to secondary care. Conclusions Five diagnostic pulmonary embolism prediction models that are easily applicable in primary care were validated in this setting. Whereas efficiency was comparable for all rules, the Wells rules gave the best performance in terms of lower failure rates.
    BMJ (online) 09/2015; 351. DOI:10.1136/bmj.h4438 · 17.45 Impact Factor
  • Karel G M Moons · Douglas G Altman · Johannes B Reitsma · Gary S Collins ·
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    ABSTRACT: Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include models to predict treatment outcomes or responses; in the cancer literature often referred to as predictive models. Clinical prediction models have become abundant. Pathology measurement or results are frequently included as predictors in such prediction models, certainly in the cancer domain. Only when full information on all aspects of a prediction modeling study are clearly reported, risk of bias and potential usefulness of the prediction model can be adequately assessed. Many reviews have illustrated that the quality of reports on the development, validation, and/or adjusting (updating) of prediction models, is very poor. Hence, the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative has developed a comprehensive and user-friendly checklist for the reporting of studies on, both diagnostic and prognostic, prediction models. The TRIPOD Statement intends to improve the transparency and completeness of reporting of studies that report solely on development, both development and validation, and solely on the validation (with or without updating) of diagnostic or prognostic, including predictive, models.
    Advances in anatomic pathology 09/2015; 22(5):303-305. DOI:10.1097/PAP.0000000000000072 · 3.23 Impact Factor
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    ABSTRACT: Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995-2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
    BMJ Open 08/2015; 5(8). DOI:10.1136/bmjopen-2015-008424 · 2.27 Impact Factor
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    ABSTRACT: Individual participant data (IPD) meta-analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta-analysis (IPD-MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD-MA using evidence from clinical trials or non-randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD-MA. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
    Research Synthesis Methods 08/2015; DOI:10.1002/jrsm.1160
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    ABSTRACT: Healthcare provision is increasingly focused on the prediction of patients’ individual risk for developing a particular health outcome in planning further tests and treatments. There has been a steady increase in the development and publication of prognostic models for various maternal and fetal outcomes in obstetrics. We undertook a systematic review to give an overview of the current status of available prognostic models in obstetrics in the context of their potential advantages and the process of developing and validating models. Important aspects to consider when assessing a prognostic model are discussed and recommendations on how to proceed on this within the obstetric domain are given.
    American journal of obstetrics and gynecology 06/2015; DOI:10.1016/j.ajog.2015.06.013 · 4.70 Impact Factor
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    ABSTRACT: Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
    Journal of Clinical Epidemiology 05/2015; 338. DOI:10.1016/j.jclinepi.2015.05.009 · 3.42 Impact Factor

  • Journal of the American Geriatrics Society 05/2015; 63(5). DOI:10.1111/jgs.13419 · 4.57 Impact Factor
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    ABSTRACT: BACKGROUND Valid comparison between hospitals for benchmarking or pay-for-performance incentives requires accurate correction for underlying disease severity (case-mix). However, existing models are either very simplistic or require extensive manual data collection. OBJECTIVE To develop a disease severity prediction model based solely on data routinely available in electronic health records for risk-adjustment in mechanically ventilated patients. DESIGN Retrospective cohort study. PARTICIPANTS Mechanically ventilated patients from a single tertiary medical center (2006-2012). METHODS Predictors were extracted from electronic data repositories (demographic characteristics, laboratory tests, medications, microbiology results, procedure codes, and comorbidities) and assessed for feasibility and generalizability of data collection. Models for in-hospital mortality of increasing complexity were built using logistic regression. Estimated disease severity from these models was linked to rates of ventilator-associated events. RESULTS A total of 20,028 patients were initiated on mechanical ventilation, of whom 3,027 deceased in hospital. For models of incremental complexity, area under the receiver operating characteristic curve ranged from 0.83 to 0.88. A simple model including demographic characteristics, type of intensive care unit, time to intubation, blood culture sampling, 8 common laboratory tests, and surgical status achieved an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.86-0.88) with adequate calibration. The estimated disease severity was associated with occurrence of ventilator-associated events. CONCLUSIONS Accurate estimation of disease severity in ventilated patients using electronic, routine care data was feasible using simple models. These estimates may be useful for risk-adjustment in ventilated patients. Additional research is necessary to validate and refine these models.
    Infection Control and Hospital Epidemiology 04/2015; 36(07):1-9. DOI:10.1017/ice.2015.75 · 4.18 Impact Factor
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    ABSTRACT: Background General practitioners (GP) can safely exclude pulmonary embolism (PE) using the Wells PE-rule combined with D-dimer testing.Objective To compare the accuracy of a strategy using the Wells rule combined with either a qualitative point of care (POC) D-dimer test performed in primary care or a quantitative laboratory based D-dimer test.Methods We used data from a prospective cohort study including 598 adults suspected of PE in primary care in the Netherlands. GPs scored the Wells rule and carried out a qualitative POC test. All patients were referred to hospital for reference testing. We obtained quantitative D-dimer-test results as performed in hospital laboratories. The primary outcome was the prevalence of venous thrombo-embolism in low-risk patients.ResultsPrevalence of PE was 12.2%. POC D-dimer-test results were available in 582 patients (97%). Quantitative test results were available in 401 patients (67%). We imputed results in 197 patients. The quantitative test and POC-test missed 1 (0.4%) and 4 patients (1.5%), respectively, with a negative strategy (Wells ≤4 points and D-dimer test negative)(p=0.20). The POC-test could exclude 23 more patients (4%)(p=0.05). The sensitivity and specificity of the Wells rule combined with a POC test was 94.5% and 51.0%, combined with a quantitative test 98.6% and 47.2%, respectively.Conclusions Combined with the Wells PE-rule both tests are safe in excluding PE. The quantitative test seemed to be safer than the POC test, albeit not statistically significant. The specificity of the POC test was higher resulting in more patients in whom PE could be excluded.This article is protected by copyright. All rights reserved.
    Journal of Thrombosis and Haemostasis 04/2015; 13(6). DOI:10.1111/jth.12951 · 5.72 Impact Factor
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    ABSTRACT: Nagaan of een screening op COPD bij thuiswonende kwetsbare ouderen met klachten van kortademigheid of verminderd inspanningsvermogen zinvol zou kunnen zijn. Daartoe bepaalden we het medicatiegebruik, het aantal ziekenhuisopnames en de sterfte onder patiënten bij wie de screening leidde tot een eerste diagnose COPD. Een panel van deskundigen bepaalde de diagnose op basis van alle screeningsgegevens, inclusief spirometrie. Follow-upgegevens verzamelden we via de deelnemende huisartsen. De screening werd uitgevoerd bij 386 oudere huisartspatiënten met kortademigheid of inspanningstolerantie. Bij 84 (21,8%) patiënten leidde de screening tot een niet eerder gestelde diagnose COPD. Van deze 84 waren er 15 (17,9%) binnen zes maanden na de diagnose gestart met inhalatiemedicatie of ze hadden hun bestaande medicatie aangepast, en waren er 27 (32,1%) binnen twaalf maanden opgenomen in een ziekenhuis. In de groep bij wie de screening geen COPD had aangetoond, lag dit laatste percentage significant lager (22,9%). De mortaliteit was in beide groepen vergelijkbaar. Door kwetsbare ouderen te screenen kunnen veel nieuwe gevallen van COPD worden ontdekt. De screening heeft echter weinig consequenties voor de daaropvolgende behandeling. Een mogelijke verklaring is dat patiënten die niet zelf met hun klachten naar de huisarts stappen, waarschijnlijk toch al minder gemotiveerd zijn voor behandeling.
    Huisarts en wetenschap 04/2015; 58(5):242-244. DOI:10.1007/s12445-015-0131-4
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    Karel G M Moons · Ewoud Schuit ·

    03/2015; 3(5). DOI:10.1016/S2213-8587(15)00002-9
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    ABSTRACT: Right ventricular pacing (RVP) is associated with an increased risk of heart failure (HF) events. However, the extent and shape of this association is hardly assessed. We quantified whether the undesired effects of RVP are confirmed in an unselected population of first bradycardia pacemaker recipients. Furthermore, we studied the shape of the association between RVP and HF death and cardiac death. Cumulative percentage RVP (%RVP) was measured in 1395 patients. Using multivariable Cox regression analysis with %RVP as time-dependant co-variate we evaluated the association between %RVP and HF- and cardiac death, both unadjusted and adjusted for confounders, including age, gender, pacemaker-indication, cardiac disease, HF at baseline, diabetes, hypertension, atrio-ventricular synchrony, usage of beta-blocking drugs, anti-arrhythmic medication, HF medication, and prior atrial fibrillation/flutter. Non-linear associations were evaluated with restricted cubic splines. During a mean follow-up of 5.8 (SD 1.1) years 104 HF deaths and 144 cardiac deaths were observed. %RVP was significantly associated with HF- and cardiac death in both unadjusted (p<0.001 and p<0.001, respectively) and adjusted analyses (p=0.046 and p=0.009, respectively). Our results show a linear association between %RVP and HF- and cardiac death. We observed a constant increase of 8% risk of HF death per 10% increase in RVP. A model incorporating various non-linear transformations of %RVP using restrictive cubic splines showed no improved model fit over linear associations. This long-term, prospective study observed a significant, though linear association between %RVP and risk of HF death and/or cardiac death in unselected bradycardia pacing recipients. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    International journal of cardiology 03/2015; 185:95-100. DOI:10.1016/j.ijcard.2015.03.053 · 4.04 Impact Factor
  • Karel G M Moons · Douglas G Altman · Johannes B Reitsma · Gary S Collins ·

    Clinical Chemistry 03/2015; 61(3):565-6. DOI:10.1373/clinchem.2014.237883 · 7.91 Impact Factor
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    ABSTRACT: This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models. Researchers frequently combine data from several centers to develop clinical prediction models. In our simulation study, we developed models from clustered training data using multilevel logistic regression and validated them in external data. The amount of clustering was not meaningfully associated with the models' predictive performance. The median calibration slope of models built in samples with EPV = 5 and strong clustering (ICC = 20%) was 0.71. With EPV = 5 and ICC = 0%, it was 0.72. A higher EPV related to an increased performance: the calibration slope was 0.85 at EPV = 10 and ICC = 20% and 0.96 at EPV = 50 and ICC = 20%. Variable selection sometimes led to a substantial relative bias in the estimated predictor effects (up to 118% at EPV = 5), but this had little influence on the model's performance in our simulations. We recommend at least 10 EPV to fit prediction models in clustered data using logistic regression. Up to 50 EPV may be needed when variable selection is performed. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of clinical epidemiology 02/2015; DOI:10.1016/j.jclinepi.2015.02.002 · 3.42 Impact Factor
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    ABSTRACT: Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a consequence, it is no longer possible to evaluate between-study heterogeneity and to estimate study-specific predictor effects, or to include all individual studies, which severely hampers the development and validation of prediction models. Here, we describe a novel approach for imputing systematically missing data and adopt a generalized linear mixed model to allow for between-study heterogeneity. This approach can be viewed as an extension of Resche-Rigon's method (Stat Med 2013), relaxing their assumptions regarding variance components and allowing imputation of linear and nonlinear predictors. We illustrate our approach using a case study with IPD-MA of 13 studies to develop and validate a diagnostic prediction model for the presence of deep venous thrombosis. We compare the results after applying four methods for dealing with systematically missing predictors in one or more individual studies: complete case analysis where studies with systematically missing predictors are removed, traditional multiple imputation ignoring heterogeneity across studies, stratified multiple imputation accounting for heterogeneity in predictor prevalence, and multilevel multiple imputation (MLMI) fully accounting for between-study heterogeneity. We conclude that MLMI may substantially improve the estimation of between-study heterogeneity parameters and allow for imputation of systematically missing predictors in IPD-MA aimed at the development and validation of prediction models. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
    Statistics in Medicine 02/2015; 34(11). DOI:10.1002/sim.6451 · 1.83 Impact Factor
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    ABSTRACT: OBJECTIVE Manual surveillance of healthcare-associated infections is cumbersome and vulnerable to subjective interpretation. Automated systems are under development to improve efficiency and reliability of surveillance, for example by selecting high-risk patients requiring manual chart review. In this study, we aimed to validate a previously developed multivariable prediction modeling approach for detecting drain-related meningitis (DRM) in neurosurgical patients and to assess its merits compared to conventional methods of automated surveillance. METHODS Prospective cohort study in 3 hospitals assessing the accuracy and efficiency of 2 automated surveillance methods for detecting DRM, the multivariable prediction model and a classification algorithm, using manual chart review as the reference standard. All 3 methods of surveillance were performed independently. Patients receiving cerebrospinal fluid drains were included (2012-2013), except children, and patients deceased within 24 hours or with pre-existing meningitis. Data required by automated surveillance methods were extracted from routine care clinical data warehouses. RESULTS In total, DRM occurred in 37 of 366 external cerebrospinal fluid drainage episodes (12.3/1000 drain days at risk). The multivariable prediction model had good discriminatory power (area under the ROC curve 0.91-1.00 by hospital), had adequate overall calibration, and could identify high-risk patients requiring manual confirmation with 97.3% sensitivity and 52.2% positive predictive value, decreasing the workload for manual surveillance by 81%. The multivariable approach was more efficient than classification algorithms in 2 of 3 hospitals. CONCLUSIONS Automated surveillance of DRM using a multivariable prediction model in multiple hospitals considerably reduced the burden for manual chart review at near-perfect sensitivity. Infect Control Hosp Epidemiol 2015;36(1): 65-75.
    Infection Control and Hospital Epidemiology 01/2015; 36(1):65-75. DOI:10.1017/ice.2014.5 · 4.18 Impact Factor

  • Chest 01/2015; 147(1):e22. DOI:10.1378/chest.14-2064 · 7.48 Impact Factor

Publication Stats

13k Citations
2,086.34 Total Impact Points


  • 2001-2015
    • University Medical Center Utrecht
      • • Julius Center for Health Sciences and Primary Care
      • • Department of Psychiatry
      • • Department of Anesthesiology
      Utrecht, Utrecht, Netherlands
  • 2013
    • University of Oxford
      Oxford, England, United Kingdom
  • 2012
    • Radboud University Medical Centre (Radboudumc)
      Nymegen, Gelderland, Netherlands
    • Council for Public Health and Health Care, Netherlands
      's-Gravenhage, South Holland, Netherlands
  • 2011
    • Academisch Medisch Centrum Universiteit van Amsterdam
      Amsterdamo, North Holland, Netherlands
  • 1997-2011
    • Universiteit Utrecht
      • • Department of Methodology and Statistics
      • • Department of Epidemiology
      Utrecht, Utrecht, Netherlands
  • 2010
    • Maastricht University
      Maestricht, Limburg, Netherlands
  • 2009
    • Medical Research Council (UK)
      • MRC Clinical Trials Unit
      London, ENG, United Kingdom
    • Kitasato University
      Edo, Tōkyō, Japan
  • 2008
    • Wageningen University
      • Division of Human Nutrition
      Wageningen, Provincie Gelderland, Netherlands
  • 2006
    • Vanderbilt University
      • Department of Biostatistics
      Нашвилл, Michigan, United States
  • 2002
    • VU University Amsterdam
      Amsterdamo, North Holland, Netherlands
    • Erasmus MC
      Rotterdam, South Holland, Netherlands
  • 1997-2001
    • Erasmus Universiteit Rotterdam
      Rotterdam, South Holland, Netherlands
  • 2000
    • St. Antonius Ziekenhuis
      Nieuwegen, Utrecht, Netherlands