Observational Study in a Real-World Setting of Targeted Therapy in the Systemic Treatment of Progressive Unresectable or Metastatic Well-Differentiated Pancreatic Neuroendocrine Tumors (pNETs) in France: OPALINE Study

INTRODUCTION

Approval of sunitinib and everolimus for the treatment of progressive, unresectable or metastatic well-differentiated pancreatic neuroendocrine tumors (pNETs) was obtained in France in 2011 and 2012, respectively. OPALINE was set up as an observational study to evaluate the efficacy of sunitinib and everolimus compared to usual pNET treatments of chemotherapies and somatostatin analogues that had been previously recommended by the health authorities.

METHODS

The OPALINE study assessed the efficacy of everolimus and sunitinib in terms of survival, disease progression and tolerance. Patients (N = 144) were enrolled from May 2015 to September 2017, and their disease characteristics were analyzed from diagnosis to 2 years post-enrollment.

RESULTS

At inclusion most patients had comorbidities, and about 95% presented metastases. Patients received on average 3.2 lines of treatment from diagnosis to inclusion and two lines throughout the 2-year follow-up. Seventy-nine patients (59.0%) received at least one targeted therapy (TT) during their care path. For these patients, the overall survival (OS) was approximatively 176.5 months (95% CI: 97.2-not evaluable), with a 2-year survival rate estimated at 93.6% (SD 2.6%). Similar survival rates were observed whether the TTs were prescribed sooner or later in the treatment path. The main reasons for discontinuation of TTs were disease progression (54 patients) and adverse events (26 patients). Most patients receiving TTs did not change their dose during the follow-up reflecting the good treatment tolerability over time. No new safety alert was reported for everolimus and sunitinib during this study.

CONCLUSION

Given their good tolerance and positive impact on estimated OS, the two TTs have an important role to play in the care path of patients with pNETs.

CLINICALTRIALS

GOV NATIONAL CLINICAL TRIAL NUMBER

NCT02264665.

A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data

Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable. We therefore describe a machine learning algorithm to screen for EI, as actimetry measurements are difficult to implement. Complete datasets for 1409 COPD patients were obtained from COLIBRI-COPD, a database of clinicopathological data submitted by French pulmonologists. Patient- and pulmonologist-reported estimates of PA quantity (daily walking time) and intensity (domestic, recreational, or fitness-directed) were first used to assign patients to one of four PA groups (extremely inactive [EI], overtly active [OA], intermediate [INT], inconclusive [INC]). The algorithm was developed by (i) using data from 80% of patients in the EI and OA groups to identify ‘phenotype signatures’ of non-PA-related clinical variables most closely associated with EI or OA; (ii) testing its predictive validity using data from the remaining 20% of EI and OA patients; and (iii) applying the algorithm to identify EI patients in the INT and INC groups. The algorithm’s overall error for predicting EI status among EI and OA patients was 13.7%, with an area under the receiver operating characteristic curve of 0.84 (95% confidence intervals: 0.75–0.92). Of the 577 patients in the INT/INC groups, 306 (53%) were reclassified as EI by the algorithm. Patient- and physician- reported estimation may underestimate EI in a large proportion of COPD patients. This algorithm may assist physicians in identifying patients in urgent need of interventions to promote PA.

A French Value Set for the EQ-5D-5L

OBJECTIVE

The objective of this study was to develop a French value set for the EQ-5D-5L, for academic and clinical research, and for regulatory requirements for price-setting of drugs and medical devices.

METHOD

This study used the standardized valuation protocol developed by EuroQol, using computer-assisted personal interview software. A representative sample of 1048 French residents were interviewed by a market research company, under the supervision of the research team. Health states were valued using composite time trade-off and a discrete choice experiment. Modeling was used to create values for the 3125 possible health states. The composite time trade-off data were modeled using a Tobit model with censored observations at -1 and correcting for heteroscedasticity. A conditional logit model was used for the discrete choice results, and both models were combined using a hybrid model. An adjusted hybrid model was tested to correct for imbalance in the sample on age and sex compared with the general population. A comparison with the 3-level (3L) value set was performed.

RESULTS

The adjusted model was preferred to comply with the representativeness of the general population. It provided a value set for which all coefficients were logically consistent. Values ranged from - 0.525 to 1. The distribution of values presented a shift towards higher values versus the 3L value set. Ranking of dimensions changed. Pain and discomfort and mobility were the dimensions with the highest potential for disutility compared with mobility and self-care for the 3L instrument.

CONCLUSIONS

This study provides a value set based on societal preferences of the French population, using an improved descriptive instrument of health-related quality-of-life health states. It will contribute to improve the quality of cost-effectiveness analysis in the French context and help stimulate disease-specific quality-of-life references for academic-, institutional-, and industry-promoted studies.