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Wilson A, Chiorean A, Aguiar M, Sekulic D, Pavlick P, Shah N, Sniderman King L, Génin M, Rollot M, Blanchon M, Gosset S, Montmerle M, Molony C, Dumitriu A. Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States. Orphanet J Rare Dis 2023; 18:280. [PMID: 37689674 PMCID: PMC10492341 DOI: 10.1186/s13023-023-02868-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 08/23/2023] [Indexed: 09/11/2023] Open
Abstract
BACKGROUND Early diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records. METHODS We utilized Optum's de-identified Integrated Claims-Clinical dataset (2007-2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients. RESULTS Splenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients. CONCLUSIONS The age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10-20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients.
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Affiliation(s)
- Amanda Wilson
- Health Economics and Value Assessment, Sanofi, Cambridge, MA USA
| | | | - Mario Aguiar
- Global Medical Affairs, RD Hematology, Sanofi, Cambridge, MA USA
| | - Davorka Sekulic
- Global Medical Affairs, RD Hematology, Sanofi, Cambridge, MA USA
| | | | - Neha Shah
- Medical Diagnostics, Sanofi, Cambridge, MA USA
| | | | | | | | | | | | | | | | - Alexandra Dumitriu
- Global Medical Affairs, Medical Evidence Generation, Sanofi, Cambridge, MA USA
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Ibald-Mulli A, Seufert J, Grimsmann JM, Laimer M, Bramlage P, Civet A, Blanchon M, Gosset S, Templier A, Paar WD, Zhou FL, Lanzinger S. Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm. Diabetes Obes Metab 2023. [PMID: 36867100 DOI: 10.1111/dom.15039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 02/27/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023]
Abstract
AIMS Diabetic ketoacidosis (DKA) is a serious and potentially fatal complication of type 1 diabetes and it is difficult to identify individuals at increased risk. The aim of this study was to identify predictive factors for DKA by retrospective analysis of registry data and use of a subgroup discovery algorithm. MATERIALS AND METHODS Data from adults and children with type 1 diabetes and >2 diabetes-related visits were analyzed from the Diabetes Prospective Follow-up Registry. Q-Finder®, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH <7.3 during a hospitalization event. RESULTS Data for 108,223 adults and children, of whom 5,609 (5.2%) had DKA, were studied. Q-Finder® analysis identified 11 profiles associated with increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; HbA1c ≥8.87 [73 mmol/mol]; no fast-acting insulin intake; age <15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycemia; hypoglycemic coma; and autoimmune thyroiditis. Risk of DKA increased with number of risk profiles matching patients' characteristics. CONCLUSIONS Q-Finder® confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Angela Ibald-Mulli
- Real World Evidence and Clinical Outcome Generation, Sanofi, Paris, France
| | - Jochen Seufert
- Division of Endocrinology and Diabetology, Department of Medicine II, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julia M Grimsmann
- Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany
- German Centre for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Markus Laimer
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Peter Bramlage
- Institute for Pharmacology and Preventive Medicine, Cloppenburg, Germany
| | | | | | | | | | | | | | - Stefanie Lanzinger
- Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany
- German Centre for Diabetes Research (DZD), München-Neuherberg, Germany
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Gosset S, Glatigny A, Gallopin M, Yi Z, Salé M, Mucchielli-Giorgi MH. APPINetwork: an R package for building and computational analysis of protein-protein interaction networks. PeerJ 2022; 10:e14204. [PMID: 36353604 PMCID: PMC9639416 DOI: 10.7717/peerj.14204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/19/2022] [Indexed: 11/06/2022] Open
Abstract
Background Protein-protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein-protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.
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Affiliation(s)
- Simon Gosset
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France,Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
| | - Annie Glatigny
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Mélina Gallopin
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Zhou Yi
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Marion Salé
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Marie-Hélène Mucchielli-Giorgi
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France,Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
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Schweke H, Mucchielli MH, Chevrollier N, Gosset S, Lopes A. SURFMAP: A Software for Mapping in Two Dimensions Protein Surface Features. J Chem Inf Model 2022; 62:1595-1601. [DOI: 10.1021/acs.jcim.1c01269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hugo Schweke
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette 91198, France
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Marie-Hélène Mucchielli
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
| | | | - Simon Gosset
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
| | - Anne Lopes
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette 91198, France
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Bittoni M, Divan H, Krishna A, Gosset S, Carbone D. 105P US real-world treatment patterns and outcomes of metastatic nonsquamous non-small cell lung cancer (mNSQ NSCLC) patients (pts) after progression on standard of care (SOC). Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.10.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Amarenco P, Davis S, Jones EF, Cohen AA, Heiss WD, Kaste M, Laouénan C, Young D, Macleod M, Donnan GA, Bladin CF, Chambers BR, Frayne J, Hankey GJ, Levi CR, Read SJ, Ravaud P, Tatlisumak T, Soinne L, Laine M, Syvänne M, Vikatmaa P, Lepäntalo M, Mentré F, Gosset S, Churilov L, De Broucker T, Favrole P, Mawet J, Mocquard Y, Obadia M, Godefroy O, Hosseini H, Pico F, Garnier P, Malbec M, Pinel JF, Ille O, Vadamme X, Macian-Montoro F, Servan J, Viallet F, Rosolacci T, Lecoz P, Clavelou P, Detante O, Cho TH, Saudeau D, Michel P, D’Ombrogio S, Serisier D, Sturm J, Kimber T, Marcus R, Schwartz R, Helme R, Blacker D, Wood J. Clopidogrel Plus Aspirin Versus Warfarin in Patients With Stroke and Aortic Arch Plaques. Stroke 2014; 45:1248-57. [DOI: 10.1161/strokeaha.113.004251] [Citation(s) in RCA: 148] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Pierre Amarenco
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Stephen Davis
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Elizabeth F. Jones
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Ariel A. Cohen
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Wolf-Dieter Heiss
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Markku Kaste
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Cédric Laouénan
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Dennis Young
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Malcolm Macleod
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
| | - Geoffrey A. Donnan
- From the Department of Neurology, Stroke Centre, DHU FIRE, INSERM U 1148, Paris Diderot–Sorbonne University, Hôpital Bichat (P.A.), Department of Cardiology, Saint-Antoine Hospital and Medical School, Univeristé Pierre et Marie Curie (A.A.C.), and Department of Biostatistics, Paris-Diderot–Sorbonne University, Hôpital Bichat (C.L.), Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Neurology, Royal Melbourne Hospital (S.D.) and Florey Institute of Neuroscience and Mental Health (D
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Gosset S, Carjuzaa A, Seguin P, Guigui J, Lambrescak P. [Severe poisoning caused by chloralose]. Cah Anesthesiol 1989; 37:293-4. [PMID: 2790554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- S Gosset
- Service de réanimation du CHA René le Bas, Cherbourg-Naval
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Gosset S, Goursaud R, Carjuzaa A, Martin J, Guigui J, Seguin P. [Pleural localization of multiresistant non-typhoid Salmonella]. Presse Med 1988; 17:2200. [PMID: 2974581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
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Coursange F, Aubert M, Gosset S, Carpentier JP, Cornen L. [Rupture of an intracranial aneurysm after spinal anesthesia]. Ann Fr Anesth Reanim 1987; 6:113-4. [PMID: 3592315 DOI: 10.1016/s0750-7658(87)80113-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A case is reported of an intracranial aneurysm which ruptured after spinal anaesthesia. A 36-year-old man underwent several locoregional anaesthesias for the surgical treatment of an infected lower limb fracture (8 epidural blocks and 1 spinal anaesthesia). After a further spinal anaesthesia, he suffered violent headaches, a meningeal syndrome, restlessness, left hemiplegia and coma. The relationship between such an accident and the anesthesia is discussed in the light of similar cases previously published.
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Gosset S, Coursange F, Aubert M, Carpentier JP, Malgras G. [Postoperative peridural analgesia in thoracic surgery using repeated low doses of morphine hydrochloride]. Cah Anesthesiol 1986; 34:243. [PMID: 3742309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Coursange F, Gosset S, Carpentier JP, Vincent M, Aubert M. [Severe hypoxemia associated with cytolytic hepatitis in Q fever]. Presse Med 1986; 15:801. [PMID: 2940530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
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Gosset S, Coursange F, Carpentier JP, Malgras G, Aubert M, Vincent M. [Valium-Acupan combination--its value at an isolated health center]. Med Trop (Mars) 1986; 46:195-7. [PMID: 3724416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Twelve anaesthesias of the diazo-analgesia type have been carried out using a combination of two non depressive analgesics: a narcotic one and a powerful central one. These interventions are classified; used technique is described and results are commented on. Indications are not too painful quick surgical acts performed on patients with an empty stomach.
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Coursange F, Gosset S. [Rehabilitation of the malnourished patient]. Soins Pathol Trop 1986:13-6. [PMID: 3088732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Coursange F, Théobald X, Pats B, Gosset S, Carpentier JP, Vincent M, Aubert M. [Transitory right bundle branch block during a hemodynamic study using the Swan-Ganz catheter]. Cah Anesthesiol 1985; 33:509-11. [PMID: 4084844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Froment A, Milon H, Gallavardin P, Gosset S, Gravier C. [Comparison of hospitalized hypertensive patients and those consulting in a specialized unit. Initial clinical state and patient compliance]. Arch Mal Coeur Vaiss 1979; 72:1137-45. [PMID: 120721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This study is based on a series of 1919 hypertensive patients examined consecutively either in hospital or in the out patient clinic between 1969 and 1977 and reviewed in February 1977 by a postal questionnaire. The initial presentation of in-patients and out-patients differed very significantly in the same specialised department: the patients hospitalised were seen at a more advanced stage of their hypertensive illness. Neither group was truly representative of the general population of hypertensive patients. The percentage of patient compliance was only slightly lower in the out-patients. The mortality rate observed did not differ significantly from the expected mortality rate in out-patients; despite treatment it remained over 200% greater in the hospitalised group. It would seem desirable to develop out-patient rather than in-patient hospital facilities for the treatment of hypertension, despite the foreseeable practical difficulties.
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