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Aims Clinical guidelines often recommend treating individuals based on their cardiovascular risk. We revisit this paradigm and quantify the efficacy of three treatment strategies: (i) overall prescription, i.e. treatment to all individuals sharing the eligibility criteria of a trial; (ii) risk-stratified prescription, i.e. treatment only to those at an elevated outcome risk; and (iii) prescription based on predicted treatment responsiveness. Methods and results We reanalysed the PROSPER randomized controlled trial, which included individuals aged 70–82 years with a history of, or risk factors for, vascular diseases. We conducted the derivation and internal–external validation of a model predicting treatment responsiveness. We compared with placebo (n = 2913): (i) pravastatin (n = 2891); (ii) pravastatin in the presence of previous vascular diseases and placebo in the absence thereof (n = 2925); and (iii) pravastatin in the presence of a favourable prediction of treatment response and placebo in the absence thereof (n = 2890). We found an absolute difference in primary outcome events composed of coronary death, non-fatal myocardial infarction, and fatal or non-fatal stroke, per 10 000 person-years equal to: −78 events (95% CI, −144 to −12) when prescribing pravastatin to all participants; −66 events (95% CI, −114 to −18) when treating only individuals with an elevated vascular risk; and −103 events (95% CI, −162 to −44) when restricting pravastatin to individuals with a favourable prediction of treatment response. Conclusion Pravastatin prescription based on predicted responsiveness may have an encouraging potential for cardiovascular prevention. Further external validation of our results and clinical experiments are needed., BACKGROUND & AIMS: Clinical guidelines often recommend to treat individuals based on their cardiovascular risk. We revisit this paradigm and quantify the efficacy of three treatment strategies: (i) overall prescription, i.e. treatment to all individuals sharing the eligibility criteria of a trial; (ii) risk-stratified prescription, i.e. treatment only to those at an elevated outcome risk; and (iii) prescription based on predicted treatment responsiveness.METHODS: We reanalysed the PROSPER randomised controlled trial, which included individuals aged 70-82 years with a history of, or risk factors for, vascular diseases. We conducted the derivation and internal-external validation of a model predicting treatment responsiveness. We compared to placebo (n= 2913): (i) pravastatin (n= 2891); (ii) pravastatin in the presence of previous vascular diseases and placebo in the absence thereof (n= 2925); and (iii) pravastatin in the presence of a favourable prediction of treatment response and placebo in the absence thereof (n= 2890).RESULTS: We found an absolute difference in primary outcome events composed of coronary death, non-fatal myocardial infarction, fatal or non-fatal stroke, per 10 000 person-years equal to: -78 events (95% CI, -144 to -12) when prescribing pravastatin to all participants; -66 events (95% CI, -114 to -18) when treating only individuals with an elevated vascular risk; and -103 events (95% CI, -162 to -44) when restricting pravastatin to individuals with a favourable prediction of treatment response.CONCLUSIONS: Pravastatin prescription based on predicted responsiveness may have an encouraging potential for cardiovascular prevention. Further external validation of our results and clinical experiments are needed.

Contains fulltext : 296153.pdf (Publisher’s version ) (Open Access), BACKGROUND: A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS. METHODS: An IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8-15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions. RESULTS: Results indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50). CONCLUSIONS: In conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.

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Background: Antibiotics are overused in patients with acute rhinosinusitis (ARS) as it is difficult to identify those who benefit from antibiotic treatment. Aim: To develop prediction models for computed tomography (CT)-confirmed ARS and culture-confirmed acute bacterial rhinosinusitis (ABRS) in adults presenting to primary care with symptoms suggestive of ARS. Design and setting: This was a systematic review and individual participant data meta-analysis. Method: CT-confirmed ARS was defined as the presence of fluid level or total opacification in any maxillary sinuses, whereas culture-confirmed ABRS was defined by culture of fluid from antral puncture. Prediction models were derived using logistic regression modelling. Results: Among 426 patients from three studies, 140 patients (32.9%) had CT-confirmed ARS. A model consisting of seven variables: previous diagnosis of ARS, preceding upper respiratory tract infection, anosmia, double sickening, purulent nasal discharge on examination, need for antibiotics as judged by a physician, and C-reactive protein (CRP) showed an optimism-corrected c-statistic of 0.73 (95% confidence interval [CI] = 0.69 to 0.78) and a calibration slope of 0.99 (95% CI = 0.72 to 1.19). Among 225 patients from two studies, 68 patients (30.2%) had culture-confirmed ABRS. A model consisting of three variables: pain in teeth, purulent nasal discharge, and CRP showed an optimism-corrected c-statistic of 0.70 (95% CI = 0.63 to 0.77) and a calibration slope of 1.00 (95% CI = 0.66 to 1.52). Clinical utility analysis showed that both models could be useful to rule out the target condition. Conclusion: Simple prediction models for CT-confirmed ARS and culture-confirmed ABRS can be useful to safely reduce antibiotic use in adults with ARS in high-prescribing countries.

The identification of individual patients at risk of disease has become an integral part of recent trends towards a more personalized healthcare system. A healthcare system that is personalized allows us to administer the most applicable treatment to an individual patient given their risk profile and, in turn, make our healthcare much more efficient. To that end, clinical prediction models are situated as prime candidates to assist clinicians with accurate risk estimates. By harnessing the information captured in various patient or disease related properties, these risk prediction models are able to chart a likely path that a disease might take (i.e., prognosis) or identify whether a specific disease is likely present in individual patients (i.e., diagnosis). Recent efforts to computerize the use of various clinical prediction models in clinical practice have provided clinical decision support systems (CDSS) that are already usable in clinical practice. These CDSS already allow clinicians to potentially inform their clinical decision making by providing individual risk probabilities. However, because currently available risk prediction models require complete information to generate predictions, these models are severely hampered whenever any patient or disease properties are missing. Luckily, the ample guidance that exists on the handling of missing data provides useful stepping stones to develop flexible or missing data handling techniques usable in real-time clinical practice. The development of several imputation methods for missing predictor values in real-time were presented previously. In a case-study with a real-world empirical data set for cardiovascular risk prediction, the accuracy of two common imputation methods which were adjusted for use in real time clinical practice were compared. These consisted of conditional modeling imputation (CMI, where for each predictor a separate multivariable imputation model is derived) and joint modeling imputation (J

Item does not contain fulltext, Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

Contains fulltext : 208805.pdf (publisher's version ) (Open Access), OBJECTIVES: With the increasing interest in personalised medicine, the use of subgroup analyses is likely to increase. Subgroup analyses are challenging and often misused, possibly leading to false interpretations of the effect. It remains unclear to what extent key organisations warn for such pitfalls and translate current methodological research to detect these effects into research guidelines. The aim of this scoping review is to determine and evaluate the current guidance used by organisations for exploring, confirming and interpreting subgroup effects. DESIGN: Scoping review. ELIGIBILITY CRITERIA: We identified four types of key stakeholder organisations: industry, health technology assessment organisations (HTA), academic/non-profit research organisations and regulatory bodies. After literature search and expert consultation, we identified international and national organisations of each type. For each organisation that was identified, we searched for official research guidance documents and contacted the organisation for additional guidance. RESULTS: Twenty-seven (45%) of the 60 organisations that we included had relevant research guidance documents. We observed large differences between organisation types: 18% (n=2) of the industry organisations, 64% (n=9) of the HTA organisations, 38% (n=8) of academic/non-profit research organisations and 57% (n=8) of regulatory bodies provided guidance documents. The majority of the documents (n=33, 63%) mentioned one or more challenges in subgroup analyses, such as false positive findings or ecological bias with variations across the organisation types. Statistical recommendations were less common (n=19, 37%) and often limited to a formal test of interaction. CONCLUSIONS: Almost half of the organisations included in this scoping review provided guidance on subgroup effect research in their guidelines. However, there were large differences between organisations in the amount and level of detail of their guidance. Effort i

Studies into organizational networks and governance tend to analyze professional behavior through the lens of rational (self)interest, resources, conflict, and power relations. However legitimate, this viewpoint overlooks the normative dimensions of networks. Therefore, in studying nodal security governance, the authors introduce the concept of “social practice,” which highlights the intrinsic normativity of what networked actors do. Social practices, they argue, deepen the theory of nodal governance by focusing more precise attention on the mentalities and value-laden characteristics of actors in highly complex settings. Drawing on this insight, the chapter presents a theoretical framework for analyzing social practices in nodal security governance, after which an empirical example concretizes our rather abstract line of reasoning.