Cost-effectiveness and cost-utility analysis of somatrogon once-weekly injections vs. daily growth hormone injection for treating paediatric growth hormone deficiency in Ireland.

OBJECTIVES: Paediatric growth hormone deficiency (pGHD) manifests as growth failure associated with inadequate growth hormone (GH) production. Daily injections of recombinant human GH (dGH) [somatropin] is the current standard of care, which has been shown to be well tolerated and effective, but associated with suboptimal adherence, leading to reduced effectiveness. Somatrogon, a once-weekly injectable long-acting human GH, has demonstrated clinical non-inferiority and significantly lower life interference (i.e. treatment burden) vs. somatropin in two Phase 3 studies. This work evaluated cost-effectiveness and cost-utility of somatrogon vs dGHs from an Irish payer perspective. METHODS: A Markov model was developed for patients starting somatrogon or dGHs treatment at 3-12 years and continuing up to achievement of near adult height (NAH), with growth driven by trial-based height velocity (HV) and treatment-specific adherence. Patients could discontinue treatment at the end of Year 1 (4%). DGH adherence (95.3%-65% over treatment duration) and adherence-growth relationship were based on published evidence. Higher Year 1 adherence of 4%, tapering over time, for somatrogon vs. dGHs was based on clinical consultation. Treatment costs, monitoring costs and costs due to different wastage types (device setting and adherence) were sourced from local data. Health utilities based on height and injection frequency were derived from published literature. Scenario analysis, deterministic and probabilistic sensitivity analysis were performed. RESULTS: Somatrogon treatment led to 1.87-3.66 cm greater NAH gain and 0.21-0.50 higher quality adjusted life years (QALYs) vs. dGHs, across the base case and scenarios evaluated. Somatrogon treatment was associated with cost savings of €5,699-€21,974 and lower cost per cm gained vs. dGHs (€197-€527), per patient. Somatrogon was cost-effective vs. dGHs, with the result consistent across the sensitivity analyses conducted. CONCLUSION: Somatrogon weekly injections were estimated to result in higher NAH, higher QALYs, lower overall costs and lower costs per cm gained than dGHs, in pGHD.

Risk Prediction of Cognitive Decline after Stroke.

BACKGROUND AND PURPOSE: Cognitive decline is one of the major outcomes after stroke. We have developed and evaluated a risk predictive tool of post-stroke cognitive decline and assessed its clinical utility. METHODS: In this population-based cohort, 4,783 patients with first-ever stroke from the South London Stroke Register (1995-2010) were included in developing the model. Cognitive impairment was measured using the Mini Mental State Examination (cut off 24/30) and the Abbreviated Mental Test (cut off 8/10) at 3-months and yearly thereafter. A penalised mixed-effects linear model was developed and temporal-validated in a new cohort consisted of 1,718 stroke register participants recruited from (2011-2018). Prediction errors on discrimination and calibration were assessed. The clinical utility of the model was evaluated using prognostic accuracy measurements and decision curve analysis. RESULTS: The overall predictive model showed good accuracy, with root mean squared error of 0.12 and R2 of 73%. Good prognostic accuracy for predicting severe cognitive decline was observed AUC: (88%, 95% CI [85-90]), (89.6%, 95% CI [86-92]), (87%, 95% CI [85-91]) at 3 months, one and 5 years respectively. Average predicted recovery patterns were analysed by age, stroke subtype, Glasgow-coma scale, and left-stroke and showed variability. DECISION: curve analysis showed an increased clinical benefit, particularly at threshold probabilities of above 15% for predictive risk of cognitive impairment. CONCLUSIONS: The derived prognostic model seems to accurately screen the risk of post-stroke cognitive decline. Such prediction could support the development of more tailored management evaluations and identify groups for further study and future trials.

Predicting Re-Exacerbation Timing and Understanding Prolonged Exacerbations: An Analysis of Patients with COPD in the ECLIPSE Cohort.

PURPOSE: Understanding risk factors for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is important for optimizing patient care. We re-analyzed data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (NCT00292552) to identify factors predictive of re-exacerbations and associated with prolonged AECOPDs. METHODS: Patients with COPD from ECLIPSE with moderate/severe AECOPDs were included. The end of the first exacerbation was the index date. Timing of re-exacerbation risk was assessed in patients with 180 days' post-index-date follow-up data. Factors predictive of early (1-90 days) vs late (91-180 days) vs no re-exacerbation were identified using a multivariable partial-proportional-odds-predictive model. Explanatory logistic-regression modeling identified factors associated with prolonged AECOPDs. RESULTS: Of the 1,554 eligible patients from ECLIPSE, 1,420 had 180 days' follow-up data: more patients experienced early (30.9%) than late (18.7%) re-exacerbations; 50.4% had no re-exacerbation within 180 days. Lower post-bronchodilator FEV1 (P=0.0019), a higher number of moderate/severe exacerbations on/before index date (P<0.0001), higher St. George's Respiratory Questionnaire total score (P=0.0036), and season of index exacerbation (autumn vs winter, P=0.00164) were identified as predictors of early (vs late/none) re-exacerbation risk within 180 days. Similarly, these were all predictors of any (vs none) re-exacerbation risk within 180 days. Median moderate/severe AECOPD duration was 12 days; 22.7% of patients experienced a prolonged AECOPD. The odds of experiencing a prolonged AECOPD were greater for severe vs moderate AECOPDs (adjusted odds ratio=1.917, P=0.002) and lower for spring vs winter AECOPDs (adjusted odds ratio=0.578, P=0.017). CONCLUSION: Prior exacerbation history, reduced lung function, poorer respiratory-related quality-of-life (greater disease burden), and season may help identify patients who will re-exacerbate within 90 days of an AECOPD. Severe AECOPDs and winter AECOPDs are likely to be prolonged and may require close monitoring.

A Dynamic Model for Predicting Survival up to 1 Year After Ischemic Stroke.

BACKGROUND: This study developed and validated a dynamic prediction model for survival after ischaemic stroke up to 1 year. METHODS: Patients with stroke (n = 425) who participated in a sub-study (2002-2004) from the South London Stroke Register (SLSR) were selected for model derivation. The model was developed using the extended Cox model with time-dependent covariates. The two temporal validation cohorts from SLSR included 1735 (1995-2002) and 2155 patients (2004-2016). The discrimination, calibration and clinical utility of the model were assessed. RESULTS: Six strong predictors were used in the model, namely, age, sex, stroke subtype, stroke severity and pre-stroke and post-stroke disabilities. The c-statistics was 0.822 at 1 year in the derivation cohort. The model had a fair performance with prognostic accuracies of 77%-83% in the validation 1 cohort and 70%-75% in the validation 2 cohort. A good calibration was observed in the derivation cohort. CONCLUSION: The proposed model can accurately predict survival up to 1 year after ischaemic stroke.

Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis.

OBJECTIVE: We aim to identify and critically appraise clinical prediction models of mortality and function following ischaemic stroke. METHODS: Electronic databases, reference lists, citations were searched from inception to September 2015. Studies were selected for inclusion, according to pre-specified criteria and critically appraised by independent, blinded reviewers. The discrimination of the prediction models was measured by the area under the curve receiver operating characteristic curve or c-statistic in random effects meta-analysis. Heterogeneity was measured using I2. Appropriate appraisal tools and reporting guidelines were used in this review. RESULTS: 31395 references were screened, of which 109 articles were included in the review. These articles described 66 different predictive risk models. Appraisal identified poor methodological quality and a high risk of bias for most models. However, all models precede the development of reporting guidelines for prediction modelling studies. Generalisability of models could be improved, less than half of the included models have been externally validated(n = 27/66). 152 predictors of mortality and 192 predictors and functional outcome were identified. No studies assessing ability to improve patient outcome (model impact studies) were identified. CONCLUSIONS: Further external validation and model impact studies to confirm the utility of existing models in supporting decision-making is required. Existing models have much potential. Those wishing to predict stroke outcome are advised to build on previous work, to update and adapt validated models to their specific contexts opposed to designing new ones.

Development and validation of clinical prediction models for mortality, functional outcome and cognitive impairment after stroke: a study protocol.

INTRODUCTION: Stroke is a leading cause of adult disability and death worldwide. The neurological impairments associated with stroke prevent patients from performing basic daily activities and have enormous impact on families and caregivers. Practical and accurate tools to assist in predicting outcome after stroke at patient level can provide significant aid for patient management. Furthermore, prediction models of this kind can be useful for clinical research, health economics, policymaking and clinical decision support. METHODS: 2869 patients with first-ever stroke from South London Stroke Register (SLSR) (1995-2004) will be included in the development cohort. We will use information captured after baseline to construct multilevel models and a Cox proportional hazard model to predict cognitive impairment, functional outcome and mortality up to 5 years after stroke. Repeated random subsampling validation (Monte Carlo cross-validation) will be evaluated in model development. Data from participants recruited to the stroke register (2005-2014) will be used for temporal validation of the models. Data from participants recruited to the Dijon Stroke Register (1985-2015) will be used for external validation. Discrimination, calibration and clinical utility of the models will be presented. ETHICS: Patients, or for patients who cannot consent their relatives, gave written informed consent to participate in stroke-related studies within the SLSR. The SLSR design was approved by the ethics committees of Guy's and St Thomas' NHS Foundation Trust, Kings College Hospital, Queens Square and Westminster Hospitals (London). The Dijon Stroke Registry was approved by the Comité National des Registres and the InVS and has authorisation of the Commission Nationale de l'Informatique et des Libertés.

Psychological Determinants of Medication Adherence in Stroke Survivors: a Systematic Review of Observational Studies.

BACKGROUND: Medications targeting stroke risk factors have shown good efficacy, yet adherence is suboptimal. To improve adherence, its determinants must be understood. To date, no systematic review has mapped identified determinants into the Theoretical Domains Framework (TDF) in order to establish a more complete understanding of medication adherence. PURPOSE: The aim of this study was to identify psychological determinants that most influence stroke survivors' medication adherence. METHODS: In line with the prospectively registered protocol (PROSPERO CRD42015016222), five electronic databases were searched (1953-2015). Hand searches of included full text references were undertaken. Two reviewers conducted screening, data extraction and quality assessment. Determinants were mapped into the TDF. RESULTS: Of 32,825 articles, 12 fulfilled selection criteria (N = 43,984 stroke survivors). Tested determinants mapped into 8/14 TDF domains. Studies were too heterogeneous for meta-analysis. Three TDF domains appeared most influential. Negative emotions ('Emotions' domain) such as anxiety and concerns about medications ('Beliefs about Consequences' domain) were associated with reduced adherence. Increased adherence was associated with better knowledge of medications ('Knowledge' domain) and stronger beliefs about medication necessity ('Beliefs about Consequences' domain). Study quality varied, often lacking information on sample size calculations. CONCLUSIONS: This review provides foundations for evidence-based intervention design by establishing psychological determinants most influential in stroke survivors' medication adherence. Six TDF domains do not appear to have been tested, possibly representing gaps in research design. Future research should standardise and clearly report determinant and medication adherence measurement to facilitate meta-analysis. The range of determinants explored should be broadened to enable more complete understanding of stroke survivors' medication adherence.