Structural equation modeling for identifying the drivers of health-related quality of life improvement experienced by patients with migraine receiving eptinezumab

BACKGROUND

As new migraine therapies emerge, it is crucial for measures to capture the complexities of health-related quality of life (HRQoL) improvement beyond improvements in monthly migraine day (MMD) reduction. Investigations into the correlations between MMD reduction, symptom management, and HRQoL are lacking, particularly those that focus on improvements in canonical symptoms and improvement in patient-identified most-bothersome symptoms (PI-MBS), in patients treated with eptinezumab. This exploratory analysis identified efficacy measures mediating the effect of eptinezumab on HRQoL improvements in patients with migraine.

METHODS

Data from the DELIVER study of patients with 2-4 prior preventive migraine treatment failures (NCT04418765) were inputted to two structural equation models describing sources of HRQoL improvement via Migraine-Specific Quality-of-Life Questionnaire (MSQ) scores. A single latent variable was defined to represent HRQoL and describe the sources of HRQoL in DELIVER. One model included all migraine symptoms while the second model included the PI-MBS as the only migraine symptom. Mediating variables capturing different aspects of efficacy included MMDs, other canonical symptoms, and PI-MBS.

RESULTS

In the first model, reductions in MMDs and other canonical symptoms accounted for 35% (standardized effect size [SES] - 0.11) and 25% (SES - 0.08) of HRQoL improvement, respectively, with 41% (SES - 0.13) of improvement comprising "direct treatment effect," i.e., unexplained by mediators. In the second model, substantial HRQoL improvement with eptinezumab (86%; SES - 0.26) is due to MMD reduction (17%; SES - 0.05) and change in PI-MBS (69%; SES - 0.21).

CONCLUSIONS

Improvements in HRQoL experienced by patients treated with eptinezumab can be substantially explained by its effect on migraine frequency and PI-MBS. Therefore, in addition to MMD reduction, healthcare providers should discuss PI-MBS improvements, since this may impact HRQoL. Health technology policymakers should consider implications of these findings in economic evaluation, as they point to alternative measurement of quality-adjusted life years to capture fully treatment benefits in cost-utility analyses.

TRIAL REGISTRATION

ClinicalTrials.gov (Identifier: NCT04418765 ; EudraCT (Identifier: 2019-004497-25; URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-004497-25 ).

Persistence to anti-CGRP monoclonal antibodies and onabotulinumtoxinA among patients with migraine: a retrospective cohort study

BACKGROUND

To date, real-world evidence on persistence to anti-calcitonin gene-related peptide (anti-CGRP) monoclonal antibodies (mAbs) or onabotulinumtoxinA have excluded eptinezumab. This retrospective cohort study was performed to compare treatment persistency among patients with migraine on anti-CGRP mAbs (erenumab, fremanezumab, galcanezumab, or eptinezumab) or onabotulinumtoxinA.

METHODS

This retrospective study used IQVIA PharmMetrics data. Adult patients with migraine treated with an anti-CGRP mAb or onabotulinumtoxinA who had 12 months of continuous insurance enrollment before starting treatment were included. A "most recent treatment episode" analysis was used in which the most recent episode was defined as the latest treatment period with the same drug (anti-CGRP mAb or onabotulinumtoxinA) without a ≥ 15-day gap in medication supply on/after June 25, 2020, to December 31, 2021. Patients were indexed at the start of their most recent episode. Patients were considered non-persistent and discontinued the therapy associated with their most recent episode if there was ≥ 15-day gap in medication supply. A Cox proportional-hazards model estimated the discontinuation hazard between treatments. The gap periods and cohort definition were varied in sensitivity analyses.

RESULTS

The study included 66,576 patients (median age 46 years, 88.6% female). More eptinezumab-treated patients had chronic migraine (727/1074), ≥ 3 previous acute (323/1074) or preventive (333/1074) therapies, and more prior treatment episodes (3) than other treatment groups. Based on a 15-day treatment gap, patients on subcutaneous anti-CGRP mAbs had a 32% (95% CI: 1.19, 1.49; erenumab), 42% (95% CI: 1.27, 1.61; galcanezumab), and 58% (95% CI: 1.42, 1.80; fremanezumab) higher discontinuation hazard than those receiving eptinezumab, with this relationship attenuated, but still statistically significant based on 30-day and 60-day treatment gaps. There was no significant difference in the discontinuation hazard between eptinezumab and onabotulinumtoxinA. Based on a 15-day treatment gap among patients who newly initiated therapy, the discontinuation hazard of subcutaneous anti-CGRP mAbs remained significantly higher compared to eptinezumab and onabotulinumtoxinA.

CONCLUSION

Patients treated with eptinezumab demonstrated persistency that was higher than subcutaneous anti-CGRP mAbs and similar to onabotulinumtoxinA.

Comparison of indirect treatment methods in migraine prevention to address differences in mode of administration

Aim: Indirect treatment comparisons (ITCs) are anchored on a placebo comparator, and the placebo response may vary according to drug administration route. Migraine preventive treatment studies were used to evaluate ITCs and determine whether mode of administration influences placebo response and the overall study findings. Materials & methods: Change from baseline in monthly migraine days produced by monoclonal antibody treatments (subcutaneous, intravenous) was compared using fixed-effects Bayesian network meta-analysis (NMA), network meta-regression (NMR), and unanchored simulated treatment comparison (STC). Results: NMA and NMR provide mixed, rarely differentiated results between treatments, whereas unanchored STC strongly favors eptinezumab over other preventive treatments. Conclusion: Further investigations are needed to determine which ITC best reflects the impact of mode of administration on placebo.

Estimating treatment effects on health utility scores for patients living with migraine: a post hoc analysis of the DELIVER trial

BACKGROUND

This post hoc analysis aimed to estimate eptinezumab's therapeutic effect on health utilities and determined to which extent monthly migraine days (MMDs) explain changes in health utilities.

RESEARCH DESIGN/METHODS

DELIVER, a randomized, double-blind, placebo-controlled phase 3b trial (NCT04418765), investigated eptinezumab efficacy and safety in patients with 2-4 prior migraine treatment failures. Regression analysis explored the relationship between utility scores and MMDs, with eptinezumab treatment as a covariate along with MMDs to identify any MMD-independent effect on utilities. Path analysis quantified eptinezumab's impact as mediated through MMD reduction.

RESULTS

The base case model showed that each reduction in MMD was associated with a mean utility score increase (0.0189; 95% CI: 0.0180, 0.0198; P < 0.001). Mean utility score was generally higher for eptinezumab versus placebo, justifying addition of treatment effect to the base case model. Patients administered eptinezumab had on average 0.0562 (95% CI: 0.0382, 0.0742; P < 0.001) higher utility versus placebo when controlling for number of MMDs. From path analysis, MMD reduction resulting from eptinezumab treatment accounted for 53% additional utility gain observed in patients.

CONCLUSIONS

Changes in MMDs alone inadequately captured migraine's impact on patient utility, as there was also a positive eptinezumab-driven, treatment-specific impact on utility score.

TRIAL REGISTRATION

The trial is registered at ClinicalTrials.gov (CT.gov identifier: NCT04418765).

NASHmap: clinical utility of a machine learning model to identify patients at risk of NASH in real-world settings

The NASHmap model is a non-invasive tool using 14 variables (features) collected in standard clinical practice to classify patients as probable nonalcoholic steatohepatitis (NASH) or non-NASH, and here we have explored its performance and prediction accuracy. The National Institute of Diabetes and Digestive Kidney Diseases (NIDDK) NAFLD Adult Database and the Optum Electronic Health Record (EHR) were used for patient data. Model performance metrics were calculated from correct and incorrect classifications for 281 NIDDK (biopsy-confirmed NASH and non-NASH, with and without stratification by type 2 diabetes status) and 1,016 Optum (biopsy-confirmed NASH) patients. NASHmap sensitivity in NIDDK is 81%, with a slightly higher sensitivity in T2DM patients (86%) than non-T2DM patients (77%). NIDDK patients misclassified by NASHmap had mean feature values distinct from correctly predicted patients, particularly for aspartate transaminase (AST; 75.88 U/L true positive vs 34.94 U/L false negative), and alanine transaminase (ALT; 104.09 U/L vs 47.99 U/L). Sensitivity was slightly lower in Optum at 72%. In an undiagnosed Optum cohort at risk for NASH (n = 2.9 M), NASHmap predicted 31% of patients as NASH. This predicted NASH group had AST and ALT mean levels above normal range of 0-35 U/L, and 87% had HbA1C levels > 5.7%. Overall, NASHmap demonstrates good sensitivity in predicting NASH status in both datasets, and NASH patients misclassified as non-NASH by NASHmap have clinical profiles closer to non-NASH patients.

Mendelian randomization: estimation of inpatient hospital costs attributable to obesity

BACKGROUND

Mendelian Randomization is a type of instrumental variable (IV) analysis that uses inherited genetic variants as instruments to estimate causal effects attributable to genetic factors. This study aims to estimate the impact of obesity on annual inpatient healthcare costs in the UK using linked data from the UK Biobank and Hospital Episode Statistics (HES).

METHODS

UK Biobank data for 482,127 subjects was linked with HES inpatient admission records, and costs were assigned to episodes of care. A two-stage least squares (TSLS) IV model and a TSLS two-part cost model were compared to a naïve regression of inpatient healthcare costs on body mass index (BMI).

RESULTS

The naïve analysis of annual cost on continuous BMI predicted an annual cost of £21.61 [95% CI £20.33 - £22.89] greater cost per unit increase in BMI. The TSLS IV model predicted an annual cost of £14.36 [95% CI £0.31 - £28.42] greater cost per unit increase in BMI. Modelled with a binary obesity variable, the naïve analysis predicted that obese subjects incurred £205.53 [95% CI £191.45 - £219.60] greater costs than non-obese subjects. The TSLS model predicted a cost £201.58 [95% CI £4.32 - £398.84] greater for obese subjects compared to non-obese subjects.

CONCLUSIONS

The IV models provide evidence for a causal relationship between obesity and higher inpatient healthcare costs. Compared to the naïve models, the binary IV model found a slightly smaller marginal effect of obesity, and the continuous IV model found a slightly smaller marginal effect of a single unit increase in BMI.

Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis

Objective

To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML).

Materials and Methods

This retrospective study utilized two databases: a) the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease (NAFLD) adult database (2004-2009), and b) the Optum^® de-identified Electronic Health Record dataset (2007-2018), a real-world dataset representative of common electronic health records in the United States. We developed an ML model to predict NASH, using confirmed NASH and non-NASH based on liver histology results in the NIDDK dataset to train the model.

Results

Models were trained and tested on NIDDK NAFLD data (704 patients) and the best-performing models evaluated on Optum data (~3,000,000 patients). An eXtreme Gradient Boosting model (XGBoost) consisting of 14 features exhibited high performance as measured by area under the curve (0.82), sensitivity (81%), and precision (81%) in predicting NASH. Slightly reduced performance was observed with an abbreviated feature set of 5 variables (0.79, 80%, 80%, respectively). The full model demonstrated good performance (AUC 0.76) to predict NASH in Optum data.

Discussion

The proposed model, named NASHmap, is the first ML model developed with confirmed NASH and non-NASH cases as determined through liver biopsy and validated on a large, real-world patient dataset. Both the 14 and 5-feature versions exhibit high performance.

Conclusion

The NASHmap model is a convenient and high performing tool that could be used to identify patients likely to have NASH in clinical settings, allowing better patient management and optimal allocation of clinical resources.

Mapping the Kansas City Cardiomyopathy Questionnaire (KCCQ) Onto EQ-5D-3L in Heart Failure Patients: Results for the Japanese and UK Value Sets

Background. Health technology assessment bodies in several countries, including Japan and the United Kingdom, recommend mapping techniques to obtain utility scores in clinical trials that do not have a preference-based measure of health. This study sought to develop mapping algorithms to predict EQ-5D-3L scores from the Kansas City Cardiomyopathy Questionnaire (KCCQ) in patients with heart failure (HF). Methods. Data from the randomized, double-blind PARADIGM-HF trial were analyzed, and EQ-5D-3L scores were calculated using the Japanese and UK value sets. Several different model specifications were explored to best fit EQ-5D data collected at baseline with KCCQ scores, including ordinary least square regression, two-part, Tobit, and three-part models. Generalized estimating equations models were also fitted to analyze longitudinal EQ-5D data. To validate model predictions, the data set was split into a derivation (n = 4,465) from which the models were developed and a separate sample (n = 1,892) for validation. Results. There were only small differences between the different model classes tested. Model performance and predictive power was better for the item-level models than for the models including KCCQ domain scores. R ² statistics for the item-level models ranged from 0.45 to 0.52. Mean absolute error in the validation sample was 0.10 for the models using the Japanese value set and 0.114 for the UK models. All models showed some underprediction of utility above 0.75 and overprediction of utility below 0.5, but performed well for population-level estimates. Conclusions. Using data from a large clinical trial in HF, we found that EQ-5D-3L scores can be estimated from responses to the KCCQ and can facilitate cost-utility analysis from existing HF trials where only the KCCQ was administered. Future validation in other HF populations is warranted.

Review and comparison of methodologies for indirect comparison of clinical trial results: an illustration with ranibizumab and aflibercept

AIM

To review and compare methods for indirect comparison of aflibercept and ranibizumab in patients with diabetic macular edema.

METHODS

Post-stratification, inverse probability weighting based on simulated data, weight optimization, and regression model techniques were used to compare pooled individual patient-level data from the RESTORE and RESPOND (ranibizumab 0.5 mg as needed after 3 initial monthly doses) studies with summary-level data from the VIVID and VISTA (aflibercept 2.0 mg every 8 weeks after 5 initial monthly doses, 2q8) studies. The impact of adjusting for up to two baseline characteristics was assessed.

RESULTS

All methods provided similar results. After adjustment for baseline best-corrected visual acuity and central retinal thickness, no statistically significant difference in average gain in baseline best-corrected visual acuity from baseline at month 12 was found between ranibizumab 0.5 mg and aflibercept 2q8.

CONCLUSIONS

Weight optimization and regression methods are useful options to adjust for more than one baseline characteristic.

Comparative efficacy and safety of approved treatments for macular oedema secondary to branch retinal vein occlusion: a network meta-analysis

OBJECTIVE

To compare the efficacy and safety of approved treatments for macular oedema secondary to branch retinal vein occlusion (BRVO).

DESIGN

Randomised controlled trials (RCTs) evaluating the efficacy and safety of approved treatments for macular oedema secondary to BRVO were identified from an updated systematic review.

SETTING

A Bayesian network meta-analysis of RCTs of treatments for macular oedema secondary to BRVO.

INTERVENTIONS

Ranibizumab 0.5 mg pro re nata, aflibercept 2 mg monthly (2q4), dexamethasone 0.7 mg implant, laser photocoagulation, ranibizumab+laser, or sham intervention. Bevacizumab and triamcinolone were excluded.

OUTCOME MEASURES

Efficacy outcomes were mean change in best corrected visual acuity (Early Treatment Diabetic Retinopathy Study scale) and the percentage of patients gaining ≥ 15 letters. Safety outcome was the percentage of patients with increased intraocular pressure (IOP)/ocular hypertension (OH).

RESULTS

8 RCTs were identified for inclusion with 1743 adult patients. The probability of being the most efficacious treatment at month 6 or 12 based on letters gained was 54% for ranibizumab monotherapy, 30% for aflibercept, 16% for ranibizumab plus laser (adjunctive or prompt), and 0% for dexamethasone implant, laser or sham. The probability of being the most efficacious treatment for patients gaining ≥ 15 letters was 39% for aflibercept, 35% for ranibizumab monotherapy, 24% for ranibizumab plus laser, 2% for dexamethasone implant, and less than 1% for laser or sham. There was no statistical difference between ranibizumab monotherapy and aflibercept for letters gained (+1.4 letters for ranibizumab vs aflibercept with 95% credible interval (CrI) of -5.2 to +8.5 letters) or the OR for gaining ≥ 15 letters: 1.06 (95% CrI 0.16 to 8.94)). Dexamethasone implant was associated with significantly higher IOP/OH than antivascular endothelial growth factor agents (OR 13.1 (95% CrI 1.7 to 116.9)).

CONCLUSIONS

There was no statistically significant difference between ranibizumab and aflibercept.