Restricted mean survival time over 15 years for patients starting renal replacement therapy

International audienceBackground:The restricted mean survival time (RMST) estimates life expectancy up to a given time horizon and can thus express the impact of a disease. The aim of this study was to estimate the 15-year RMST of a hypothetical cohort of incident patients starting renal replacement therapy (RRT), according to their age, gender and diabetes status, and to compare it with the expected RMST of the general population.Methods:Using data from 67 258 adult patients in the French Renal Epidemiology and Information Network (REIN) registry, we estimated the RMST of a hypothetical patient cohort (and its subgroups) for the first 15 years after starting RRT (cRMST) and used the general population mortality tables to estimate the expected RMST (pRMST). Results were expressed in three different ways: the cRMST, which calculates the years of life gained under the hypothesis of 100% death without RRT treatment, the difference between the pRMST and the cRMST (the years lost), and a ratio expressing the percentage reduction of the expected RMST: (pRMST - cRMST)/pRMST.Results:Over their first 15 years of RRT, the RMST of end-stage renal disease (ESRD) patients decreased with age, ranging from 14.3 years in patients without diabetes aged 18 years at ESRD to 1.8 years for those aged 90 years, and from 12.7 to 1.6 years, respectively, for those with diabetes; expected RMST varied from 15.0 to 4.1 years between 18 and 90 years. The number of years lost in all subgroups followed a bell curve that was highest for patients aged 70 years. After the age of 55 years in patients with and 70 years in patients without diabetes, the reduction of the expected RMST was >50%.Conclusion:While neither a clinician nor a survival curve can predict with absolute certainty how long a patient will live, providing estimates on years gained or lost, or percentage reduction of expected RMST, may improve the accuracy of the prognostic estimates that influence clinical decisions and information given to patients

Centralization errors in comparative genomic hybridization array analysis of pituitary tumor samples

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COVID-19 outbreaks in nursing homes: A strong link with the coronavirus spread in the surrounding population, France, March to July 2020

International audienceBackground Worldwide, COVID-19 outbreaks in nursing homes have often been sudden and massive. The study investigated the role SARS-CoV-2 virus spread in nearby population plays in introducing the disease in nursing homes. Material and methods This was carried out through modelling the occurrences of first cases in each of 943 nursing homes of Auvergne-Rhône-Alpes French Region over the first epidemic wave (March-July, 2020). The cumulative probabilities of COVID-19 outbreak in the nursing homes and those of hospitalization for the disease in the population were modelled in each of the twelve Départements of the Region over period March-July 2020. This allowed estimating the duration of the active outbreak period, the dates and heights of the peaks of outbreak probabilities in nursing homes, and the dates and heights of the peaks of hospitalization probabilities in the population. Spearman coefficient estimated the correlation between the two peak series. Results The cumulative proportion of nursing homes with COVID-19 outbreaks was 52% (490/943; range: 22–70% acc. Département). The active outbreak period in the nursing homes lasted 11 to 21 days (acc. Département) and ended before lockdown end. Spearman correlation between outbreak probability peaks in nursing homes and hospitalization probability peaks in the population (surrogate of the incidence peaks) was estimated at 0.71 (95% CI: [0.66; 0.78]). Conclusion The modelling highlighted a strong correlation between the outbreak in nursing homes and the external pressure of the disease. It indicated that avoiding disease outbreaks in nursing homes requires a tight control of virus spread in the surrounding populations

Using repeated-prevalence data in multi-state modeling of renal replacement therapy

<div><p>Multi-state models help predict future numbers of patients requiring specific treatments but these models require exhaustive incidence data. Deriving reliable predictions from repeated-prevalence data would be helpful. A new method to model the number of patients that switch between therapeutic modalities using repeated-prevalence data is presented and illustrated. The parameters and goodness of fit obtained with the new method and repeated-prevalence data were compared to those obtained with the classical method and incidence data. The multi-state model parameters’ confidence intervals obtained with annually collected repeated-prevalence data were wider than those obtained with incidence data and six out of nine pairs of confidence intervals did not overlap. However, most parameters were of the same order of magnitude and the predicted patient distributions among various renal replacement therapies were similar regardless of the type of data used. In the absence of incidence data, a multi-state model can still be successfully built with annually collected repeated-prevalence data to predict the numbers of patients requiring specific treatments. This modeling technique can be extended to other chronic diseases.</p></div

Dany2014.Supplementay.Materials

<p>Supplementary information for paper:</p> <p>http://dx.doi.org/10.1080/02664763.2014.999648 .</p

Stronger Impact of COVID-19 in Nursing Homes of a French Region During the Second Pandemic Wave

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Progressive fibrosing interstitial lung disease: a clinical cohort (the PROGRESS study)

International audienceIn patients with chronic fibrosing interstitial lung disease (ILD), a progressive fibrosing phenotype (PF-ILD) may develop, but information on the frequency and characteristics of this population outside clinical trials is lacking. We assessed the characteristics and outcomes of patients with PF-ILD other than idiopathic pulmonary fibrosis (IPF) in a real-world, single-centre clinical cohort. The files of all consecutive adult patients with fibrosing ILD (2010–2017) were examined retrospectively for pre-defined criteria of ≥10% fibrosis on high-resolution computed tomography and progressive disease during overlapping windows of 2 years. Baseline was defined as the date disease progression was identified. Patients receiving nintedanib or pirfenidone were censored from survival and progression analyses. In total, 1395 patients were screened; 617 had ILD other than IPF or combined pulmonary fibrosis and emphysema, and 168 had progressive fibrosing phenotypes. In 165 evaluable patients, median age was 61 years; 57% were female. Baseline mean forced vital capacity (FVC) was 74±22% predicted. Median duration of follow-up was 46.2 months. Annualised FVC decline during the first year was estimated at 136±328 mL using a linear mixed model. Overall survival was 83% at 3 years and 72% at 5 years. Using multivariate Cox regression analysis, mortality was significantly associated with relative FVC decline ≥10% in the previous 24 months (p<0.05), age ≥50 years (p<0.01) and diagnosis subgroup (p<0.01). In this cohort of patients with PF-ILD not receiving antifibrotic therapy, the disease followed a course characterised by continued decline in lung function, which predicted mortality

Long‐term psycho‐traumatic consequences of the COVID‐19 health crisis among emergency department healthcare workers

International audienceAbstract Assess the changes in post‐traumatic stress disorder (PTSD), burnout, anxiety, depression, jobstrain, and isostrain levels over time among healthcare workers in emergency departments (EDs) after successive outbreaks of COVID‐19. A prospective, multicenter study was conducted in 3 EDs and an emergency medical service. Healthcare workers who participated in our previous study were invited to participate in a follow‐up 16 and 18 months and completed the questionnaires to assess symptoms of PTSD, burnout, anxiety, depression, jobstrain, and isostrain. Among the 485 healthcare workers asked to participate, 211 (43.5%) completed the survey at inclusion (122 were followed up at 3 months) and 59 participate to the follow‐up study. At 16 months, 10.9% of healthcare workers had symptoms of PTSD and 17.4% at 18 months. At inclusion, 33.5% and 11.7% of healthcare workers had symptoms of anxiety and depression, respectively. A decrease in anxiety between inclusion and 16 months ( p = 0.02) and an increase between 16 and 18 months ( p = 0.009) was observed. At inclusion, 40.8% of all healthcare workers had symptoms of burnout. There was an increase in symptoms of burnout between inclusion and 18 months ( p = 0.006). At inclusion, 43.2% and 29.5% of healthcare workers were exposed to jobstrain and isostrain, respectively. Jobstrain were higher among paramedics and administrative staff compared to physicians ( p = 0.001 and p = 0.026, respectively). Successive outbreaks of COVID‐19 led to long‐term mental health consequences among ED healthcare workers that differed according to occupation. This must be taken into account to rethink the management of teams