Thyroidal effect of metformin treatment in patients with polycystic ovary syndrome.

OBJECTIVE: Metformin is widely used for the treatment of type 2 diabetes. Growing evidence supports the beneficial effects of metformin also in patients with polycystic ovary syndrome (PCOS). It was recently reported that metformin has a TSH-lowering effect in hypothyroid patients with diabetes being treated with metformin. DESIGN: Aim of this study was to evaluate the effect of metformin treatment on the thyroid hormone profile in patients with PCOS. PATIENTS AND MEASUREMENTS: Thirty-three patients with PCOS were specifically selected for being either treated with levothyroxine for a previous diagnosis of hypothyroidism (n = 7), untreated subclinically hypothyroid (n = 2) or euthyroid without levothyroxine treatment (n = 24) before the starting of metformin. The serum levels of TSH and FT(4) were measured before and after a 4-month period of metformin therapy. RESULTS: Thyroid function parameters did not change after starting metformin therapy in euthyroid patients with PCOS. In the 9 hypothyroid patients with PCOS, the basal median serum levels of TSH (3·2 mIU/l, range = 0·4-7·1 mIU/l) significantly (P < 0·05) decreased after a 4-month course of metformin treatment (1·7 mIU/l, range = 0·5-5·2 mIU/l). No significant change in the serum levels of FT4 was observed in these patients. The TSH-lowering effect of metformin was not related to the administered dose of the drug, which was similar in euthyroid as compared with hypothyroid patients with PCOS (1406 ± 589 vs 1322 ± 402 mg/day, respectively; NS). CONCLUSIONS: These results indicate that metformin treatment has a TSH-lowering effect in hypothyroid patients with PCOS, both treated with l-thyroxine and untreated

Holography, Pade Approximants and Deconstruction

We investigate the relation between holographic calculations in 5D and the Migdal approach to correlation functions in large N theories. The latter employs Pade approximation to extrapolate short distance correlation functions to large distances. We make the Migdal/5D relation more precise by quantifying the correspondence between Pade approximation and the background and boundary conditions in 5D. We also establish a connection between the Migdal approach and the models of deconstructed dimensions.Comment: 28 page

A dashboard-based system for supporting diabetes care

[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. J., Roze, S., Valentine, W. J., Minshall, M. E., Foos, V., Lurati, F. M., … Spinas, G. A. (2004). The CORE Diabetes Model: Projecting Long-term Clinical Outcomes, Costs and Costeffectiveness of Interventions in Diabetes Mellitus (Types 1 and 2) to Support Clinical and Reimbursement Decision-making. Current Medical Research and Opinion, 20(sup1), S5-S26. doi:10.1185/030079904x1980O’Connor, P. J., Bodkin, N. L., Fradkin, J., Glasgow, R. E., Greenfield, S., Gregg, E., … Wysham, C. H. (2011). Diabetes Performance Measures: Current Status and Future Directions. Diabetes Care, 34(7), 1651-1659. doi:10.2337/dc11-0735Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Plank, J., … Pieber, T. R. (2016). Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. International Journal of Medical Informatics, 90, 58-67. doi:10.1016/j.ijmedinf.2016.03.007Sáenz, A., Brito, M., Morón, I., Torralba, A., García-Sanz, E., & Redondo, J. (2012). Development and Validation of a Computer Application to Aid the Physician’s Decision-Making Process at the Start of and during Treatment with Insulin in Type 2 Diabetes: A Randomized and Controlled Trial. Journal of Diabetes Science and Technology, 6(3), 581-588. doi:10.1177/193229681200600313Ampudia-Blasco, F. J., Benhamou, P. Y., Charpentier, G., Consoli, A., Diamant, M., Gallwitz, B., … Stoevelaar, H. (2015). A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes. Diabetes Technology & Therapeutics, 17(3), 194-202. doi:10.1089/dia.2014.0260Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Lipton, J. A., Barendse, R. J., Akkerhuis, K. M., Schinkel, A. F. L., & Simoons, M. L. (2010). Evaluation of a Clinical Decision Support System for Glucose Control. Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, 9(3), 140-147. doi:10.1097/hpc.0b013e3181e7d7caNeubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., … Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology & Therapeutics, 17(10), 685-692. doi:10.1089/dia.2015.0027Rodbard, D., & Vigersky, R. A. (2011). Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 5(2), 402-411. doi:10.1177/193229681100500230Augstein, P., Vogt, L., Kohnert, K.-D., Heinke, P., & Salzsieder, E. (2010). Translation of Personalized Decision Support into Routine Diabetes Care. Journal of Diabetes Science and Technology, 4(6), 1532-1539. doi:10.1177/193229681000400631Reza, A. W., & Eswaran, C. (2009). A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. Journal of Medical Systems, 35(1), 17-24. doi:10.1007/s10916-009-9337-yKumar, S. J. J., & Madheswaran, M. (2012). An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images. Journal of Medical Systems, 36(6), 3573-3581. doi:10.1007/s10916-012-9833-3Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial Intelligence in Medicine, 42(1), 37-53. doi:10.1016/j.artmed.2007.09.005Cleveringa, F. G. W., Gorter, K. J., van den Donk, M., & Rutten, G. E. H. M. (2008). Combined Task Delegation, Computerized Decision Support, and Feedback Improve Cardiovascular Risk for Type 2 Diabetic Patients: A cluster randomized trial in primary care. Diabetes Care, 31(12), 2273-2275. doi:10.2337/dc08-0312Haussler, B., Fischer, G. C., Meyer, S., & Sturm, D. (2007). Risk assessment in diabetes management: how do general practitioners estimate risks due to diabetes? Quality and Safety in Health Care, 16(3), 208-212. doi:10.1136/qshc.2006.019539Heselmans, A., Van de Velde, S., Ramaekers, D., Vander Stichele, R., & Aertgeerts, B. (2013). Feasibility and impact of an evidence-based electronic decision support system for diabetes care in family medicine: protocol for a cluster randomized controlled trial. Implementation Science, 8(1). doi:10.1186/1748-5908-8-83Koopman, R. J., Kochendorfer, K. M., Moore, J. L., Mehr, D. R., Wakefield, D. S., Yadamsuren, B., … Belden, J. L. (2011). A Diabetes Dashboard and Physician Efficiency and Accuracy in Accessing Data Needed for High-Quality Diabetes Care. The Annals of Family Medicine, 9(5), 398-405. doi:10.1370/afm.1286Den Ouden, H., Vos, R. C., Reidsma, C., & Rutten, G. E. (2015). Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial. BMC Family Practice, 16(1). doi:10.1186/s12875-015-0230-0Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., … Chan, D. (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Canadian Medical Association Journal, 181(1-2), 37-44. doi:10.1503/cmaj.081272O’Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2012). Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records. Journal of the American Medical Informatics Association, 19(3), 341-345. doi:10.1136/amiajnl-2011-000371Parker, R. F., Mohamed, A. Z., Hassoun, S. A., Miles, S., & Fernando, D. J. S. (2014). The Effect of Using a Shared Electronic Health Record on Quality of Care in People With Type 2 Diabetes. Journal of Diabetes Science and Technology, 8(5), 1064-1065. doi:10.1177/1932296814536880Caban, J. J., & Gotz, D. (2015). Visual analytics in healthcare - opportunities and research challenges. Journal of the American Medical Informatics Association, 22(2), 260-262. doi:10.1093/jamia/ocv006Mick, J. (2011). Data-Driven Decision Making. JONA: The Journal of Nursing Administration, 41(10), 391-393. doi:10.1097/nna.0b013e31822edb8cBatley, N. J., Osman, H. O., Kazzi, A. A., & Musallam, K. M. (2011). Implementation of an Emergency Department Computer System: Design Features That Users Value. The Journal of Emergency Medicine, 41(6), 693-700. doi:10.1016/j.jemermed.2010.05.014Sprague, A. E., Dunn, S. I., Fell, D. B., Harrold, J., Walker, M. C., Kelly, S., & Smith, G. N. (2013). Measuring Quality in Maternal-Newborn Care: Developing a Clinical Dashboard. Journal of Obstetrics and Gynaecology Canada, 35(1), 29-38. doi:10.1016/s1701-2163(15)31045-8WILBANKS, B. A., & LANGFORD, P. A. (2014). A Review of Dashboards for Data Analytics in Nursing. CIN: Computers, Informatics, Nursing, 32(11), 545-549. doi:10.1097/cin.0000000000000106Hartzler, A. L., Izard, J. P., Dalkin, B. L., Mikles, S. P., & Gore, J. L. (2015). Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. Journal of the American Medical Informatics Association, 23(1), 38-47. doi:10.1093/jamia/ocv101Dixon, B. E., Jabour, A. M., Phillips, E. O., & Marrero, D. G. (2014). An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data. Journal of the American Medical Informatics Association, 21(3), 517-521. doi:10.1136/amiajnl-2013-001959Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267-298. doi:10.1016/0933-3657(95)00036-4Sacchi, L., Capozzi, D., Bellazzi, R., & Larizza, C. (2015). JTSA: An open source framework for time series abstractions. Computer Methods and Programs in Biomedicine, 121(3), 175-188. doi:10.1016/j.cmpb.2015.05.006Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J. H., & Bellazzi, R. (2017). Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics, 66, 136-147. doi:10.1016/j.jbi.2016.12.012Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Bijlsma, M. J., Janssen, F., & Hak, E. (2015). Estimating time-varying drug adherence using electronic records: extending the proportion of days covered (PDC) method. Pharmacoepidemiology and Drug Safety, 25(3), 325-332. doi:10.1002/pds.3935Robusto, F., Lepore, V., D’Ettorre, A., Lucisano, G., De Berardis, G., Bisceglia, L., … Nicolucci, A. (2016). The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level. PLOS ONE, 11(2), e0149203. doi:10.1371/journal.pone.0149203De Berardis, G., D’Ettorre, A., Graziano, G., Lucisano, G., Pellegrini, F., Cammarota, S., … Nicolucci, A. (2012). The burden of hospitalization related to diabetes mellitus: A population-based study. Nutrition, Metabolism and Cardiovascular Diseases, 22(7), 605-612. doi:10.1016/j.numecd.2010.10.016Van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011). A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies. Journal of Medical Internet Research, 13(4), e111. doi:10.2196/jmir.1672Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Tenenbaum, J. D., Avillach, P., Benham-Hutchins, M., Breitenstein, M. K., Crowgey, E. L., Hoffman, M. A., … Freimuth, R. R. (2016). An informatics research agenda to support precision medicine: seven key areas. Journal of the American Medical Informatics Association, 23(4), 791-795. doi:10.1093/jamia/ocv213Bottomly, D., McWeeney, S. K., & Wilmot, B. (2015). HitWalker2: visual analytics for precision medicine and beyond. Bioinformatics, 32(8), 1253-1255. doi:10.1093/bioinformatics/btv739Fabris, C., Facchinetti, A., Fico, G., Sambo, F., Arredondo, M. T., & Cobelli, C. (2015). Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis. Journal of Diabetes Science and Technology, 10(1), 119-124. doi:10.1177/1932296815596173Hassenzahl, M., Wiklund-Engblom, A., Bengs, A., Hägglund, S., & Diefenbach, S. (2015). Experience-Oriented and Product-Oriented Evaluation: Psychological Need Fulfillment, Positive Affect, and Product Perception. International Journal of Human-Computer Interaction, 31(8), 530-544. doi:10.1080/10447318.2015.106466

Developing expert international consensus statements for opioid-sparing analgesia using the Delphi method

Introduction: The management of postoperative pain in anaesthesia is evolving with a deeper understanding of associating multiple modalities and analgesic medications. However, the motivations and barriers regarding the adoption of opioid-sparing analgesia are not well known. Methods: We designed a modified Delphi survey to explore the perspectives and opinions of expert panellists with regard to opioid-sparing multimodal analgesia. 29 anaesthetists underwent an evolving three-round questionnaire to determine the level of agreement on certain aspects of multimodal analgesia, with the last round deciding if each statement was a priority. Results: The results were aggregated and a consensus, defined as achievement of over 75% on the Likert scale, was reached for five out of eight statements. The panellists agreed there was a strong body of evidence supporting opioid-sparing multimodal analgesia. However, there existed multiple barriers to widespread adoption, foremost the lack of training and education, as well as the reluctance to change existing practices. Practical issues such as cost effectiveness, increased workload, or the lack of supply of anaesthetic agents were not perceived to be as critical in preventing adoption. Conclusion: Thus, a focus on developing specific guidelines for multimodal analgesia and addressing gaps in education may improve the adoption of opioid-sparing analgesia

Effect of Colchicine on the Risk of Perioperative Acute Kidney Injury : Clinical Protocol of a Substudy of the Colchicine for the Prevention of Perioperative Atrial Fibrillation Randomized Clinical Trial

Inflammation during and after surgery can lead to organ damage including acute kidney injury. Colchicine, an established inexpensive anti-inflammatory medication, may help to protect the organs from pro-inflammatory damage. This protocol describes a kidney substudy of the colchicine for the prevention of perioperative atrial fibrillation (COP-AF) study, which is testing the effect of colchicine versus placebo on the risk of atrial fibrillation and myocardial injury among patients undergoing thoracic surgery. Objective: Our kidney substudy of COP-AF will determine whether colchicine reduces the risk of perioperative acute kidney injury compared with a placebo. We will also examine whether colchicine has a larger absolute benefit in patients with pre-existing chronic kidney disease, the most prominent risk factor for acute kidney injury. Design and Setting: Randomized, superiority clinical trial conducted in 40 centers in 11 countries from 2018 to 2023. Patients (~3200) aged 55 years and older having major thoracic surgery. Intervention: Patients are randomized 1:1 to receive oral colchicine (0.5 mg tablet) or a matching placebo, given twice daily starting 2 to 4 hours before surgery for a total of 10 days. Patients, health care providers, data collectors, and outcome adjudicators will be blinded to the randomized treatment allocation. Serum creatinine concentrations will be measured before surgery and on postoperative days 1, 2, and 3 (or until hospital discharge). The primary outcome of the substudy is perioperative acute kidney injury, defined as an increase (from the prerandomization value) in serum creatinine concentration of either ≥26.5 μmol/L (≥0.3 mg/dL) within 48 hours of surgery or ≥50% within 7 days of surgery. The primary analysis (intention-to-treat) will examine the relative risk of acute kidney injury in patients allocated to receive colchicine versus placebo. We will repeat the primary analysis using alternative definitions of acute kidney injury and examine effect modification by pre-existing chronic kidney disease, defined as a prerandomization estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m. The substudy will be underpowered to detect small effects on more severe forms of acute kidney injury treated with dialysis. Substudy results will be reported in 2024. This substudy will estimate the effect of colchicine on the risk of perioperative acute kidney injury in older adults undergoing major thoracic surgery. Clinical trial registration number: NCT0331012

The LBNO long-baseline oscillation sensitivities with two conventional neutrino beams at different baselines

The proposed Long Baseline Neutrino Observatory (LBNO) initially consists of $\sim 20$ kton liquid double phase TPC complemented by a magnetised iron calorimeter, to be installed at the Pyh\"asalmi mine, at a distance of 2300 km from CERN. The conventional neutrino beam is produced by 400 GeV protons accelerated at the SPS accelerator delivering 700 kW of power. The long baseline provides a unique opportunity to study neutrino flavour oscillations over their 1st and 2nd oscillation maxima exploring the $L/E$ behaviour, and distinguishing effects arising from $\delta_{CP}$ and matter. In this paper we show how this comprehensive physics case can be further enhanced and complemented if a neutrino beam produced at the Protvino IHEP accelerator complex, at a distance of 1160 km, and with modest power of 450 kW is aimed towards the same far detectors. We show that the coupling of two independent sub-MW conventional neutrino and antineutrino beams at different baselines from CERN and Protvino will allow to measure CP violation in the leptonic sector at a confidence level of at least $3\sigma$ for 50\% of the true values of $\delta_{CP}$ with a 20 kton detector. With a far detector of 70 kton, the combination allows a $3\sigma$ sensitivity for 75\% of the true values of $\delta_{CP}$ after 10 years of running. Running two independent neutrino beams, each at a power below 1 MW, is more within today's state of the art than the long-term operation of a new single high-energy multi-MW facility, which has several technical challenges and will likely require a learning curve.Comment: 21 pages, 12 figure

International multicenter observational study on assessment of ventilatory management during general anaesthesia for robotic surgery and its effects on postoperative pulmonary complication (AVATaR) : study protocol and statistical analysis plan

Introduction: Robotic-assisted surgery (RAS) has emerged as an alternative minimally invasive surgical option. Despite its growing applicability, the frequent need for pneumoperitoneum and Trendelenburg position could significantly affect respiratory mechanics during RAS. AVATaR is an international multicenter observational study aiming to assess the incidence of postoperative pulmonary complications (PPC), to characterise current practices of mechanical ventilation (MV) and to evaluate a possible association between ventilatory parameters and PPC in patients undergoing RAS. Methods and analysis: AVATaR is an observational study of surgical patients undergoing MV for general anaesthesia for RAS. The primary outcome is the incidence of PPC during the first five postoperative days. Secondary outcomes include practice of MV, effect of surgical positioning on MV, effect of MV on clinical outcome and intraoperative complications. Ethics and dissemination: This study was approved by the Institutional Review Board of the Hospital Israelita Albert Einstein. The study results will be published in peer-reviewed journals and disseminated at international conferences. Trial registration number: NCT02989415; Pre-results

Altered time structure of neuro-endocrine-immune system function in lung cancer patients

<p>Abstract</p> <p>Background</p> <p>The onset and the development of neoplastic disease may be influenced by many physiological, biological and immunological factors. The nervous, endocrine and immune system might act as an integrated unit to mantain body defense against this pathological process and reciprocal influences have been evidenced among hypothalamus, pituitary, thyroid, adrenal, pineal gland and immune system. In this study we evaluated differences among healthy subjects and subjects suffering from lung cancer in the 24-hour secretory profile of melatonin, cortisol, TRH, TSH, FT4, GH, IGF-1 and IL-2 and circadian variations of lymphocyte subpopulations. </p> <p>Methods</p> <p>In ten healthy male volunteers (age range 45-66) and ten male patients with untreated non small cell lung cancer (age range 46-65) we measured melatonin, cortisol, TRH, TSH, FT4, GH, IGF-1 and IL-2 serum levels and percentages of lymphocyte subpopulations on blood samples collected every four hours for 24 hours. One-way ANOVA between the timepoints for each variable and each group was performed to look for a time-effect, the presence of circadian rhythmicity was evaluated, MESOR, amplitude and acrophase values, mean diurnal levels and mean nocturnal levels were compared.</p> <p>Results</p> <p>A clear circadian rhythm was validated in the control group for hormone serum level and for lymphocyte subsets variation. Melatonin, TRH, TSH, GH, CD3, CD4, HLA-DR, CD20 and CD25 expressing cells presented circadian rhythmicity with acrophase during the night. Cortisol, CD8, CD8^bright, CD8^dim, CD16, TcRδ1 and δTcS1 presented circadian rhythmicity with acrophase in the morning/at noon. FT4, IGF-1 and IL-2 variation did not show circadian rhythmicity. In lung cancer patients cortisol, TRH, TSH and GH serum level and all the lymphocyte subsubsets variation (except for CD4) showed loss of circadian rhythmicity. MESOR of cortisol, TRH, GH, IL-2 and CD16 was increased, whereas MESOR of TSH, IGF-1, CD8, CD8^bright, TcRδ1 and δTcS1 was decreased in cancer patients. The melatonin/cortisol mean nocturnal level ratio was decreased in cancer patients.</p> <p>Conclusion</p> <p>The altered secretion and loss of circadian rhythmicity of many studied factors observed in the subjects suffering from neoplastic disease may be expression of gradual alteration of the integrated function of the neuro-immune-endocrine system</p

Ventilation and outcomes following robotic-assisted abdominal surgery: an international, multicentre observational study

Background: International data on the epidemiology, ventilation practice, and outcomes in patients undergoing abdominal robotic-assisted surgery (RAS) are lacking. The aim of the study was to assess the incidence of postoperative pulmonary complications (PPCs), and to describe ventilator management after abdominal RAS. Methods: This was an international, multicentre, prospective study in 34 centres in nine countries. Patients ≥18 yr of age undergoing abdominal RAS were enrolled between April 2017 and March 2019. The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score was used to stratify for higher risk of PPCs (≥26). The primary outcome was the incidence of PPCs. Secondary endpoints included the preoperative risk for PPCs and ventilator management. Results: Of 1167 subjects screened, 905 abdominal RAS patients were included. Overall, 590 (65.2%) patients were at increased risk for PPCs. Meanwhile, 172 (19%) patients sustained PPCs, which occurred more frequently in 132 (22.4%) patients at increased risk, compared with 40 (12.7%) patients at lower risk of PPCs (absolute risk difference: 12.2% [95% confidence intervals (CI), 6.8–17.6%]; P&lt;0.001). Plateau and driving pressures were higher in patients at increased risk, compared with patients at low risk of PPCs, but no ventilatory variables were independently associated with increased occurrence of PPCs. Development of PPCs was associated with a longer hospital stay. Conclusions: One in five patients developed one or more PPCs (chiefly unplanned oxygen requirement), which was associated with a longer hospital stay. No ventilatory variables were independently associated with PPCs. Clinical trial registration: NCT02989415