Proteomic Modeling for HIV-1 Infected Microglia-Astrocyte Crosstalk

Background: HIV-1-infected and immune competent brain mononuclear phagocytes (MP; macrophages and microglia) secrete cellular and viral toxins that affect neuronal damage during advanced disease. In contrast, astrocytes can affect disease by modulating the nervous system’s microenvironment. Interestingly, little is known how astrocytes communicate with MP to influence disease. Methods and Findings: MP-astrocyte crosstalk was investigated by a proteomic platform analysis using vesicular stomatitis virus pseudotyped HIV infected murine microglia. The microglial-astrocyte dialogue was significant and affected microglial cytoskeleton by modulation of cell death and migratory pathways. These were mediated, in part, through F-actin polymerization and filament formation. Astrocyte secretions attenuated HIV-1 infected microglia neurotoxicity and viral growth linked to the regulation of reactive oxygen species. Conclusions: These observations provide unique insights into glial crosstalk during disease by supporting astrocytemediated regulation of microglial function and its influence on the onset and progression of neuroAIDS. The results open new insights into previously undisclosed pathogenic mechanisms and open the potential for biomarker discovery an

Clinical outcomes and direct costs after transcatheter aortic valve implantation in French centres : a longitudinal study of 1332 patients using a national database

Objectives: To describe the clinical outcomes of patients undergoing transcatheter aortic valve implantation (TAVI) and to determine the direct costs before and after TAVI. Methods: A nationwide longitudinal study using data extracted from the French Hospital Information System. Selection criteria: all patients who underwent TAVI between 1 January 2010 and 31 December 2010. Period of follow-up: 12 months preceding TAVI to 36 months after. End-points: mortality, morbidity and total costs of acute and rehabilitation care from the perspective of the hospital. Results: A total of 1332 patients (mean age: 82.0 ± 7.2 years; 50.2% men) were identified. The mean hospitalization length of stay was 13.5 ± 9.3 days. The intrahospital mortality from any cause was 7.9% during the index hospitalization, 8.8% at 30 days, 14.8% at 6 months, 18.4% at 1 year, 24.8% at 2 years and 32.3% at 3 years. The mean number of hospital stays per patient was 4.79 the year preceding TAVI and 4.11 the year after. The cumulated number of hospital stays at 2 and 3 years post-TAVI was 6.88 and 9.69, respectively. The mean hospitalization costs were 14 665€ the year preceding, 26 575€ for the index procedure and 12 308€ the year after TAVI. The cumulated hospitalization costs per patient at 3 years after TAVI were 22 110€ for acute hospitalizations and 5689€ for rehabilitation. Conclusions: Mortality at 3 years is consistent with other published studies. After TAVI, hospitalization stays in both acute and rehabilitation settings, and the associated costs do not appear to be reduced compared with the year preceding TAVI. The total cost for patients undergoing TAVI is substantial at 3 years

Predicting length of stay with administrative data from acute and emergency care: an embedding approach

International audienceHospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly

Predicting length of stay with administrative data from acute and emergency care: an embedding approach

International audienceHospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly

Health Care Institutions Volume Is Significantly Associated with Postoperative Outcomes in Bariatric Surgery

International audiencePURPOSE: The volume of bariatric surgery has significantly increased over the past decade with concomitant postoperative outcomes improvement. The goal of this nationwide study was to estimate the volume-outcome relationship in bariatric surgery at the hospital level. MATERIALS AND METHODS: A cross-sectional analysis of all patients who underwent bariatric surgery procedure in France from January 2011 to December 2014 was designed. Volume-outcome relationship was analyzed using generalized estimating equations. RESULTS: We identified 184,332 inpatient stays for bariatric surgical procedures performed in 606 hospitals. Health care institutions performing more than 200 bariatric cases per year were significantly associated with shorter average length of stay (p~\textless~0.001) and less frequent need for intensive or critical care unit (p~=~0.003) during the index stay in comparison with lower volume institutions. Reoperations rate increased from 3.1% [95% CI, 2.8-3.3] (n~=~5627) at 1~month to 4.9% [4.6-5.2] at 3~months and 8.2% [7.8-8.7] at 6~months. The risk of reoperation after gastric bypass was 1.37 times less frequent in higher volume institutions (>=~200 inpatient stays per year, p~=~0.003), while it was 1.26 times more frequent after gastric banding in higher volume institutions (p~=~0.057) and was unaltered regarding sleeve gastrectomy (p~=~0.819). CONCLUSION: This study showed for the first time in bariatric surgery that reoperation rate after gastric bypass or sleeve significantly increased at 3 and 6~months postoperatively. Health care institutions performing more than 200 bariatric cases per year were significantly associated with improved postoperative outcomes and less frequent need for reoperation

Breast cancer incidence using administrative data: correction with sensitivity and specificity.

International audienceOBJECTIVE: To estimate breast cancer incidence in the general population using a method that corrects for lack of sensitivity and specificity in the identification of incident breast cancer in inpatient claims data. STUDY DESIGN AND SETTINGS: Two-phase study: phase 1 to identify incident cases in claims data, and phase 2 to estimate sensitivity and specificity in a subset of the population. Two algorithms (1: principal diagnosis; 2: principal diagnosis+specific surgery procedures) were used to identify incident cases in claims of women aged 20 years or older, living in a French district covered by a cancer registry. Sensitivity and specificity were estimated in one district and used to correct incident cases identified. RESULTS: The sensitivity and specificity for algorithms 1 and 2 were 69.0% and 99.89%, and 64.4% and 99.93%, respectively. In contrast to specificity, the sensitivity for both algorithms was lower for women younger than 40 years and older than 65 years. Cases reported by cancer registries were closer to cases identified with algorithm 2 (-3.2% to +20.1%) and to corrected numbers with algorithm 1 (-1% to +15%). CONCLUSION: To obtain reliable estimates of breast cancer incidence in the general population, sensitivity and specificity, which reflect medical and coding practice variations, are necessary