[Reliable diagnostics: clinical gestalt or prediction rule?]

Diagnostic prediction models can support the diagnostic process, both for experienced physicians and for physicians with little experience. More attention should be paid to the incorporation of diagnostic prediction models in the electronic patient record, so that a more accurate probability estimate can be made without simplification to rounded sumscores. A uniform cut-off of sum scores with associated categorization is also undesirable, because it does not take the context of the individual patient sufficiently into account. In the case of a very strong gut feeling, the outcome of a diagnostic prediction model rule alone cannot be sufficient for further policy. Diagnostic prediction models 'only' generate individual objectively estimated probabilities; the clinical decision-making based on these probabilities always needs to be made by the doctor in shared decision making with the patient. Conflict of interest and financial support: none declared.

Management of superficial venous thrombosis based on individual risk profiles: protocol for the development and validation of three prognostic prediction models in large primary care cohorts

BACKGROUND

Superficial venous thrombosis (SVT) is considered a benign thrombotic condition in most patients. However, it also can cause serious complications, such as clot progression to deep venous thrombosis (DVT) and pulmonary embolism (PE). Although most SVT patients are encountered in primary healthcare, studies on SVT nearly all were focused on patients seen in the hospital setting. This paper describes the protocol of the development and external validation of three prognostic prediction models for relevant clinical outcomes in SVT patients seen in primary care: (i) prolonged (painful) symptoms within 14 days since SVT diagnosis, (ii) for clot progression to DVT or PE within 45 days and (iii) for clot recurrence within 12 months.

METHODS

Data will be used from four primary care routine healthcare registries from both the Netherlands and the UK; one UK registry will be used for the development of the prediction models and the remaining three will be used as external validation cohorts. The study population will consist of patients ≥18 years with a diagnosis of SVT. Selection of SVT cases will be based on a combination of ICPC/READ/Snowmed coding and free text clinical symptoms. Predictors considered are sex, age, body mass index, clinical SVT characteristics, and co-morbidities including (history of any) cardiovascular disease, diabetes, autoimmune disease, malignancy, thrombophilia, pregnancy or puerperium and presence of varicose veins. The prediction models will be developed using multivariable logistic regression analysis techniques for models i and ii, and for model iii, a Cox proportional hazards model will be used. They will be validated by internal-external cross-validation as well as external validation.

DISCUSSION

There are currently no prediction models available for predicting the risk of serious complications for SVT patients presenting in primary care settings. We aim to develop and validate new prediction models that should help identify patients at highest risk for complications and to support clinical decision making for this understudied thrombo-embolic disorder. Challenges that we anticipate to encounter are mostly related to performing research in large, routine healthcare databases, such as patient selection, endpoint classification, data harmonisation, missing data and avoiding (predictor) measurement heterogeneity.

Interpretation of CVD risk predictions in clinical practice: Mission impossible?

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.

Ruling out pulmonary embolism across different subgroups of patients and healthcare settings: protocol for a systematic review and individual patient data meta-analysis (IPDMA)

BACKGROUND

Diagnosing pulmonary embolism in suspected patients is notoriously difficult as signs and symptoms are non-specific. Different diagnostic strategies have been developed, usually combining clinical probability assessment with D-dimer testing. However, their predictive performance differs across different healthcare settings, patient subgroups, and clinical presentation, which are currently not accounted for in the available diagnostic approaches.

METHODS

This is a protocol for a large diagnostic individual patient data meta-analysis (IPDMA) of currently available diagnostic studies in the field of pulmonary embolism. We searched MEDLINE (search date January 1, 1995, till August 25, 2016) to retrieve all primary diagnostic studies that had evaluated diagnostic strategies for pulmonary embolism. Two authors independently screened titles, abstracts, and subsequently full-text articles for eligibility from 3145 individual studies. A total of 40 studies were deemed eligible for inclusion into our IPDMA set, and principal investigators from these studies were invited to participate in a meeting at the 2017 conference from the International Society on Thrombosis and Haemostasis. All authors agreed on data sharing and participation into this project. The process of data collection of available datasets as well as potential identification of additional new datasets based upon personal contacts and an updated search will be finalized early 2018. The aim is to evaluate diagnostic strategies across three research domains: (i) the optimal diagnostic approach for different healthcare settings, (ii) influence of comorbidity on the predictive performance of each diagnostic strategy, and (iii) optimize and tailor the efficiency and safety of ruling out PE across a broad spectrum of patients with a new, patient-tailored clinical decision model that combines clinical items with quantitative D-dimer testing.

DISCUSSION

This pre-planned individual patient data meta-analysis aims to contribute in resolving remaining diagnostic challenges of time-efficient diagnosis of pulmonary embolism by tailoring available diagnostic strategies for different healthcare settings and comorbidity.

SYSTEMATIC REVIEW REGISTRATION

Prospero trial registration: ID 89366.

Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis

Essentials The widely recommended CHA2DS2-VASc shows conflicting results in contemporary validation studies. We performed a systematic review and meta-analysis of 19 studies validating CHA2DS2-VASc. There was high heterogeneity in stroke risks for different CHA2DS2-VASc scores. This was not explained by differences between setting of care, or by performing meta-regression.

SUMMARY

Background The CHA2DS2-VASc decision rule is widely recommended for estimating stroke risk in patients with atrial fibrillation (AF), although validation studies show ambiguous and conflicting results. Objectives To: (i) review existing studies validating CHA2DS2-VASc in AF patients who are not (yet) anticoagulated; (ii) meta-analyze estimates of stroke risk per score; and (iii) explore sources of heterogeneity across the validation studies. Methods We performed a systematic literature review and random effects meta-analysis of studies externally validating CHA2DS2-VASc in AF patients not receiving anticoagulants. To explore between-study heterogeneity in stroke risk, we stratified studies to the clinical setting in which patient enrollment started, and performed meta-regression. Results In total, 19 studies were evaluated, with over two million person-years of follow-up. In studies recruiting AF patients in hospitals, stroke risks for scores of 0, 1 and 2 were 0.4% (approximate 95% prediction interval [PI] 0.2-3.2%), 1.2% (95% PI 0.1-3.8%), and 2.2% (95% PI 0.03-7.8%), respectively. These were consistently higher than those in studies recruiting patients from the open general population, with risks of 0.2% (95% PI 0.0-0.9%), 0.7% (95% PI 0.3-1.2%) and 1.5% (95% PI 0.4-3.3%) for scores of 0, 1, and 2, respectively. Heterogeneity, as reflected by the wide PIs, could not be fully explained by meta-regression. Conclusions Studies validating CHA2DS2-VASc show high heterogeneity in predicted stroke risks for different scores.

The effects of misclassification in routine healthcare databases on the accuracy of prognostic prediction models: a case study of the CHA2DS2-VASc score in atrial fibrillation

BACKGROUND

Research on prognostic prediction models frequently uses data from routine healthcare. However, potential misclassification of predictors when using such data may strongly affect the studied associations. There is no doubt that such misclassification could lead to the derivation of suboptimal prediction models. The extent to which misclassification affects the validation of existing prediction models is currently unclear.We aimed to quantify the amount of misclassification in routine care data and its effect on the validation of the existing risk prediction model. As an illustrative example, we validated the CHA2DS2-VASc prediction rule for predicting mortality in patients with atrial fibrillation (AF).

METHODS

In a prospective cohort in general practice in the Netherlands, we used computerized retrieved data from the electronic medical records of patients known with AF as index predictors. Additionally, manually collected data after scrutinizing all complete medical files were used as reference predictors. Comparing the index with the reference predictors, we assessed misclassification in individual predictors by calculating Cohen's kappas and other diagnostic test accuracy measures. Predictive performance was quantified by the c-statistic and by determining calibration of multivariable models.

RESULTS

In total, 2363 AF patients were included. After a median follow-up of 2.7 (IQR 2.3-3.0) years, 368 patients died (incidence rate 6.2 deaths per 100 person-years). Misclassification in individual predictors ranged from substantial (Cohen's kappa 0.56 for prior history of heart failure) to minor (kappa 0.90 for a history of type 2 diabetes). The overall model performance was not affected when using either index or reference predictors, with a c-statistic of 0.684 and 0.681, respectively, and similar calibration.

CONCLUSION

In a case study validating the CHA2DS2-VASc prediction model, we found substantial predictor misclassification in routine healthcare data with only limited effect on overall model performance. Our study should be repeated for other often applied prediction models to further evaluate the usefulness of routinely available healthcare data for validating prognostic models in the presence of predictor misclassification.

[Systematic detection of physical child abuse at emergency rooms]

OBJECTIVE

The aim of our diagnostic accuracy study Child Abuse Inventory at Emergency Rooms (CHAIN-ER) was to establish whether a widely used checklist accurately detects or excludes physical abuse among children presenting to ERs with physical injury.

DESIGN

A large multicentre study with a 6-month follow-up in 4 ERs in The Netherlands.

METHOD

Participants were 4290 children aged 0-7 years, attending the ER because of physical injury. All children were systematically tested with an easy-to-use child abuse checklist (index test). A national expert panel (reference standard) retrospectively assessed all children with positive screens and a 15% random sample of the children with negative screens for physical abuse, using additional information, namely, an injury history taken by a paediatrician, information provided by the general practitioner, youth doctor and social services by structured questionnaires, and 6-month follow-up information. Our main outcome measure was physical child abuse; secondary outcome measure was injury due to neglect and need for help.

RESULTS

4253/4290 (99%) parents agreed to follow-up. At a prevalence of 0.07% (3/4253) for inflicted injury by expert panel decision, the positive predictive value of the checklist was 0.03 (95% CI 0.006 to 0.085), and the negative predictive value 1.0 (0.994 to 1.0). There was 100% (93 to 100) agreement about inflicted injury in children, with positive screens between the expert panel and child abuse experts.

CONCLUSION

Rare cases of inflicted injury among preschool children presenting at ERs for injury are very likely captured by easy-to-use checklists, but at very high false-positive rates. Subsequent assessment by child abuse experts can be safely restricted to children with positive screens at very low risk of missing cases of inflicted injury. Because of the high false positive rate, we do advise careful prior consideration of cost-effectiveness and clinical and societal implications before de novo implementation.

Qualitative point-of-care D-dimer testing compared with quantitative D-dimer testing in excluding pulmonary embolism in primary care

BACKGROUND

General practitioners can safely exclude pulmonary embolism (PE) by 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. General practitioners 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 thromboembolism in low-risk patients.

RESULTS

Prevalence 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 one (0.4%) and four 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 were 94.5% and 51.0% and, combined with a quantitative test, 98.6% and 47.2%, respectively.

CONCLUSIONS

Combined with the Wells PE rule, both tests are safe to use 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.

A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data

OBJECTIVES

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.

STUDY DESIGN AND SETTING

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.

RESULTS

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.

CONCLUSION

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.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement

BACKGROUND

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed.

METHODS

An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors.

RESULTS

The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study.

CONCLUSION

The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod-statement.org.

Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals

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.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Improved prediction of inhibitor development in previously untreated patients with severe haemophilia A

Treatment of previously untreated patients (PUPs) with severe haemophilia A is complicated by the formation of inhibitors. Prediction of PUPs with high risk is important to allow altering treatment with the intention to reduce the occurrence of inhibitors. An unselected multicentre cohort of 825 PUPs with severe haemophilia A (FVIII<0.01 IU mL(-1) ) was used. Patients were followed until 50 exposure days (EDs) or inhibitor development. All predictors of the existing prediction model including three new potential predictors were studied using multivariable logistic regression. Model performance was quantified [area under the curve (AUC), calibration plot] and internal validation (bootstrapping) was performed. A nomogram for clinical application was developed. Of the 825 patients, 225 (28%) developed inhibitors. The predictors family history of inhibitors, F8 gene mutation and an interaction variable of dose and number of EDs of intensive treatment were independently associated with inhibitor development. Age and reason for first treatment were not associated with inhibitor development. The AUC was 0.69 (95% CI 0.65-0.72) and calibration was good. An improved prediction model for inhibitor development and a nomogram for clinical use were developed in a cohort of 825 PUPs with severe haemophilia A. Clinical applicability was improved by combining dose and duration of intensive treatment, allowing the assessment of the effects of treatment decisions on inhibitor risk and potentially modify treatment.

Breast-feeding and health consequences in early childhood: is there an impact of time-dependent confounding?

BACKGROUND

Estimated effects of breast-feeding on childhood health vary between studies, possibly due to confounding by baseline maternal and child characteristics. Possible time-dependent confounding has received little consideration. Our aim was to evaluate the impact of such confounding.

METHODS

We estimated the relationship between cumulative exclusive breast-feeding up to 6 months and wheezing, rash and body mass index (BMI) at 12 months [in the Whistler cohort (n = 494) and PROBIT (n = 11,463)], and wheezing, rash, asthma, hay fever, eczema, allergy and BMI at age 6.5 years (PROBIT). We adjusted for time-dependent confounding by weight, length, rash, respiratory illness and day care attendance using marginal structural models (MSMs).

RESULTS

Weight and day care attendance appeared potential time-dependent confounders, since these predicted breast-feeding status and were influenced by previous breast-feeding. However, adjustment for time-dependent confounders did not markedly change the estimated associations. For example, in PROBIT the adjusted increase in BMI at 12 months per 1-month increase in exclusive breast-feeding was 0.04 (95% CI -0.09 to 0.01) using logistic regression and -0.06 (95% CI -0.11 to -0.01) using MSM. In Whistler, these estimates were each -0.05 (95% CI -0.10 to 0.00).

CONCLUSIONS

In two cohort studies, there was little evidence of time-dependent confounding by weight, length, rash, respiratory illness or day care attendance of the effects of breast-feeding on early childhood health.

Impact of adding therapeutic recommendations to risk assessments from a prediction model for postoperative nausea and vomiting

BACKGROUND

In a large cluster-randomized trial on the impact of a prediction model, presenting the calculated risk of postoperative nausea and vomiting (PONV) on-screen (assistive approach) increased the administration of risk-dependent PONV prophylaxis by anaesthetists. This change in therapeutic decision-making did not improve the patient outcome; that is, the incidence of PONV. The present study aimed to quantify the effects of adding a specific therapeutic recommendation to the predicted risk (directive approach) on PONV prophylaxis decision-making and the incidence of PONV.

METHODS

A prospective before-after study was conducted in 1483 elective surgical inpatients. The before-period included care-as-usual and the after-period included the directive risk-based (intervention) strategy. Risk-dependent effects on the administered number of prophylactic antiemetics and incidence of PONV were analysed by mixed-effects regression analysis.

RESULTS

During the intervention period anaesthetists administered 0.5 [95% confidence intervals (CIs): 0.4-0.6] more antiemetics for patients identified as being at greater risk of PONV. This directive approach led to a reduction in PONV [odds ratio (OR): 0.60, 95% CI: 0.43-0.83], with an even greater reduction in PONV in high-risk patients (OR: 0.45, 95% CI: 0.28-0.72).

CONCLUSIONS

Anaesthetists administered more prophylactic antiemetics when a directive approach was used for risk-tailored intervention compared with care-as-usual. In contrast to the previously studied assistive approach, the increase in PONV prophylaxis now resulted in a lower PONV incidence, particularly in high-risk patients. When one aims for a truly 'PONV-free hospital', a more liberal use of prophylactic antiemetics must be accepted and lower-risk thresholds should be set for the actionable recommendations.

The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus

OBJECTIVE

Various cardiovascular prediction models have been developed for patients with type 2 diabetes. Their predictive performance in new patients is mostly not investigated. This study aims to quantify the predictive performance of all cardiovascular prediction models developed specifically for diabetes patients.

DESIGN AND METHODS

Follow-up data of 453, 1174 and 584 type 2 diabetes patients without pre-existing cardiovascular disease (CVD) in the EPIC-NL, EPIC-Potsdam and Secondary Manifestations of ARTerial disease cohorts, respectively, were used to validate 10 prediction models to estimate risk of CVD or coronary heart disease (CHD). Discrimination was assessed by the c-statistic for time-to-event data. Calibration was assessed by calibration plots, the Hosmer-Lemeshow goodness-of-fit statistic and expected to observed ratios.

RESULTS

There was a large variation in performance of CVD and CHD scores between different cohorts. Discrimination was moderate for all 10 prediction models, with c-statistics ranging from 0.54 (95% CI 0.46 to 0.63) to 0.76 (95% CI 0.67 to 0.84). Calibration of the original models was poor. After simple recalibration to the disease incidence of the target populations, predicted and observed risks were close. Expected to observed ratios of the recalibrated models ranged from 1.06 (95% CI 0.81 to 1.40) to 1.55 (95% CI 0.95 to 2.54), mainly driven by an overestimation of risk in high-risk patients.

CONCLUSIONS

All 10 evaluated models had a comparable and moderate discriminative ability. The recalibrated, but not the original, prediction models provided accurate risk estimates. These models can assist clinicians in identifying type 2 diabetes patients who are at low or high risk of developing CVD.

Effectiveness of progestogens to improve perinatal outcome in twin pregnancies: an individual participant data meta-analysis

BACKGROUND

In twin pregnancies, the rates of adverse perinatal outcome and subsequent long-term morbidity are substantial, and mainly result from preterm birth (PTB).

OBJECTIVES

To assess the effectiveness of progestogen treatment in the prevention of neonatal morbidity or PTB in twin pregnancies using individual participant data meta-analysis (IPDMA).

SEARCH STRATEGY

We searched international scientific databases, trial registration websites, and references of identified articles.

SELECTION CRITERIA

Randomised clinical trials (RCTs) of 17-hydroxyprogesterone caproate (17Pc) or vaginally administered natural progesterone, compared with placebo or no treatment.

DATA COLLECTION AND ANALYSIS

Investigators of identified RCTs were asked to share their IPD. The primary outcome was a composite of perinatal mortality and severe neonatal morbidity. Prespecified subgroup analyses were performed for chorionicity, cervical length, and prior spontaneous PTB.

MAIN RESULTS

Thirteen trials included 3768 women and their 7536 babies. Neither 17Pc nor vaginal progesterone reduced the incidence of adverse perinatal outcome (17Pc relative risk, RR 1.1; 95% confidence interval, 95% CI 0.97-1.4, vaginal progesterone RR 0.97; 95% CI 0.77-1.2). In a subgroup of women with a cervical length of ≤25 mm, vaginal progesterone reduced adverse perinatal outcome when cervical length was measured at randomisation (15/56 versus 22/60; RR 0.57; 95% CI 0.47-0.70) or before 24 weeks of gestation (14/52 versus 21/56; RR 0.56; 95% CI 0.42-0.75).

AUTHOR'S CONCLUSIONS

In unselected women with an uncomplicated twin gestation, treatment with progestogens (intramuscular 17Pc or vaginal natural progesterone) does not improve perinatal outcome. Vaginal progesterone may be effective in the reduction of adverse perinatal outcome in women with a cervical length of ≤25 mm; however, further research is warranted to confirm this finding.

Exclusion of deep vein thrombosis using the Wells rule in clinically important subgroups: individual patient data meta-analysis

OBJECTIVE

To assess the accuracy of the Wells rule for excluding deep vein thrombosis and whether this accuracy applies to different subgroups of patients.

DESIGN

Meta-analysis of individual patient data.

DATA SOURCES

Authors of 13 studies (n = 10,002) provided their datasets, and these individual patient data were merged into one dataset.

ELIGIBILITY CRITERIA

Studies were eligible if they enrolled consecutive outpatients with suspected deep vein thrombosis, scored all variables of the Wells rule, and performed an appropriate reference standard.

MAIN OUTCOME MEASURES

Multilevel logistic regression models, including an interaction term for each subgroup, were used to estimate differences in predicted probabilities of deep vein thrombosis by the Wells rule. In addition, D-dimer testing was added to assess differences in the ability to exclude deep vein thrombosis using an unlikely score on the Wells rule combined with a negative D-dimer test result.

RESULTS

Overall, increasing scores on the Wells rule were associated with an increasing probability of having deep vein thrombosis. Estimated probabilities were almost twofold higher in patients with cancer, in patients with suspected recurrent events, and (to a lesser extent) in males. An unlikely score on the Wells rule (≤ 1) combined with a negative D-dimer test result was associated with an extremely low probability of deep vein thrombosis (1.2%, 95% confidence interval 0.7% to 1.8%). This combination occurred in 29% (95% confidence interval 20% to 40%) of patients. These findings were consistent in subgroups defined by type of D-dimer assay (quantitative or qualitative), sex, and care setting (primary or hospital care). For patients with cancer, the combination of an unlikely score on the Wells rule and a negative D-dimer test result occurred in only 9% of patients and was associated with a 2.2% probability of deep vein thrombosis being present. In patients with suspected recurrent events, only the modified Wells rule (adding one point for the previous event) is safe.

CONCLUSION

Combined with a negative D-dimer test result (both quantitative and qualitative), deep vein thrombosis can be excluded in patients with an unlikely score on the Wells rule. This finding is true for both sexes, as well as for patients presenting in primary and hospital care. In patients with cancer, the combination is neither safe nor efficient. For patients with suspected recurrent disease, one extra point should be added to the rule to enable a safe exclusion.

The Dutch hospital standardised mortality ratio (HSMR) method and cardiac surgery: benchmarking in a national cohort using hospital administration data versus a clinical database

Objective

To compare the accuracy of data from hospital administration databases and a national clinical cardiac surgery database and to compare the performance of the Dutch hospital standardised mortality ratio (HSMR) method and the logistic European System for Cardiac Operative Risk Evaluation, for the purpose of benchmarking of mortality across hospitals.

Methods

Information on all patients undergoing cardiac surgery between 1 January 2007 and 31 December 2010 in 10 centres was extracted from The Netherlands Association for Cardio-Thoracic Surgery database and the Hospital Discharge Registry. The number of cardiac surgery interventions was compared between both databases. The European System for Cardiac Operative Risk Evaluation and hospital standardised mortality ratio models were updated in the study population and compared using the C-statistic, calibration plots and the Brier-score.

Results

The number of cardiac surgery interventions performed could not be assessed using the administrative database as the intervention code was incorrect in 1.4–26.3%, depending on the type of intervention. In 7.3% no intervention code was registered. The updated administrative model was inferior to the updated clinical model with respect to discrimination (c-statistic of 0.77 vs 0.85, p<0.001) and calibration (Brier Score of 2.8% vs 2.6%, p<0.001, maximum score 3.0%). Two average performing hospitals according to the clinical model became outliers when benchmarking was performed using the administrative model.

Conclusions

In cardiac surgery, administrative data are less suitable than clinical data for the purpose of benchmarking. The use of either administrative or clinical risk-adjustment models can affect the outlier status of hospitals. Risk-adjustment models including procedure-specific clinical risk factors are recommended.

Including post-discharge mortality in calculation of hospital standardised mortality ratios: retrospective analysis of hospital episode statistics

OBJECTIVES

To assess the consequences of applying different mortality timeframes on standardised mortality ratios of individual hospitals and, secondarily, to evaluate the association between in-hospital standardised mortality ratios and early post-discharge mortality rate, length of hospital stay, and transfer rate.

DESIGN

Retrospective analysis of routinely collected hospital data to compare observed deaths in 50 diagnostic categories with deaths predicted by a case mix adjustment method.

SETTING

60 Dutch hospitals.

PARTICIPANTS

1 228 815 patients discharged in the period 2008 to 2010.

MAIN OUTCOME MEASURES

In-hospital standardised mortality ratio, 30 days post-admission standardised mortality ratio, and 30 days post-discharge standardised mortality ratio.

RESULTS

Compared with the in-hospital standardised mortality ratio, 33% of the hospitals were categorised differently with the 30 days post-admission standardised mortality ratio and 22% were categorised differently with the 30 days post-discharge standardised mortality ratio. A positive association was found between in-hospital standardised mortality ratio and length of hospital stay (Pearson correlation coefficient 0.33; P=0.01), and an inverse association was found between in-hospital standardised mortality ratio and early post-discharge mortality (Pearson correlation coefficient -0.37; P=0.004).

CONCLUSIONS

Applying different mortality timeframes resulted in differences in standardised mortality ratios and differences in judgment regarding the performance of individual hospitals. Furthermore, associations between in-hospital standardised mortality rates, length of stay, and early post-discharge mortality rates were found. Combining these findings suggests that standardised mortality ratios based on in-hospital mortality are subject to so-called "discharge bias." Hence, early post-discharge mortality should be included in the calculation of standardised mortality ratios.

Diagnostic and prognostic prediction models

Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo-embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re-) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case-mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).

Internal validation of risk models in clustered data: a comparison of bootstrap schemes

Internal validity of a risk model can be studied efficiently with bootstrapping to assess possible optimism in model performance. Assumptions of the regular bootstrap are violated when the development data are clustered. We compared alternative resampling schemes in clustered data for the estimation of optimism in model performance. A simulation study was conducted to compare regular resampling on only the patient level with resampling on only the cluster level and with resampling sequentially on both the cluster and patient levels (2-step approach). Optimism for the concordance index and calibration slope was estimated. Resampling of only patients or only clusters showed accurate estimates of optimism in model performance. The 2-step approach overestimated the optimism in model performance. If the number of centers or intraclass correlation coefficient was high, resampling of clusters showed more accurate estimates than resampling of patients. The 3 bootstrap schemes also were applied to empirical data that were clustered. The results presented in this paper support the use of resampling on only the clusters for estimation of optimism in model performance when data are clustered.

Patients' characteristics associated with readmission to a surgical intensive care unit

BACKGROUND

Readmission within 48 hours is a leading performance indicator of the quality of care in an intensive care unit.

OBJECTIVE

To investigate variables that might be associated with readmission to a surgical intensive care unit.

METHODS

Demographic characteristics, severity-of-illness scores, and survival rates were collected for all patients admitted to a surgical intensive care unit between 1995 and 2000. Long-term survival and quality of life were determined for patients who were readmitted within 30 days after discharge from the unit. Quality of life was measured with the EuroQol-6D questionnaire. Multivariate logistic analysis was used to calculate the independent association of expected covariates.

RESULTS

Mean follow-up time was 8 years. Of the 1682 patients alive at discharge, 141 (8%) were readmitted. The main causes of readmission were respiratory decompensation (48%) and cardiac conditions (16%). Compared with the total sample, patients readmitted were older, mostly had vascular (39%) or gastrointestinal (26%) disease, and had significantly higher initial severity of illness (P = .003, .007) and significantly more comorbid conditions (P = .005). For all surgical classifications except general surgery, readmission was independently associated with type of admission and need for mechanical ventilation. Long-term mortality was higher among patients who were readmitted than among the total sample. Nevertheless, quality-of-life scores were the same for patients who were readmitted and patients who were not.

CONCLUSION

The adverse effect of readmission to the intensive care unit on survival appears to be long-lasting, and predictors of readmission are scarce.

Prognosis and prognostic factors of patients with mesothelioma: a population-based study

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.

A clinical prediction model to assess the risk of operative delivery

OBJECTIVE

To predict instrumental vaginal delivery or caesarean section for suspected fetal distress or failure to progress.

DESIGN

Secondary analysis of a randomised trial.

SETTING

Three academic and six non-academic teaching hospitals in the Netherlands.

POPULATION

5667 labouring women with a singleton term pregnancy in cephalic presentation.

METHODS

We developed multinomial prediction models to assess the risk of operative delivery using both antepartum (model 1) and antepartum plus intrapartum characteristics (model 2). The models were validated by bootstrapping techniques and adjusted for overfitting. Predictive performance was assessed by calibration and discrimination (area under the receiver operating characteristic), and easy-to-use nomograms were developed.

MAIN OUTCOME MEASURES

Incidence of instrumental vaginal delivery or caesarean section for fetal distress or failure to progress with respect to a spontaneous vaginal delivery (reference).

RESULTS

  375 (6.6%) and 212 (3.6%) women had an instrumental vaginal delivery or caesarean section due to fetal distress, and 433 (7.6%) and 571 (10.1%) due to failure to progress, respectively. Predictors were age, parity, previous caesarean section, diabetes, gestational age, gender, estimated birthweight (model 1) and induction of labour, oxytocin augmentation, intrapartum fever, prolonged rupture of membranes, meconium stained amniotic fluid, epidural anaesthesia, and use of ST-analysis (model 2). Both models showed excellent calibration and the receiver operating characteristics areas were 0.70-0.78 and 0.73-0.81, respectively.

CONCLUSION

  In Dutch women with a singleton term pregnancy in cephalic presentation, antepartum and intrapartum characteristics can assist in the prediction of the need for an instrumental vaginal delivery or caesarean section for fetal distress or failure to progress.

Common alternative diagnoses in general practice when deep venous thrombosis is excluded

BACKGROUND

In patients initially suspected of deep venous thrombosis (DVT) the diagnosis can be confirmed in approximately 10 to 30% of cases. For the majority of patients this means that eventually an alternative diagnosis is assigned.

OBJECTIVE

To assess the frequency distribution of alternative diagnoses and subsequent management of patients in primary care after initial exclusion of DVT. In addition, assess the value of ultrasound examination for the allocation of alternative diagnoses.

METHODS

Data were recorded by general practitioners alongside a diagnostic study in primary care in the Netherlands (AMUSE). Additional data were retrieved from a three-month follow-up questionnaire. A descriptive analysis was performed using these combined data.

RESULTS

The most prevalent diagnoses were muscle rupture (18.5%), chronic venous insufficiency (CVI) (14.6%), erysipelas/cellulitis (12.6%) and superficial venous thrombosis (SVT) (10.9%). Alternative diagnoses were based mainly on physical examination; ultrasound examination (US) did not improve the diagnostic yield for the allocation of alternative diagnoses. In about 30% of all cases, a wait and see approach was used (27 to 41%). During the three-month follow-up nine patients were diagnosed with venous thromboembolic disease, three of which occurred in patients with the working diagnosis of SVT (p=0.026).

CONCLUSIONS

We found that after exclusion of DVT in general practice a wait and see policy in the primary care setting is uneventful for almost one third of patients, but with the alternative diagnosis of SVT, patients may require closer surveillance since we found a significant association with thrombosis in these patients.

Diagnostic management of chronic obstructive pulmonary disease

Detection of early chronic obstructive pulmonary disease (COPD) in patients presenting with respiratory symptoms is recommended; however, diagnosing COPD is difficult because a single gold standard is not available. The aim of this article is to review and interpret the existing evidence, theories and consensus on the individual parts of the diagnostic work-up for COPD. Relevant articles are discussed under the subheadings: history taking, physical examination, spirometry and additional lung function assessment. Wheezing, cough, phlegm and breathlessness on exertion are suggestive signs for COPD. The diagnostic value of the physical examination is limited, except for auscultated pulmonary wheezing or reduced breath sounds, increasing the probability of COPD. Spirometric airflow obstruction after bronchodilation, defined as a lowered ratio of the forced volume in one second to the forced vital capacity (FEV1/FVC ratio), is a prerequisite, but can only confirm COPD in combination with suggestive symptoms. Different thresholds are being recommended to define low FEV1/FVC, including a fixed threshold, and one varying with gender and age; however, the way physicians interpret these thresholds in their assessment is not well known. Body plethysmography allows a more complete assessment of pulmonary function, providing results on the total lung capacity and the residual volume and is indicated when conventional spirometry results are inconclusive. Chest radiography has no diagnostic value for COPD but is useful to exclude alternative diagnoses such as heart failure or lung cancer. Extensive history taking is of key importance in diagnosing COPD.

Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review

CONTEXT

A recent overview of all CVD models applicable to diabetes patients is not available.

OBJECTIVE

To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes.

DESIGN

Systematic review.

DATA SOURCES

Medline was searched from 1966 to 1 April 2011.

STUDY SELECTION

A study was eligible when it described the development, validation or impact assessment of a model that was constructed to predict the occurrence of cardiovascular disease in people with type 2 diabetes, or when the model was designed for use in the general population but included diabetes as a predictor.

DATA EXTRACTION

A standardized form was sued to extract all data of the CVD models.

RESULTS

45 prediction models were identified, of which 12 were specifically developed for patients with type 2 diabetes. Only 31% of the risk scores has been externally validated in a diabetes population, with an area under the curve ranging from 0.61 to 0.86 and 0.59 to 0.80 for models developed in a diabetes population and in the general population, respectively. Only one risk score has been studied for its effect on patient management and outcomes. 10% of the risk scores are advocated in national diabetes guidelines.

CONCLUSION

Many cardiovascular risk scores are available that can be applied to patients with type 2 diabetes. A minority of these risk scores has been validated and tested for its predictive accuracy, with only a few showing a discriminative value of ≥0.80. The impact of applying these risk scores in clinical practice is almost completely unknown, but their use is recommended in various national guidelines.

Markers for the non-invasive diagnosis of mesothelioma: a systematic review

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.

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes

AIMS/HYPOTHESIS

Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes.

METHODS

The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ(2) statistic.

RESULTS

The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor.

CONCLUSIONS/INTERPRETATION

We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.

Progression to microalbuminuria in type 1 diabetes: development and validation of a prediction rule

AIMS/HYPOTHESIS

Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical prediction rule that estimates the absolute risk of microalbuminuria.

METHODS

Data from the European Diabetes Prospective Complications Study (n = 1115) were used to develop the prediction rule (development set). Multivariable logistic regression analysis was used to assess the association between potential predictors and progression to microalbuminuria within 7 years. The performance of the prediction rule was assessed with calibration and discrimination (concordance statistic [c-statistic]) measures. The rule was validated in three other diabetes studies (Pittsburgh Epidemiology of Diabetes Complications [EDC] study, Finnish Diabetic Nephropathy [FinnDiane] study and Coronary Artery Calcification in Type 1 Diabetes [CACTI] study).

RESULTS

Of patients in the development set, 13% were microalbuminuric after 7 years. Glycosylated haemoglobin, AER, WHR, BMI and ever smoking were found to be the most important predictors. A high-risk group (n = 87 [8%]) was identified with a risk of progression to microalbuminuria of 32%. Predictions showed reasonable discriminative ability, with c-statistic of 0.71. The rule showed good calibration and discrimination in EDC, FinnDiane and CACTI (c-statistic 0.71, 0.79 and 0.79, respectively).

CONCLUSIONS/INTERPRETATION

We developed and validated a clinical prediction rule that uses relatively easily obtainable patient characteristics to predict microalbuminuria in patients with type 1 diabetes. This rule can help clinicians to decide on more frequent check-ups for patients at high risk of microalbuminuria in order to prevent long-term chronic complications.

Cost-effectiveness of ruling out deep venous thrombosis in primary care versus care as usual

BACKGROUND

Referral for ultrasound testing in all patients suspected of DVT is inefficient, because 80-90% have no DVT.

OBJECTIVE

To assess the incremental cost-effectiveness of a diagnostic strategy to select patients at first presentation in primary care based on a point of care D-dimer test combined with a clinical decision rule (AMUSE strategy), compared with hospital-based strategies.

PATIENTS/METHODS

A Markov-type cost-effectiveness model with a societal perspective and a 5-year time horizon was used to compare the AMUSE strategy with hospital-based strategies. Data were derived from the AMUSE study (2005-2007), the literature, and a direct survey of costs (2005-2007).

RESULTS OF BASE-CASE ANALYSIS

Adherence to the AMUSE strategy on average results in savings of euro138 ($185) per patient at the expense of a very small health loss (0.002 QALYs) compared with the best hospital strategy. The iCER is euro55 753($74 848). The cost-effectiveness acceptability curves show that the AMUSE strategy has the highest probability of being cost-effective.

RESULTS OF SENSITIVITY ANALYSIS

Results are sensitive to decreases in sensitivity of the diagnostic strategy, but are not sensitive to increase in age (range 30-80), the costs for health states, and events.

CONCLUSION

A diagnostic management strategy based on a clinical decision rule and a point of care D-dimer assay to exclude DVT in primary care is not only safe, but also cost-effective as compared with hospital-based strategies.

Excluding venous thromboembolism using point of care D-dimer tests in outpatients: a diagnostic meta-analysis

OBJECTIVE

To review the evidence on the diagnostic accuracy of the currently available point of care D-dimer tests for excluding venous thromboembolism.

DESIGN

Systematic review of research on the accuracy of point of care D-dimer tests, using bivariate regression to examine sources of variation and to estimate sensitivity and specificity.

DATA SOURCES

Studies on the diagnostic accuracy of point of care D-dimer tests published between January 1995 and September 2008 and available in either Medline or Embase. Review methods The analysis included studies that compared point of care D-dimer tests with predefined reference criteria for venous thromboembolism, enrolled consecutive outpatients, and allowed for construction of a 2x2 table.

RESULTS

23 studies (total number of patients 13 959, range in mean age 38-65 years, range of venous thromboembolism prevalence 4-51%) were included in the meta-analysis. The studies reported two qualitative point of care D-dimer tests (SimpliRED D-dimer (n=12) and Clearview Simplify D-dimer (n=7)) and two quantitative point of care D-dimer tests (Cardiac D-dimer (n=4) and Triage D-dimer (n=2)). Overall sensitivity ranged from 0.85 (95% confidence interval 0.78 to 0.90) to 0.96 (0.91 to 0.98) and overall specificity from 0.48 (0.33 to 0.62) to 0.74 (0.69 to 0.78). The two quantitative tests Cardiac D-dimer and Triage D-dimer scored most favourably.

CONCLUSIONS

In outpatients suspected of venous thromboembolism, point of care D-dimer tests can contribute important information and guide patient management, notably in low risk patients (that is, those patients with a low score on a clinical decision rule).

Inter- and intra-observer agreement of intrapartum ST analysis of the fetal electrocardiogram in women monitored by STAN

OBJECTIVE

The objective of this study was to quantify inter- and intra-observer agreement on classification of the intrapartum cardiotocogram (CTG) and decision to intervene following STAN guidelines.

DESIGN

A prospective, observational study.

SETTING

Obstetrics Department of a tertiary referral hospital.

POPULATION

STAN recordings of 73 women after 36 weeks of gestation with a high-risk pregnancy, induced or oxytocin-augmented labour, meconium-stained amniotic fluid or epidural analgesia.

METHODS

Six observers classified 73 STAN recordings and decided if and when they would suggest an intervention. Proportions of specific agreement (Ps) and kappa values (Kappa) were calculated.

MAIN OUTCOME MEASURES

Agreement upon classification of the intrapartum CTG and decision to perform an intervention.

RESULTS

Agreement for classification of a normal and a (pre)terminal CTG was good (Ps range 0.50-0.84), but poor for the intermediary and abnormal CTG (Ps range 0.34-0.56). Agreement on the decision to intervene was higher, especially on the decision to perform 'no intervention' (Ps range 0.76-0.94). Overall inter-observer agreement on the decision to intervene was considered moderate in five of six observer combinations according to the kappa (Kappa range 0.42-0.73). Intra-observer agreement for CTG classification and decision to intervene was moderate (Kappa range 0.52-0.67 and 0.61-0.75).

CONCLUSIONS

Inter-observer agreement on classification of the intrapartum CTG is poor, but addition of information regarding fetal electrocardiogram, especially in case of intermediary or abnormal CTG traces, results in a more standardised decision to intervene.

Validation, updating and impact of clinical prediction rules: a review

OBJECTIVE

To provide an overview of the research steps that need to follow the development of diagnostic or prognostic prediction rules. These steps include validity assessment, updating (if necessary), and impact assessment of clinical prediction rules.

STUDY DESIGN AND SETTING

Narrative review covering methodological and empirical prediction studies from primary and secondary care.

RESULTS

In general, three types of validation of previously developed prediction rules can be distinguished: temporal, geographical, and domain validations. In case of poor validation, the validation data can be used to update or adjust the previously developed prediction rule to the new circumstances. These update methods differ in extensiveness, with the easiest method a change in model intercept to the outcome occurrence at hand. Prediction rules -- with or without updating -- showing good performance in (various) validation studies may subsequently be subjected to an impact study, to demonstrate whether they change physicians' decisions, improve clinically relevant process parameters, patient outcome, or reduce costs. Finally, whether a prediction rule is implemented successfully in clinical practice depends on several potential barriers to the use of the rule.

CONCLUSION

The development of a diagnostic or prognostic prediction rule is just a first step. We reviewed important aspects of the subsequent steps in prediction research.

The intracarotid amobarbital or Wada test: unilateral or bilateral?

OBJECTIVE

In the Netherlands, presurgical screening for temporal lobe epilepsy (TLE) includes the intracarotid amobarbital procedure (IAP), consisting of two consecutive injections of amobarbital, ipsilateral and contralateral to the epileptic focus. We studied whether a bilateral IAP has added value to a unilateral, ipsilateral IAP.

METHODS

This population-based study included 183 consecutive patients referred for screening for TLE surgery who underwent bilateral IAP. Using multivariable modeling, we assessed the added value of bilateral IAP on the decision for surgery, resection size, amygdalohippocampectomy, post-operative seizure freedom, memory performance, and IQ change.

RESULTS

Given the results from the unilateral IAP, the bilateral IAP had added prognostic value for postoperative change in verbal memory (P < 0.01) and verbal IQ (P < 0.01), especially if patients had a left-sided focus. In contrast, information provided by the contralateral IAP was not associated with decision-making or surgical strategy.

CONCLUSIONS

A bilateral IAP has added value in predicting post-operative verbal memory and IQ. A bilateral IAP is currently not used to guide surgical strategy, but may be used for this purpose when verbal capacity is of particular concern in patients with a left-sided focus. In other cases, IAP is best performed unilaterally.

General and abdominal adiposity and risk of death in Europe

BACKGROUND

Previous studies have relied predominantly on the body-mass index (BMI, the weight in kilograms divided by the square of the height in meters) to assess the association of adiposity with the risk of death, but few have examined whether the distribution of body fat contributes to the prediction of death.

METHODS

We examined the association of BMI, waist circumference, and waist-to-hip ratio with the risk of death among 359,387 participants from nine countries in the European Prospective Investigation into Cancer and Nutrition (EPIC). We used a Cox regression analysis, with age as the time variable, and stratified the models according to study center and age at recruitment, with further adjustment for educational level, smoking status, alcohol consumption, physical activity, and height.

RESULTS

During a mean follow-up of 9.7 years, 14,723 participants died. The lowest risks of death related to BMI were observed at a BMI of 25.3 for men and 24.3 for women. After adjustment for BMI, waist circumference and waist-to-hip ratio were strongly associated with the risk of death. Relative risks among men and women in the highest quintile of waist circumference were 2.05 (95% confidence interval [CI], 1.80 to 2.33) and 1.78 (95% CI, 1.56 to 2.04), respectively, and in the highest quintile of waist-to-hip ratio, the relative risks were 1.68 (95% CI, 1.53 to 1.84) and 1.51 (95% CI, 1.37 to 1.66), respectively. BMI remained significantly associated with the risk of death in models that included waist circumference or waist-to-hip ratio (P<0.001).

CONCLUSIONS

These data suggest that both general adiposity and abdominal adiposity are associated with the risk of death and support the use of waist circumference or waist-to-hip ratio in addition to BMI in assessing the risk of death.