Developing a Natural History Model for Duchenne Muscular Dystrophy.

BACKGROUND: The aim of this study was to pool multiple data sets to build a patient-centric, data-informed, natural history model (NHM) for Duchenne muscular dystrophy (DMD) to estimate disease trajectory across patient lifetime under current standard of care in future economic evaluations. The study was conducted as part of Project HERCULES, a multi-stakeholder collaboration to develop tools to support health technology assessments of new treatments for DMD. METHODS: Health states were informed by a review of NHMs for DMD and input from clinicians, patients and caregivers, and defined using common outcomes in clinical trials and real-world practice. The primary source informing the NHM was the Critical Path Institute Duchenne Regulatory Science Consortium (D-RSC) database. This was supplemented with expert input obtained via an elicitation exercise, and a systematic literature review and meta-analysis of mortality data. RESULTS: The NHM includes ambulatory, transfer and non-ambulatory phases, which capture loss of ambulation, ability to weight bear and upper body and respiratory function, respectively. The NHM estimates patients spend approximately 9.5 years in ambulatory states, 1.5 years in the transfer state and the remainder of their lives in non-ambulatory states. Median predicted survival is 34.8 years (95% CI 34.1-35.8). CONCLUSION: The model includes a detailed disease pathway for DMD, including the clinically and economically important transfer state. The NHM may be used to estimate the current trajectory of DMD in economic evaluations of new treatments, facilitating inclusion of a lifetime time horizon, and will help identify areas for further research.

Perfusion Scotoma: A Potential Core Underestimation in CT Perfusion in the Delayed Time Window in Patients with Acute Ischemic Stroke.

With the growing rise in utilization of CT perfusion for selecting patients for thrombectomy in acute ischemic stroke from large vessel occlusion, some potential pitfalls are becoming more commonly seen particularly when it comes to estimating the core infarct size on CT perfusion. Ghost infarct core has been described to account for overestimating core infarct size in the early time period (<3 hours). Herein, we describe the phenomenon of underestimating core infarct size on CT perfusion in the later time period (>6 hours), which we have termed perfusion scotoma.

Jointly modelling longitudinally measured urinary human chorionic gonadotrophin and early pregnancy outcomes.

Human chorionic gonadotrophin (hCG) is largely used to confirm pregnancy. Yet evidence shows that longitudinal hCG profiles are distinguishable between healthy and failing pregnancies. We retrospectively fitted a joint longitudinal-survival model to data from 127 (85 healthy and 42 failing pregnancies) US women, aged 18-45, who were attempting to conceive, to quantify the association between longitudinally measured urinary hCG and early miscarriage. Using subject-specific predictions, obtained uniquely from the joint model, we investigated the plausibility of adaptively monitoring early pregnancy outcomes based on updating hCG measurements. Volunteers collected daily early morning urine samples for their menstrual cycle and up to 28 days post day of missed period. The longitudinal submodel for log hCG included a random intercept and slope and fixed linear and quadratic time terms. The survival submodel included maternal age and cycle length covariates. Unit increases in log hCG corresponded to a 63.9% (HR 0.36, 95% CI 0.16, 0.47) decrease in the risk of miscarriage, confirming a strong association between hCG and miscarriage. Outputted conditional survival probabilities gave individualised risk estimates for the early pregnancy outcomes in the short term. However, longer term monitoring would require a larger sample size and prospectively followed up data, focusing on emerging extensions to the joint model, which allow assessment of the specificity and sensitivity.

Assessing causal treatment effect estimation when using large observational datasets.

BACKGROUND: Recently, there has been a heightened interest in developing and evaluating different methods for analysing observational data. This has been driven by the increased availability of large data resources such as Electronic Health Record (EHR) data alongside known limitations and changing characteristics of randomised controlled trials (RCTs). A wide range of methods are available for analysing observational data. However, various, sometimes strict, and often unverifiable assumptions must be made in order for the resulting effect estimates to have a causal interpretation. In this paper we will compare some common approaches to estimating treatment effects from observational data in order to highlight the importance of considering, and justifying, the relevant assumptions prior to conducting an observational analysis. METHODS: A simulation study was conducted based upon a small cohort of patients with chronic obstructive pulmonary disease. Two-stage least squares instrumental variables, propensity score, and linear regression models were compared under a range of different scenarios including different strengths of instrumental variable and unmeasured confounding. The effects of violating the assumptions of the instrumental variables analysis were also assessed. Sample sizes of up to 200,000 patients were considered. RESULTS: Two-stage least squares instrumental variable methods can yield unbiased treatment effect estimates in the presence of unmeasured confounding provided the sample size is sufficiently large. Adjusting for measured covariates in the analysis reduces the variability in the two-stage least squares estimates. In the simulation study, propensity score methods produced very similar results to linear regression for all scenarios. A weak instrument or strong unmeasured confounding led to an increase in uncertainty in the two-stage least squares instrumental variable effect estimates. A violation of the instrumental variable assumptions led to bias in the two-stage least squares effect estimates. Indeed, these were sometimes even more biased than those from a naïve linear regression model. CONCLUSIONS: Instrumental variable methods can perform better than naïve regression and propensity scores. However, the assumptions need to be carefully considered and justified prior to conducting an analysis or performance may be worse than if the problem of unmeasured confounding had been ignored altogether.

Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.

BACKGROUND: Treatment switching is common in randomised trials of oncology treatments, with control group patients switching onto the experimental treatment during follow-up. This distorts an intention-to-treat comparison of the treatments under investigation. Two-stage estimation (TSE) can be used to estimate counterfactual survival times for patients who switch treatments - that is, survival times that would have been observed in the absence of switching. However, when switchers do not die during the study, counterfactual censoring times are estimated, inducing informative censoring. Re-censoring is usually applied alongside TSE to resolve this problem, but results in lost longer-term information - a major concern if the objective is to estimate long-term treatment effects, as is usually the case in health technology assessment. Inverse probability of censoring weights (IPCW) represents an alternative technique for addressing informative censoring but has not before been combined with TSE. We aim to determine whether combining TSE with IPCW (TSEipcw) represents a valid alternative to re-censoring. METHODS: We conducted a simulation study to compare TSEipcw to TSE with and without re-censoring. We simulated 48 scenarios where control group patients could switch onto the experimental treatment, with switching affected by prognosis. We investigated various switching proportions, treatment effects, survival function shapes, disease severities and switcher prognoses. We assessed the alternative TSE applications according to their estimation of control group restricted mean survival (RMST) that would have been observed in the absence of switching up to the end of trial follow-up. RESULTS: TSEipcw performed well when its weights had a low coefficient of variation, but performed poorly when the coefficient of variation was high. Re-censored analyses usually under-estimated control group RMST, whereas non-re-censored analyses usually produced over-estimates, with bias more serious when the treatment effect was high. In scenarios where TSEipcw performed well, it produced low bias that was often between the two extremes associated with the re-censoring and non-recensoring options. CONCLUSIONS: Treatment switching adjustment analyses using TSE should be conducted with re-censoring, without re-censoring, and with IPCW to explore the sensitivity in results to these application options. This should allow analysts and decision-makers to better interpret the results of adjustment analyses.

Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

Treatment switching often has a crucial impact on estimates of effectiveness and cost-effectiveness of new oncology treatments. Rank preserving structural failure time models (RPSFTM) and two-stage estimation (TSE) methods estimate 'counterfactual' (i.e. had there been no switching) survival times and incorporate re-censoring to guard against informative censoring in the counterfactual dataset. However, re-censoring causes a loss of longer term survival information which is problematic when estimates of long-term survival effects are required, as is often the case for health technology assessment decision making. We present a simulation study designed to investigate applications of the RPSFTM and TSE with and without re-censoring, to determine whether re-censoring should always be recommended within adjustment analyses. We investigate a context where switching is from the control group onto the experimental treatment in scenarios with varying switch proportions, treatment effect sizes, treatment effect changes over time, survival function shapes, disease severity and switcher prognosis. Methods were assessed according to their estimation of control group restricted mean survival that would be observed in the absence of switching, up to the end of trial follow-up. We found that analyses which re-censored usually produced negative bias (i.e. underestimating control group restricted mean survival and overestimating the treatment effect), whereas analyses that did not re-censor consistently produced positive bias which was often smaller in magnitude than the bias associated with re-censored analyses, particularly when the treatment effect was high and the switching proportion was low. The RPSFTM with re-censoring generally resulted in increased bias compared to the other methods. We believe that analyses should be conducted with and without re-censoring, as this may provide decision-makers with useful information on where the true treatment effect is likely to lie. Incorporating re-censoring should not always represent the default approach when the objective is to estimate long-term survival times and treatment effects.

Health Information Management: Changing with Time.

Objective: With the evolution of patient medical records from paper to electronic media and the changes to the way data is sourced, used, and managed, there is an opportunity for health information management (HIM) to learn and facilitate the increasing expanse of available patient data. Methods: This paper discusses the emerging trends and lessons learnt in relation with the following four areas: 1) data and information governance, 2) terminology standards certification, 3) International Classification of Diseases, 11th edition (ICD-11), and 4) data analytics and HIM. Results: The governance of patient data and information increasingly requires the HIM profession to incorporate the roles of data scientists and data stewards into its portfolio to ensure data analytics and digital transformation is appropriately managed. Not only are terminology standards required to facilitate the structure and primary use of this data, developments in Canada in relation with the standards, role descriptions, framework and curricula in the form of certification provide one prime example of ensuring the quality of the secondary use of patient data. The impending introduction of ICD-11 brings with it the need for the HIM profession to manage the transition between ICD versions and country modifications incorporating changes to standards and tools, and the availability and type of patient data available for secondary use. Conclusions: In summary, the health information management profession now requires abilities in leadership, data, and informatics in addition to health information science and coding skills to facilitate the expanding secondary use of patient data.

Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method.

Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) - whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment. However, it is unclear which methods are likely to produce least bias in realistic RCT-based scenarios. We simulated RCTs in which switching, associated with patient prognosis, was permitted. Treatment effect size and time dependency, switching proportions and disease severity were varied across scenarios. We assessed the performance of alternative adjustment methods based upon bias, coverage and mean squared error, related to the estimation of true restricted mean survival in the absence of switching in the control group. We found that when the treatment effect was not time-dependent, rank preserving structural failure time models (RPSFTM) and iterative parameter estimation methods produced low levels of bias. However, in the presence of a time-dependent treatment effect, these methods produced higher levels of bias, similar to those produced by an inverse probability of censoring weights method. The inverse probability of censoring weights and structural nested models produced high levels of bias when switching proportions exceeded 85%. A simplified two-stage Weibull method produced low bias across all scenarios and provided the treatment switching mechanism is suitable, represents an appropriate adjustment method.

Angle selective backscattered electron contrast in the low-voltage scanning electron microscope: Simulation and experiment for polymers.

Recently developed detectors can deliver high resolution and high contrast images of nanostructured carbon based materials in low voltage scanning electron microscopes (LVSEM) with beam deceleration. Monte Carlo Simulations are also used to predict under which exact imaging conditions purely compositional contrast can be obtained and optimised. This allows the prediction of the electron signal intensity in angle selective conditions for back-scattered electron (BSE) imaging in LVSEM and compares it to experimental signals. Angle selective detection with a concentric back scattered (CBS) detector is considered in the model in the absence and presence of a deceleration field, respectively. The validity of the model prediction for both cases was tested experimentally for amorphous C and Cu and applied to complex nanostructured carbon based materials, namely a Poly(N-isopropylacrylamide)/Poly(ethylene glycol) Diacrylate (PNIPAM/PEGDA) semi-interpenetration network (IPN) and a Poly(3-hexylthiophene-2,5-diyl) (P3HT) film, to map nano-scale composition and crystallinity distribution by avoiding experimental imaging conditions that lead to a mixed topographical and compositional contrast.

Further studies to evaluate methods of leucoreduction to prevent alloimmune platelet refractoriness and induce tolerance in a dog platelet transfusion model.

OBJECTIVES: Three leucoreduction filters were evaluated - when used alone or combined with centrifuge leucoreduction (C-LR) - to prevent alloimmune platelet refractoriness in a dog platelet transfusion model. MATERIALS AND METHODS: Donor platelet-rich plasma (PRP) or buffy coat (BC) platelets were either filter leucoreduced (F-LR) or F-LR/C-LR, (51) Cr radiolabelled and transfused. Weekly transfusions were given for up to 8 weeks or until platelet refractoriness. Recipients who accepted treated transfusions were then given non-leucoreduced (non-LR) platelets to determine whether donor-specific tolerance had been induced. RESULTS: Acceptance of F-LR PRP transfusions ranged from 29% to 66%. F-LR/C-LR transfusions prepared from PRP were accepted by 92%, from BC by 63% and from pooled PRP by 75% of recipients (p=NS); overall acceptance rate of F-LR/C-LR transfusions was 83%. Tolerance to subsequent non-LR transfusions occurred in 45% of the F-LR-/C-LR-accepting recipients unrelated to DR-B compatibility between donors and recipients (P = 0·18). CONCLUSION: In a dog platelet transfusion model, acceptance of donor platelets required combining F-LR with C-LR as apparently each process removes different immunizing WBCs.

Can illness beliefs, from the common-sense model, prospectively predict adherence to self-management behaviours? A systematic review and meta-analysis.

OBJECTIVE: To determine whether people's beliefs about their illness, conceptualised by the common sense model (CSM), can prospectively predict adherence to self-management behaviours (including, attendance, medication, diet and exercise) in adults with acute and chronic physical illnesses. DESIGN AND MAIN OUTCOME MEASURES: Electronic databases were searched in September 2014, for papers specifying the use of the 'CSM' in relation to 'self-management', 'rehabilitation' and 'adherence' in the context of physical illness. Six hundred abstracts emerged. Data from 52 relevant studies were extracted. Twenty-one studies were meta-analysed, using correlation coefficients in random effects models. The remainder were descriptively synthesised. RESULTS: The effect sizes for individual illness belief domains and adherence to self-management behaviours ranged from .04 to .13, indicating very weak, predictive relationships. Further analysis revealed that predictive relationships did not differ by the: type of self-management behaviour; acute or chronic illness; or duration of follow-up. CONCLUSION: Individual illness belief domains, outlined by the CSM, did not predict adherence to self-management behaviours in adults with physical illnesses. Prospective relationships, controlling for past behaviour, also did not emerge. Other factors, including patients' treatment beliefs and inter-relationships between individual illness beliefs domains, may have influenced potential associations with adherence to self-management behaviours.

Convergent evolution of health information management and health informatics: a perspective on the future of information professionals in health care.

Clearly defined boundaries are disappearing among the activities, sources, and uses of health care data and information managed by health information management (HIM) and health informatics (HI) professionals. Definitions of the professional domains and scopes of practice for HIM and HI are converging with the proliferation of information and communication technologies in health care settings. Convergence is changing both the roles that HIM and HI professionals serve in their organizations as well as the competencies necessary for training future professionals. Many of these changes suggest a blurring of roles and responsibilities with increasingly overlapping curricula, job descriptions, and research agendas. Blurred lines in a highly competitive market create confusion for students and employers. In this essay, we provide some perspective on the changing landscape and suggest a course for the future. First we review the evolving definitions of HIM and HI. We next compare the current domains and competencies, review the characteristics as well as the education and credentialing of both disciplines, and examine areas of convergence. Given the current state, we suggest a path forward to strengthen the contributions HIM and HI professionals and educators make to the evolving health care environment.

Dynamic modulation of spatially structured polarization fields for real-time control of ultrafast laser-material interactions.

The polarization state of an ultrafast laser is dynamically controlled using two Spatial Light Modulators and additional waveplates. Consequently, four states of polarization, linear horizontal and vertical, radial and azimuthal, all with a ring intensity distribution, were dynamically switched at a frequency ν = 12.5 Hz while synchronized with a motion control system. This technique, demonstrated here for the first time, enables a remarkable level of real-time control of the properties of light waves and applied to real-time surface patterning, shows that highly controlled nanostructuring is possible. Laser ablation of Induced Periodic Surface Structures is used to directly verify the state of polarization at the focal plane.

Validation of the Patient Global Impression scales for use in detrusor overactivity: secondary analysis of the RELAX study.

OBJECTIVE: To assess construct validity of the Patient Global Impression scales (Severity [PGI-S], Bother [PGI-B] and Improvement [PGI-I]) for symptoms of detrusor overactivity (DO). DESIGN: Secondary analysis of a randomised trial of onabotulinum toxin A. SETTING: Eight UK urogynaecology departments. POPULATION: A total of 240 women with DO refractory to medical treatment randomised to receive 200 iu onabotulinum toxin A or placebo in the RELAX trial and followed up for 6 months. MAIN OUTCOME MEASURES: Urinary diaries and disease-specific quality of life (QoL) questionnaires were completed at baseline and during follow up. Discriminatory ability of the PGI-S, PGI-B and PGI-I scales to identify symptom severity and change in severity was assessed by comparing mean diary and QoL outcomes across the response categories, analysed by one-way analysis of variance. RESULTS: Data were available from 237 women (98.8%) for validation of PGI-S and PGI-B at baseline, and 192 women (80%) at 6 weeks follow up for validation of PGI-I. Leakage episodes (P = 0.01), urgency episodes (P = 0.019), urgency severity (P = 0.012), and QoL scores (all P < 0.001) were greater in women with more severe problems on PGI-S. Similar results were seen for PGI-B: leakage (P = 0.051), urgency episodes (P < 0.001), urgency severity (P < 0.001), and QoL scores (all P < 0.001). PGI-I responses demonstrated significant relationships with size of change of all variables (P < 0.001). The generic instrument EQ-5D had weaker relationships (PGI-S, P = 0.09; PGI-B, P = 0.004; PGI-I, P = 0.06), suggesting that it was less sensitive. CONCLUSIONS: The PGI scales are robust and valid instruments to assess disease severity, bother and improvement after treatment in women with detrusor overactivity.