Recurrence up to 3.5 years after antibiotic treatment of acute otitis media in very young Dutch children: survey of trial participants

Objective To determine the long term effects of antibiotic treatment for acute otitis media in young children

Early Health Economic Modeling of Novel Therapeutics in Age-Related Hearing Loss

Background: Health systems face challenges to accelerate access to innovations that add value and avoid those unlikely to do so. This is very timely to the field of age-related sensorineural hearing loss (ARHL), where a significant unmet market need has been identified and sizeable investments made to promote the development of novel hearing therapeutics (NT). This study aims to apply health economic modeling to inform the development of cost-effective NT. Methods: We developed a decision-analytic model to assess the potential costs and effects of using regenerative NT in patients ≥50 with ARHL. This was compared to the current standard of care including hearing aids and cochlear implants. Input data was collected from systematic literature searches and expert opinion. A UK NHS healthcare perspective was adopted. Three different but related analyses were performed using probabilistic modeling: (1) headroom analysis, (2) scenario analyses, and (3) threshold analyses. Results: The headroom analysis shows an incremental net monetary benefit (iNMB) of £20,017[£11,299–£28,737] compared to the standard of care due to quality-adjusted life-years (QALY) gains and cost savings. Higher therapeutic efficacy and access for patients with all degrees of hearing loss yields higher iNMBs. Threshold analyses shows that the ceiling price of the therapeutic increases with more severe degrees of hearing loss. Conclusion: NT for ARHL are potentially cost-effective under current willingness-to-pay (WTP) thresholds with considerable room for improvement in the current standard of care pathway. Our model can be used to help decision makers decide which therapeutics represent value for money and are worth commissioning, thereby paving the way for urgently needed NT

Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling

Background: To assess whether early health economic modeling helps to distinguish those healthcare innovations that are potentially cost-effective from those that are not potentially cost-effective. We will also study what information is retrieved from the health economic models to inform further development, research and implementation decisions.Methods: We performed secondary analyses on an existing database of 32 health economic modeling assessments of 30 innovations, performed by our group. First, we explored whether the assessments could distinguish innovations with potential cost-effectiveness from innovations without potential cost-effectiveness. Second, we explored which recommendations were made regarding development, implementation and further research of the innovation. Results: Of the 30 innovations, 1 (3%) was an idea that was not yet being developed and 14 (47%) were under development. Eight (27%) innovations had finished development, and another 7 (23%) innovations were on the market. Although all assessments showed that the innovation had the potential to become cost-effective, due to improved patient outcomes, cost savings or both, differences were found in the magnitude of the potential benefits, and the likelihood of reaching this potential. The assessments informed how the innovation could be further developed or positioned to maximize its cost-effectiveness, and informed further research.Conclusion: The early health economic assessments provided insight in the potential cost-effectiveness of an innovation in its intended context, and the associated uncertainty. None of the assessments resulted in a firm ‘no-go’ recommendation, but recommendations could be provided on further research and development in order to maximize value for money

An integrated framework of personalized medicine: from individual genomes to participatory health care

Abstract Promising research developments in both basic and applied sciences, such as genomics and participatory health care approaches, have generated widespread interest in personalized medicine among almost all scientific areas and clinicians. The term personalized medicine is, however, frequently used without defining a clear theoretical and methodological background. In addition, to date most personalized medicine approaches still lack convincing empirical evidence regarding their contribution and advantages in comparison to traditional models. Here, we propose that personalized medicine can only fulfill the promise of optimizing our health care system by an interdisciplinary and translational view that extends beyond traditional diagnostic and classification systems

Subgroup effects despite homogeneous heterogeneity test results

Background. Statistical tests of heterogeneity are very popular in meta-analyses, as heterogeneity might indicate subgroup effects. Lack of demonstrable statistical heterogeneity, however, might obscure clinical heterogeneity, meaning clinically relevant subgroup effects. Methods. A qualitative, visual method to explore the potential for subgroup effects was provided by a modification of the forest plot, i.e., adding a vertical axis indicating the proportion of a subgroup variable in the individual trials. Such a plot was used to assess the potential for clinically relevant subgroup effects and was illustrated by a clinical example on the effects of antibiotics in children with acute otitis media. Results. Statistical tests did not indicate heterogeneity in the meta-analysis on the effects of amoxicillin on acute otitis media (Q = 3.29, p = 0.51; I2 = 0%; T2 = 0). Nevertheless, in a modified forest plot, in which the individual trials were ordered by the proportion of children with bilateral otitis, a clear relation between bilaterality and treatment effects was observed (which was also found in an individual patient data meta-analysis of the included trials: p-value for interaction 0.021). Conclusions. A modification of the forest plot, by including an additional (vertical) axis indicating the proportion of a certain subgroup variable, is a qualitative, visual, and easy-to-interpret method to explore potential subgroup effects in studies included in meta-analyse

Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics : protocol of an individual patient data meta-analysis using multivariable risk prediction modelling

Introduction Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials. Methods and analysis This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk. Ethics and dissemination In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals. PROSPERO registration number CRD42020220108.Peer reviewe

An integrated framework of personalized medicine: from individual genomes to participatory health care

Abstract Promising research developments in both basic and applied sciences, such as genomics and participatory health care approaches, have generated widespread interest in personalized medicine among almost all scientific areas and clinicians. The term personalized medicine is, however, frequently used without defining a clear theoretical and methodological background. In addition, to date most personalized medicine approaches still lack convincing empirical evidence regarding their contribution and advantages in comparison to traditional models. Here, we propose that personalized medicine can only fulfill the promise of optimizing our health care system by an interdisciplinary and translational view that extends beyond traditional diagnostic and classification systems

Estimating measures of interaction on an additive scale for preventive exposures

Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: −0.30; 0.82], AP = 0.30 [95%CI: −0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = −0.37 [95%CI: −1.23; 0.49], AP = −0.29 [95%CI: −0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding