Immunizations with pneumococcal surface protein A and pneumolysin are protective against pneumonia in a murine model of pulmonary infection with Streptococcus pneumoniae

Intranasal infection of mice with certain strains of capsular group 19 Streptococcus pneumoniae can result in focal pneumonia in the absence of bacteremia. Using this model of murine pneumonia, we demonstrated that immunization with recombinant forms of either pneumococcal surface protein A (PspA) or PdB (a genetically detoxified derivative of pneumolysin) elicited significant protection against focal pulmonary infection. This may be the first demonstration that a proposed vaccine antigen can protect against pneumococcal pneumonia. The best protection was obtained by immunizing mice with a mixture of PspA and PdB, indicating that the protection elicited by these antigens can complement each other. This result is in agreement with previous studies that used pneumococcal sepsis and nasal colonization models and demonstrate that the best protein vaccines for prevention of infection may be those that include more than one protection-eliciting pneumococcal protein.David E. Briles, Susan K. Hollingshead, James C. Paton, Edwin W. Ades, Lea Novak, Frederik W. van Ginkel, and William H. Benjamin, Jr

Applying multiple criteria decision analysis to comparative benefit-risk assessment: choosing among statins in primary prevention

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefitrisk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis

Immunizations with pneumococcal surface protein A and pneumolysin are protective against pneumonia in a murine model of pulmonary infection with Streptococcus pneumoniae

Intranasal infection of mice with certain strains of capsular group 19 Streptococcus pneumoniae can result in focal pneumonia in the absence of bacteremia. Using this model of murine pneumonia, we demonstrated that immunization with recombinant forms of either pneumococcal surface protein A (PspA) or PdB (a genetically detoxified derivative of pneumolysin) elicited significant protection against focal pulmonary infection. This may be the first demonstration that a proposed vaccine antigen can protect against pneumococcal pneumonia. The best protection was obtained by immunizing mice with a mixture of PspA and PdB, indicating that the protection elicited by these antigens can complement each other. This result is in agreement with previous studies that used pneumococcal sepsis and nasal colonization models and demonstrate that the best protein vaccines for prevention of infection may be those that include more than one protection-eliciting pneumococcal protein.David E. Briles, Susan K. Hollingshead, James C. Paton, Edwin W. Ades, Lea Novak, Frederik W. van Ginkel, and William H. Benjamin, Jr

The HTA risk analysis chart: visualising the need for and potential value of managed entry agreements in health technology assessment

Background Recent changes to the regulatory landscape of pharmaceuticals may sometimes require reimbursement authorities to issue guidance on technologies that have a less mature evidence base. Decision makers need to be aware of risks associated with such health technology assessment (HTA) decisions and the potential to manage this risk through managed entry agreements (MEAs). Objective This work develops methods for quantifying risk associated with specific MEAs and for clearly communicating this to decision makers. Methods We develop the ‘HTA risk analysis chart’, in which we present the payer strategy and uncertainty burden (P-SUB) as a measure of overall risk. The P-SUB consists of the payer uncertainty burden (PUB), the risk stemming from decision uncertainty as to which is the truly optimal technology from the relevant set of technologies, and the payer strategy burden (PSB), the additional risk of approving a technology that is not expected to be optimal. We demonstrate the approach using three recent technology appraisals from the UK National Institute for Health and Clinical Excellence (NICE), each of which considered a price-based MEA. Results The HTA risk analysis chart was calculated using results from standard probabilistic sensitivity analyses. In all three HTAs, the new interventions were associated with substantial risk as measured by the P-SUB. For one of these technologies, the P-SUB was reduced to zero with the proposed price reduction, making this intervention cost effective with near complete certainty. For the other two, the risk reduced substantially with a much reduced PSB and a slightly increased PUB. Conclusions The HTA risk analysis chart shows the risk that the healthcare payer incurs under unresolved decision uncertainty and when considering recommending a technology that is not expected to be optimal given current evidence. This allows the simultaneous consideration of financial and data-collection MEA schemes in an easily understood format. The use of HTA risk analysis charts will help to ensure that MEAs are considered within a standard utility-maximising health economic decision-making framework

RfaH Suppresses Small RNA MicA Inhibition of fimB Expression in Escherichia coli K-12

The phase variation (reversible on-off switching) of the type 1 fimbrial adhesin of Escherichia coli involves a DNA inversion catalyzed by FimB (switching in either direction) or FimE (on-to-off switching). Here, we demonstrate that RfaH activates expression of a FimB-LacZ protein fusion while having a modest inhibitory effect on a comparable fimB-lacZ operon construct and on a FimE-LacZ protein fusion, indicating that RfaH selectively controls fimB expression at the posttranscriptional level. Further work demonstrates that loss of RfaH enables small RNA (sRNA) MicA inhibition of fimB expression even in the absence of exogenous inducing stress. This effect is explained by induction of σE , and hence MicA, in the absence of RfaH. Additional work con- firms that the procaine-dependent induction of micA requires OmpR, as reported previously (A. Coornaert et al., Mol. Microbiol. 76:467–479, 2010, doi:10.1111/j.1365-2958.2010.07115.x), but also demonstrates that RfaH inhibition of fimB transcription is enhanced by procaine independently of OmpR. While the effect of procaine on fimB transcription is shown to be independent of RcsB, it was found to require SlyA, another known regulator of fimB transcription. These results demonstrate a complex role for RfaH as a regulator of fimB expression

Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to `map' the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardised for age and sex. We develop three methods for mapping between aggregate BMI data using known relationships between individual measurements on different scales. The first is an analytical method based on the mathematical definitions of z-scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual-level relationships. We verify and contrast our methods using trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non-convergence.Comment: Main text: 15 pages, 3 figures, 2 tables Supplementary material: 10 pages, 10 figures, 3 table

Clinical history and management recommendations of the smooth muscle dysfunction syndrome due to ACTA2 arginine 179 alterations

Smooth muscle dysfunction syndrome (SMDS) due to heterozygous ACTA2 arginine 179 alterations is characterized by patent ductus arteriosus, vasculopathy (aneurysm and occlusive lesions), pulmonary arterial hypertension, and other complications in smooth muscle-dependent organs. We sought to define the clinical history of SMDS to develop recommendations for evaluation and management. Medical records of 33 patients with SMDS (median age 12 years) were abstracted and analyzed. All patients had congenital mydriasis and related pupillary abnormalities at birth and presented in infancy with a patent ductus arteriosus or aortopulmonary window. Patients had cerebrovascular disease characterized by small vessel disease (hyperintense periventricular white matter lesions; 95%), intracranial artery stenosis (77%), ischemic strokes (27%), and seizures (18%). Twelve (36%) patients had thoracic aortic aneurysm repair or dissection at median age of 14 years and aortic disease was fully penetrant by the age of 25 years. Three (9%) patients had axillary artery aneurysms complicated by thromboembolic episodes. Nine patients died between the ages of 0.5 and 32 years due to aortic, pulmonary, or stroke complications, or unknown causes. Based on these data, recommendations are provided for the surveillance and management of SMDS to help prevent early-onset life-threatening complications

The Relative Importance of Clinical, Economic, Patient Values and Feasibility Criteria in Cancer Drug Reimbursement in Canada:A Revealed Preferences Analysis of Recommendations of the Pan-Canadian Oncology Drug Review 2011–2017

Background: Most Canadian provinces and territories rely on the pan-Canadian Oncology Drug Review (pCODR) to provide recommendations regarding public reimbursement of cancer drugs. The pCODR review process considers four dimensions of value—clinical benefit, economic evaluation, patient-based values and adoption feasibility—but they do not define weights for individual decision criteria or an acceptable threshold for any of the criteria. Given this implicit review process, it is of interest to understand which factors appear to carry the most weight in pCODR recommendations using a revealed preferences approach. Methods: Using publicly available decision summaries (n = 91) describing submissions and resulting recommendations 2011–2017, we extracted ten attributes that characterized each submission. Using logistic regression, we identified statistically significant attributes and estimated their relative impact in final recommendations. Results: Clinical aspects appear to carry the greatest weight in the decision to reject or not reject, along with aspects of patient value (treatments with no alternatives were less likely to be rejected). Cost effectiveness does not appear to play a role in the initial decision to reject or not reject but is critical in full versus conditional approvals. There is evidence of a maximum acceptable threshold of around $Can140,000 per quality-adjusted life-year (QALY) gained. Conclusion: A set of factors driving pCODR recommendations is identifiable, supporting the consistency of the review process. However, the implicit nature of the review process and the difficulty of extracting and interpreting some of the attribute levels used in the analysis suggests that the process may still lack full transparency