[Reliable diagnostics: clinical gestalt or prediction rule?] / Klinische blik of beslisregel.

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.

Effectiveness of CHA2DS2-VASc based decision support on stroke prevention in atrial fibrillation: A cluster randomised trial in general practice.

BACKGROUND: Guidelines on atrial fibrillation (AF) recommend the CHA2DS2-VASc rule for anticoagulant decision-making, but underuse exists. We studied the impact of an automated decision support on stroke prevention in patients with AF in a cluster randomised trial in general practice. METHODS: Intervention practices were provided with a CHA2DS2-VASc based anticoagulant treatment recommendation. Reference practices provided care as usual. The primary outcome was incidence of ischaemic stroke, transient ischaemic attack (TIA) and/or thromboembolism (TE). Secondary outcomes were bleeding and the proportion of patients on guideline recommended anticoagulant treatment. RESULTS: In total, 1129 AF patients were included in the 19 intervention practices and 1226 AF patients in the 19 reference practices. The median age was 77 (interquartile range (IQR) 68-75) years, the median CHA2DS2-VASc score was 3.0 (IQR 2.0-5.0). Underuse of anticoagulants in patients with CHA2DS2-VASc score ≥ 2 was 6.6%. After a median follow-up of 2.7 years (IQR 2.3-3.0), the incidence rate per 100 person-years of ischaemic stroke/TIA/TE was 1.96 in the intervention group and 1.42 in the reference group (hazard ratio (HR) 1.3, 95% C.I. 0.8-2.1). No difference was observed in the rate of bleeding (0.79 versus 0.82), or in the underuse (7.2% versus 8.2%) or overuse (8.0% versus 7.9%) of anticoagulation. CONCLUSIONS: In this study in patients with AF in general practice, underuse of anticoagulants was relatively low. Providing practitioners with CHA2DS2-VASc based decision support did not result in a reduction in stroke incidence, affect bleeding risk or anticoagulant over- or underuse.

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.

Long-term outcomes and cost effectiveness of high-dose dexamethasone for cardiac surgery: a randomised trial.

Prophylactic intra-operative administration of dexamethasone may improve short-term clinical outcomes in cardiac surgical patients. The purpose of this study was to evaluate long-term clinical outcomes and cost effectiveness of dexamethasone versus placebo. Patients included in the multicentre, randomised, double-blind, placebo-controlled DExamethasone for Cardiac Surgery (DECS) trial were followed up for 12 months after their cardiac surgical procedure. In the DECS trial, patients received a single intra-operative dose of dexamethasone 1 mg.kg-1 (n = 2239) or placebo (n = 2255). The effects on the incidence of major postoperative events were evaluated. Also, overall costs for the 12-month postoperative period, and cost effectiveness, were compared between groups. Of 4494 randomised patients, 4457 patients (99%) were followed up until 12 months after surgery. There was no difference in the incidence of major postoperative events, the relative risk (95%CI) being 0.86 (0.72-1.03); p = 0.1. Treatment with dexamethasone reduced costs per patient by £921 [€1084] (95%CI £-1672 to -137; p = 0.02), mainly through reduction of postoperative respiratory failure and duration of postoperative hospital stay. The probability of dexamethasone being cost effective compared with placebo was 97% at a threshold value of £17,000 [€20,000] per quality-adjusted life year. We conclude that intra-operative high-dose dexamethasone did not have an effect on major adverse events at 12 months after cardiac surgery, but was associated with a reduction in costs. Routine dexamethasone administration is expected to be cost effective at commonly accepted threshold levels for cost effectiveness.

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]. / Screening op kindermishandeling op de SEH.

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.

Patient selection for cardiac surgery: Time to consider subgroups within risk categories?

BACKGROUND: Medical guidelines increasingly use risk stratification and implicitly assume that individuals classified in the same risk category form a homogeneous group, while individuals with similar, or even identical, predicted risks can still be very different. We evaluate a strategy to identify homogeneous subgroups typically comprising predicted risk categories to allow further tailoring of treatment allocation and illustrate this strategy empirically for cardiac surgery patients with high postoperative mortality risk. METHODS: Using a dataset of cardiac surgery patients (n=6517) we applied cluster analysis to identify homogenous subgroups of patients comprising the high postoperative mortality risk group (EuroSCORE ≥ 15%). Cluster analyses were performed separately within younger (<75 years) and older (≥ 75 years) patients. Validity measures were calculated to evaluate quality and robustness of the identified subgroups. RESULTS: Within younger patients two distinct and robust subgroups were identified, differing mainly in preoperative state and indication of recent myocardial infarction or unstable angina. In older patients, two distinct and robust subgroups were identified as well, differing mainly in preoperative state, presence of chronic pulmonary disease, previous cardiac surgery, neurological dysfunction disease and pulmonary hypertension. CONCLUSIONS: We illustrated a feasible method to identify homogeneous subgroups of individuals typically comprising risk categories. This allows a single treatment strategy--optimal only on average, across all individuals in a risk category--to be replaced by subgroup-specific treatment strategies, bringing us another step closer to individualized care. Discussions on allocation of cardiac surgery patients to different interventions may benefit from focusing on such specific subgroups.

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).

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.

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.

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).

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.