Prescription errors by emergency physicians for inpatients are associated with emergency department length of stay.

OBJECTIVES: Adverse drug events are the sixth-leading cause of death in Western countries and are also more frequent in emergency departments (EDs). In some hospitals or on some occasions, ED physicians prescribe for patients who they have admitted. These prescriptions are then followed by the wards and can persist for several days. Our objectives were to determine the frequency of prescription errors for patients over 18years old hospitalized from ED to medical or surgical wards, and whether there exists a relationship between those prescription errors and ED LOS. METHODS: This was a single center retrospective study that was conduct in the ED of a university hospital with an annual census of 65 000 patients. The population studied consisted of patients over 18years old hospitalized from ED to medical or surgical wards between January 1st, 2012 and January 21st, 2012. RESULTS: Six hundred eight patients were included. One hundred fifty-four (25%) patients had prescription errors. Prescription errors were associated with increased ED length of stay (OR=2.47; 95% CIs [1.58; 3.92]) and polypharmacy (OR=1.78; 95% CIs [1.20; 2.66]). Fewer prescription errors were found when the patient was examined in the ED by a consultant (OR=0.61; 95% CIs [0.41; 0.91]) and when the medical history was known (OR=0.28; 95% CIs [0.10; 0.88]). CONCLUSION: Prescription errors occurred frequently in the ED. We assume that a clear communication and cooperation between EPs and consultants may help improve prescription accuracy.

RaDiCo, the French national research program on rare disease cohorts.

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.

Determination of thresholds of risk in women at average risk of breast cancer to personalize the organized screening program.

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.

Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years.

Acute coronary syndrome (ACS) in women is a growing public health issue and a death leading cause. We explored whether the hospital healthcare trajectory was characterizable using a longitudinal clustering approach in women with ACS. From the 2009-2014 French nationwide hospital database, we extracted spatio-temporal patterns in ACS patient trajectories, by replacing the spatiality by their hospitalization cause. We used these patterns to characterize hospital healthcare flows in a visualization tool. We clustered these trajectories with kmlShape to identify time gap and tariff profiles. ACS hospital healthcare flows have three key categories: Angina pectoris, Myocardial Infarction or Ischemia. Elderly flows were more complex. Time gap profiles showed that readmissions were closer together as time goes by. Tariff profiles were different according to age and initial event. Our approach might be applied to monitoring other chronic diseases. Further work is needed to integrate these results into a medical decision-making tool.

10 years of CEMARA database in the AnDDI-Rares network: a unique resource facilitating research and epidemiology in developmental disorders in France.

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.

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome.

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach.

Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events' prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.

Equivalence Randomized Trial to Compare Treatment on the Basis of Sentinel Node Biopsy Versus Neck Node Dissection in Operable T1-T2N0 Oral and Oropharyngeal Cancer.

PURPOSE: Sentinel node (SN) biopsy is accurate in operable oral and oropharyngeal cT1-T2N0 cancer (OC), but, to our knowledge, the oncologic equivalence of SN biopsy and neck lymph node dissection (ND; standard treatment) has never been evaluated. METHODS: In this phase III multicenter trial, 307 patients with OC were randomly assigned to (1) the ND arm or (2) the SN arm (experimental arm: biopsy alone if negative, or followed by ND if positive, during primary tumor surgery). The primary outcome was neck node recurrence-free survival (RFS) at 2 years. Secondary outcomes were 5-year neck node RFS, 2- and 5-year disease-specific survival (DSS), and overall survival (OS). Other outcomes were hospital stay length, neck and shoulder morbidity, and number of physiotherapy prescriptions during the 2 years after surgery. RESULTS: Data on 279 patients (139 ND and 140 SN) could be analyzed. Neck node RFS was 89.6% (95% CI, 0.83% to 0.94%) at 2 years in the ND arm and 90.7% (95% CI, 0.84% to 0.95%) in the SN arm, confirming the equivalence with P < .01. The 5-year RFS and the 2- and 5-year DSS and OS were not significantly different between arms. The median hospital stay length was 8 days in the ND arm and 7 days in the SN arm (P < .01). The functional outcomes were significantly worse in the ND arm until 6 months after surgery. CONCLUSION: This study demonstrated the oncologic equivalence of the SN and ND approaches, with lower morbidity in the SN arm during the first 6 months after surgery, thus establishing SN as the standard of care in OC.

Should we use liver grafts repeatedly refused by other transplant teams?

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.

Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-An illustration with the International Stroke Trial.

OBJECTIVE: Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects. STUDY DESIGN AND SETTING: As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors. RESULTS: The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin-despite an average benefit-may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients. CONCLUSIONS: Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.

Matching Graft Quality to Recipient's Disease Severity Based on the Survival Benefit in Liver Transplantation.

Persistent shortage and heterogeneous quality of liver grafts encourages the optimization of donor-recipient matching in liver transplantation (LT). We explored whether or not there was a survival benefit (SB) of LT according to the quality of grafts assessed by the Donor Quality Index (DQI) and recipients' disease severity, using the Model for End-Stage Liver Disease (MELD) in 8387 French patients wait-listed between 2009 and 2014. SB associated with LT was estimated using the sequential stratification method in different categories of MELD and DQI. For each transplantation, a stratum was created that matched one transplanted patient with all eligible control candidates. Strata were thereafter combined, and a stratified Cox model, adjusted for covariates, was fitted in order to estimate hazard ratios that qualified the SB according to each MELD and DQI sub-group. A significant SB was observed for all MELD and DQI sub-groups, with the exception of high MELD patients transplanted with "high-risk" grafts. More specifically, in decompensated-cirrhosis patients, "high-risk" grafts did not appear to be detrimental in medium MELD patients. Interestingly, in hepatocellular-carcinoma (HCC) patients, a significant SB was found for all MELD-DQI combinations. For MELD exceptions no SB was found. In terms of SB, "low-risk" grafts appeared appropriate for most severe patients (MELD > 30). Conversely, low/medium MELD and HCC patients presented an SB while allocated "high-risk" grafts. Thus, SB based matching rules for LT candidates might improve the survival of the LT population as a whole.

Circulating Tumor Cells as a Prognostic Factor in Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: The CIRCUTEC Prospective Study.

BACKGROUND: This prospective multicenter study evaluated the prognostic value of circulating tumor cells (CTCs) in relapsing nonoperable or metastatic head and neck squamous cell carcinoma (rHNSCC) treated by chemotherapy and cetuximab. METHODS: In 65 patients suitable for analyses, peripheral blood was taken at day 0 (D0) D7, and D21 of treatment for CTC detection by CellSearch®, EPISPOT, and flow cytometry (FCM). Progression-free survival (PFS) was assessed with the Kaplan-Meier method and compared with the log-rank test (P < 0.05). RESULTS: At D0, CTCs were detected with EPISPOT, CellSearch, and FCM in 69% (45/65), 21% (12/58), and 11% (7/61) of patients, respectively. In the patients tested with all 3 methods, EPISPOT identified 92% (36/39), 92% (35/38), and 90% (25/28) of all positive samples at D0, D7, and D21, respectively. Median PFS time was significantly lower in (a) patients with increasing or stable CTC counts (36/54) from D0 to D7 with EPISPOTEGFR (3.9 vs 6.2 months; 95% CI, 5.0-6.9; P = 0.0103) and (b) patients with ≥1 CTC detected with EPISPOT or CellSearch® (37/51) (P = 0.0311), EPISPOT or FCM (38/54) (P = 0.0480), and CellSearch or FCM (11/51) (P = 0.0005) at D7. CONCLUSIONS: CTCs can be detected before and during chemotherapy in patients with rHNSCC. D0-D7 CTC kinetics evaluated with EPISPOTEGFR are associated with the response to treatment. This study indicates that CTCs can be used as a real-time liquid biopsy to monitor the early response to chemotherapy in rHNSCC. CLINICALTRIALSGOV IDENTIFIER: NCT02119559.

Regional Professionals Network to Support the Renal Epidemiology and Information Registry in Ile-de-France.

We present the regional professional network to support the Renal Epidemiology Information Network (REIN) registry in maintaining high quality data production and information analyses in Ile-De-France region. The network is based on a long term partnership between the nephrologists and a regional methodology support unit. It integrates clinical research assistants for data quality control. We also present organizational methods on maintaining the registry and enhancing information analyses and automating analyses reports.

Hospital burden of coronary artery disease: Trends of myocardial infarction and/or percutaneous coronary interventions in France 2009-2014.

BACKGROUND: Currently, 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). METHODS: MI/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. RESULTS: Over 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 <40 or >80. CONCLUSION: We outlined the public health burden of this condition and the importance of improving the trajectories of care as an aid for better care.

Increased incidence of acute kidney injury requiring dialysis in metropolitan France.

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<0.001), respectively. In-hospital mortality was high but stable (47%) and higher in patients with an ICU stay. CONCLUSIONS: This is the first exhaustive study in metropolitan France of the SIR for AKI-D. It shows this SIR has increased significantly over 6 years, together with ICU care and continuous RRT. In-hospital mortality is high but stable.

Federating patients identities: the case of rare diseases.

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.

Author Correction: A Donor Quality Index for liver transplantation: development, internal and external validation.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Recommendations for Improving the Quality of Rare Disease Registries.

Rare diseases (RD) patient registries are powerful instruments that help develop clinical research, facilitate the planning of appropriate clinical trials, improve patient care, and support healthcare management. They constitute a key information system that supports the activities of European Reference Networks (ERNs) on rare diseases. A rapid proliferation of RD registries has occurred during the last years and there is a need to develop guidance for the minimum requirements, recommendations and standards necessary to maintain a high-quality registry. In response to these heterogeneities, in the framework of RD-Connect, a European platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research, we report on a list of recommendations, developed by a group of experts, including members of patient organizations, to be used as a framework for improving the quality of RD registries. This list includes aspects of governance, Findable, Accessible, Interoperable and Reusable (FAIR) data and information, infrastructure, documentation, training, and quality audit. The list is intended to be used by established as well as new RD registries. Further work includes the development of a toolkit to enable continuous assessment and improvement of their organizational and data quality.

A Donor Quality Index for liver transplantation: development, internal and external validation.

Organ shortage leads to using non-optimal liver grafts. Thus, to determine the graft quality, the Donor Risk Index and the Eurotransplant Donor Risk Index have been proposed. In a previous study we showed that neither could be validated on the French database. Our aim was then dedicated to propose an adaptive Donor Quality Index (DQI) using data from 3961 liver transplantation (LT) performed in France between 2009 and 2013, with an external validation based on 1048 French LT performed in 2014. Using Cox models and three different methods of selection, we developed a new score and defined groups at risk. Model performance was assessed by means of three measures of discrimination corrected by the optimism using a bootstrap procedure. An external validation was also performed in order to evaluate its calibration and discrimination. Five donor covariates were retained: age, cause of death, intensive care unit stay, lowest MDRD creatinine clearance, and liver type. Three groups at risk could be discriminated. The performances of the model were satisfactory after internal validation. Calibration and discrimination were preserved in the external validation dataset. The DQI exhibited good properties and is potentially adaptive as an aid for better guiding decision making for LT.

PaFloChar: An Innovating Approach to Characterise Patient Flows in Myocardial Infarction.

A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies.