Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.Comment: The Lancet Digital Health, 202

Modélisation de la stéatose hépatique (NAFLD) et de ses facteurs de risque par apprentissage sur des données de santé

Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease which is a combination of simple, slowly progressing steatosis, and non-alcoholic steatohepatitis (NASH), an inflammatory form which accelerates its progression. It is estimated that one in four people in the world is affected by NAFLD, and its prevalence is increasing rapidly, in parallel with the prevalence of its main risk factors: overweight, obesity and type 2 diabetes.This pathology is asymptomatic up to the complications, cirrhosis and liver cancer (hepatocellular carcinoma, HCC), which leads to late diagnosis and a negative impact on the associated morbidity and mortality. Furthermore, the reference diagnosis requires a liver biopsy, an invasive examination that cannot be performed routinely. As a result, the progression of the disease is poorly known and its estimation may suffer from a selection bias, towards patients with significant risk factors, who require a biopsy in the first place. A better understanding would allow the implementation of strategies to reduce its burden.The modelling approach is appropriate to take into account all susceptible patients, without having to carry out a large-scale follow-up study using liver biopsies in patients who are mostly asymptomatic. The objectives of this thesis are to describe and quantify the progression of NAFLD, to predict the associated morbidity and mortality, and to identify the population at risk, using Markov models. To do this, it is necessary to fill in some of the progression parameters via a literature review, to characterise the initial states (population likely to develop NAFLD) and the final states (mortality due to NAFLD), in order to deduce the missing progression parameters between the onset of the disease and mortality, by back-calculation.To exhaustively characterise NAFLD mortality, we identified all patients with cirrhosis or HCC from national hospital databases, representing more than 380,000 patients. We then developed an identification algorithm to determine the etiology underlying the hepatic complication, based on all the stays of the identified patients. This algorithm requires the identification of patients with cirrhosis or HCC of alcoholic or viral origin, to obtain by elimination only NAFLD patients. Once the specific mortality data had been obtained, we estimated the population likely to develop NAFLD, defined as all individuals with overweight or type 2 diabetes, excluding the population of excessive drinkers. We estimated the prevalence and incidence of this population, and modelled its evolution with age and years, based on individual data from surveys representative of the French population.Finally, we quantified the progression of NAFLD, and the impact of risk factors, using two approaches: from the literature, and from biopsy data from more than 1,800 obese patients who were candidates for bariatric surgery, resulting in a tool for predicting the progression of NAFLD in this population. We chose to back-calculate the progression parameters corresponding to the asymptomatic states, which are the most susceptible to selection bias.We obtained a model of the progression of NAFLD, taking into account the dynamic distribution of the population among weight classes and diabetes status, and resulting in the observed statistics of NAFLD deaths. The model takes into account gender, age, year, BMI (body mass index) class, diabetes status and the presence of a genetic polymorphism (PNPLA3 rs738409, C→G) as covariates of progression. It is a tool for assessing the impact of a possible treatment or public health policy on morbidity and mortality.La stéatose hépatique non-alcoolique (NAFLD) est une maladie chronique du foie regroupant la stéatose simple à évolution lente, et la stéatohépatite non-alcoolique (NASH), forme inflammatoire accélérant son évolution. On estime qu’une personne sur quatre dans le monde est atteinte de NAFLD, et cette prévalence augmente rapidement, en parallèle avec celle de ses principaux facteurs de risque : le surpoids, l’obésité et le diabète. Cette pathologie est asymptomatique jusqu’aux complications, la cirrhose et le cancer du foie (carcinome hépatocellulaire, CHC), ce qui induit un diagnostic tardif et un impact négatif sur la morbidité et mortalité associées. De plus, le diagnostic de référence nécessite une biopsie hépatique, un examen invasif qui ne peut être réalisé en routine. En conséquence, la progression de la maladie est mal connue et son estimation peut souffrir d’un biais de sélection, vers les patients présentant des facteurs de risques importants, qui nécessitaient une biopsie en premier lieu. Mieux l’appréhender permettrait de mettre en place des stratégies diminuant son fardeau.L’approche par modélisation est appropriée pour prendre en compte l’ensemble des patients susceptibles, sans avoir à réaliser d’étude de suivi à large échelle par biopsie hépatique chez des patients en majorité asymptomatiques. Les objectifs de cette thèse sont de décrire et quantifier la progression de la NAFLD, de prédire la morbidité et mortalité associées, ainsi que d’identifier la population à risque, par modèles de Markov. Pour cela, il est nécessaire de renseigner une partie des paramètres de progression via une revue de la littérature, de caractériser les états initiaux (population susceptible de développer la NAFLD) et les états finaux (mortalité due à la NAFLD), pour en déduire les paramètres de progression manquants entre l’entrée dans la maladie et la mortalité, par rétro-calcul.Pour caractériser la mortalité due à la NAFLD de manière exhaustive, nous avons identifié tous les patients avec une cirrhose ou un CHC à partir des bases de données nationales des hôpitaux, soit plus de 380 000 patients. Nous avons ensuite élaboré un algorithme d’identification pour déterminer l’étiologie sous-jacente à la complication hépatique, à partir de l’ensemble des séjours des patients identifiés. Cet algorithme nécessite d’identifier les patients avec cirrhose ou CHC d’origine alcoolique ou virale, pour obtenir par élimination uniquement les patients NAFLD.Une fois les données de mortalité spécifiques obtenues, nous avons estimé la population susceptible de développer la NAFLD, définie comme l’ensemble des individus avec un surpoids ou un diabète de type 2, en excluant la population de buveurs excessifs. Nous avons estimé la prévalence et l’incidence de cette population, et modélisé son évolution avec l’âge et les années, à partir de données individuelles d’enquêtes représentatives de la population française.Enfin, nous avons quantifié la progression de la NAFLD, et l’impact des facteurs de risque, via deux approches : à partir de la littérature, et à partir de données de biopsies de plus de 1 800 patients obèses candidats à la chirurgie bariatrique, aboutissant à un outil de prédiction de la progression de la NAFLD dans cette population. Nous avons choisi de rétro-calculer les paramètres de progression correspondant aux états asymptomatiques, les plus susceptibles au biais de sélection.Nous avons obtenu un modèle de l’évolution de la NAFLD, prenant en compte la distribution dynamique de la population parmi les classes de poids et le statut de diabète, et aboutissant aux statistiques observées de décès dus à la NAFLD. Le modèle prend en compte le sexe, l’âge, l’année, la classe d’IMC, le statut de diabète et la présence d’un polymorphisme génétique (PNPLA3 rs738409, C→G) comme covariables de progression. Il constitue un outil permettant d’évaluer l’impact d’un éventuel traitement ou d’une politique de santé publique sur la morbi-mortalité

Modelling of hepatic steatosis (NAFLD) and its risk factors by learning from health data

La stéatose hépatique non-alcoolique (NAFLD) est une maladie chronique du foie regroupant la stéatose simple à évolution lente, et la stéatohépatite non-alcoolique (NASH), forme inflammatoire accélérant son évolution. On estime qu’une personne sur quatre dans le monde est atteinte de NAFLD, et cette prévalence augmente rapidement, en parallèle avec celle de ses principaux facteurs de risque : le surpoids, l’obésité et le diabète. Cette pathologie est asymptomatique jusqu’aux complications, la cirrhose et le cancer du foie (carcinome hépatocellulaire, CHC), ce qui induit un diagnostic tardif et un impact négatif sur la morbidité et mortalité associées. De plus, le diagnostic de référence nécessite une biopsie hépatique, un examen invasif qui ne peut être réalisé en routine. En conséquence, la progression de la maladie est mal connue et son estimation peut souffrir d’un biais de sélection, vers les patients présentant des facteurs de risques importants, qui nécessitaient une biopsie en premier lieu. Mieux l’appréhender permettrait de mettre en place des stratégies diminuant son fardeau.L’approche par modélisation est appropriée pour prendre en compte l’ensemble des patients susceptibles, sans avoir à réaliser d’étude de suivi à large échelle par biopsie hépatique chez des patients en majorité asymptomatiques. Les objectifs de cette thèse sont de décrire et quantifier la progression de la NAFLD, de prédire la morbidité et mortalité associées, ainsi que d’identifier la population à risque, par modèles de Markov. Pour cela, il est nécessaire de renseigner une partie des paramètres de progression via une revue de la littérature, de caractériser les états initiaux (population susceptible de développer la NAFLD) et les états finaux (mortalité due à la NAFLD), pour en déduire les paramètres de progression manquants entre l’entrée dans la maladie et la mortalité, par rétro-calcul.Pour caractériser la mortalité due à la NAFLD de manière exhaustive, nous avons identifié tous les patients avec une cirrhose ou un CHC à partir des bases de données nationales des hôpitaux, soit plus de 380 000 patients. Nous avons ensuite élaboré un algorithme d’identification pour déterminer l’étiologie sous-jacente à la complication hépatique, à partir de l’ensemble des séjours des patients identifiés. Cet algorithme nécessite d’identifier les patients avec cirrhose ou CHC d’origine alcoolique ou virale, pour obtenir par élimination uniquement les patients NAFLD.Une fois les données de mortalité spécifiques obtenues, nous avons estimé la population susceptible de développer la NAFLD, définie comme l’ensemble des individus avec un surpoids ou un diabète de type 2, en excluant la population de buveurs excessifs. Nous avons estimé la prévalence et l’incidence de cette population, et modélisé son évolution avec l’âge et les années, à partir de données individuelles d’enquêtes représentatives de la population française.Enfin, nous avons quantifié la progression de la NAFLD, et l’impact des facteurs de risque, via deux approches : à partir de la littérature, et à partir de données de biopsies de plus de 1 800 patients obèses candidats à la chirurgie bariatrique, aboutissant à un outil de prédiction de la progression de la NAFLD dans cette population. Nous avons choisi de rétro-calculer les paramètres de progression correspondant aux états asymptomatiques, les plus susceptibles au biais de sélection.Nous avons obtenu un modèle de l’évolution de la NAFLD, prenant en compte la distribution dynamique de la population parmi les classes de poids et le statut de diabète, et aboutissant aux statistiques observées de décès dus à la NAFLD. Le modèle prend en compte le sexe, l’âge, l’année, la classe d’IMC, le statut de diabète et la présence d’un polymorphisme génétique (PNPLA3 rs738409, C→G) comme covariables de progression. Il constitue un outil permettant d’évaluer l’impact d’un éventuel traitement ou d’une politique de santé publique sur la morbi-mortalité.Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease which is a combination of simple, slowly progressing steatosis, and non-alcoholic steatohepatitis (NASH), an inflammatory form which accelerates its progression. It is estimated that one in four people in the world is affected by NAFLD, and its prevalence is increasing rapidly, in parallel with the prevalence of its main risk factors: overweight, obesity and type 2 diabetes.This pathology is asymptomatic up to the complications, cirrhosis and liver cancer (hepatocellular carcinoma, HCC), which leads to late diagnosis and a negative impact on the associated morbidity and mortality. Furthermore, the reference diagnosis requires a liver biopsy, an invasive examination that cannot be performed routinely. As a result, the progression of the disease is poorly known and its estimation may suffer from a selection bias, towards patients with significant risk factors, who require a biopsy in the first place. A better understanding would allow the implementation of strategies to reduce its burden.The modelling approach is appropriate to take into account all susceptible patients, without having to carry out a large-scale follow-up study using liver biopsies in patients who are mostly asymptomatic. The objectives of this thesis are to describe and quantify the progression of NAFLD, to predict the associated morbidity and mortality, and to identify the population at risk, using Markov models. To do this, it is necessary to fill in some of the progression parameters via a literature review, to characterise the initial states (population likely to develop NAFLD) and the final states (mortality due to NAFLD), in order to deduce the missing progression parameters between the onset of the disease and mortality, by back-calculation.To exhaustively characterise NAFLD mortality, we identified all patients with cirrhosis or HCC from national hospital databases, representing more than 380,000 patients. We then developed an identification algorithm to determine the etiology underlying the hepatic complication, based on all the stays of the identified patients. This algorithm requires the identification of patients with cirrhosis or HCC of alcoholic or viral origin, to obtain by elimination only NAFLD patients. Once the specific mortality data had been obtained, we estimated the population likely to develop NAFLD, defined as all individuals with overweight or type 2 diabetes, excluding the population of excessive drinkers. We estimated the prevalence and incidence of this population, and modelled its evolution with age and years, based on individual data from surveys representative of the French population.Finally, we quantified the progression of NAFLD, and the impact of risk factors, using two approaches: from the literature, and from biopsy data from more than 1,800 obese patients who were candidates for bariatric surgery, resulting in a tool for predicting the progression of NAFLD in this population. We chose to back-calculate the progression parameters corresponding to the asymptomatic states, which are the most susceptible to selection bias.We obtained a model of the progression of NAFLD, taking into account the dynamic distribution of the population among weight classes and diabetes status, and resulting in the observed statistics of NAFLD deaths. The model takes into account gender, age, year, BMI (body mass index) class, diabetes status and the presence of a genetic polymorphism (PNPLA3 rs738409, C→G) as covariates of progression. It is a tool for assessing the impact of a possible treatment or public health policy on morbidity and mortality

Early prediction of the impact of public health policies on obesity and lifetime risk of type 2 diabetes: A modelling approach.

ObjectiveHelp public health decision-making requires a better understanding of the dynamics of obesity and type 2 diabetes and an assessement of different strategies to decrease their burdens.MethodsBased on 97,848 individual data, collected in the French Health, Health Care and Insurance Survey over 1998-2014, a Markov model was developed to describe the progression of being overweight to obesity, and the onset of type 2 diabetes. This model traces and predicts 2022-2027 burdens of obesity and type 2 diabetes, and lifetime risk of diabetes, according to different scenarios aiming at minimum to stabilize obesity at 5 years.ResultsEstimated risks of type 2 diabetes increase from 0.09% (normal weight) to 1.56% (obesity II-III). Compared to the before 1995 period, progression risks are estimated to have nearly doubled for obesity and tripled for type 2 diabetes. Consequently, over 2022-2027, the prevalence of obesity and type 2 diabetes will continue to increase from 17.3% to 18.2% and from 7.3% to 8.1%, respectively. Scenarios statibilizing obesity would require a 22%-decrease in the probability of move up (scenario 1) or a 33%-increase in the probability of move down (scenario 2) one BMI class. However, this stabilization will not affect the increase of diabetes prevalence whereas lifetime risk of diabetes would decrease (30.9% to 27.0%). Combining both scenarios would decrease obesity by 9.9%. Only the prevalence of obesity III shows early change able to predict the outcome of a strategy: for example, 6.7%-decrease at one year, 13.3%-decrease at two years with scenario 1 stabilizing obesity at 5 years.ConclusionsPrevalences of obesity and type 2 diabetes will still increase over the next 5 years. Stabilizing obesity may decrease lifetime risks of type 2 diabetes without affecting its short-term prevalence. Our study highlights that, to early assess the effectiveness of their program, public health policy makers should rely on the change in prevalence of obesity III

Methodological Considerations in Cost of Prostate Cancer Studies: A Systematic Review

AbstractObjectivesCost-of-illness (COI) studies estimate the overall economic burden of a specific disease, rather than simply treatment-related costs. While having been criticized for not allowing resource prioritization, COI studies can provide useful guidance, so long as they adhere to accepted methodology. Prostate cancer is an important disease in terms of economic implications because of its increasing incidence and health-care costs and therefore provides a relevant example with which to review COI study methodologies. The aim of this study was to review published COI studies on prostate cancer to analyze the methods used.MethodsFirst, we provide a general description of the COI method. COI studies relating to prostate cancer were then systematically reviewed, focussing on an analysis of the different methods used.ResultsThe methods, data sources, and estimated cost categories in each study varied widely. The review showed that COI studies adopted significantly different approaches to estimate the costs of prostate cancer, reflecting a lack of consensus on the methodology of COI studies in this area.ConclusionTo increase its credibility, closer agreement among researchers on the methodological principles of the COI studies would be desirable

Un modèle pour identifier les buveurs excessifs à haut risque de progression de la maladie du foie

International audienceBACKGROUND & AIMS:Alcohol-related liver disease (ALD) causes chronic liver disease. We investigated how information on patients' drinking history and amount, stage of liver disease, and demographic feature can be used to determine risk of disease progression.METHODS:We collected data from 2334 heavy drinkers (50 g/day or more) with persistently abnormal results from liver tests who had been admitted to a hepato-gastroenterology unit in France from January 1982 through December 1997; patients with a recorded duration of alcohol abuse were assigned to the development cohort (n=1599; 75% men) or the validation cohort (n=735; 75% men), based on presence of a liver biopsy. We collected data from both cohorts on patient history and disease stage at the time of hospitalization. For the development cohort, severity of the disease was scored by the METAVIR (due to the availability of liver histology reports); in the validation cohort only the presence of liver complications was assessed. We developed a model of ALD progression and occurrence of liver complications (hepatocellular carcinoma and/or liver decompensation) in association with exposure to alcohol, age at the onset of heavy drinking, amount of alcohol intake, sex and body mass index. The model was fitted to the development cohort and then evaluated in the validation cohort. We then tested the ability of the model to predict disease progression for any patient profile (baseline evaluation). Patients with a 5-y weighted risk of liver complications greater than 5% were considered at high risk for disease progression.RESULTS:Model results are given for the following patient profiles: men and women, 40 y old, who started drinking at an age of 25 y, drank 150 g/day, and had a body mass index of 22 kg/m2 according to the disease severity at baseline evaluation. For men with baseline F0-F2 fibrosis, the model estimated the probabilities of normal liver, steatosis, or steatohepatitis at baseline to be 31.8%, 61.5% and 6.7%, respectively. The 5-y weighted risk of liver complications was 1.9%, ranging from 0.2% for men with normal liver at baseline evaluation to 10.3% for patients with steatohepatitis at baseline. For women with baseline F0-F2 fibrosis, probabilities of normal liver, steatosis, or steatohepatitis at baseline were 25.1%, 66.5% and 8.4%, respectively; the 5-y weighted risk of liver complications was 3.2%, ranging from 0.5% for women with normal liver at baseline to 14.7% for patients with steatohepatitis at baseline. Based on the model, men with F3-F4 fibrosis at baseline have a 24.5% 5-y weighted risk of complications (ranging from 20.2% to 34.5%) and women have a 30.1% 5-y weighted risk of complications (ranging from 24.7% to 41.0%).CONCLUSIONS:We developed a Markov model that integrates data on level and duration of alcohol use to identify patients at high risk of liver disease progression. This model might be used to adapt patient care pathways

A tool to predict progression of non-alcoholic fatty liver disease in severely obese patients

International audienceBackground & AimsSeverely obese patients are a growing population at risk of non-alcoholic fatty liver disease (NAFLD). Considering the increasing burden, a predictive tool of NAFLD progression would be of interest. Our objective was to provide a tool allowing general practitioners to identify and refer the patients most at risk, and specialists to estimate disease progression and adapt the therapeutic strategy.MethodsThis predictive tool is based on a Markov model simulating steatosis, fibrosis and non-alcoholic steatohepatitis (NASH) evolution. This model was developped from data of 1801 severely obese, bariatric surgery candidates, with histological assessment, integrating duration of exposure to risk factors. It is then able to predict current disease severity in the absence of assessment, and future cirrhosis risk based on current stage.ResultsThe model quantifies the impact of sex, body-mass index at 20, diabetes, age of overweight onset, on progression. For example, for 40-year-old severely obese patients seen by the general practitioners: (a) non-diabetic woman overweight at 20, and (b) diabetic man overweight at 10, without disease assessment, the model predicts their current risk to have NASH or F3-F4: for (a) 5.7% and 0.6%, for (b) 16.1% and 10.0% respectively. If those patients have been diagnosed F2 by the specialist, the model predicts the 5-year cirrhosis risk: 1.8% in the absence of NASH and 6.0% in its presence for (a), 10.3% and 26.7% respectively, for (b).ConclusionsThis model provides a decision-making tool to predict the risk of liver disease that could help manage severely obese patients

Impact de l'assistance robotique sur les complications après chirurgie bariatrique en centres experts en chirurgie laparoscopique : une étude rétrospective comparative avec score de propension

International audienceObjective: To investigate the way robotic assistance affected rate of complications in bariatric surgery at expert robotic and laparoscopic surgery facilities. Background: While the benefits of robotic assistance were established at the beginning of surgical training, there is limited data on the robot’s influence on experienced bariatric laparoscopic surgeons. Methods: We conducted a retrospective study using the BRO clinical database (2008–2022) collecting data of patients operated on in expert centers. We compared the serious complication rate (defined as a Clavien score≥3) in patients undergoing metabolic bariatric surgery with or without robotic assistance. We used a directed acyclic graph to identify the variables adjustment set used in a multivariable linear regression, and a propensity score matching to calculate the average treatment effect (ATE) of robotic assistance. Results: The study included 35,043 patients [24,428 sleeve gastrectomy (SG); 10,452 Roux-en-Y gastric bypass (RYGB); 163 single anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S)], with 938 operated on with robotic assistance (801 SG; 134 RYGB; 3 SADI-S), among 142 centers. Overall, we found no benefit of robotic assistance regarding the risk of complications (average treatment effect=−0.05, P =0.794), with no difference in the RYGB+SADI group ( P =0.322) but a negative trend in the SG group (more complications, P =0.060). Length of hospital stay was decreased in the robot group (3.7±11.1 vs 4.0±9.0 days, P <0.001). Conclusions: Robotic assistance reduced the length of stay but did not statistically significantly reduce postoperative complications (Clavien score≥3) following either GBP or SG. A tendency toward an elevated risk of complications following SG requires more supporting studies

Repenser la prise en charge des sujets âgés atteints d’un cancer : propositions du groupe Priorités Âge Cancer

International audienceThe growing incidence of cancer associated with an aging population implies important health challenges that require questioning on the care management of older adults with cancer. There is a need to rethink the care management of older cancer patients with patient-centered decisions and an adjustment of the care pathway for this population. The Priorities Age Cancer (PAC) French group, made up of physicians, pharmacists and researchers in geriatric oncology, set up proposals to answer this need. First, the heterogeneity and the specificities of older adults as well as their preferences regarding cancer treatment goals, care management decisions must be patient-centered. The frailty screening tools should be generalized in clinical practice to provide geriatric assessment-guided recommendations and help for treatment decisions, and patients' involvement and shared decision should be developed. Second, older adults with cancer confront a complex health care system that demands a high level of health literacy. The caregivers, playing an essential role, may not be prepared for all these challenges. Thus, there is a need to promote health literacy by patient education, and patient-experts should be involved in health pathway. Third, there is a need to deal with dedicated partners and adjust the care pathway. New pathway careers as case-management nurses and specialized pharmacists should be involved in patient care and may play a central role together with other careers. Community-Hospital coordination should also be reinforced.Plus de la moitié des cancers sont diagnostiqués chez des sujets âgés, et cette part va croître dans les prochaines années. La prise en charge des patients âgés atteints d’un cancer constitue un défi majeur, qui nécessite de placer le patient au cœur des décisions et de réorganiser le parcours de soins, en repensant la collaboration entre les différents partenaires. Le groupe Priorités Âge Cancer, composé de praticiens, de pharmaciens et de chercheurs en oncogériatrie, a émis plusieurs propositions afin de répondre à ces besoins. Considérant l’hétérogénéité et les spécificités des sujets âgés, mais également leurs préférences, les décisions thérapeutiques doivent être individualisées. Une gradation coordonnée des soins doit être réalisée en généralisant les outils de repérage de la fragilité. L’implication des patients doit être renforcée afin de développer une meilleure décision partagée. Les patients âgés sont confrontés à un système de soins complexe qui exige un niveau élevé de littératie pour comprendre les traitements et les différentes étapes du parcours de soins. Les aidants participent à la prise en charge de leurs proches, mais peuvent ne pas être préparés à relever les défis, que ce rôle essentiel implique. Il est nécessaire de renforcer l’information des patients, et promouvoir le rôle des patients experts, mais également de soutenir, former et intégrer les aidants au parcours de soins. Il semble également nécessaire d’impliquer de nouveaux partenaires comme les pharmaciens ou les infirmiers formés à la gestion de cas. Le lien ville-hôpital doit être renforcé, notamment avec les acteurs du premier recours