Development of a prediction model to target screening for high blood pressure in children.

Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. Data from 5359 children in a population-based prospective cohort study were used. High blood pressure was defined as systolic or diastolic blood pressure ≥ 95th percentile for gender, age, and height. Logistic regression with backward selection was used to identify the strongest predictors related to pregnancy, child, and parent characteristics. Internal validation was performed using bootstrapping. 227 children (4.2%) had high blood pressure. The diagnostic model included maternal hypertensive disease during pregnancy, maternal BMI, maternal educational level, parental hypertension, parental smoking, child birth weight standard deviation score (SDS), child BMI SDS, and child ethnicity. The area under the ROC curve was 0.73, compared to 0.65 when using only child overweight. Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only.

The association between palliative care team consultation and hospital costs for patients with advanced cancer: An observational study in 12 Dutch hospitals.

BACKGROUND: Early palliative care team consultation has been shown to reduce costs of hospital care. The objective of this study was to investigate the association between palliative care team (PCT) consultation and the content and costs of hospital care in patients with advanced cancer. MATERIAL AND METHODS: A prospective, observational study was conducted in 12 Dutch hospitals. Patients with advanced cancer and an estimated life expectancy of less than 1 year were included. We compared hospital care during 3 months of follow-up for patients with and without PCT involvement. Propensity score matching was used to estimate the effect of PCTs on costs of hospital care. Additionally, gamma regression models were estimated to assess predictors of hospital costs. RESULTS: We included 535 patients of whom 126 received PCT consultation. Patients with PCT had a worse life expectancy (life expectancy <3 months: 62% vs. 31%, p < .01) and performance status (p < .01, e.g., WHO status higher than 2:54% vs. 28%) and more often had no more options for anti-tumour therapy (57% vs. 30%, p < .01). Hospital length of stay, use of most diagnostic procedures, medication and other therapeutic interventions were similar. The total mean hospital costs were €8,393 for patients with and €8,631 for patients without PCT consultation. Analyses using propensity scores to control for observed confounding showed no significant difference in hospital costs. CONCLUSIONS: PCT consultation for patients with cancer in Dutch hospitals often occurs late in the patients' disease trajectories, which might explain why we found no effect of PCT consultation on costs of hospital care. Earlier consultation could be beneficial to patients and reduce costs of care.

Development of a clinical prediction model for an international normalised ratio ≥ 4·5 in hospitalised patients using vitamin K antagonists.

Vitamin K antagonists (VKAs) used for the prevention and treatment of thromboembolic disease, increase the risk of bleeding complications. We developed and validated a model to predict the risk of an international normalised ratio (INR) ≥ 4·5 during a hospital stay. Adult patients admitted to a tertiary hospital and treated with VKAs between 2006 and 2010 were analysed. Bleeding risk was operationalised as an INR value ≥4·5. Multivariable logistic regression analysis was used to assess the association between potential predictors and an INR ≥ 4·5 and validated in an independent cohort of patients from the same hospital between 2011 and 2014. We identified 8996 admissions of patients treated with VKAs, of which 1507 (17%) involved an INR ≥ 4·5. The final model included the following predictors: gender, age, concomitant medication and several biochemical parameters. Temporal validation showed a c statistic of 0·71. We developed and validated a clinical prediction model for an INR ≥ 4·5 in VKA-treated patients admitted to our hospital. The model includes factors that are collected during routine care and are extractable from electronic patient records, enabling easy use of this model to predict an increased bleeding risk in clinical practice.

Validation of the Feverkidstool and procalcitonin for detecting serious bacterial infections in febrile children.

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.

Prediction of Multiple Recurrent Events: A Comparison of Extended Cox Models in Bladder Cancer.

Recurrence of bladder cancer can occur repeatedly in the same patient after treatment of the primary tumor. Models predicting the risk of a next recurrence may inform individualized decision-making on surveillance frequency. We aimed to assess the usefulness of extensions of the Cox proportional hazards model for repeated events in this context. We analyzed 531 Dutch patients with bladder cancer (1990-2012) with information on 7 prespecified predictors at the time of diagnosis of the primary and recurrent tumors. We considered 3 aspects of model variants: how to model time to the repeated events (calendar time, gap time, elapsed time); the number of preceding events (predictor, stratum variable); and the within-subject correlation (ignored in a simple Cox model, robust standard errors in a variance-correction model, random effect in a frailty model). First to fourth recurrences of bladder cancer occurred in 313, 174, 103, and 66 patients, respectively, with median calendar follow-up times of 1.1, 2.5, 3.8, and 4.5 years, respectively. We focused on gap time in the detailed analyses, allowing for clinically meaningful predictions. Variance-correction models may be useful if predictor selection is part of the model development. Frailty models may be useful when within-subject correlation is strong.

FGFR3, TERT and OTX1 as a Urinary Biomarker Combination for Surveillance of Patients with Bladder Cancer in a Large Prospective Multicenter Study.

PURPOSE: Patients with nonmuscle invasive bladder cancer are followed with frequent cystoscopies. In this study FGFR3, TERT and OTX1 were investigated as a diagnostic urinary marker combination during followup of patients with primary nonmuscle invasive bladder cancer. MATERIALS AND METHODS: In this international, multicenter, prospective study 977 patients with nonmuscle invasive bladder cancer were included. A total of 2,496 urine samples were collected prior to cystoscopy during regular visits. Sensitivity was estimated to detect concomitant recurrences. Kaplan-Meier curves were used to estimate the development of future recurrences after urinalysis and a negative cystoscopy. RESULTS: Sensitivity of the assay combination for recurrence detection was 57% in patients with primary low grade, nonmuscle invasive bladder cancer. However, sensitivity was 83% for recurrences that were pT1 or muscle invasive bladder cancer. Of the cases 2% progressed to muscle invasive bladder cancer. Sensitivity for recurrence detection in patients with primary high grade disease was 72% and 7% of them had progression to muscle invasive bladder cancer. When no concomitant tumor was found by cystoscopy, positive urine samples were more frequently followed by a recurrence over time compared to a negative urine sample (58% vs 36%, p <0.001). High stage recurrences were identified within 1 year after a positive urine test and a negative cystoscopy. CONCLUSIONS: Recurrences in patients with primary nonmuscle invasive bladder cancer can be detected by a combination of urine assays. This study supports the value of urinalysis as an alternative diagnostic tool in patients presenting with low grade tumors and as a means to identify high stage tumors earlier.

A closed testing procedure to select an appropriate method for updating prediction models.

Prediction models fitted with logistic regression often show poor performance when applied in populations other than the development population. Model updating may improve predictions. Previously suggested methods vary in their extensiveness of updating the model. We aim to define a strategy in selecting an appropriate update method that considers the balance between the amount of evidence for updating in the new patient sample and the danger of overfitting. We consider recalibration in the large (re-estimation of model intercept); recalibration (re-estimation of intercept and slope) and model revision (re-estimation of all coefficients) as update methods. We propose a closed testing procedure that allows the extensiveness of the updating to increase progressively from a minimum (the original model) to a maximum (a completely revised model). The procedure involves multiple testing with maintaining approximately the chosen type I error rate. We illustrate this approach with three clinical examples: patients with prostate cancer, traumatic brain injury and children presenting with fever. The need for updating the prostate cancer model was completely driven by a different model intercept in the update sample (adjustment: 2.58). Separate testing of model revision against the original model showed statistically significant results, but led to overfitting (calibration slope at internal validation = 0.86). The closed testing procedure selected recalibration in the large as update method, without overfitting. The advantage of the closed testing procedure was confirmed by the other two examples. We conclude that the proposed closed testing procedure may be useful in selecting appropriate update methods for previously developed prediction models. Copyright © 2016 John Wiley & Sons, Ltd.

Diagnostic Accuracy of the Combination of Clinical Symptoms and CT or MR Angiography in Patients With Chronic Gastrointestinal Ischemia.

BACKGROUND: No golden diagnostic standard is available to diagnose chronic gastrointestinal ischemia (CGI). GOALS: We aimed to establish an accurate prediction model for CGI, based on clinical symptoms and radiologic evaluation of the amount of stenosis in the celiac artery (CA) and superior mesenteric artery (SMA) by means of computed tomography-angiography or magnetic resonance (MR)-angiography. STUDY: We prospectively included 436 consecutive patients with clinical suspicion of CGI in a tertiary referral center. Predictors for CGI were obtained by comparing clinical parameters to the diagnosis of CGI. Multivariable logistic regression was used to combine the strongest predictors in a model. A score chart based on the prediction model was provided to calculate the risk of CGI. RESULTS: CGI was present in 171/436 (39%) patients (67 y; range, 54 to 74 y; 27% male). Strongest predictors for CGI were female gender [odds ratio (OR)=1.44; 95% confidence interval (CI), 0.85-2.43], weight loss (OR=1.63, 95% CI, 0.98-2.72), concomitant cardiovascular disease (OR=1.70, 95% CI, 1.04-2.78), duration of symptoms (OR=0.88, 95% CI, 0.79-0.99), and stenosis of CA and SMA (50% to 70% stenosis CA: OR=1.33, 95% CI, 0.56-3.19; >70% stenosis CA: OR=5.79, 95% CI, 3.42-9.81; 50% to 70% stenosis SMA: OR=3.21, 95% CI, 0.81-12.74; >70% stenosis SMA: OR=4.39, 95% CI, 2.30-8.41). A model based on clinical symptoms alone showed limited discriminative ability for diagnosing CGI (c-statistic 0.62). Adding radiologic imaging of the mesenteric arteries improved the discriminative ability (c-statistic 0.79). CONCLUSIONS: Clinical symptoms alone are insufficient to predict the risk of CGI. Radiologic evaluation of the mesenteric arteries is essential. This tool may be useful for clinicians to assess the risk of CGI and to decide whether further diagnostic work-up for CGI is needed.

Prediction of Chlamydia trachomatis infection to facilitate selective screening on population and individual level: a cross-sectional study of a population-based screening programme.

OBJECTIVES: To develop prediction models for Chlamydia trachomatis (Ct) infection with different levels of detail in information, that is, from readily available data in registries and from additional questionnaires. METHODS: All inhabitants of Rotterdam and Amsterdam aged 16-29 were invited yearly from 2008 until 2011 for home-based testing. Their registry data included gender, age, ethnicity and neighbourhood-level socioeconomic status (SES). Participants were asked to fill in a questionnaire on education, sexually transmitted infection history, symptoms, partner information and sexual behaviour. We developed prediction models for Ct infection using first-time participant data-including registry variables only and with additional questionnaire variables-by multilevel logistic regression analysis to account for clustering within neighbourhoods. We assessed the discriminative ability by the area under the receiver operating characteristic curve (AUC). RESULTS: Four per cent (3540/80 385) of the participants was infected. The strongest registry predictors for Ct infection were young age (especially for women) and Surinamese, Antillean or sub-Saharan African ethnicity. Neighbourhood-level SES was of minor importance. Strong questionnaire predictors were low to intermediate education level, ethnicity of the partner (non-Dutch) and having sex with casual partners. When using a prediction model including questionnaire risk factors (AUC 0.74, 95% CI 0.736 to 0.752) for selective screening, 48% of the participating population needed to be screened to find 80% (95% CI 78.4% to 81.0%) of Ct infections. The model with registry risk factors only (AUC 0.67, 95% CI 0.656 to 0.675) required 60% to be screened to find 78% (95% CI 76.6% to 79.4%) of Ct infections. CONCLUSIONS: A registry-based prediction model can facilitate selective Ct screening at population level, with further refinement at the individual level by including questionnaire risk factors.

A new concordance measure for risk prediction models in external validation settings.

Concordance measures are frequently used for assessing the discriminative ability of risk prediction models. The interpretation of estimated concordance at external validation is difficult if the case-mix differs from the model development setting. We aimed to develop a concordance measure that provides insight into the influence of case-mix heterogeneity and is robust to censoring of time-to-event data. We first derived a model-based concordance (mbc) measure that allows for quantification of the influence of case-mix heterogeneity on discriminative ability of proportional hazards and logistic regression models. This mbc can also be calculated including a regression slope that calibrates the predictions at external validation (c-mbc), hence assessing the influence of overall regression coefficient validity on discriminative ability. We derived variance formulas for both mbc and c-mbc. We compared the mbc and the c-mbc with commonly used concordance measures in a simulation study and in two external validation settings. The mbc was asymptotically equivalent to a previously proposed resampling-based case-mix corrected c-index. The c-mbc remained stable at the true value with increasing proportions of censoring, while Harrell's c-index and to a lesser extent Uno's concordance measure increased unfavorably. Variance estimates of mbc and c-mbc were well in agreement with the simulated empirical variances. We conclude that the mbc is an attractive closed-form measure that allows for a straightforward quantification of the expected change in a model's discriminative ability due to case-mix heterogeneity. The c-mbc also reflects regression coefficient validity and is a censoring-robust alternative for the c-index when the proportional hazards assumption holds. Copyright © 2016 John Wiley & Sons, Ltd.

Study protocol for a cluster randomized controlled trial to evaluate a referral strategy for axial spondyloarthritis in young primary care patients with chronic low back pain; an impact study.

BACKGROUND: Axial spondyloarthritis (axSpA) is a disabling inflammatory joint disease with chronic low back pain (CLBP) as leading symptom. Recognizing axSpA in the large amount of CLBP patients is difficult for general practioners (GP). This evaluation aims to assess the effect of a referral strategy for axSpA in young primary care patients with CLBP by comparing the use of the strategy with usual care. The effect is measured at three different levels; by patient reported outcomes (the clinical effect), process and costs evaluation. METHODS/DESIGN: This study design is a cluster randomized controlled trial with GP as clusters. GPs throughout the Netherlands are invited to participate and randomized to either the intervention or the control group. Patients from participating GPs are invited to participate if they have ever been registered with low back pain, without radiation (ICPC L03) and aged 18-45 years. To be included in the study, patients need to have current low back pain and chronic low back pain (>12 weeks). In the intervention arm a referral strategy for axSpA will be applied in CLBP patients, in the control arm care as usual will be provided for CLBP patients. The referral strategy consists of four easy to use variables. All are questions about the back pain complaints of the patients. Data is prospectively collected in an online database at baseline (T0), 4 months (T1), 12 months (T2) and 24 months (T3). After time point T1 (4 months) patients from the control group will also receive the intervention i.e. the application of a referral strategy for axSpA. The effect of the referral strategy is measured at three different levels, by patient outcomes (e.g. pain scores, quality of life), process measures (e.g. number of axSpA diagnoses by rheumatologists) and by costs (work productivity and health care resources use). Our primary outcome is the Roland Morris Disability Questionnaire after 4 months, secondary outcomes are pain and quality of life. Costs will be assessed before and after the use of the referral strategy, to estimate if the use of the strategy will lead to a reduction in health care costs and improvement in work participation. DISCUSSION: It is anticipated that using the axSpA referral strategy for primary care CLBP patients will increase the quality of life of CLBP patients, will result in more (correct) diagnoses of axSpA by the rheumatologists, and will be cost-effective. Ultimately, the results of this study may contribute to the startup of a national implementation of the axSpA referral strategy to identify timely CLBP patients with axSpA. TRIAL REGISTRATION: NCT01944163 , date of registration; September 6, 2013 (Clinicaltrials.gov).

Epilepsy in children with tuberous sclerosis complex: Chance of remission and response to antiepileptic drugs.

OBJECTIVE: To describe treatment and outcome of epilepsy in children with tuberous sclerosis complex (TSC). METHODS: Seventy-one children with TSC and epilepsy treated at the ENCORE TSC Expertise Center between 1988 and 2014 were included. Patient characteristics and duration and effectiveness of antiepileptic treatments were extracted from our clinical database. Correlations were made between recurrence of seizures after response to treatment, and several patient characteristics. RESULTS: Median age at time of inclusion was 9.4 years (range 0.9-18.0). Seizure history showed that 55 children (77%) of 71 became seizure-free for longer than 1 month, and 21 (30%) of 71 for longer than 24 months. Remission of seizures was associated with higher IQ, and a trend was observed between seizure remission and age at onset of seizures. A total of 19 antiepileptic drugs (AEDs) were used. Valproic acid, vigabatrin, levetiracetam, and carbamazepine were used most frequently. Nonpharmacologic therapies (ketogenic diet, epilepsy surgery, and vagus nerve stimulation) were used 13 times. Epilepsy surgery was most effective, with four of five children becoming seizure-free. AEDs prescribed as first and second treatment were most effective. Valproic acid was prescribed most frequently as first and second treatment, followed by vigabatrin. Thirty-one children had infantile spasms, preceded by focal seizures in 18 children (58%). Vigabatrin was used by 29 children (94%), and was first treatment in 15 (48%). Vigabatrin was more effective than other AEDs when prescribed as first treatment. SIGNIFICANCE: We showed that, although 77% of children with epilepsy due to TSC reached seizure remission, usually after their first or second AED, this was sustained for at least 24 months in only 38%. Almost half of those with 24 months of remission later had relapse of seizures. Our results support vigabatrin as first choice drug, and show the need for better treatment options for these children.

A head to head comparison of nine tools predicting non-sentinel lymph node status in sentinel node positive breast cancer women.

BACKGROUND AND OBJECTIVES: This study was conducted to evaluate the performance of available tools predicting non-sentinel lymph node (non-SLN) status in women with SLN positive breast cancer and to see if they can be safely used in everyday clinical practice. METHODS: Data of 220 women with breast cancer who underwent a SLN biopsy at the Máxima Medical Centre between 2000-2008 were analysed. Tools evaluated were: the models from Memorial Sloan Kettering Cancer Centre, Stanford, Mayo, Cambridge, Gur, and MOU, and the scores from Saidi, Tenon, and MDA. Model performance was assessed using calibration, discrimination and Nagelkerke's explained variation. RESULTS: The MSKCC nomogram showed best overall performance with best discrimination (AUC 0.69), second best calibration, and highest explained variation (31%). The 10% low risk threshold led to defining only 22% (38/176) of the women as being low risk while in fact 66% (116/176) were non-SLN negative. The false negative rate was 13% (5/38). CONCLUSIONS: Current models for predicting non-SLN metastases in SLN positive breast cancer are not yet ready for implementation in general practice. Further research efforts should improve model performance in selecting patients or perhaps find a role in support in the paradigm shift to a "treat none unless" approach.

Palliative care consultation services in hospitals in the Netherlands: the design of the COMPASS study.

BACKGROUND: Patients with an advanced incurable disease are often hospitalised for some time during the last phase of life. Care in hospitals is generally focussed at curing disease and prolonging life and may therefore not in all cases adequately address the needs of such patients. We present the COMPASS study, a study on the effects and costs of consultation teams for palliative care in hospitals. This observational study aims to investigate the use, effects and costs of PCT consultation services for hospitalized patients with incurable cancer in the Netherlands. METHODS/DESIGN: The study consists of 3 parts: 1. A questionnaire, interviews and a focus group discussion to investigate the characteristics of PCT consultation in 12 hospitals. PCTs will register their activities to calculate the costs of PCT consultation. 2. Cancer patients for whom the attending physician would not be surprised that they would die within 12 month will be included in a medical file search in three hospitals. Medical records will be investigated to compare care, treatment and hospital costs between patients with and patients without PCT consultation. 3. In the other nine hospitals, we will perform a longitudinal study, and compare quality of life between 100 patients for whom a PCT was consulted with 200 patients without PCT consultation. Propensity score matching will be used to adjust for differences between both patient groups. Patients will be followed for three months after inclusion. Quality of life will be assessed with the Palliative Outcome Scale, the EuroQol-5d and the EORTC-QLQ-C15 PAL. Satisfaction with care in the hospital is measured with the IN-PATSAT32. The cost impact of PCT consultation will also be explored. DISCUSSION: This is the first multicenter study on PCT consultation in the Netherlands. The study will give valuable insight in the process, effects and costs of PCT consultation in hospitals. It is anticipated that PCT consultation has a positive effect on patients' quality of life and satisfaction with care and will lead to less hospital care costs.

Towards better clinical prediction models: seven steps for development and an ABCD for validation.

Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an event in the future course of disease (prognosis) for individual patients. Although publications that present and evaluate such models are becoming more frequent, the methodology is often suboptimal. We propose that seven steps should be considered in developing prediction models: (i) consideration of the research question and initial data inspection; (ii) coding of predictors; (iii) model specification; (iv) model estimation; (v) evaluation of model performance; (vi) internal validation; and (vii) model presentation. The validity of a prediction model is ideally assessed in fully independent data, where we propose four key measures to evaluate model performance: calibration-in-the-large, or the model intercept (A); calibration slope (B); discrimination, with a concordance statistic (C); and clinical usefulness, with decision-curve analysis (D). As an application, we develop and validate prediction models for 30-day mortality in patients with an acute myocardial infarction. This illustrates the usefulness of the proposed framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research.

C-Reactive Protein Bedside Testing in Febrile Children Lowers Length of Stay at the Emergency Department.

BACKGROUND: C-Reactive protein (CRP) is an important diagnostic marker for serious bacterial infections in febrile children. C-Reactive protein bedside testing could potentially accelerate the diagnostic evaluation and shorten length of stay (LOS). OBJECTIVE: The aim of the study was to study the effect of introducing CRP bedside testing on the LOS of febrile children at the emergency department (ED). DESIGN AND INTERVENTION: A prospective observational study with a preimplementation cohort (2008) with traditional CRP testing and a postimplementation cohort (2009-2011) in which CRP bedside testing was introduced. PATIENTS AND SETTING: All previously healthy children with fever, aged 1 month to 16 years, attending the ED of a university hospital were included; non-ill-appearing children with an upper airway infection were not eligible for CRP bedside testing. ANALYSIS AND MAIN OUTCOME MEASURE: Multivariable linear regression and propensity score analyses were used to determine the effect of CRP bedside testing on the logarithmic transformation length of stay [(log)LOS]. RESULTS: The preimplementation cohort included 609 children of whom 286 (47%) had traditional CRP. The postimplementation cohort included the following 1330 children: 728 (55%) children had bedside CRP and 156 (12%) children had traditional CRP. Bedside CRP significantly lowered the median LOS of children in whom an additional diagnostic CRP test was performed, from 178 minutes (interquartile range, 135-232 minutes) to 148 minutes (interquartile range, 108-200 minutes) (30 minutes, 19% of total LOS). A significant reduction of 15% of the (log)LOS remained after adjusting for other determinants of (log)LOS; propensity score analysis showed a 16% reduction. CONCLUSIONS: C-Reactive protein bedside testing substantially lowered the LOS of children with fever at the ED in whom an additional diagnostic CRP test was performed.

Predicting major outcomes in type 1 diabetes: a model development and validation study.

AIMS/HYPOTHESIS: Type 1 diabetes is associated with a higher risk of major vascular complications and death. A reliable method that predicted these outcomes early in the disease process would help in risk classification. We therefore developed such a prognostic model and quantified its performance in independent cohorts. METHODS: Data were analysed from 1,973 participants with type 1 diabetes followed for 7 years in the EURODIAB Prospective Complications Study. Strong prognostic factors for major outcomes were combined in a Weibull regression model. The performance of the model was tested in three different prospective cohorts: the Pittsburgh Epidemiology of Diabetes Complications study (EDC, n = 554), the Finnish Diabetic Nephropathy study (FinnDiane, n = 2,999) and the Coronary Artery Calcification in Type 1 Diabetes study (CACTI, n = 580). Major outcomes included major CHD, stroke, end-stage renal failure, amputations, blindness and all-cause death. RESULTS: A total of 95 EURODIAB patients with type 1 diabetes developed major outcomes during follow-up. Prognostic factors were age, HbA1c, WHR, albumin/creatinine ratio and HDL-cholesterol level. The discriminative ability of the model was adequate, with a concordance statistic (C-statistic) of 0.74. Discrimination was similar or even better in the independent cohorts, the C-statistics being: EDC, 0.79; FinnDiane, 0.82; and CACTI, 0.73. CONCLUSIONS/INTERPRETATION: Our prognostic model, which uses easily accessible clinical features can discriminate between type 1 diabetes patients who have a good or a poor prognosis. Such a prognostic model may be helpful in clinical practice and for risk stratification in clinical trials.

Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist.

Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors' Summary.

Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II.

BACKGROUND: 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. METHODS: SYNTAX score II was developed by applying a Cox proportional hazards model to results of the randomised all comers SYNTAX trial (n=1800). Baseline features with strong associations to 4-year mortality in either the CABG or the PCI settings (interactions), or in both (predictive accuracy), were added to the anatomical SYNTAX score. Comparisons of 4-year mortality predictions between CABG and PCI were made for each patient. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. External validation was done in the multinational all comers DELTA registry (n=2891), a heterogeneous population that included patients with three-vessel disease (26%) or complex coronary artery disease (anatomical SYNTAX score ≥33, 30%) who underwent CABG or PCI. The SYNTAX trial is registered with ClinicalTrials.gov, number NCT00114972. FINDINGS: SYNTAX score II contained eight predictors: anatomical SYNTAX score, age, creatinine clearance, left ventricular ejection fraction (LVEF), presence of unprotected left main coronary artery (ULMCA) disease, peripheral vascular disease, female sex, and chronic obstructive pulmonary disease (COPD). SYNTAX score II significantly predicted a difference in 4-year mortality between patients undergoing CABG and those undergoing PCI (p(interaction) 0·0037). To achieve similar 4-year mortality after CABG or PCI, younger patients, women, and patients with reduced LVEF required lower anatomical SYNTAX scores, whereas older patients, patients with ULMCA disease, and those with COPD, required higher anatomical SYNTAX scores. Presence of diabetes was not important for decision making between CABG and PCI (p(interaction) 0·67). SYNTAX score II discriminated well in all patients who underwent CABG or PCI, with concordance indices for internal (SYNTAX trial) validation of 0·725 and for external (DELTA registry) validation of 0·716, which were substantially higher than for the anatomical SYNTAX score alone (concordance indices of 0·567 and 0·612, respectively). A nomogram was constructed that allowed for an accurate individualised prediction of 4-year mortality in patients proposing to undergo CABG or PCI. INTERPRETATION: Long-term (4-year) mortality in patients with complex coronary artery disease can be well predicted by a combination of anatomical and clinical factors in SYNTAX score II. SYNTAX score II can better guide decision making between CABG and PCI than the original anatomical SYNTAX score. FUNDING: Boston Scientific Corporation.

Prediction of hemoglobin levels in whole blood donors: how to model donation history.

BACKGROUND: Recently, prediction models for hemoglobin (Hb) deferral risk have been developed. These models consider the previous Hb level plus change in Hb. Here, we investigated if the performance of models could be improved by considering more information on Hb level history. STUDY DESIGN AND METHODS: Data of 166,497 Dutch whole blood donors with sequential Hb measurements during 2 years (760,444 in total) were used to develop and internally validate three different regression models: two simple linear models with Hb level history included as 1) Hb at the previous visit plus change in Hb or 2) mean of all previous Hb levels and one mixed-effect model including measurements of all previous Hb levels. RESULTS: Thirteen percent of men and 21% of women were deferred because of a low Hb level at least once in 2 years. The simple linear models and the mixed-effect model performed similar, if an estimate of the random intercept of the mixed-effect model was used for individual donors to calculate the predicted Hb level. In men, the concordance (c)-statistic ranged from 0.87 to 0.89 and the R(2) ranged from 0.42 to 0.45. In women, the c-statistic ranged from 0.81 to 0.84. Values of R(2) in women were higher for the simple linear models than for the mixed-effect model, 0.37 and 0.40 versus 0.30, respectively. CONCLUSION: Previous Hb levels could be summarized with one predictor as the mean value of all previous Hb levels. This predictor can be used in an easy-to-use simple linear regression model.