Lippincott Williams & Wilkins



Incidence of and Risk Factors for Systemic Adverse Events after Screening or Primary Diagnostic Colonoscopy: A Nationwide Cohort Studysupplemental digital contentSupplemental Methods: Presentation of the SNDS databasesDescriptive statisticsSupplemental Table 1: Procedure codes for colonoscopies, from French medical classification of medical proceduresSupplemental Table 2: Procedure codes for major colic or rectal surgery procedures leading to exclusion, from French medical classification of medical proceduresSupplemental Table 3: ICD-10 codes for definition of adverse eventsSupplemental Table 4a: Standardized incidence ratios (SIRs) of serious adverse events after colonoscopy.Supplemental Table 4b: Standardized incidence ratios (SIRs) of serious adverse events after colonoscopy, in the further selected population excluding colonoscopies performed on day 2 of hospitalization or without identified bowel preparation.Supplemental Table 5: Characteristics associated with cardiovascular adverse events: unadjusted logistic regression models (N = 4,088,799)Supplemental Table 6: Characteristics associated with renal adverse events: unadjusted logistic regression models (N = 4,088,799)Supplemental Table 7: Characteristics associated with myocardial infarction: sensitivity analysis using a broader definition for myocardial infarction (4,088,799 patients, including 476 with myocardial infarction)Supplemental Table 8: Characteristics associated with acute renal failure: sensitivity analysis using a more stringent definition for acute renal failure (4,088,799 patients, including 522 with acute renal failure)Supplemental Methods Presentation of the SNDS databasesThe study was conducted using French medical administrative databases SNDS (Système National des Données de Santé, National Heath Data System), consisting of individual data from both the SNIIRAM (Système National d’Information Inter-Régime de l’Assurance Maladie, National Health Insurance claims information system) and PMSI (Programme de Médicalisation des Systèmes d’Information, national hospital discharge database). These databases were linked by means of a unique anonymous number allocated to each individual, providing detailed information on health insurance claims for inpatient (PMSI) and outpatient (SNIIRAM) care for 99% of the population living in France (about 67,000,000 people). Further description of these databases and their use is available in English in the papers by Bezin et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"B063qrWF","properties":{"formattedCitation":"\\super 1\\nosupersub{}","plainCitation":"1","noteIndex":0},"citationItems":[{"id":92,"uris":[""],"uri":[""],"itemData":{"id":92,"type":"article-journal","title":"The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology","container-title":"Pharmacoepidemiology and Drug Safety","page":"954-962","volume":"26","issue":"8","source":"onlinelibrary-wiley-com.gate2.inist.fr (Atypon)","abstract":"Abstract The French health care system is based on universal coverage by one of several health care insurance plans. The SNIIRAM database merges anonymous information of reimbursed claims from all these plans, linked to the national hospital?discharge summaries database system (PMSI) and the national death registry. It now covers 98.8% of the French population, over 66 million persons, from birth (or immigration) to death (or emigration), making it possibly the world's largest continuous homogeneous claims database. The database includes demographic data; health care encounters such as physician or paramedical visits, medicines, medical devices, and lab tests (without results); chronic medical conditions (ICD10 codes); hospitalisations with ICD10 codes for primary, linked and associated diagnoses, date and duration, procedures, diagnostic?related groups, and cost coding; date but currently not cause of death. The power of the database is correlatively great, and its representativeness is near perfect, since it essentially includes the whole country's population. The main difficulty in using the database, beyond its sheer size and complexity, is the administrative process necessary to access it. Recent legislative advances are making this easier. EGB (Echantillon Généraliste de Bénéficiaires) is the 1/97th random permanent representative sample of SNIIRAM, with planned 20?year longitudinal data (10?years at this time). Access time is 1 to 3?months, but its power is less (780?000 subjects). This is enough to study common issues with older drugs but may be limited for new products or rare events.","DOI":"10.1002/pds.4233","ISSN":"1053-8569","title-short":"The national healthcare system claims databases in France, SNIIRAM and EGB","journalAbbreviation":"Pharmacoepidemiology and Drug Safety","author":[{"family":"Bezin","given":"Julien"},{"family":"Duong","given":"Mai"},{"family":"Lassalle","given":"Régis"},{"family":"Droz","given":"Cécile"},{"family":"Pariente","given":"Antoine"},{"family":"Blin","given":"Patrick"},{"family":"Moore","given":"Nicholas"}],"issued":{"date-parts":[["2017",8,8]]}}}],"schema":""} 1 and Moulis et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2zMl2u28","properties":{"formattedCitation":"\\super 2\\nosupersub{}","plainCitation":"2","noteIndex":0},"citationItems":[{"id":604,"uris":[""],"uri":[""],"itemData":{"id":604,"type":"article-journal","title":"French health insurance databases: What interest for medical research?","container-title":"La Revue De Medecine Interne","page":"411-417","volume":"36","issue":"6","source":"PubMed","abstract":"French health insurance databases are organized since 2003 into a huge digital data warehouse, the Système national d'information inter-régime de l'assurance maladie (SNIIR-AM). It covers the entire French population (65 million inhabitants). In order to facilitate studies on more frequent conditions, a random sample of 1/97th of national health system beneficiaries has been built since 2005, called the échantillon généraliste des bénéficiaires (EGB). The aim of this article is to describe the main characteristics of the SNIIR-AM and the EGB, to detail their accessibility according to French law, and to present their strengths and limits. It is illustrated with the most recent studies conducted in these databases. These databases include demographic, out-hospital reimbursement (including drug dispensing), medical (costly long-term diseases, occupational diseases, sick-leaves…), and in-hospital data. All these data are prospectively recorded, individualized, made anonymous and linkable. Consequently, the SNIIR-AM is a very useful data source for epidemiological, pharmacoepidemiological and health economics studies, particularly for rare diseases. The EGB is appropriate for long-term research on more frequent diseases.","DOI":"10.1016/j.revmed.2014.11.009","ISSN":"1768-3122","note":"PMID: 25547954","title-short":"French health insurance databases","journalAbbreviation":"Rev Med Interne","language":"eng","author":[{"family":"Moulis","given":"G."},{"family":"Lapeyre-Mestre","given":"M."},{"family":"Palmaro","given":"A."},{"family":"Pugnet","given":"G."},{"family":"Montastruc","given":"J.-L."},{"family":"Sailler","given":"L."}],"issued":{"date-parts":[["2015",6]]}}}],"schema":""} 2, and more extensively in the paper by Tuppin et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"9nxpqwtL","properties":{"formattedCitation":"\\super 3\\nosupersub{}","plainCitation":"3","noteIndex":0},"citationItems":[{"id":607,"uris":[""],"uri":[""],"itemData":{"id":607,"type":"article-journal","title":"Value of a national administrative database to guide public decisions: From the système national d'information interrégimes de l'Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France","container-title":"Revue D'epidemiologie Et De Sante Publique","page":"S149-S167","volume":"65 Suppl 4","source":"PubMed","abstract":"In 1999, French legislators asked health insurance funds to develop a système national d'information interrégimes de l'Assurance Maladie (SNIIRAM) [national health insurance information system] in order to more precisely determine and evaluate health care utilization and health care expenditure of beneficiaries. These data, based on almost 66 million inhabitants in 2015, have already been the subject of numerous international publications on various topics: prevalence and incidence of diseases, patient care pathways, health status and health care utilization of specific populations, real-life use of drugs, assessment of adverse effects of drugs or other health care procedures, monitoring of national health insurance expenditure, etc. SNIIRAM comprises individual information on the sociodemographic and medical characteristics of beneficiaries and all hospital care and office medicine reimbursements, coded according to various systems. Access to data is controlled by permissions dependent on the type of data requested or used, their temporality and the researcher's status. In general, data can be analyzed by accredited agencies over a period covering the last three years plus the current year, and specific requests can be submitted to extract data over longer periods. A 1/97th random sample of SNIIRAM, the échantillon généraliste des bénéficiaires (EGB), representative of the national population of health insurance beneficiaries, was composed in 2005 to allow 20-year follow-up with facilitated access for medical research. The EGB is an open cohort, which includes new beneficiaries and newborn infants. SNIIRAM has continued to grow and extend to become, in 2016, the cornerstone of the future système national des données de santé (SNDS) [national health data system], which will gradually integrate new information (causes of death, social and medical data and complementary health insurance). In parallel, the modalities of data access and protection systems have also evolved. This article describes the SNIIRAM data warehouse and its transformation into SNDS, the data collected, the tools developed in order to facilitate data analysis, the limitations encountered, and changing access permissions.","DOI":"10.1016/j.respe.2017.05.004","ISSN":"0398-7620","note":"PMID: 28756037","title-short":"Value of a national administrative database to guide public decisions","journalAbbreviation":"Rev Epidemiol Sante Publique","language":"eng","author":[{"family":"Tuppin","given":"P."},{"family":"Rudant","given":"J."},{"family":"Constantinou","given":"P."},{"family":"Gastaldi-Ménager","given":"C."},{"family":"Rachas","given":"A."},{"family":"Roquefeuil","given":"L.","non-dropping-particle":"de"},{"family":"Maura","given":"G."},{"family":"Caillol","given":"H."},{"family":"Tajahmady","given":"A."},{"family":"Coste","given":"J."},{"family":"Gissot","given":"C."},{"family":"Weill","given":"A."},{"family":"Fagot-Campagna","given":"A."}],"issued":{"date-parts":[["2017",10]]}}}],"schema":""} 3.The database contains data about all outpatient services reimbursed by National Health Insurance, including drugs, physician visits, and laboratory investigations, but does not provide any information about the medical indications. Patients with chronic diseases (LTD: long-term diseases), such as cancer or Crohn’s disease, are 100% reimbursed for their health expenditure, and the diagnosis is recorded (coded according to International Classification of Diseases (ICD)-10 codes ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"gOhBaCn6","properties":{"formattedCitation":"\\super 4\\nosupersub{}","plainCitation":"4","noteIndex":0},"citationItems":[{"id":2336,"uris":[""],"uri":[""],"itemData":{"id":2336,"type":"book","title":"ICD-10: International statistical classification of diseases and health-related problems","publisher":"WHO","volume":"2","source":"Google Scholar","note":"00002","title-short":"ICD-10","issued":{"date-parts":[["1992"]]}}}],"schema":""} 4). The accuracy of medication use estimated on the basis of SNDS data has been previously investigated: drugs with the best agreements between SNDS data and patient interviews were antidiabetic and cardiovascular drugs (Kappa values >0.80) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"odqVzBJB","properties":{"formattedCitation":"\\super 5\\nosupersub{}","plainCitation":"5","noteIndex":0},"citationItems":[{"id":93,"uris":[""],"uri":[""],"itemData":{"id":93,"type":"article-journal","title":"Comparison of health insurance claims and patient interviews in assessing drug use: data from the Three‐City (3C) Study","container-title":"Pharmacoepidemiology and Drug Safety","page":"310-319","volume":"18","issue":"4","source":"onlinelibrary. (Atypon)","abstract":"Abstract Purpose Precise determination of drug exposure is fundamental in pharmacoepidemiology. Drug exposure is often presumed from health insurance claims but this may not correspond exactly to what subjects actually take. This study was designed to investigate French reimbursement databases in assessing drug use. Methods Between 1999 and 2001, 9294 subjects were included in the Three?City (3C) Study, a French cohort studying the relationship between vascular risk factors and dementia. Of these, 4112 subjects had data available from both clinical interviews and the reimbursement databases of the French national health insurance system. Agreement between drugs reported as used at interview and drugs reimbursed during the previous 30 or 60?days was measured with ? coefficients. Using calculations of sensitivity (Se), specificity (Sp), positive predictive values (PPVs) and negative predictive values (NPVs), the validity of reimbursement data for the 30 or 60?days preceding the interview was investigated taking drugs reported at interview as the ?gold standard?. Results Declared drug use at interview was less well predicted by 30?day than by 60?day reimbursement data. Agreement between reimbursement data and interviews as well as validity of reimbursement data with reference to interviews were substantial for drugs used in cardiovascular diseases, diabetes, rheumatic disorders or neuropsychiatric conditions and were poor for laxatives, vitamins, vasculoprotectives, first and second line analgesics, anti?infective products or dermatologicals. Conclusions Reimbursement data with an appropriate time frame and interviews estimate exposure to chronically used drugs similarly. Self?medication was better described with interviews whereas reimbursement data seem more useful for drugs used topically or intermittently. Copyright ? 2009 John Wiley & Sons, Ltd.","DOI":"10.1002/pds.1717","ISSN":"1053-8569","title-short":"Comparison of health insurance claims and patient interviews in assessing drug use","journalAbbreviation":"Pharmacoepidemiology and Drug Safety","author":[{"family":"Noize","given":"Pernelle"},{"family":"Bazin","given":"Fabienne"},{"family":"Dufouil","given":"Carole"},{"family":"Lechevallier‐Michel","given":"Nathalie"},{"family":"Ancelin","given":"Marie‐Laure"},{"literal":"Dartigues Jean‐Fran?ois"},{"literal":"Tzourio Christophe"},{"literal":"Moore Nicholas"},{"literal":"Fourrier‐Réglat Annie"}],"issued":{"date-parts":[["2009",2,24]]}}}],"schema":""} 5; the sensitivity of drug exposure was 85.3% with a specificity of 91% for antithrombotics ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"mSPaCrNh","properties":{"formattedCitation":"\\super 6\\nosupersub{}","plainCitation":"6","noteIndex":0},"citationItems":[{"id":541,"uris":[""],"uri":[""],"itemData":{"id":541,"type":"article-journal","title":"Validity of chronic drug exposure presumed from repeated patient interviews varied according to drug class","container-title":"Journal of Clinical Epidemiology","page":"1061-1068","volume":"65","issue":"10","source":"","DOI":"10.1016/j.jclinepi.2012.04.009","ISSN":"0895-4356, 1878-5921","note":"PMID: 22819646","journalAbbreviation":"Journal of Clinical Epidemiology","language":"English","author":[{"family":"Noize","given":"Pernelle"},{"family":"Bazin","given":"Fabienne"},{"family":"Pariente","given":"Antoine"},{"family":"Dufouil","given":"Carole"},{"family":"Ancelin","given":"Marie-Laure"},{"family":"Helmer","given":"Catherine"},{"family":"Moore","given":"Nicholas"},{"family":"Fourrier-Réglat","given":"Annie"}],"issued":{"date-parts":[["2012",10,1]]}}}],"schema":""} 6. The database also contains the procedures performed during all hospital stays (notably, colonoscopies), and the principal (DP), related (DR), and associated (DAS) diagnoses (coded according to ICD-10). DP and DR correspond to the diseases justifying admission to hospital, and DAS are comorbidities or adverse events treated during the hospitalization. In order to be recorded as a hospitalization diagnosis in the database (DP, DR, or DAS), the diagnosis therefore had to have an impact on medical care. Procedures are coded according to the French medical classification for clinical procedures (CCAM, Classification commune des actes médicaux ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"8tw2Rmfh","properties":{"formattedCitation":"\\super 7\\nosupersub{}","plainCitation":"7","noteIndex":0},"citationItems":[{"id":908,"uris":[""],"uri":[""],"itemData":{"id":908,"type":"article-journal","title":"Classification commune des actes médicaux","container-title":"Bulletin officiel","issue":"2007/3 bis","issued":{"date-parts":[["2007",2]]}}}],"schema":""} 7). The database does not contain any information about the inpatient or outpatient status, which cannot be deduced from other data. Drugs used during hospitalization (notably, those used for sedation) are not available, except for certain expensive drugs (such as some types of cancer chemotherapy). Dates available for hospital stays are admission date, discharge date, and procedure (e.g. colonoscopy) date. For the purposes of this study, admission date was used as an approximation for the SAE date.Almost all persons living in France are covered by one of the various national health insurance schemes. Each national health insurance scheme reimburses almost all medical care (including colonoscopies) at the same rate for both public and private care. Persons with limited income or LTD are 100% reimbursed. The general scheme is the largest health insurance scheme in France (covering 76% of the population living in France). This scheme includes employed workers, retired, unemployed, and disadvantaged people. Follow-up of beneficiaries is stable, as people rarely leave this scheme. Other schemes (mainly farmers and self-employed) are more specific. The present study was restricted to the general scheme, as reliable information (notably on death date) for the entire study period is only available for this scheme.SNDS data are commonly used for non-interventional studies in various conditions. To date, more than 400 published studies have used these databases ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"LvdFOY3Z","properties":{"formattedCitation":"\\super 3\\nosupersub{}","plainCitation":"3","noteIndex":0},"citationItems":[{"id":607,"uris":[""],"uri":[""],"itemData":{"id":607,"type":"article-journal","title":"Value of a national administrative database to guide public decisions: From the système national d'information interrégimes de l'Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France","container-title":"Revue D'epidemiologie Et De Sante Publique","page":"S149-S167","volume":"65 Suppl 4","source":"PubMed","abstract":"In 1999, French legislators asked health insurance funds to develop a système national d'information interrégimes de l'Assurance Maladie (SNIIRAM) [national health insurance information system] in order to more precisely determine and evaluate health care utilization and health care expenditure of beneficiaries. These data, based on almost 66 million inhabitants in 2015, have already been the subject of numerous international publications on various topics: prevalence and incidence of diseases, patient care pathways, health status and health care utilization of specific populations, real-life use of drugs, assessment of adverse effects of drugs or other health care procedures, monitoring of national health insurance expenditure, etc. SNIIRAM comprises individual information on the sociodemographic and medical characteristics of beneficiaries and all hospital care and office medicine reimbursements, coded according to various systems. Access to data is controlled by permissions dependent on the type of data requested or used, their temporality and the researcher's status. In general, data can be analyzed by accredited agencies over a period covering the last three years plus the current year, and specific requests can be submitted to extract data over longer periods. A 1/97th random sample of SNIIRAM, the échantillon généraliste des bénéficiaires (EGB), representative of the national population of health insurance beneficiaries, was composed in 2005 to allow 20-year follow-up with facilitated access for medical research. The EGB is an open cohort, which includes new beneficiaries and newborn infants. SNIIRAM has continued to grow and extend to become, in 2016, the cornerstone of the future système national des données de santé (SNDS) [national health data system], which will gradually integrate new information (causes of death, social and medical data and complementary health insurance). In parallel, the modalities of data access and protection systems have also evolved. This article describes the SNIIRAM data warehouse and its transformation into SNDS, the data collected, the tools developed in order to facilitate data analysis, the limitations encountered, and changing access permissions.","DOI":"10.1016/j.respe.2017.05.004","ISSN":"0398-7620","note":"PMID: 28756037","title-short":"Value of a national administrative database to guide public decisions","journalAbbreviation":"Rev Epidemiol Sante Publique","language":"eng","author":[{"family":"Tuppin","given":"P."},{"family":"Rudant","given":"J."},{"family":"Constantinou","given":"P."},{"family":"Gastaldi-Ménager","given":"C."},{"family":"Rachas","given":"A."},{"family":"Roquefeuil","given":"L.","non-dropping-particle":"de"},{"family":"Maura","given":"G."},{"family":"Caillol","given":"H."},{"family":"Tajahmady","given":"A."},{"family":"Coste","given":"J."},{"family":"Gissot","given":"C."},{"family":"Weill","given":"A."},{"family":"Fagot-Campagna","given":"A."}],"issued":{"date-parts":[["2017",10]]}}}],"schema":""} 3 (see for example ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"43yTfAtU","properties":{"unsorted":true,"formattedCitation":"\\super 8\\uc0\\u8211{}15\\nosupersub{}","plainCitation":"8–15","noteIndex":0},"citationItems":[{"id":98,"uris":[""],"uri":[""],"itemData":{"id":98,"type":"article-journal","title":"Association of Hysteroscopic vs Laparoscopic Sterilization With Procedural, Gynecological, and Medical Outcomes","container-title":"JAMA","page":"375-387","volume":"319","issue":"4","source":"PubMed","abstract":"Importance: Safety of hysteroscopic sterilization has been recently questioned following reports of general symptoms such as allergy, tiredness, and depression in addition to associated gynecological results such as pelvic pain, perforation of fallopian tubes or uterus, and unwanted pregnancy.\nObjective: To compare the risk of reported adverse events between hysteroscopic and laparoscopic sterilization.\nDesign, Setting, and Participants: French nationwide cohort study using the national hospital discharge database linked to the health insurance claims database. Women aged 30 to 54 years receiving a first hysteroscopic or laparoscopic sterilization between 2010 and 2014 were included and were followed up through December 2015.\nExposures: Hysteroscopic sterilization vs laparoscopic sterilization.\nMain Outcomes and Measures: Risks of procedural complications (surgical and medical) and of gynecological (sterilization failure that includes salpingectomy, second sterilization procedure, or pregnancy; pregnancy; reoperation) and medical outcomes (all types of allergy; autoimmune diseases; thyroid disorder; use of analgesics, antimigraines, antidepressants, benzodiazepines; outpatient visits; sickness absence; suicide attempts; death) that occurred within 1 and 3 years after sterilization were compared using inverse probability of treatment-weighted Cox models.\nResults: Of the 105?357 women included (95.5% of eligible participants; mean age, 41.3 years [SD, 3.7 years]), 71?303 (67.7% ) underwent hysteroscopic sterilization, and 34?054 (32.3%) underwent laparoscopic sterilization. During the hospitalization for sterilization, risk of surgical complications for hysteroscopic sterilization was lower: 0.13% for hysteroscopic sterilization vs 0.78% for laparoscopic sterilization (adjusted risk difference [RD], -0.64; 95% CI, -0.67 to -0.60) and was lower for medical complications: 0.06% vs 0.11% (adjusted RD, -0.05; 95% CI, -0.08 to -0.01). During the first year after sterilization, 4.83% of women who underwent hysteroscopic sterilization had a higher risk of sterilization failure than the 0.69% who underwent laparoscopic sterilization (adjusted hazard ratio [HR], 7.11; 95% CI, 5.92 to 8.54; adjusted RD, 4.23 per 100 person-years; 95% CI, 3.40 to 5.22). Additionally, 5.65% of women who underwent hysteroscopic sterilization required gynecological reoperation vs 1.76% of women who underwent laparoscopic sterilization (adjusted HR, 3.26; 95% CI, 2.90 to 3.67; adjusted RD, 4.63 per 100 person-years; 95% CI, 3.38 to 4.75); these differences persisted after 3 years, although attenuated. Hysteroscopic sterilization was associated with a lower risk of pregnancy within the first year of the procedure but was not significantly associated with a difference in risk of pregnancy by the third year (adjusted HR, 1.04; 95% CI, 0.83-1.30; adjusted RD, 0.01 per 100 person-years; 95% CI, -0.04 to 0.07). Risks of medical outcomes were not significantly increased with hysteroscopic sterilization compared with laparoscopic sterilization.\nConclusions and Relevance: Among women undergoing first sterilization, the use of hysteroscopic sterilization was significantly associated with higher risk of gynecological complications over 1 year and over 3 years than was laparoscopic sterilization. Risk of medical outcomes was not significantly increased over 1 year or over 3 years. These findings do not support increased medical risks associated with hysteroscopic sterilization.","DOI":"10.1001/jama.2017.21269","ISSN":"1538-3598","note":"PMID: 29362796\nPMCID: PMC5833563","journalAbbreviation":"JAMA","language":"eng","author":[{"family":"Bouillon","given":"Kim"},{"family":"Bertrand","given":"Marion"},{"family":"Bader","given":"Georges"},{"family":"Lucot","given":"Jean-Philippe"},{"family":"Dray-Spira","given":"Rosemary"},{"family":"Zureik","given":"Mahmoud"}],"issued":{"date-parts":[["2018"]],"season":"23"}}},{"id":540,"uris":[""],"uri":[""],"itemData":{"id":540,"type":"article-journal","title":"Association Between Use of Thiopurines or Tumor Necrosis Factor Antagonists Alone or in Combination and Risk of Lymphoma in Patients With Inflammatory Bowel Disease","container-title":"JAMA","page":"1679-1686","volume":"318","issue":"17","source":"jamanetwork-com.gate2.inist.fr","abstract":"<h3>Importance</h3><p>An increased risk of lymphoma has been reported among patients receiving thiopurines for inflammatory bowel disease (IBD). The risk of lymphoma associated with anti–tumor necrosis factor (TNF) agents either alone or in combination with thiopurines is uncertain.</p><h3>Objective</h3><p>To assess the risk of lymphoma associated with thiopurines and anti-TNF agents, used alone or in combination, for the management of IBD.</p><h3>Design, Setting, and Participants</h3><p>Nationwide cohort study based on French National Health Insurance databases. Patients aged 18 years or older identified with IBD were included from January 1, 2009, through December 31, 2013, and followed up until December 31, 2015.</p><h3>Exposures</h3><p>At each time of the follow-up, patients were categorized as being exposed to thiopurine monotherapy, anti-TNF monotherapy, or combination therapy, or being unexposed.</p><h3>Main Outcomes and Measures</h3><p>The primary outcome was incident lymphoma.</p><h3>Results</h3><p>Among the 189 289 patients included (54% women; median age, 43 years [interquartile range, 32-56 years]) and followed up for a median of 6.7 years, 123 069 were never exposed during follow-up, 50 405 were exposed to thiopurine monotherapy, 30 294 to anti-TNF monotherapy, and 14 229 to combination therapy. Overall, 336 lymphoma cases occurred: 220 in unexposed patients (incidence rate [IR] per 1000 person-years, 0.26; 95% CI, 0.23-0.29), 70 in patients exposed to thiopurine monotherapy (IR, 0.54; 95% CI, 0.41-0.67), 32 in patients exposed to anti-TNF monotherapy (IR, 0.41; 95% CI, 0.27-0.55), and 14 in patients exposed to combination therapy (IR, 0.95; 95% CI, 0.45-1.45). In a multivariable Cox model, compared with unexposed patients, the risk of lymphoma was higher among those exposed to thiopurine monotherapy (adjusted hazard ratio [aHR], 2.60; 95% CI, 1.96-3.44;<i>P</i> &lt; .001), anti-TNF monotherapy (aHR, 2.41; 95% CI, 1.60-3.64;<i>P</i> &lt; .001), or combination therapy (aHR, 6.11; 95% CI, 3.46-10.8;<i>P</i> &lt; .001). The risk was higher in patients exposed to combination therapy vs those exposed to thiopurine monotherapy (aHR, 2.35; 95% CI, 1.31-4.22;<i>P</i> &lt; .001) or anti-TNF monotherapy (aHR, 2.53; 95% CI, 1.35-4.77;<i>P</i> &lt; .001).</p><h3>Conclusions and Relevance</h3><p>Among adults with IBD, the use of thiopurine monotherapy or anti-TNF monotherapy was associated with a small but statistically significant increased risk of lymphoma compared with exposure to neither medication, and this risk was higher with combination therapy than with each of these treatments used alone. These findings may inform decisions regarding the benefits and risks of treatment.</p>","DOI":"10.1001/jama.2017.16071","ISSN":"0098-7484","journalAbbreviation":"JAMA","language":"en","author":[{"family":"Lemaitre","given":"Magali"},{"family":"Kirchgesner","given":"Julien"},{"family":"Rudnichi","given":"Annie"},{"family":"Carrat","given":"Fabrice"},{"family":"Zureik","given":"Mahmoud"},{"family":"Carbonnel","given":"Franck"},{"family":"Dray-Spira","given":"Rosemary"}],"issued":{"date-parts":[["2017",11,7]]}}},{"id":97,"uris":[""],"uri":[""],"itemData":{"id":97,"type":"article-journal","title":"Association Between Bariatric Surgery and Rates of Continuation, Discontinuation, or Initiation of Antidiabetes Treatment 6 Years Later","container-title":"JAMA surgery","source":"PubMed","abstract":"Importance: Few large-scale long-term prospective cohort studies have assessed changes in antidiabetes treatment after bariatric surgery.\nObjective: To describe the association between bariatric surgery and rates of continuation, discontinuation, or initiation of antidiabetes treatment 6 years after bariatric surgery compared with a matched control obese group.\nDesign, Setting, and Participants: This nationwide observational population-based cohort study extracted health care reimbursement data from the French national health insurance database from January 1, 2008, to December 31, 2015. All patients undergoing primary bariatric surgery in France between January 1 and December 31, 2009, were matched on age, sex, body mass index category, and antidiabetes treatment with control patients hospitalized for obesity in 2009 with no bariatric surgery between 2005 and 2015.\nExposures: Bariatric surgery, including adjustable gastric banding (AGB), gastric bypass (GBP), and sleeve gastrectomy (SG).\nMain Outcome and Measure: Reimbursement for antidiabetes drugs. Mixed-effects logistic regression models estimated factors of discontinuation or initiation of antidiabetes treatment over a period of 6 years.\nResults: In 2009, a total of 15?650 patients (mean [SD] age, 38.9 [11.2] years; 84.6% female; 1633 receiving antidiabetes treatment) underwent primary bariatric surgery, with 48.5% undergoing AGB, 27.7% undergoing GBP, and 22.0% undergoing SG. Among patients receiving antidiabetes treatment at baseline, the antidiabetes treatment discontinuation rate was higher 6 years after bariatric surgery than in controls (-49.9% vs -9.0%, P?<?.001). In multivariable analysis, the main predictive factors for discontinuation were the following: GBP (odds ratio [OR], 16.7; 95% CI, 13.0-21.4), SG (OR, 7.30; 95% CI, 5.50-9.50), and AGB (OR, 4.30; 95% CI, 3.30-5.60) compared with no bariatric surgery, as well as insulin use (OR, 0.17; 95% CI, 0.13-0.22), dual therapy without insulin (OR, 0.38; 95% CI, 0.32-0.45) vs monotherapy, lipid-lowering treatment (OR, 0.76; 95% CI, 0.63-0.91), antidepressant treatment (OR, 0.67; 95% CI, 0.55-0.81), and age (OR, 0.96; 95% CI, 0.95-0.97) per year. For patients without antidiabetes treatment at baseline, the 6-year antidiabetes treatment initiation rate was much lower after bariatric surgery than in controls (1.4% vs 12.0%, P?<?.001). In multivariable analysis, protective factors were GBP (OR, 0.06; 95% CI, 0.04-0.09), SG (OR, 0.08; 95% CI, 0.06-0.11), and AGB (OR, 0.16; 95% CI, 0.14-0.20) vs controls, and risk factors were as follows: body mass index category (OR, 2.04; 95% CI, 1.68-2.47 for ≥50.0 vs 30.0-39.9 and OR, 1.68; 95% CI, 1.49-1.90 for 40.0-49.9 vs 30.0-39.9), antihypertensive treatment (OR, 1.49; 95% CI, 1.33-1.67), low income (OR, 1.43; 95 % CI, 1.26-1.62), and age (OR, 1.04; 95 % CI, 1.03-1.05) per year.\nConclusions and Relevance: Bariatric surgery was associated with a significantly higher 6-year postoperative antidiabetes treatment discontinuation rate compared with baseline and with an obese control group without bariatric surgery.","DOI":"10.1001/jamasurg.2017.6163","ISSN":"2168-6262","note":"PMID: 29450469\nPMCID: PMC5875344","journalAbbreviation":"JAMA Surg","language":"eng","author":[{"family":"Thereaux","given":"Jérémie"},{"family":"Lesuffleur","given":"Thomas"},{"family":"Czernichow","given":"Sébastien"},{"family":"Basdevant","given":"Arnaud"},{"family":"Msika","given":"Simon"},{"family":"Nocca","given":"David"},{"family":"Millat","given":"Bertrand"},{"family":"Fagot-Campagna","given":"Anne"}],"issued":{"date-parts":[["2018",2,14]]}}},{"id":539,"uris":[""],"uri":[""],"itemData":{"id":539,"type":"article-journal","title":"Dental procedures, antibiotic prophylaxis, and endocarditis among people with prosthetic heart valves: nationwide population based cohort and a case crossover study","container-title":"BMJ (Clinical research ed.)","page":"j3776","volume":"358","source":"PubMed","abstract":"Objective\n?To assess the relation between invasive dental procedures and infective endocarditis associated with oral streptococci among people with prosthetic heart valves.Design?Nationwide population based cohort and a case crossover study.Setting?French national health insurance administrative data linked with the national hospital discharge database.Participants?All adults aged more than 18 years, living in France, with medical procedure codes for positioning or replacement of prosthetic heart valves between July 2008 and July 2014.Main outcome measures?Oral streptococcal infective endocarditis was identified using primary discharge diagnosis codes. In the cohort study, Poisson regression models were performed to estimate the rate of oral streptococcal infective endocarditis during the three month period after invasive dental procedures compared with non-exposure periods. In the case crossover study, conditional logistic regression models calculated the odds ratio and 95% confidence intervals comparing exposure to invasive dental procedures during the three month period preceding oral streptococcal infective endocarditis (case period) with three earlier control periods.Results?The cohort included 138?876 adults with prosthetic heart valves (285?034 person years); 69?303 (49.9%) underwent at least one dental procedure. Among the 396?615 dental procedures performed, 103?463 (26.0%) were invasive and therefore presented an indication for antibiotic prophylaxis, which was performed in 52?280 (50.1%). With a median follow-up of 1.7 years, 267 people developed infective endocarditis associated with oral streptococci (incidence rate 93.7 per 100?000 person years, 95% confidence interval 82.4 to 104.9). Compared with non-exposure periods, no statistically significant increased rate of oral streptococcal infective endocarditis was observed during the three months after an invasive dental procedure (relative rate 1.25, 95% confidence interval 0.82 to 1.82; P=0.26) and after an invasive dental procedure without antibiotic prophylaxis (1.57, 0.90 to 2.53; P=0.08). In the case crossover analysis, exposure to invasive dental procedures was more frequent during case periods than during matched control periods (5.1%v3.2%; odds ratio 1.66, 95% confidence interval 1.05 to 2.63; P=0.03).Conclusion?Invasive dental procedures may contribute to the development of infective endocarditis in adults with prosthetic heart valves.","ISSN":"1756-1833","note":"PMID: 28882817\nPMCID: PMC5588045","title-short":"Dental procedures, antibiotic prophylaxis, and endocarditis among people with prosthetic heart valves","journalAbbreviation":"BMJ","language":"eng","author":[{"family":"Tubiana","given":"Sarah"},{"family":"Blotière","given":"Pierre-Olivier"},{"family":"Hoen","given":"Bruno"},{"family":"Lesclous","given":"Philippe"},{"family":"Millot","given":"Sarah"},{"family":"Rudant","given":"Jérémie"},{"family":"Weill","given":"Alain"},{"family":"Coste","given":"Joel"},{"family":"Alla","given":"Fran?ois"},{"family":"Duval","given":"Xavier"}],"issued":{"date-parts":[["2017",9,7]]}}},{"id":96,"uris":[""],"uri":[""],"itemData":{"id":96,"type":"article-journal","title":"Low dose oestrogen combined oral contraception and risk of pulmonary embolism, stroke, and myocardial infarction in five million French women: cohort study","container-title":"BMJ (Clinical research ed.)","page":"i2002","volume":"353","source":"PubMed","abstract":"OBJECTIVE: To assess the risk of pulmonary embolism, ischaemic stroke, and myocardial infarction associated with combined oral contraceptives according to dose of oestrogen (ethinylestradiol) and progestogen.\nDESIGN: Observational cohort study.\nSETTING: Data from the French national health insurance database linked with data from the French national hospital discharge database.\nPARTICIPANTS: 4?945?088 women aged 15-49 years, living in France, with at least one reimbursement for oral contraceptives and no previous hospital admission for cancer, pulmonary embolism, ischaemic stroke, or myocardial infarction, between July 2010 and September 2012.\nMAIN OUTCOME MEASURES: Relative and absolute risks of first pulmonary embolism, ischaemic stroke, and myocardial infarction.\nRESULTS: The cohort generated 5?443?916 women years of oral contraceptive use, and 3253 events were observed: 1800 pulmonary embolisms (33 per 100?000 women years), 1046 ischaemic strokes (19 per 100?000 women years), and 407 myocardial infarctions (7 per 100?000 women years). After adjustment for progestogen and risk factors, the relative risks for women using low dose oestrogen (20 ?g v 30-40 ?g) were 0.75 (95% confidence interval 0.67 to 0.85) for pulmonary embolism, 0.82 (0.70 to 0.96) for ischaemic stroke, and 0.56 (0.39 to 0.79) for myocardial infarction. After adjustment for oestrogen dose and risk factors, desogestrel and gestodene were associated with statistically significantly higher relative risks for pulmonary embolism (2.16, 1.93 to 2.41 and 1.63, 1.34 to 1.97, respectively) compared with levonorgestrel. Levonorgestrel combined with 20 ?g oestrogen was associated with a statistically significantly lower risk than levonorgestrel with 30-40 ?g oestrogen for each of the three serious adverse events.\nCONCLUSIONS: For the same dose of oestrogen, desogestrel and gestodene were associated with statistically significantly higher risks of pulmonary embolism but not arterial thromboembolism compared with levonorgestrel. For the same type of progestogen, an oestrogen dose of 20 ?g versus 30-40 ?g was associated with lower risks of pulmonary embolism, ischaemic stroke, and myocardial infarction.","ISSN":"1756-1833","note":"PMID: 27164970\nPMCID: PMC4862376","title-short":"Low dose oestrogen combined oral contraception and risk of pulmonary embolism, stroke, and myocardial infarction in five million French women","journalAbbreviation":"BMJ","language":"eng","author":[{"family":"Weill","given":"Alain"},{"family":"Dalichampt","given":"Marie"},{"family":"Raguideau","given":"Fanny"},{"family":"Ricordeau","given":"Philippe"},{"family":"Blotière","given":"Pierre-Olivier"},{"family":"Rudant","given":"Jérémie"},{"family":"Alla","given":"Fran?ois"},{"family":"Zureik","given":"Mahmoud"}],"issued":{"date-parts":[["2016"]],"season":"10"}}},{"id":538,"uris":[""],"uri":[""],"itemData":{"id":538,"type":"article-journal","title":"Severe intestinal malabsorption associated with olmesartan: a French nationwide observational cohort study","container-title":"Gut","page":"1664-1669","volume":"65","issue":"10","source":"PubMed","abstract":"OBJECTIVES: Severe sprue-like enteropathy associated with olmesartan has been reported, but there has been no demonstration of an increased risk by epidemiological studies.\nAIM: To assess, in a nationwide patient cohort, the risk of hospitalisation for intestinal malabsorption associated with olmesartan compared with other angiotensin receptor blockers (ARB) and ACE inhibitors (ACEIs).\nDESIGN: From the French National Health Insurance claim database, all adult patients initiating ARB or ACEI between 1 January 2007 and 31 December 2012 with no prior hospitalisation for intestinal malabsorption, no serology testing for coeliac disease and no prescription for a gluten-free diet product were included. Incidence of hospitalisation with a discharge diagnosis of intestinal malabsorption was the primary endpoint.\nRESULTS: 4?546?680 patients (9?010?303 person-years) were included, and 218 events observed. Compared with ACEI, the adjusted rate ratio of hospitalisation with a discharge diagnosis of intestinal malabsorption was 2.49 (95% CI 1.73 to 3.57, p<0.0001) in olmesartan users. This adjusted rate ratio was 0.76 (95% CI 0.39 to 1.49, p=0.43) for treatment duration shorter than 1?year, 3.66 (95% CI 1.84 to 7.29, p<0.001) between 1 and 2?years and 10.65 (95% CI 5.05 to 22.46, p<0.0001) beyond 2?years of exposure. Median length of hospital stay for intestinal malabsorption was longer in the olmesartan group than in the other groups (p=0.02). Compared with ACEI, the adjusted rate ratio of hospitalisation for coeliac disease was 4.39 (95% CI 2.77 to 6.96, p<0.0001) in olmesartan users and increased with treatment duration.\nCONCLUSIONS: Olmesartan is associated with an increased risk of hospitalisation for intestinal malabsorption and coeliac disease.","DOI":"10.1136/gutjnl-2015-309690","ISSN":"1468-3288","note":"PMID: 26250345","title-short":"Severe intestinal malabsorption associated with olmesartan","journalAbbreviation":"Gut","language":"eng","author":[{"family":"Basson","given":"Mickael"},{"family":"Mezzarobba","given":"Myriam"},{"family":"Weill","given":"Alain"},{"family":"Ricordeau","given":"Philippe"},{"family":"Allemand","given":"Hubert"},{"family":"Alla","given":"Francois"},{"family":"Carbonnel","given":"Franck"}],"issued":{"date-parts":[["2016"]]}}},{"id":537,"uris":[""],"uri":[""],"itemData":{"id":537,"type":"article-journal","title":"Comparison of the short-term risk of bleeding and arterial thromboembolic events in nonvalvular atrial fibrillation patients newly treated with dabigatran or rivaroxaban versus vitamin K antagonists: a French nationwide propensity-matched cohort study","container-title":"Circulation","page":"1252-1260","volume":"132","issue":"13","source":"PubMed","abstract":"BACKGROUND: The safety and effectiveness of non-vitamin K antagonist (VKA) oral anticoagulants, dabigatran or rivaroxaban, were compared with VKA in anticoagulant-naive patients with nonvalvular atrial fibrillation during the early phase of anticoagulant therapy.\nMETHODS AND RESULTS: With the use of the French medico-administrative databases (SNIIRAM and PMSI), this nationwide cohort study included patients with nonvalvular atrial fibrillation who initiated dabigatran or rivaroxaban between July and November 2012 or VKA between July and November 2011. Patients presenting a contraindication to oral anticoagulants were excluded. Dabigatran and rivaroxaban new users were matched to VKA new users by the use of 1:2 matching on the propensity score. Patients were followed for up to 90 days until outcome, death, loss to follow-up, or December 31 of the inclusion year. Hazard ratios of hospitalizations for bleeding and arterial thromboembolic events were estimated in an intent-to-treat analysis using Cox regression models. The population was composed of 19 713 VKA, 8443 dabigatran, and 4651 rivaroxaban new users. All dabigatran- and rivaroxaban-treated patients were matched to 16 014 and 9301 VKA-treated patients, respectively. Among dabigatran-, rivaroxaban-, and their VKA-matched-treated patients, 55 and 122 and 31 and 68 bleeding events and 33 and 58 and 12 and 28 arterial thromboembolic events were observed during follow-up, respectively. After matching, no statistically significant difference in bleeding (hazard ratio, 0.88; 95% confidence interval, 0.64-1.21) or thromboembolic (hazard ratio, 1.10; 95% confidence interval, 0.72-1.69) risk was observed between dabigatran and VKA new users. Bleeding (hazard ratio, 0.98; 95% confidence interval, 0.64-1.51) and ischemic (hazard ratio, 0.93; 95% confidence interval, 0.47-1.85) risks were comparable between rivaroxaban and VKA new users.\nCONCLUSIONS: In this propensity-matched cohort study, our findings suggest that physicians should exercise caution when initiating either non-VKA oral anticoagulants or VKA in patients with nonvalvular atrial fibrillation.","DOI":"10.1161/CIRCULATIONAHA.115.015710","ISSN":"1524-4539","note":"PMID: 26199338\nPMCID: PMC4885525","title-short":"Comparison of the short-term risk of bleeding and arterial thromboembolic events in nonvalvular atrial fibrillation patients newly treated with dabigatran or rivaroxaban versus vitamin K antagonists","journalAbbreviation":"Circulation","language":"eng","author":[{"family":"Maura","given":"Géric"},{"family":"Blotière","given":"Pierre-Olivier"},{"family":"Bouillon","given":"Kim"},{"family":"Billionnet","given":"Cécile"},{"family":"Ricordeau","given":"Philippe"},{"family":"Alla","given":"Fran?ois"},{"family":"Zureik","given":"Mahmoud"}],"issued":{"date-parts":[["2015",9,29]]}}},{"id":99,"uris":[""],"uri":[""],"itemData":{"id":99,"type":"article-journal","title":"Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012","container-title":"Diabetologia","page":"636-644","volume":"60","issue":"4","source":"PubMed","abstract":"AIMS/HYPOTHESIS: The aim of this study was to assess the risk of adverse perinatal outcomes in gestational diabetes mellitus (GDM) in a large national cohort.\nMETHODS: All deliveries taking place after 22?weeks in France in 2012 were included by extracting data from the hospital discharge database and the national health insurance system. The diabetic status of mothers was determined by the use of glucose-lowering agents and by hospital diagnosis. Outcomes were analysed according to the type of diabetes and, in the GDM group, whether or not diabetes was insulin-treated.\nRESULTS: The cohort of 796,346 deliveries involved 57,629 (7.24%) mothers with GDM. Mother-infant linkage was obtained for 705,198 deliveries. The risks of adverse outcomes were much lower with GDM than with pregestational diabetes. After limiting the analysis to deliveries after 28?weeks to reduce immortal time bias, the risks of preterm birth (OR 1.3 [95% CI 1.3, 1.4]), Caesarean section (OR 1.4 [95% CI 1.4, 1.4]), pre-eclampsia/eclampsia (OR 1.7 [95% CI 1.6, 1.7]), macrosomia (OR 1.8 [95% CI 1.7, 1.8]), respiratory distress (OR 1.1 [95% CI 1.0, 1.3]), birth trauma (OR 1.3 [95% CI 1.1, 1.5]) and cardiac malformations (OR 1.3 [95% CI 1.1, 1.4]) were increased in women with GDM compared with the non-diabetic population. Higher risks were observed in women with insulin-treated GDM than those with diet-treated GDM. After limiting the analysis to term deliveries, an increased risk of perinatal mortality was observed. After excluding women suspected to have undiagnosed pregestational diabetes, the risk remained moderately increased only for those with diet-treated GDM (OR 1.3 [95% CI 1.0, 1.6]).\nCONCLUSIONS/INTERPRETATION: GDM is associated with a moderately increased risk of adverse perinatal outcomes, which is higher in insulin-treated GDM than in non-insulin-treated GDM for most outcomes.","DOI":"10.1007/s00125-017-4206-6","ISSN":"1432-0428","note":"PMID: 28197657","journalAbbreviation":"Diabetologia","language":"eng","author":[{"family":"Billionnet","given":"Cécile"},{"family":"Mitanchez","given":"Delphine"},{"family":"Weill","given":"Alain"},{"family":"Nizard","given":"Jacky"},{"family":"Alla","given":"Fran?ois"},{"family":"Hartemann","given":"Agnès"},{"family":"Jacqueminet","given":"Sophie"}],"issued":{"date-parts":[["2017"]]}}}],"schema":""} 8–15). These comprehensive national databases are powerful tools to assess the prevalence of rare events. As the primary purpose of these databases is financial, any false statements constitute serious frauds, liable to legal proceedings, which also tends to ensure the accuracy of the data recorded. Furthermore, quality controls and audits are performed before transmission to National Health Insurance (CNAM, Caisse Nationale de l’Assurance Maladie), mainly for processing of aberrant or missing data. Descriptive statisticsCrude SAE rates per 10,000 colonoscopies were calculated by dividing the total numbers of adverse events by the total number of colonoscopies in the cohort. Standardized incidence ratios (SIRs) were calculated to compare these incidence rates with the incidence rates of these events in 2013 in the general population (general scheme beneficiaries aged 30 years and over, i.e. 32,490,167 individuals), which served as the reference group (reference rates were stratified by gender and 5-year age-groups). More precisely, SIRs were estimated by dividing the observed number of SAEs by the expected number of SAEs, obtained by applying the incidence rate observed in the general population (in this case, the general scheme beneficiaries) to the number of individuals in our cohort in each age and gender group considered. Crude 5-day and 30-day mortality rates per 1,000 SAE were calculated for each SAE by dividing the total numbers of deaths occurring within 5 or 30 days after a SAE by the total number of SAE in the cohort. Crude 5-day and 30-day mortality rates per 100,000 procedures were also calculated for each SAE by dividing the total numbers of deaths occurring within 5 or 30 days after a SAE by the total number of colonoscopies in the cohort. Crude odds ratios (OR) for each SAE studied were estimated by logistic regression models for patient, colonoscopy, endoscopist, and facility characteristics.References1. Bezin J, Duong M, Lassalle R, et al. The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology. Pharmacoepidemiol Drug Saf. 2017;26(8):954-962. doi:10.1002/pds.42332. Moulis G, Lapeyre-Mestre M, Palmaro A, Pugnet G, Montastruc J-L, Sailler L. French health insurance databases: What interest for medical research? Rev Med Interne. 2015;36(6):411-417. doi:10.1016/j.revmed.2014.11.0093. Tuppin P, Rudant J, Constantinou P, et al. Value of a national administrative database to guide public decisions: From the système national d’information interrégimes de l’Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France. Rev Epidemiol Sante Publique. 2017;65 Suppl 4:S149-S167. doi:10.1016/j.respe.2017.05.0044. ICD-10: International Statistical Classification of Diseases and Health-Related Problems. Vol 2. WHO; 1992.5. Noize P, Bazin F, Dufouil C, et al. Comparison of health insurance claims and patient interviews in assessing drug use: data from the Three‐City (3C) Study. Pharmacoepidemiol Drug Saf. 2009;18(4):310-319. doi:10.1002/pds.17176. Noize P, Bazin F, Pariente A, et al. Validity of chronic drug exposure presumed from repeated patient interviews varied according to drug class. J Clin Epidemiol. 2012;65(10):1061-1068. doi:10.1016/j.jclinepi.2012.04.0097. Classification commune des actes médicaux. Bull Off. 2007;(2007/3 bis).8. Bouillon K, Bertrand M, Bader G, Lucot J-P, Dray-Spira R, Zureik M. Association of Hysteroscopic vs Laparoscopic Sterilization With Procedural, Gynecological, and Medical Outcomes. JAMA. 2018;319(4):375-387. doi:10.1001/jama.2017.212699. Lemaitre M, Kirchgesner J, Rudnichi A, et al. Association Between Use of Thiopurines or Tumor Necrosis Factor Antagonists Alone or in Combination and Risk of Lymphoma in Patients With Inflammatory Bowel Disease. JAMA. 2017;318(17):1679-1686. doi:10.1001/jama.2017.1607110. Thereaux J, Lesuffleur T, Czernichow S, et al. Association Between Bariatric Surgery and Rates of Continuation, Discontinuation, or Initiation of Antidiabetes Treatment 6 Years Later. JAMA Surg. February 2018. doi:10.1001/jamasurg.2017.616311. Tubiana S, Blotière P-O, Hoen B, et al. Dental procedures, antibiotic prophylaxis, and endocarditis among people with prosthetic heart valves: nationwide population based cohort and a case crossover study. BMJ. 2017;358:j3776.12. Weill A, Dalichampt M, Raguideau F, et al. Low dose oestrogen combined oral contraception and risk of pulmonary embolism, stroke, and myocardial infarction in five million French women: cohort study. BMJ. 2016;353:i2002.13. Basson M, Mezzarobba M, Weill A, et al. Severe intestinal malabsorption associated with olmesartan: a French nationwide observational cohort study. Gut. 2016;65(10):1664-1669. doi:10.1136/gutjnl-2015-30969014. Maura G, Blotière P-O, Bouillon K, et al. Comparison of the short-term risk of bleeding and arterial thromboembolic events in nonvalvular atrial fibrillation patients newly treated with dabigatran or rivaroxaban versus vitamin K antagonists: a French nationwide propensity-matched cohort study. Circulation. 2015;132(13):1252-1260. doi:10.1161/CIRCULATIONAHA.115.01571015. Billionnet C, Mitanchez D, Weill A, et al. Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012. Diabetologia. 2017;60(4):636-644. doi:10.1007/s00125-017-4206-6Supplemental Table SEQ Supplementary_table \* ARABIC 1: Procedure codes for colonoscopies, from French medical classification of medical procedures. Procedure codeWordingIncomplete colonoscopiesWithout polypectomyHHQE004Incomplete colonoscopy beyond the sigmoid colonWith polypectomyHHFE001Resection of 1 to 3 polyps less than 1 cm in diameter from the colon and/or rectum, by recto-sigmoidoscopy or incomplete colonoscopyHHFE005Resection of a polyp larger than 1 cm in diameter or 4 or more polyps from the colon and/or rectum, by recto-sigmoidoscopy or incomplete colonoscopyComplete colonoscopiesWithout polypectomyHHQE002Complete colonoscopy, beyond the ileocecal valveHHQE005Complete colonoscopy with visualization of the base of the caecum, without crossing the ileocecal valveWith polypectomyHHFE002Resection of 1 to 3 polyps less than 1 cm in diameter from the colon and/or rectum, by complete colonoscopyHHFE004Resection of a polyp larger than 1 cm in diameter or 4 or more polyps from the colon and/or rectum, by complete colonoscopyCCAM (Classification Commune des Actes Médicaux).Supplemental Table SEQ Supplementary_table \* ARABIC 2: Procedure codes for major colic or rectal surgery procedures leading to exclusion, from French medical classification of medical procedures.Procedure codeWordingHHCC007Cutaneous colostomy, by laparoscopyHHCA002Cutaneous colostomy, by laparotomyHHCC011Colocolostomy diversion [Colocolic anastomosis without colonic resection], by laparoscopyHHCA003Colocolostomy diversion [Colocolic anastomosis without colonic resection], by laparotomyHHFA026Right colectomy without restoration of continuity, by laparotomyHHFA009Right colectomy with restoration of continuity, by laparotomyHHFA008Right colectomy with restoration of continuity, by laparoscopy, or laparotomy with laparoscopic preparationHHFA018Transverse colectomy, by laparotomyHHFA023Transverse colectomy, by laparoscopy, or laparotomy with laparoscopic preparationHHFA014Left hemicolectomy without release of splenic flexure, without restoration of continuity, by laparotomyHHFA017Left hemicolectomy without release of splenic flexure, with restoration of continuity, by laparotomyHHFA010Left hemicolectomy without release of splenic flexure, with restoration of continuity, by laparoscopy, or laparotomy with laparoscopic preparationHHFA024Left hemicolectomy with release of splenic flexure, without restoration of continuity, by laparotomyHHFA006Left hemicolectomy with release of splenic flexure, with restoration of continuity, by laparotomyHHFA002Left hemicolectomy with release of splenic flexure, with restoration of continuity, by laparoscopy, or laparotomy with laparoscopic preparationHHFA021Complete colectomy with preservation of the rectum, without restoration of continuity, by laparotomyHHFA005Complete colectomy with preservation of the rectum, without restoration of continuity, by laparoscopy, or laparotomy with laparoscopic preparationHHFA022Complete colectomy with preservation of the rectum, with ileorectal anastomosis, by laparotomyHHFA004Complete colectomy with preservation of the rectum, with ileorectal anastomosis, by laparoscopy, or laparotomy with laparoscopic preparationHJFC031Recto-sigmoid resection extending beyond the pouch of Douglas, without restoration of continuity, by laparoscopyHJFA011Recto-sigmoid resection extending beyond the pouch of Douglas, without restoration of continuity, by laparotomyHJFA002Recto-sigmoid resection with infraperitoneal colorectal anastomosis, by laparotomyHJFA004Recto-sigmoid resection with infraperitoneal colorectal anastomosis, by laparoscopy, or by laparotomy with laparoscopic preparationHJFA006Recto-sigmoid resection by laparotomy, with coloanal anastomosis by anal or trans-sphincter approachHJFA017Laparoscopic or open recto-sigmoid resection with laparoscopic preparation, with coloanal anastomosis by anal approachHJFA001Retrocolic resection with retrorectal lowering of the colon by laparotomy, with colorectal anastomosis by anal approachHJFA005Amputation of the rectum, by perineal approachHJFA007Amputation of the rectum, by laparotomy and perineal approachHJFA019Amputation of the rectum, by laparoscopy, or laparotomy with preparation by laparoscopy and perineal approachCCAM (Classification Commune des Actes Médicaux).Supplemental Table SEQ Supplementary_table \* ARABIC 3: ICD10 codes for definition of adverse events.ICD10 codeWordingCardiovascular adverse eventsShockR57.0Cardiogenic shockR57.1Hypovolemic shockR57.9Shock, unspecifiedMyocardial infarctionI21Acute myocardial infarctionI22Subsequent myocardial infarctionStrokeI63Cerebral infarctionI64Stroke, not specified as hemorrhage or infarctionPulmonary embolismI26Pulmonary embolismAcute renal failureN17Acute renal failureUrolithiasisN20Calculus of kidney and ureterN21Calculus of lower urinary tractN22Calculus of urinary tract in diseases classified elsewhereN23Unspecified renal colicICD10: International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Supplemental Table SEQ Supplementary_table \* ARABIC 4a: Standardized incidence ratios (SIRs) of serious adverse events after colonoscopy (n=4,088,799).?SIR95% CICardiovascular adverse events shock15.8[14.9-16.7] myocardial infarction3.3[3.0-3.7] stroke5.1[4.7-5.5] pulmonary embolism12.8[12.1-13.6]Renal adverse events acute renal failure11.8[11.4-12.3] urolithiasis7.3[6.9-7.7]Standard population: general scheme beneficiaries aged 30 years and over.CI: confidence interval.Supplemental Table 4b: Rates and standardized incidence ratios (SIRs) of serious adverse events after colonoscopy, in the further selected population excluding colonoscopies performed on day 2 of hospitalization or without identified bowel preparation (n=3,549,206).?Numberper 10,000 proceduresSIR95% CICardiovascular adverse events shock1160.331.9[1.6-2.3] myocardial infarction1360.381.5[1.3-1.8] stroke2140.601.8[1.6-2.1] pulmonary embolism3781.15.0[4.6-5.6]Renal adverse events acute renal failure3160.892.1[1.8-2.3] urolithiasis9242.65.6[5.3-6.0]Standard population: general scheme beneficiaries aged 30 years and over.CI: confidence interval.Patients excluded from this analysis were older (14.3% aged 80 or older) and had a higher number of pre-existing conditions (27.9% with a Charlson index score ≥2).Supplemental Table SEQ Supplementary_table \* ARABIC 5: Characteristics associated with cardiovascular adverse events: unadjusted logistic regression models (N = 4,088,799).?Shock Myocardial infarction Stroke Pulmonary embolism?OR [95% CI]p value?OR [95% CI]p value?OR [95% CI]p value?OR [95% CI]p valueYear 20101.13 [0.93-1.38]0.231.01 [0.73-1.40]0.931.39 [1.1-1.74]0.0051.14 [0.94-1.37]0.18 20111.00 [0.82-1.23]1.000.76 [0.54-1.08]0.131.10 [0.87-1.4]0.420.98 [0.81-1.19]0.83 20121.001.001.001.00 20131.26 [1.04-1.53]0.020.86 [0.61-1.21]0.381.08 [0.84-1.38]0.561.01 [0.83-1.23]0.94 20141.05 [0.85-1.29]0.640.72 [0.50-1.04]0.0831.04 [0.81-1.34]0.741.03 [0.84-1.26]0.77 20151.31 [1.07-1.61]0.0090.85 [0.59-1.23]0.390.74 [0.55-0.98]0.0371.05 [0.85-1.29]0.65Patient characteristics???????????Sex male1.001.001.001.00 female0.45 [0.40-0.50]<.00010.38 [0.31-0.48]<.00010.68 [0.59-0.79]<.00010.85 [0.76-0.96]0.0062Age 30-391.001.001.001.00 40-490.96 [0.66-1.40]0.854.77 [1.46-15.5]0.00961.54 [0.73-3.22]0.251.57 [1.10-2.25]0.013 50-591.20 [0.85-1.69]0.315.91 [1.86-18.8]0.00263.28 [1.66-6.47]0.00061.44 [1.02-2.04]0.041 60-691.97 [1.41-2.74]<.00018.81 [2.79-27.8]0.00025.45 [2.79-10.7]<.00011.88 [1.34-2.64]0.0003 70-793.93 [2.83-5.47]<.000115.1 [4.78-47.8]<.000114.0 [7.19-27.2]<.00014.64 [3.32-6.49]<.0001 80 and older13.6 [9.78-19.0]<.000150.1 [15.8-158]<.000143.3 [22.2-84.3]<.000111.4 [8.10-16.1]<.0001Pre-existing conditions myocardial infarction10.7 [9.41-12.1]<.000174.6 [58.8-94.7]<.00014.90 [4.04-5.95]<.00012.59 [2.13-3.15]<.0001 congestive heart failure33.4 [29.5-37.9]<.000126.8 [21.2-33.8]<.000116.1 [13.4-19.3]<.00018.60 [7.15-10.4]<.0001 peripheral vascular disease14.4 [12.7-16.2]<.000121.0 [17.0-25.9]<.00017.35 [6.15-8.77]<.00013.46 [2.88-4.17]<.0001 cerebrovascular disease9.18 [7.84-10.8]<.00015.79 [4.14-8.11]<.0001241 [199-291]<.00013.77 [3.02-4.70]<.0001 dementia8.81 [6.68-11.6]<.00017.50 [4.39-12.8]<.000115.7 [12.1-20.5]<.00018.12 [6.11-10.8]<.0001 chronic pulmonary disease2.43 [2.15-2.75]<.00011.79 [1.42-2.27]<.00011.68 [1.42-1.98]<.00011.73 [1.52-1.98]<.0001 connective tissue disease2.53 [1.83-3.50]<.00012.16 [1.15-4.04]0.0171.19 [0.67-2.11]<.00012.55 [1.85-3.50]<.0001 ulcer disease8.75 [7.39-10.4]<.00013.19 [2.01-5.06]<.00013.22 [2.35-4.41]<.00013.19 [2.47-4.11]<.0001 moderate or severe renal disease46.3 [41.1-52.2]<.000120.3 [15.6-26.4]<.000116.2 [13.3-19.6]<.00019.22 [7.63-11.2]<.0001 hemiplegia10.2 [7.98-13.0]<.00017.67 [4.64-12.7]<.000149.7 [42.0-58.8]<.00014.69 [3.32-6.63]<.0001 HIV/AIDS2.94 [1.58-5.48]0.00072.86 [0.92-8.92]0.072.23 [0.93-5.38]0.0733.76 [2.17-6.49]<.0001 liver disease8.09 [6.84-9.57]<.00012.72 [1.69-4.37]<.00012.54 [1.82-3.55]<.00012.88 [2.24-3.72]<.0001 mild liver disease4.64 [3.63-5.93]<.00013.02 [1.80-5.07]<.00012.07 [1.35-3.16]0.00082.20 [1.58-3.06]<.0001 moderate or severe liver disease18.5 [14.9-22.9]<.00011.81 [0.58-5.65]0.313.95 [2.33-6.70]<.00014.94 [3.37-7.24]<.0001 diabetes3.88 [3.41-4.41]<.00013.91 [3.10-4.92]<.00014.20 [3.60-4.91]<.00012.10 [1.80-2.44]<.0001 diabetes without end-organ damage1.70 [1.40-2.05]<.00011.40 [0.97-2.04]0.0741.41 [1.09-1.82]0.00931.79 [1.51-2.12]<.0001 diabetes with end-organ damage18.6 [15.9-21.7]<.000120.7 [15.8-27.0]<.000123.0 [19.3-27.5]<.00014.16 [3.15-5.50]<.0001 cancera3.34 [2.94-3.80]<.00012.31 [1.79-2.98]<.00012.64 [2.23-3.12]<.00014.04 [3.58-4.57]<.0001 non metastatic cancerb2.78 [2.41-3.20]<.00012.16 [1.65-2.82]<.00012.22 [1.85-2.67]<.00012.93 [2.55-3.37]<.0001 metastatic solid tumor10.8 [8.50-13.7]<.00014.32 [2.30-8.11]<.00018.12 [5.87-11.2]<.000118.8 [15.6-22.7]<.0001Supplemental Table 5: Characteristics associated with cardiovascular adverse events: unadjusted logistic regression models (N = 4,088,799) (continued).?Shock Myocardial infarction Stroke Pulmonary embolism?OR [95% CI]p value?OR [95% CI]p value?OR [95% CI]p value?OR [95% CI]p valuePatient characteristics (continued)???????????Charlson index 01.001.001.001.00 14.34 [3.50-5.39]<.00013.42 [2.49-4.68]<.000111.7 [8.66-15.8]<.00012.08 [1.75-2.47]<.0001 ≥222.7 [19.3-26.8]<.000110.2 [7.92-13.0]<.000142.7 [32.7-55.8]<.00016.49 [5.72-7.36]<.0001Medical history of urolithiasis????? no1.00??1.00?1.00?1.00 yes1.23 [0.73-2.09]0.43?1.44 [0.6-3.47]0.42?1.07 [0.54-2.15]0.84?0.60 [0.29-1.26]0.1795Previous treatment (during previous 6 months) antiplatelet drugs4.85 [4.32-5.45]<.00016.84 [5.56-8.42]<.00017.61 [6.60-8.78]<.00012.05 [1.79-2.34]<.0001 anticoagulants8.96 [7.92-10.1]<.00013.43 [2.57-4.57]<.00018.64 [7.41-10.1]<.000122.5 [20.0-25.2]<.0001Colonoscopy characteristics???????????Bowel preparation polyethylene glycol1.001.001.001.00 phosphate preparation0.69 [0.41-1.15]0.160.74 [0.45-1.21]0.230.62 [0.42-0.92]0.0160.47 [0.34-0.66]<.0001 magnesium preparation0.78 [0.52-1.16]0.220.79 [0.54-1.17]0.250.53 [0.38-0.74]0.00020.61 [0.48-0.77]<.0001 not retrieved50.3 [40.8-61.9]<.00019.66 [7.61-12.3]<.000112.7 [10.8-15.1]<.000110.5 [9.20-11.9]<.0001Polypectomy no polypectomy1.001.001.001.00 polyp <1 cm0.15 [0.12-0.20]<.00010.46 [0.35-0.62]<.00010.62 [0.51-0.74]<.00010.44 [0.37-0.52]<.0001 polyp >1 cm0.65 [0.49-0.87]0.00320.91 [0.57-1.45]0.701.30 [0.98-1.72]0.0660.92 [0.71-1.18]0.51Sedation no1.001.001.001.00 yes0.04 [0.04-0.05]<.00010.08 [0.06-0.10]<.00010.13 [0.11-0.16]<.00010.17 [0.14-0.20]<.0001Complete colonoscopy no1.001.001.001.00 yes0.05 [0.05-0.06]<.00010.09 [0.07-0.11]<.00010.14 [0.11-0.16]<.00010.16 [0.14-0.19]<.0001Supplemental Table 5: Characteristics associated with cardiovascular adverse events: unadjusted logistic regression models (N = 4,088,799) (continued).?Shock Myocardial infarction Stroke Pulmonary embolism?OR [95% CI]p value?OR [95% CI]p value?OR [95% CI]p value?OR [95% CI]p valueEndoscopist characteristics ???????????Number of years since MD graduation <5included in the next modality0.89 [0.12-6.46]0.911.19 [0.29-4.88]0.811.08 [0.34-3.41]0.89 5-141.01 [0.60-1.71]0.960.91 [0.50-1.67]0.761.62 [1.07-2.43]0.0211.21 [0.86-1.72]0.28 15-241.001.001.001.00 25-341.25 [0.88-1.78]0.220.96 [0.64-1.44]0.841.08 [0.78-1.50]0.650.88 [0.67-1.14]0.33 ≥350.81 [0.44-1.49]0.501.41 [0.82-2.44]0.211.40 [0.90-2.19]0.140.97 [0.65-1.44]0.87 not retrieved19.8 [14.8-26.3]<.00015.45 [3.91-7.59]<.00018.72 [6.7-11.36]<.00018.50 [6.92-10.4]<.0001Number of procedures performed during the previous year <1500.67 [0.15-2.94]0.591.80 [0.64-5.05]0.261.62 [0.76-3.46]0.211.25 [0.57-2.72]0.58 150-2991.03 [0.48-2.22]0.951.01 [0.45-2.26]0.970.88 [0.49-1.58]0.661.04 [0.62-1.76]0.89 300-5991.29 [0.73-2.27]0.390.89 [0.48-1.63]0.700.68 [0.44-1.06]0.0921.04 [0.70-1.54]0.86 600-9990.92 [0.51-1.66]0.780.85 [0.46-1.57]0.590.74 [0.48-1.16]0.190.76 [0.50-1.14]0.18 ≥1,0001.001.001.001.00 not retrieved19.7 [11.6-33.4]<.00014.89 [2.80-8.56]<.00015.83 [3.93-8.64]<.00018.15 [5.67-11.73]<.0001Hospital/clinic characteristics???????????Type of health facility for-profit private clinic1.001.001.001.00 not-for-profit private clinic4.69 [3.60-6.12]<.00012.11 [1.38-3.23]0.00051.51 [1.04-2.19]0.0302.71 [2.11-3.47]<.0001 local public hospital13.3 [11.2-15.8]<.00015.06 [3.96-6.47]<.00016.59 [5.52-7.87]<.00017.59 [6.56-8.79]<.0001 university public hospital63.8 [53.3-76.4]<.000116.5 [12.4-22.0]<.000127.9 [23.1-33.8]<.000129.6 [25.3-34.7]<.0001Number of procedures performed in the facility each year <4003.79 [3.07-4.67]<.00013.42 [2.24-5.22]<.00014.05 [3.10-5.31]<.00014.68 [3.75-5.84]<.0001 400-20000.84 [0.70-1.00]0.0550.98 [0.68-1.40]0.900.86 [0.68-1.09]0.210.96 [0.78-1.17]0.66 2000-49990.23 [0.19-0.29]<.00010.44 [0.30-0.65]<.00010.37 [0.28-0.48]<.00010.40 [0.32-0.49]<.0001 ≥50001.00??1.00??1.00??1.00?OR [95% CI]: Odds ratio (95% confidence interval).a except non-melanoma skin cancerb including lymphoma and leukemiaSupplemental Table 6: Characteristics associated with renal adverse events: unadjusted logistic regression models (N = 4,088,799).?Acute renal failure?Urolithiasis?OR [95%CI]p value?OR [95%CI]p valueYear 20101.31 [1.15-1.51]<.00011.31 [1.10-1.55]0.0016 20111.15 [1.00-1.33]0.0451.35 [1.14-1.60]0.0004 20121.001.00 20131.11 [0.96-1.29]0.140.86 [0.71-1.04]0.13 20141.07 [0.92-1.24]0.370.79 [0.65-0.97]0.024 20151.09 [0.93-1.27]0.27?0.73 [0.59-0.90]0.0041Patient characteristics ?? ?Sex male1.00??1.00? female0.49 [0.45-0.54]<.0001?0.47 [0.42-0.52]<.0001Age 30-391.001.00 40-491.24 [0.91-1.69]0.171.14 [0.91-1.44]0.24 50-591.66 [1.25-2.22]0.00051.04 [0.84-1.30]0.68 60-693.04 [2.30-4.02]<.00010.96 [0.77-1.20]0.77 70-795.87 [4.44-7.76]<.00010.95 [0.74-1.21]0.69 80 and older19.6 [14.8-26.0]<.00011.56 [1.17-2.09]0.002Pre-existing conditions myocardial infarction7.78 [7.06-8.58]<.00011.52 [1.21-1.91]0.0003 congestive heart failure28.4 [25.9-31.1]<.00012.30 [1.69-3.13]<.0001 peripheral vascular disease11.3 [10.3-12.4]<.00011.74 [1.38-2.20]<.0001 cerebrovascular disease7.91 [7.02-8.91]<.00011.46 [1.07-2.00]0.020 dementia9.96 [8.26-12.0]<.00012.18 [1.33-3.56]0.002 chronic pulmonary disease2.16 [1.97-2.36]<.00011.12 [0.97-1.29]0.10 connective tissue disease2.45 [1.93-3.10]<.00011.16 [0.75-1.79]0.50 ulcer disease6.04 [5.25-6.95]<.00012.11 [1.58-2.80]<.0001 moderate or severe renal disease82.8 [76.2-90.0]<.00012.70 [1.99-3.66]<.0001 hemiplegia10.5 [8.88-12.5]<.00012.18 [1.37-3.47]0.001 HIV/AIDS3.92 [2.66-5.78]<.00012.99 [1.69-5.28]0.0002 liver disease mild liver disease5.97 [5.10-6.99]<.00013.17 [2.45-4.11]<.0001 moderate or severe liver disease19.7 [16.9-22.9]<.00012.22 [1.31-3.76]0.003 diabetes diabetes without end-organ damage2.53 [2.24-2.85]<.00011.78 [1.52-2.08]<.0001 diabetes with end-organ damage24.6 [22.2-27.4]<.00011.89 [1.30-2.75]0.0009 cancera non-metastatic cancerb2.92 [2.64-3.22]<.00010.95 [0.79-1.14]0.62 metastatic solid tumor11.3 [9.62-13.4]<.00011.68 [1.04-2.72]0.030Charlson index 01.001.00 16.27 [5.34-7.36]<.00011.25 [1.08-1.44]0.003 ≥229.4 [25.8-33.5]<.00011.53 [1.33-1.75]<.0001Medical history of urolithiasis no1.001.00 yes1.03 [0.68-1.56]0.8629.1 [25.6-33.0]<.0001Previous treatment (during previous 6 months) antiplatelet drugs4.53 [4.17-4.93]<.00011.20 [1.04-1.40]0.01 anticoagulants7.10 [6.47-7.79]<.00011.50 [1.22-1.83]<.0001Supplemental Table 6: Characteristics associated with renal adverse events: unadjusted logistic regression models (N = 4,088,799) (continued).?Acute renal failure?Urolithiasis?OR [95%CI]p value?OR [95%CI]p valueColonoscopy characteristics ?? ?Polypectomy no polypectomy1.001.00 polyp <1 cm0.27 [0.23-0.31]<.00010.83 [0.73-0.94]0.004 polyp >1 cm0.54 [0.43-0.67]<.00011.09 [0.86-1.37]0.46Bowel preparation polyethylene glycol1.001.00 phosphate preparation0.43 [0.30-0.61]<.00011.04 [0.87-1.25]0.61 magnesium preparation0.50 [0.38-0.66]<.00011.07 [0.92-1.24]0.36 not retrieved29.8 [26.4-33.7]<.00013.25 [2.85-3.70]<.0001Sedation no1.001.00 yes0.05 [0.05-0.06]<.00010.60 [0.46-0.78]0.0002Complete colonoscopy no1.001.00 yes0.06 [0.05-0.06]<.00010.45 [0.37-0.56]<.0001Endoscopist characteristics ?? ?Number of years since MD graduation <51.63 [0.72-3.69]0.240.81 [0.36-1.83]0.62 5-141.03 [0.75-1.42]0.841.28 [1.04-1.58]0.020 15-241.001.00 25-340.77 [0.61-0.98]0.0391.12 [0.96-1.31]0.14 ≥351.09 [0.78-1.52]0.601.16 [0.92-1.45]0.20 not retrieved13.6 [11.4-16.3]<.00011.67 [1.43-1.94]<.0001Number of procedures performed during the previous year <1501.20 [0.55-2.62]0.631.77 [1.16-2.70]0.0073 150-2991.08 [0.65-1.80]0.761.30 [0.96-1.77]0.087 300-5991.20 [0.82-1.76]0.341.03 [0.81-1.31]0.80 600-9991.10 [0.74-1.62]0.621.03 [0.81-1.32]0.79 ≥1,0001.001.00 not retrieved16.8 [11.8-24.0]<.00011.61 [1.27-2.05]<.0001Hospital/clinic characteristics ?? ?Type of health facility for-profit private clinic1.001.00 not-for-profit private clinic3.49 [2.87-4.24]<.00011.12 [0.91-1.38]0.25 local public hospital10.9 [9.74-12.3]<.00011.46 [1.28-1.67]<.0001 university public hospital54.5 [48.2-61.5]<.00012.48 [1.98-3.10]<.0001Number of procedures performed in the facility each year <4004.60 [3.95-5.35]<.00011.13 [0.88-1.44]0.32 400-20000.84 [0.73-0.96]0.0120.55 [0.47-0.65]<.0001 2000-49990.29 [0.24-0.34]<.00010.46 [0.39-0.55]<.0001 ≥50001.00??1.00OR [95% CI]: Odds ratio [95% confidence interval].a except non-melanoma skin cancerb including lymphoma and leukemiaSupplemental Table 7: Characteristics associated with myocardial infarction: sensitivity analysis using a broader definition for myocardial infarction (4,088,799 patients, including 476 with myocardial infarction).?aOR [95% CI]p valuePatient characteristics??Sex male1.00 female0.80 [0.65-0.99]0.037Age 30-391.00 40-493.13 [1.11-8.79]0.031 50-592.65 [0.96-7.31]0.060 60-692.79 [1.01-7.66]0.047 70-792.83 [1.02-7.82]0.045 80 and older4.59 [1.65-12.8]0.0035Pre-existing conditions myocardial infarct95.4 [70.5-129]<.0001 congestive heart failure2.05 [1.61-2.60]<.0001 peripheral vascular disease2.09 [1.69-2.59]<.0001 moderate or severe renal disease1.55 [1.19-2.02]0.0010 hemiplegia1.96 [1.25-3.07]0.0033Previous treatment (within 6 months) antiplatelet drugs0.32 [0.25-0.40]<.0001 anticoagulants0.46 [0.35-0.60]<.0001Polypectomy no polypectomy1.00 polyp <1 cm0.55 [0.42-0.71]<.0001 polyp >1 cm0.68 [0.46-1.01]0.056Hospital/clinic characteristics??Type of health facility for-profit private clinic1.00 not-for-profit private clinic1.61 [0.98-2.63]0.059 local public hospital2.41 [1.74-3.34]<.0001 university public hospital7.62 [4.66-12.5]<.0001Number of procedures performed in the facility each year <4002.01 [0.86-4.69]0.11 400-20001.30 [0.60-2.81]0.51 2000-49991.04 [0.48-2.28]0.92 ≥50001.00?Multilevel logistic regression model accounting for patients nested within facility, adjusted for yearly calendar effect.aOR [95% CI]: Adjusted odds ratio [95% confidence interval].Supplemental Table 8: Characteristics associated with acute renal failure: sensitivity analysis using a more stringent definition for acute renal failure (4,088,799 patients, including 522 with acute renal failure).?aOR [95% CI]p valuePatient characteristics??Sex male1.00 female0.91 [0.76-1.11]0.36Age 30-391.00 40-490.84 [0.48-1.46]0.54 50-590.88 [0.53-1.47]0.63 60-690.86 [0.52-1.42]0.56 70-790.73 [0.44-1.22]0.23 80 and older0.27 [0.16-0.46]<.0001Pre-existing conditions congestive heart failure1.46 [1.18-1.80]0.0004 peripheral vascular disease1.46 [1.19-1.80]0.0003 moderate or severe renal disease185 [132-261]<.0001 hemiplegia1.86 [1.31-2.65]0.0006 liver disease mild0.87 [0.64-1.19]0.39 moderate or severe1.68 [1.20-2.37]0.0027 diabetes without end-organ damage0.91 [0.65-1.28]0.60 with end-organ damage0.72 [0.58-0.89]0.0026 cancer non metastatica1.08 [0.85-1.36]0.53 metastaticb1.18 [0.71-1.95]0.52Previous treatment (within 6 months) anticoagulants0.78 [0.62-0.97]0.027Bowel preparation polyethylene glycol1.00 phosphate preparation0.45 [0.11-1.90]0.28 magnesium preparation1.01 [0.46-2.21]0.98 not retrieved12.1 [8.40-17.5]<.0001Polypectomy no polypectomy1.00 polyp <1 cm0.26 [0.18-0.39]<.0001 polyp >1 cm0.20 [0.09-0.43]<.0001Hospital/clinic characteristics??Type of health facility for-profit private clinic1.00 not-for-profit private clinic2.86 [1.37-5.96]0.005 local public hospital2.43 [1.39-4.25]0.0019 university public hospital6.03 [2.71-13.4]<.0001Number of procedures performed in the facility each year <4002.41 [0.44-13.1]0.31 400-20002.09 [0.42-10.5]0.37 2000-49991.90 [0.37-9.68]0.44 ≥50001.00?Multilevel logistic regression model accounting for patients nested within facility, adjusted for yearly calendar effect.aOR [95% CI]: Adjusted odds ratio [95% confidence interval].a except non-melanoma skin cancerb including lymphoma and leukemia ................
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