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Abstract Background Rare diseases (RDs) affect nearly 3 million people in France and at least 26–30 million people in Europe. These diseases, which represent a major medical concern, are mainly of genetic origin, often chronic, progressive, degenerative, life threatening and disabling, accounting for more than one third of all deaths occurring during infancy. In this context, there are needs for coordinated information on RDs at national/international levels, based on high quality, interoperable and sharable data. The main objective of the RaDiCo (Rare Disease Cohorts) program, coordinated by Inserm, was the development of RD e-cohorts via a national platform. The cohort projects were selected through a national call in 2014. The e-cohorts are supported by an interoperable platform, equivalent to an infrastructure, constructed on the "cloud computing" principle and in compliance with the European General Data Protection Regulation. It is dedicated to allow a continuous monitoring of data quality and consistency, in line with the French Health Data Hub. Results Depending on cohorts, the objectives are to describe the natural history of the studied RD(s), identify the underlying disease genes, establish phenotype-genotype correlations, decipher their pathophysiology, assess their societal and medico-economic impact, and/or identify patients eligible for new therapeutic approaches. Inclusion of prevalent and incident cases started at the end of 2016. As of April 2021, 5558 patients have been included within 13 RD e-cohorts covering 67 diseases integrated in 10 European Reference Networks and contributing to the European Joint Program on RDs. Several original results have been obtained in relation with the secondary objectives of the RaDiCo cohorts. They deal with discovery of new disease genes, assessment of treatment management, deciphering the underlying pathophysiological mechanisms, diagnostic approaches, genotype–phenotype relationships, development and validation of questionnaires relative to disease burden, or methodological aspects. Conclusion RaDiCo currently hosts 13 RD e-cohorts on a sharable and interoperable platform constructed on the “cloud computing” principle. New RD e-cohorts at the European and international levels are targeted.

Abstract In France, more than 10 million women at ”average” risk of breast cancer (BC), are included in the organized BC screening. Existing predictive models of BC risk are not adapted to the French population. Thus, we set up a new score in the French Hérault region and looked for subgroups at a graded level of risk in women at ”average” risk. We recruited a retrospective cohort of women, aged 50 to 60, who underwent the organized BC screening, and included 2241 non-cancer women and 527 who developed a BC during a 12-year follow-up period (2006-2018). The risk factors identified were high breast density (ACR BI-RADS grading)(B vs A: HR = 1.41, 95%CI [1.05; 1.9], p = 0.023; C vs A: HR = 1.65 [1.2; 2.27], p = 0.02 ; D vs A: HR = 2.11 [1.25;3.58], p = 0.006), a history of maternal breast cancer (HR = 1.61 [1.24; 2.09], p < 0.001), and socioeconomic difficulties (HR 1.23 [1.09; 1.55], p = 0.003). While early menopause (HR = 0.36 [0.13; 0.99], p = 0.003) and an age at menarche after 12 years (HR = 0.77 [0.63; 0.95], p = 0.047) were protective factors. We identified 3 groups at risk: lower, average, and higher, respectively. A low threshold was characterized at 1.9% of 12-year risk and a high threshold at 4.5% 12-year risk. Mean 12-year risks in the 3 groups of risk were 1.37%, 2.68%, and 5.84%, respectively. Thus, 12% of women presented a level of risk different from the average risk group, corresponding to 600,000 women involved in the French organized BC screening, enabling to propose a new strategy to personalize the national BC screening. On one hand, for women at lower risk, we proposed to reduce the frequency of mammograms and on the other hand, for women at higher risk, we suggested intensifying surveillance.

Abstract Background In France, the Ministry of Health has implemented a comprehensive program for rare diseases (RD) that includes an epidemiological program as well as the establishment of expert centers for the clinical care of patients with RD. Since 2007, most of these centers have entered the data for patients with developmental disorders into the CEMARA population-based registry, a national online data repository for all rare diseases. Through the CEMARA web portal, descriptive demographic data, clinical data, and the chronology of medical follow-up can be obtained for each center. We address the interest and ongoing challenges of this national data collection system 10 years after its implementation. Methods Since 2007, clinicians and researchers have reported the “minimum dataset (MDS)” for each patient presenting to their expert center. We retrospectively analyzed administrative data, demographic data, care organization and diagnoses. Results Over 10 years, 228,243 RD patients (including healthy carriers and family members for whom experts denied any suspicion of RD) have visited an expert center. Among them, 167,361 were patients affected by a RD (median age 11 years, 54% children, 46% adults, with a balanced sex ratio), and 60,882 were unaffected relatives (median age 37 years). The majority of patients (87%) were seen no more than once a year, and 52% of visits were for a diagnostic procedure. Among the 2,869 recorded rare disorders, 1,907 (66.5%) were recorded in less than 10 patients, 802 (28%) in 10 to 100 patients, 149 (5.2%) in 100 to 1,000 patients, and 11 (0.4%) in > 1,000 patients. Overall, 45.6% of individuals had no diagnosis and 6.7% had an uncertain diagnosis. Children were mainly referred by their pediatrician (46%; n = 55,755 among the 121,136 total children referrals) and adults by a medical specialist (34%; n = 14,053 among the 41,564 total adult referrals). Given the geographical coverage of the centers, the median distance from the patient’s home was 25.1 km (IQR = 6.3 km-64.2 km). Conclusions CEMARA provides unprecedented support for epidemiological, clinical and therapeutic studies in the field of RD. Researchers can benefit from the national scope of CEMARA data, but also focus on specific diseases or patient subgroups. While this endeavor has been a major collective effort among French RD experts to gather large-scale data into a single database, it provides tremendous potential to improve patient care.

Background & Aims: In France, liver grafts that have been refused at least 5 times can be “rescued” and allocated to a centre which chooses a recipient from its own waiting list, outside the patient-based allocation framework. We explored whether these “rescued” grafts were associated with worse graft/patient survival, as well as assessing their effect on survival benefit. Methods: Among 7,895 candidates, 5,218 were transplanted between 2009 and 2014 (336 centre-allocated). We compared recipient/graft survival between patient allocation and centre allocation, considering a selection bias and the distribution of centre-allocation recipients among the transplant teams. We used a propensity score approach and a weighted Cox model using the inverse probability of treatment weighting method. We also explored the survival benefit associated with centre-allocation grafts. Results: There was a significantly higher risk of graft loss/death in the centre allocation group compared to the patient allocation group (hazard ratio 1.13; 95% CI 1.05–1.22). However, this difference was no longer significant for teams that performed more than 7% of the centre-allocation transplantations. Moreover, receiving a centre-allocation graft, compared to remaining on the waiting list and possibly later receiving a patient-allocation graft, did not convey a poorer survival benefit (hazard ratio 0.80; 95% CI 0.60–1.08). Conclusions: In centres which transplanted most of the centre-allocation grafts, using grafts repeatedly refused for top-listed candidates was not detrimental. Given the organ shortage, our findings should encourage policy makers to restrict centre-allocation grafts to targeted centres. Lay summary: “Centre allocation” (CA) made it possible to save 6 out of 100 available liver grafts that had been refused at least 5 times for use in the top-listed candidates on the national waiting list. In this series, the largest on this topic, we showed that, in centres which transplanted most of the CA grafts, using grafts repeatedly refused for top-listed candidates did not appear to be detrimental. In the context of organ shortage, our results, which could be of interest for any country using this CA strategy, should encourage policy makers to reassess some aspects of graft allocation by restricting CA grafts to targeted centres, fostering the “best” matching between grafts and candidates on the waiting list.

Abstract Background Patient information in rare disease registries is generally collected from numerous data sources, necessitating the data to be federated. In addition, data for research purposes must be de-identified. Transforming nominative data into de-identified data is thus a key issue, while minimizing the number of identity duplicates. We propose a method enabling patient identity federation and rare disease data de-identification while preserving the pertinence of the provided data. Results We developed a rare disease patient identifier. The IdMR generation process is a three-phased algorithm involving a hash function to irreversibly de-identify nominative patient data, including those of foetuses. This process minimizes collision risks and reduces variability for the purpose of identity federation. The IdMR was generated for 360,000 patients of the CEMARA database. It allowed identity federation of 1771 duplicated files. No collisions were introduced. Conclusion We examined and discussed the risks of collisions and the creation of duplicates as well as the risks of patient re-identification. We discussed our choice of nominative input information in light of that used by other patient identification solutions. The IdMR is a patient identifier that enables identity federation and file linkage. The simplicity of the algorithm and the universality and stability of the input data make it a good candidate for European cross-border rare disease projects.

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Abstract Background Clinical pharmaceutical care has long played an important role in the improvement of healthcare safety. Pharmaceutical care is a collaborative care approach, implicating all the actors of the medication circuit in order to prevent and correct drug-related problems that can lead to adverse drug events. The collaborative pharmaceutical care performed during patients’ hospitalization requires two mutually reinforcing activities: medication reconciliation and medication review. Until now, the impact of the association of these two activities has not been clearly studied. Methods This is a multicentric stepped wedge randomized study involving six care units from six French University Hospitals (each unit corresponding to a cluster) over seven consecutive 14-day periods. Each hospital unit will start with a control period and switch to an experimental period after a randomized number of 14-day periods. Patients aged at least 65 years hospitalized in one of the participating care units and having given their consent to be called for a 30-day and 90-day follow-up can be enrolled. For each 14-day period, 15 patients will be recruited in each care unit to obtain a total of 630 patients enrolled in all centers. Patients with a hospital stay of more than 21 days will be excluded. During the control period, there will be no clinical pharmacist in the care unit, whereas during the experimental period a clinical pharmacist will perform medication reconciliation and review with the healthcare team. The primary outcome will assess the impact of collaborative pharmaceutical care on preventable medication error rate. The secondary outcomes will evaluate the clinical impact of the strategy, the acceptance rate of pharmaceutical interventions, the induced and avoided costs of the strategy (cost-consequence analysis), and the healthcare team’s satisfaction. Discussion This study will assess the impact of collaborative pharmaceutical care associating medication reconciliation and review at patient admission to hospital in terms of preventable medication error rate and costs. This activity will prevent and correct medication errors arising earlier in the hospitalization. Trial registration ClinicalTrials.gov, NCT02598115 . Registered on 4 November 2015.

Abstract Background Fibrodysplasia ossificans progressiva (FOP) is a rare, severely disabling, and life-shortening genetic disorder that causes the formation of heterotopic bone within soft connective tissue. Previous studies found that the FOP prevalence was about one in every two million lives. The aim of this study is to estimate the FOP prevalence in France by probabilistic record-linkage of 2 national databases: 1) the PMSI (Programme de médicalisation des systèmes d’information), an administrative database that records all hospitalization activities in France and 2) CEMARA, a registry database developed by the French Centres of Reference for Rare Diseases. Results Using a capture-recapture methodology to adjust the crude number of patients identified in both data sources, 89 FOP patients were identified, which results in a prevalence of 1.36 per million inhabitants (CI95% = [1.10; 1.68]). FOP patients’ mean age was 25 years, only 14.9% were above 40 years, and 53% of them were males. The first symptoms – beside toe malformations- occurred after birth for 97.3% of them. Mean age at identified symptoms was 7 years and above 18 years for only 6.9% of patients. Mean age at diagnosis was 10 years, and above 18 years for 14.9% of the patients. FOP patients were distributed across France. Conclusions Despite the challenge of ascertaining patients with rare diseases, we report a much higher prevalence of FOP in France than in previous studies elsewhere. We suggest that efforts to identify patients and confirm the diagnosis of FOP should be reinforced and extended at both national and European level.

Abstract Background Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. Methods We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. Results We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. Conclusion If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering.

Abstract Background Information and theory beyond copula concepts are essential to understand the dependence relationship between several marginal covariates distributions. In a therapeutic trial data scheme, most of the time, censoring occurs. That could lead to a biased interpretation of the dependence relationship between marginal distributions. Furthermore, it could result in a biased inference of the joint probability distribution function. A particular case is the cost-effectiveness analysis (CEA), which has shown its utility in many medico-economic studies and where censoring often occurs. Methods This paper discusses a copula-based modeling of the joint density and an estimation method of the costs, and quality adjusted life years (QALY) in a cost-effectiveness analysis in case of censoring. This method is not based on any linearity assumption on the inferred variables, but on a punctual estimation obtained from the marginal distributions together with their dependence link. Results Our results show that the proposed methodology keeps only the bias resulting statistical inference and don’t have anymore a bias based on a unverified linearity assumption. An acupuncture study for chronic headache in primary care was used to show the applicability of the method and the obtained ICER keeps in the confidence interval of the standard regression methodology. Conclusion For the cost-effectiveness literature, such a technique without any linearity assumption is a progress since it does not need the specification of a global linear regression model. Hence, the estimation of the a marginal distributions for each therapeutic arm, the concordance measures between these populations and the right copulas families is now sufficient to process to the whole CEA.

BACKGROUND:Acute kidney injury requiring dialysis (AKI-D) is associated with high mortality. Information about its epidemiology is nonetheless sparse in some countries. The objective of this study was to assess its epidemiology and prognosis in metropolitan France. METHODS:Using the French hospital discharge database, the study focused on adults hospitalized in metropolitan France between 2009 and 2014 and diagnosed with AKI-D according to the codes of the French common classification of medical procedures. Crude and standardized incidence rates (SIR) by gender and age were calculated. We explored the changes in patients' characteristics, modalities of renal replacement therapy (RRT), in-hospital care, and mortality, along with their determinants. Trends over time in the SIR for AKI-D, its principal diagnoses, and comorbidities were analyzed with joinpoint models. RESULTS:Between 2009 and 2014, the AKI-D SIR increased from 475 (95% CI, 468 to 482) to 512 per million population (95% CI, 505 to 519). AKI-D was twice as high in men as women. Median age was 68 years. Over the study period, the AKI-D SIR steadily increased in all age groups, particularly in the elderly. The most common comorbidities were cardio-cerebrovascular diseases (64.8%), pulmonary disease (42.2%), CKD (33.8%), and diabetes (26.0%); all of these except CKD increased significantly over time. In 2009, heart failure (17.2%), sepsis (17.0%), AKI (13.0%), digestive diseases (10.7%), and shock (6.6%) were the most frequent principal diagnoses, with a significant increase in heart failure and digestive diseases. The proportion of patients with at least one ICU stay and continuous RRT increased from 80.3% to 83.9% and from 56.9% to 61.8% (p

BackgroundCurrently, cardiovascular disease (CVD) is widely acknowledged to be the first leading cause of fatality in the world with 31% of all deaths worldwide and is predicted to remain as such in 2030. Furthermore, CVD is also a major cause of morbidity in adults worldwide. Among these diseases, the coronary artery disease (CAD) is the most common cause, accounting for over 40% of CVD deaths. Despite a decline in mortality rates, the consequences of more effective preventive and management programs, the burden of CAD remains significant. Indeed, the rise in the prevalence of modifiable risk factors due to changes in lifestyle and health behaviors has further increased the burden of this epidemic. Our objective was to evaluate the hospital burden of CAD via MI trends and Percutaneous Coronary Intervention (PCI) in the French Prospective Payment System (PPS).MethodsMI/PCI were identified in the national PPS database from 2009 to 2014 for patients aged 20 to 99, living in metropolitan France. We examined hospitalisation, readmission and mortality trends using standardised rates.ResultsOver the six-year period, we identified 678,021 patients, representing 900,121 stays of which, 215,224 had a MI and a PCI. Admission trends increased by nearly 25%. Acute MI cases increased every year, with an alarming increase in women, and more specifically in young women. Men were 3 times more hospitalised than women, who were older. A North-South divide was noted. Twenty seven percent of patients experienced readmission within 1 month. Trajectories of care were significantly different by sex and age. Overall in-hospital death was 3.3%, decreasing by 15% during the period. The highest adjusted mortality rates were observed for inpatients aged 80.ConclusionWe outlined the public health burden of this condition and the importance of improving the trajectories of care as an aid for better care.

BACKGROUND:Preventive strategies to reduce clinically significant medication errors (MEs), such as medication review, are often limited by human resources. Identifying high-risk patients to allow for appropriate resource allocation is of the utmost importance. To this end, we developed a predictive model to identify high-risk patients and assessed its impact on clinical decision-making. METHODS:From March 1st to April 31st 2014, we conducted a prospective cohort study on adult inpatients of a 1,644-bed University Hospital Centre. After a clinical evaluation of identified MEs, we fitted and internally validated a multivariate logistic model predicting their occurrence. Through 5,000 simulated randomized controlled trials, we compared two clinical decision pathways for intervention: one supported by our model and one based on the criterion of age. RESULTS:Among 1,408 patients, 365 (25.9%) experienced at least one clinically significant ME. Eleven variables were identified using multivariable logistic regression and used to build a predictive model which demonstrated fair performance (c-statistic: 0.72). Major predictors were age and number of prescribed drugs. When compared with a decision to treat based on the criterion of age, our model enhanced the interception of potential adverse drug events by 17.5%, with a number needed to treat of 6 patients. CONCLUSION:We developed and tested a model predicting the occurrence of clinically significant MEs. Preliminary results suggest that its implementation into clinical practice could be used to focus interventions on high-risk patients. This must be confirmed on an independent set of patients and evaluated through a real clinical impact study.