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The authors validated 2 clinical risk models for perioperative mortality in patients scheduled for elective open abdominal aortic aneurysm surgery (AAA surgery). They studied 361 patients who underwent elective AAA [...]

A study was conducted to assess the sustained effect of participating in an exercise program on physical activity behavior 1 yr after completion of the program and also identify factors that predict sustained exercise participation. Sustained changes in physical activity behavior in previously low-active postmenopausal women could be possible after participation in a 1-yr exercise program.

The objective of this article is to externally validate and update a recently published score chart for chronic mesenteric ischemia (CMI). A multicenter prospective cohort analysis was conducted of 666 CMI-suspected patients referred to two Dutch specialized CMI centers. Multidisciplinary consultation resulted in expert-based consensus diagnosis after which CMI consensus patients were treated. A definitive diagnosis of CMI was established if successful treatment resulted in durable symptom relief. The absolute CMI risk was calculated and discriminative ability of the original chart was assessed by the c-statistic in the validation cohort. Thereafter the original score chart was updated based on the performance in the combined original and validation cohort with inclusion of celiac artery (CA) stenosis cause. In 8% of low-risk patients, 39% of intermediate-risk patients and 94% of high-risk patients of the validation cohort, CMI was diagnosed. Discriminative ability of the original model was acceptable (c-statistic 0.79). The total score of the updated chart ranged from 0 to 28 points (low risk 19% absolute CMI risk, intermediate risk 45%, and high risk 92%). The discriminative ability of the updated chart was slightly better (c-statistic 0.80). The CMI prediction model performs and discriminates well in the validation cohort. The updated score chart has excellent discriminative ability and is useful in clinical decision making.

Background and objective The objective of this article is to externally validate and update a recently published score chart for chronic mesenteric ischemia (CMI).Methods A multicenter prospective cohort analysis was conducted of 666 CMI-suspected patients referred to two Dutch specialized CMI centers. Multidisciplinary consultation resulted in expert-based consensus diagnosis after which CMI consensus patients were treated. A definitive diagnosis of CMI was established if successful treatment resulted in durable symptom relief. The absolute CMI risk was calculated and discriminative ability of the original chart was assessed by the c-statistic in the validation cohort. Thereafter the original score chart was updated based on the performance in the combined original and validation cohort with inclusion of celiac artery (CA) stenosis cause.Results In 8% of low-risk patients, 39% of intermediate-risk patients and 94% of high-risk patients of the validation cohort, CMI was diagnosed. Discriminative ability of the original model was acceptable (c-statistic 0.79). The total score of the updated chart ranged from 0 to 28 points (low risk 19% absolute CMI risk, intermediate risk 45%, and high risk 92%). The discriminative ability of the updated chart was slightly better (c-statistic 0.80).Conclusion The CMI prediction model performs and discriminates well in the validation cohort. The updated score chart has excellent discriminative ability and is useful in clinical decision making.

Epigenetic misprogramming is an essential component of cancer development.DNA methylation-based risk-prediction models provide novel opportunities for risk-tailored screening and prevention of cancer.Multidisciplinary collaborative research is needed to overcome the scientific challenges associated with the discovery of DNA methylation markers for risk-prediction, such as identifying surrogate tissues and developing novel analytical methods.Implementation of epigenome-based risk-tailored screening and prevention programmes requires several ethical, legal, social, organizational and economic challenges to be addressed in addition to the engagement of policymakers, health-care professionals and the public.

BackgroundTo validate the Feverkidstool, a prediction model consisting of clinical signs and symptoms and C-reactive protein (CRP) to identify serious bacterial infections (SBIs) in febrile children, and to determine the incremental diagnostic value of procalcitonin.MethodsThis prospective observational study that was carried out at two Dutch emergency departments included children with fever, aged 1 month to 16 years. The prediction models were developed with polytomous logistic regression differentiating “pneumonia” and “other SBIs” from “non-SBIs” using standardized, routinely collected data on clinical signs and symptoms, CRP, and procalcitonin.ResultsA total of 1,085 children were included with a median age of 1.6 years (interquartile range 0.8–3.4); 73 children (7%) had pneumonia and 98 children (9%) had other SBIs. The Feverkidstool showed good discriminative ability in this new population. After adding procalcitonin to the Feverkidstool, c-statistic for “pneumonia” increased from 0.85 (95% confidence interval (CI) 0.76–0.94) to 0.86 (0.77–0.94) and for “other SBI” from 0.81 (0.73–0.90) to 0.83 (0.75– 0.91). A model with clinical features and procalcitonin performed similar to the Feverkidstool.ConclusionThis study confirms the external validity of the Feverkidstool, with CRP and procalcitonin being equally valuable for predicting SBI in our population of febrile children. Our findings do not support routine dual use of CRP and procalcitonin.

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ObjectiveTo determine the agreement between peripheral and central capillary refill time (pCRT/cCRT) and their diagnostic values for detecting serious bacterial infection (SBI) in febrile children attending the paediatric emergency department (ED).DesignProspective observational study.SettingPaediatric ED, Erasmus Medium Care-Sophia Children's hospital, the Netherlands.Patients1193 consecutively included, previously healthy, febrile children (1 month–16 years) with both pCRT measurements and cCRT measurements available. SBI diagnosis was based on abnormal radiographic findings and/or positive cultures from normally sterile locations in addition to clinical criteria.Main outcome measuresAgreement between pCRT and cCRT (Cohen's κ), overall and stratified for age and body temperature. The diagnostic value of pCRT and cCRT for SBI was assessed with logistic regression.ResultsOverall agreement was 0.35 (95% CI 0.27 to 0.43; considered ‘fair’). Although not significant, agreement was lower in children aged 1–<5 years (κ: 0.15 (95% CI 0.04 to 0.27)) and decreased with higher body temperatures with κ ranging from 0.55 (95% CI 0.32 to 0.79) for temperature <37.5°C to 0.21 (95% CI 0.07 to 0.34) for temperature >39.5°C. Abnormal pCRT (>2 s) was observed in 153 (12.8%; 95% CI 10.9% to 14.7%) and abnormal cCRT in 55 (4.6%; 95% CI 3.4% to 5.8%) children. The OR of abnormal pCRT (>2 s) for predicting SBI was 1.10 (95% CI 0.65 to 1.84). For abnormal cCRT (>2 s), the OR was 0.43 (95% CI 0.13 to 1.39).ConclusionsThe pCRT and cCRT values showed only fair agreement in a general population of febrile children at the ED, and no significant association with age or body temperature was found. Only a small part of febrile children at risk for serious infections at the ED show abnormal CRT values. Both abnormal pCRT and cCRT (defined as >2 s) performed poorly and were non-significant in this study detecting SBI in a general population of febrile children.

Supplemental Digital Content is available in the text.

Supplemental Digital Content is available in the text.

C-reactive protein (CRP) and procalcitonin (PCT) are useful diagnostic tools to estimate the risk of serious bacterial infection (SBI) in febrile children at the emergency department (ED). The Lab-score combines these 2 biomarkers with urinalysis in an easy to use validated model. Kinetics of inflammatory markers suggests a differentiating role of duration of disease.

Rome criteria were formulated to define functional gastrointestinal disorders (Rome III criteria, 2006) excluding organic diagnoses when alarm symptoms were absent. The aims of the study were to validate the Rome III criteria as to their capacity to differentiate between organic and functional abdominal pain and to assess the role of alarm symptoms in this differentiation. During 2 years all of the patients (ages 4–16 years) presenting with recurrent abdominal pain (Apley criteria) and referred to secondary care were included. Clinical diagnoses were based on protocolized evaluation and intervention with 6-month follow-up. Alarm symptoms were registered. Rome III criteria for functional pain syndromes were assigned independently. Descriptive statistical analyses were performed. In 200 patients (87 boys, mean age 8.8 years), organic (17%), functional (40%), combined organic and functional (9%), spontaneous recovery (27%), and other (8%) clinical diagnoses were established. Alarm symptoms were found in 57.5% (organic causes 56%, functional causes 61%). The evaluation for Rome symptom clusters revealed symptoms of irritable bowel syndrome in 27%, functional dyspepsia in 15%, functional abdominal pain in 28%, functional abdominal pain syndrome in 14.5%, and no pain syndrome in 15.5%. Rome diagnoses, based on symptoms and absence of alarm symptoms, predicted functional clinical diagnosis with sensitivity 0.35 (95% confidence interval 0.27–0.43), specificity 0.60 (0.46–0.73), positive predictive value 0.71 (0.61–0.82), and negative predictive value of 0.24 (0.17–0.32). The Rome III criteria for abdominal pain are not specific enough to rule out organic causes. Alarm symptoms do not differentiate between organic and functional abdominal pain.

Phlebectomy of varicose tributaries is usually considered an additional treatment after or during saphenous ablation. As phlebectomies alone affect the hemodynamics of the venous system, this treatment can be effective as primary intervention in selected patients. The objective of this study was to analyze hemodynamic, clinical, and patient-reported outcomes after phlebectomies in a prospective multicenter study to determine predictors for treatment success, that is, restoration of great saphenous vein (GSV) competence.

The anatomical SYNTAX score is advocated in European and US guidelines as an instrument to help clinicians decide the optimum revascularisation method in patients with complex coronary artery disease. The absence of an individualised approach and of clinical variables to guide decision making between coronary artery bypass graft surgery (CABG) and percutaneous coronary intervention (PCI) are limitations of the SYNTAX score. SYNTAX score II aimed to overcome these limitations.

Background Left ventricular (LV) mass has a continuous relation with cardiovascular risk, and regression of LV mass induced by pharmacological treatment is associated with improved prognosis. Therefore, early identification of patients with a large LV mass is desired. We developed a model to predict LV mass in individual hypertensives at high cardiovascular risk.Design and methods We analyzed data of 536 hypertensives with symptomatic extracardiac atherosclerotic disease or marked risk factors for atherosclerosis from a cross-sectional study in a tertiary referral center. LV mass was measured by cardiac MRI. We developed the prediction rule with multivariable linear regression analysis and stepwise backward elimination. Internal validation was assessed with bootstrap sampling to obtain an estimate of model performance (R2) that may be expected for new patients.Results Important predictors for LV mass included sex, height, body mass index, systolic blood pressure, and previous aneurysm of the abdominal aorta. R2of the prediction model was 45% after internal validation, which was considerably higher than the R2of previously reported models (range 1–38%). Addition of electrocardiography data showed limited improvement of the model performance (R2=47%).Conclusion We present a prediction model for LV mass in hypertensives at high cardiovascular risk. After external validation, this model may be used in clinical practice to estimate LV mass for early identification of large LV mass. The predictions of the model may support appropriate medical care in the prevention of cardiovascular disease.

Clinical prediction models estimate the risk of having or developing a particular outcome or disease. Researchers often develop a new model when a previously developed model is validated and the performance is poor. However, the model can be adjusted (updated) using the new data. The updated model is then based on both the development and validation data. We show how a simple updating method may suffice to update a clinical prediction model. A prediction model that preoperatively predicts the risk of severe postoperative pain was developed with multivariable logistic regression from the data of 1944 surgical patients in the Academic Medical Center Amsterdam, the Netherlands. We studied the predictive performance of the model in 1,035 new patients, scheduled for surgery at a later time in the University Medical Center Utrecht, the Netherlands. We assessed the calibration (agreement between predicted risks and the observed frequencies of an outcome) and discrimination (ability of the model to distinguish between patients with and without postoperative pain). When the incidence of the outcome is different, all predicted risks may be systematically over- or underestimated. Hence, the intercept of the model can be adjusted (updating). The predicted risks were systematically higher than the observed frequencies, corresponding to a difference in the incidence of postoperative pain between the development (62%) and validation set (36%). The updated model resulted in better calibration. When a clinical prediction model in new patients does not show adequate performance, an alternative to developing a new model is to update the prediction model with new data. The updated model will be based on more patient data, and may yield better risk estimates. Les modèles de prédiction clinique évaluent le risque de présenter ou de manifester un devenir ou une maladie en particulier. Les chercheurs élaborent souvent un nouveau modèle lorsqu’un modèle élaboré précédemment est validé mais que ses performances sont peu concluantes. Toutefois, un modèle peut être ajusté (mis à jour) aux nouvelles données. Le modèle mis à jour est ensuite basé aussi bien sur les données d’élaboration que de validation. Nous montrons comment une méthode simple de mise à jour peut suffire à mettre à jour un modèle de prédiction clinique. Un modèle de prédiction qui prédit avant l’opération le risque de douleur postopératoire grave a été élaboré à l’aide de la méthode de régression logistique multivariée appliquée aux données de 1944 patients chirurgicaux du Centre médical universitaire d’Amsterdam, aux Pays-Bas. Nous avons étudié la performance prédictive du modèle chez 1 035 nouveaux patients qui devaient subir une chirurgie plus tard au Centre médical universitaire d’Utrecht, aux Pays-Bas. Nous avons évalué le calibrage (accord entre les risques prédits et les fréquences observées d’un devenir) et la discrimination (capacité du modèle de distinguer entre les patients avec ou sans douleurs postopératoires). Lorsque l’incidence du devenir est différente, tous les risques prédits peuvent être systématiquement sur- ou sous-estimés. Ainsi, le point d’intersection du modèle peut être ajusté (mise à jour). Les risques prédits étaient systématiquement plus élevés que les fréquences observées, ce qui correspond à une différence de l’incidence des douleurs postopératoires entre les données d’élaboration (62 %) et celles de validation (36 %). Le modèle mis à jour a généré un meilleur calibrage. Lorsqu’un modèle de prédiction clinique chez de nouveaux patients ne génère pas une performance adaptée, une alternative à l’élaboration d’un nouveau modèle consiste en la mise à jour du modèle de prédiction avec de nouvelles données. Le modèle mis à jour sera basé sur davantage de données patients, et pourrait donner de meilleures estimations des risques.

Because passive immunization against respiratory syncytial virus (RSV) is costly, its use should be restricted to well-defined groups of high risk children. We aimed to develop a clinical prediction rule that estimates the individual monthly risk of hospitalization for RSV infection in young children.

Reliable estimates of hospitalization costs for severe respiratory syncytial virus (RSV) infection are necessary to perform economic analyses of preventive strategies of severe RSV disease. We aimed to develop a model that predicts anticipated mean RSV hospitalization costs of groups of young children at risk for hospitalization, but not yet hospitalized, based on readily available child characteristics.

After chemotherapy for metastatic non-seminomatous testicular cancer, surgical resection is a generally accepted treatment to remove remnants of the initial metastases, since residual tumour may still be present (mature teratoma or viable cancer cells). In this paper, we review the development and external validation of a logistic regression model to predict the absence of residual tumour. Three sources of information were used. A quantitative review identified six relevant predictors from 19 published studies (996 resections). Second, a development data set included individual data of 544 patients from six centres. This data set was used to assess the predictive relationships of five continuous predictors, which resulted in dichotomization for two, and a log, square root, and linear transformation for three other predictors. The multiple logistic regression coefficients were reduced with a shrinkage factor (0.95) to improve calibration, based on a bootstrapping procedure. Third, a validation data set included 172 more recently treated patients. The model showed adequate calibration and good discrimination in the development and in the validation sample (areas under the ROC curve 0.83 and 0.82). This study illustrates that a careful modelling strategy may result in an adequate predictive model. Further study of model validity may stimulate application in clinical practice. Copyright © 2001 John Wiley Sons, Ltd.

We validated a prediction model for histology of residual retroperitoneal masses, either benign or tumor, in patients treated with chemotherapy for metastatic nonseminomatous testicular cancer.

Long-termH. pylori associated gastritis is recognized as a pathogenic factor in gastric carcinogenesis. In gastric carcinomas the amount and activity of the tissue-type plasminogen activator (t-PA) have been reported to be decreased, whereas those of the urokinase-type plasminogen activator (u-PA) were increased, contributing to the neoplastic and invasive process. The present study was performed to determine t-PA and u-PA levels and activity in gastric mucosa from 102 patients and to investigate whether these levels are influenced byH. pylori infection. The antigen concentration and activity of t-PA and u-PA in corpus mucosa were low (P<0.001) compared with those in antral mucosa, although for the u-PA activity this did not reach statistical significance. InH. pylori-associated antral gastritis the mucosal t-PA antigen concentration and activity were found to be decreased (P<0.001) compared with normal mucosa, whereas inH. pylori-associated pangastritis the corpus t-PA levels were not affected. The antigen concentration and activity of u-PA were found to be significantly (P<0.005) increased, both inH. pylori-associated gastritis of antrum and corpus mucosa. Levels of u-PA in histologically normal corpus mucosa of patients with anH. pylori-associated antral gastritis were also found to be increased (P<0.05). In conclusion, the alterations in the plasminogen activator profile found inH. pylori-associated gastritis, ie, a decrease in t-PA and an increase in u-PA, show a similar tendency as the previously found alterations in gastric carcinomas, which provides additional support for the possible involvement ofH. pylori-associated gastritis in the pathogenesis of gastric carcinoma.

ObjectiveTo derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department.DesignProspective observational diagnostic study.SettingThree paediatric emergency care units: two in the Netherlands and one in the United Kingdom.ParticipantsChildren with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein.Main outcome measuresPneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs.ResultsOxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86).ConclusionA validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections.