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

Chrysolina herbacea Modulates Terpenoid Biosynthesis of Mentha aquatica L.

Interactions between herbivorous insects and plants storing terpenoids are poorly understood. This study describes the ability of Chrysolina herbacea to use volatiles emitted by undamaged Mentha aquatica plants as attractants and the plant's response to herbivory, which involves the production of deterrent molecules. Emitted plant volatiles were analyzed by GC-MS. The insect's response to plant volatiles was tested by Y-tube olfactometer bioassays. Total RNA was extracted from control plants, mechanically damaged leaves, and leaves damaged by herbivores. The terpenoid quantitative gene expressions (qPCR) were then assayed. Upon herbivory, M. aquatica synthesizes and emits (+)-menthofuran, which acts as a deterrent to C. herbacea. Herbivory was found to up-regulate the expression of genes involved in terpenoid biosynthesis. The increased emission of (+)-menthofuran was correlated with the upregulation of (+)-menthofuran synthase

Isolation of a natural DNA virus of <i>Drosophila melanogaster</i>, and characterisation of host resistance and immune responses

<div><p><i>Drosophila melanogaster</i> has played a key role in our understanding of invertebrate immunity. However, both functional and evolutionary studies of host-virus interaction in <i>Drosophila</i> have been limited by a dearth of native virus isolates. In particular, despite a long history of virus research, DNA viruses of <i>D</i>. <i>melanogaster</i> have only recently been described, and none have been available for experimental study. Here we report the isolation and comprehensive characterisation of Kallithea virus, a large double-stranded DNA virus, and the first DNA virus to have been reported from wild populations of <i>D</i>. <i>melanogaster</i>. We find that Kallithea virus infection is costly for adult flies, reaching high titres in both sexes and disproportionately reducing survival in males, and movement and late fecundity in females. Using the <i>Drosophila</i> Genetic Reference Panel, we quantify host genetic variance for virus-induced mortality and viral titre and identify candidate host genes that may underlie this variation, including <i>Cdc42-interacting protein 4</i>. Using full transcriptome sequencing of infected males and females, we examine the transcriptional response of flies to Kallithea virus infection and describe differential regulation of virus-responsive genes. This work establishes Kallithea virus as a new tractable model to study the natural interaction between <i>D</i>. <i>melanogaster</i> and DNA viruses, and we hope it will serve as a basis for future studies of immune responses to DNA viruses in insects.</p></div

Dynamic renal echography versus urography in the follow-up of patients who have undergone ureterosigmoidostomy

The main post uretero-sigmoidostomy complications are stricture of the anastomosis, chronic infection and urolithiasis. In our institution the patients with ureterosigmodostomy undergo a follow-up protocol in which blood chemistry, ultrasonography, intravenous pyelography and C.T. are periodically performed. The aim of the present paper is to compare the accuracy of kidney sonography after diuretic stimulation with intravenous pyelography in the diagnosis of ureteral stenosis. Out of 91 patient with ureterosigmoidostomy 18 patients (34 kidneys) underwent intravenous pyelography, a basal U.S. and then a dynamic one at 5, 10, 15, 30, 45, 60, 90, 120 minutes after administration of furosemide 20 mg i.v. At basal U.S. 27 kidneys were normal and 7 showed a dilations. After diuretic stimulation we observed 16 normal kidneys, 16 dilated units and 2 intermittent hydronephrosis. Out of 16 dilated kidneys 6 became normal in 60 minutes. Out of 10 dilated units 3 were normal in 90 minutes (hipotonic), 2 were normal before 120 minutes (low grade obstruction) and 5 were dilated after 120 minutes (high grade obstruction). With intravenous pyelography we observed 27 normal kidneys and seven dilated units. Dynamic sonography have shown high sensibility (100%), specificity (88.8%) and accuracy (91%) in diagnosis of ureteral obstruction in to I.V.P. in the follow-up of this kind of divesion

Suprapubic (SP) transrectal (TR) and transvaginal (TV) echography in the diagnosis and staging of bladder carcinoma

Accuracy of suprapubic (SP), transrectal (TR), and transvaginal (TV) ultrasonography (US) in diagnosis and preoperative staging of bladder carcinoma is evaluated. From January 1990 to December 1992, 354 patients with gross haematuria, underwent bladder SP-US and cystoscopy and of this group 216 patients had transitional bladder cancer. Diagnostic sensitivity, specificity and accuracy for SP-US were, respectively, 73.5%, 88.5% and 63.8%. Clinical US and pathological staging were compared in 116 patients: downstaging was observed in 43.1% of the cases by SP-US and in 34.5% of the cases by TR-US or TV-US and overstaging in 6.9% of the cases by SP-US and in 15.4% of the cases by TR-US or TV-US. In this series accuracy of clinical staging by SP-US, TR-US, TV-US was inversely related to cancer stage and, conversely, clinical staging by pelvic CT scan accuracy is directly related to cancer stage

Temporal distribution in a tri-trophic system associated with piper amalago L. in a tropical seasonal forest

Insect seasonality is a known pattern that has intrigued ecologists for over 30 years. However, despite being well understood in general, for several taxa such as Lepidopteran caterpillars its underlying causes and mechanisms are still not fully understood. This is especially critical for Brazilian tropical forests where caterpillars have previously been shown to have a puzzling pattern of peaking in abundance only in the first months of the dry season; however, this pattern still lacks an explanation. Here, to advance our understanding of the factors underlying seasonal changes in caterpillar abundance in tropical forests, we addressed how the lepidopteran caterpillar community that feeds on Piper amalago L. plants, their host plants leaf numbers, the herbivory levels, and the parasitoid pressure all change throughout the dry and wet seasons in a Brazilian tropical semideciduous forest. We found that immature abundance and herbivory peak in the first 2 months of the dry season and then rapidly decrease and remain low throughout the remaining of the dry season and the entire wet season at the study site. However, although the proportion of parasitized immatures increased alongside caterpillar abundance, it peaked in the month that followed a drastic decrease in caterpillar abundance. These results suggest that parasitoids play a major role in the observed caterpillar abundance pattern and thus, we propose the hypothesis that high parasitoid predation pressure causes early eclosion and emergence of caterpillars and primarily drives caterpillar abundance seasonality in Brazilian tropical forests134647652CNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo303834/2015-3; 307015/2015-7; 563332/2010-72014/50316-7; 2016/01380-0; 2011/50225-3; 2013/25991-0We thank Massuo J. Kato and Martin Pareja for helping in diverse phases of the manuscript. We also thank an anonymous reviewer for the comments that substantially improved this manuscript. LGC thanks São Paulo Research Foundation (FAPESP) for an undergraduate fellowship (2016/01380-0). ARN thanks São Paulo Research Foundation (FAPESP) for a post-doc fellowship. AVLF acknowledges support from FAPESP (Biota-Fapesp Grants 2011/50225-3) and from the Brazilian Research Council—CNPq (fellowship 303834/2015-3). RC was founded by São Paulo Research Foundation (FAPESP), Grant 2013/25991-0, and CNPq (307015/2015-7). This publication is part of the RedeLep ‘Rede Nacional de Pesquisa e Conservação de Lepidópteros’ SISBIOTA-Brasil/CNPq (563332/2010-7) and of the Biota-Fapesp Program through the collaborative grant “Chemically mediated multi-trophic interaction diversity across tropical gradients” (2014/50316-7

Treatment of prostatitis with a new laser probe with optic fiber

The present treatment of the subacute abatteric prostatitis, prostatodinia, prostatosis (the most common prostatic flogistic diseases) is represented by the transrectal applications of infrared Laser. The concrete opportunity of applying such an energy directly to the prostate in cases of flogistic diseases--a very frequent pathology treated in many different and controversial ways--is a stimulating therapeutical method which we tested and that we presently use in our clinics. The thanks to the realization of an high technology equipment, easy to handle, cheap, safe, perfectly suitable, formed by a new infrared Laser probe, transrectal, atermical, made by a optical fibre, which we present. Micturition, ejaculation, fertility may draw a relevant improvement, provided that the same treatment is performed after a specific medical diagnosis and following a strict protocol

Validity of superficial echography in the study of urethral pathology

In male patients routine examination for urethral disease includes retrograde and anterograde urethrography and urethroscopy. In the patients underwent radical cystectomy, detection of cancerous cells in the urethral washing suggest cancer relapse. Nowadays we can achieve a sonographic study of the anterior male urethra, using a superficial high frequency ultrasound probe. Since September 1992 till July 1993, 12 patients underwent cystectomy at our Institution and 13 patients affected by urethral stricture, have been investigated by routine examination and sonographic urethrogram. In the first group of patients, out of 3 patients with urethral tumor, sonourethrography has confirmed the presence of tumor in 2 cases. In these second group of patients, sonourethrography has located the stricture, evaluated the length, calculated the diameter of the stricture and the depth of fibrosis. Sonourethrography is a non-invasive method that can provide valuable information about the urethral lumen and the urethral wall