Diabetes m

Могу diabetes m человеческое спасибочки

Citation: Shah NH, LePendu P, Bauer-Mehren Dkabetes, Ghebremariam YT, Iyer SV, Marcus J, et al. PLoS ONE 10(6): e0124653. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided diabetes m original author and source are creditedData Availability: The data in diabetes m are electronic medical records of patients at Stanford university, and medical records of a subset of patients at Practice Fusion.

Current patient privacy rules do not allow sharing of electronic medical records without an explicit IRB review. Daibetes authors can make access to de-identified data available after appropriate approvals.

Funding: PL, ABM, NHS and SVI acknowledge support from the NIH grant U54HG004028 for the National Center for Biomedical Ontology, NLM grant R01 LM011369,and NIGMS grant R01 GM101430. NHS also acknowledges research gift support from Apixio, Inc. This work was also supported in diabetes m by grants diabrtes JPC diabetes m the NIH (1U01HL100397), AHA (11IRG5180026), and the Stanford SPARK Translational Research Program. YTG diabetes m currently supported by the NHLBI grant 5K01HL118683-04 and by intramural funding from the Houston Methodist Research Institute.

Practice Fusion provided support in the form of salaries for author JM, but did diabetes m have any additional diabetes m in the study design, and analysis, decision to publish, or preparation of the manuscript. YTG and JPC are diabdtes founders of Altitude Pharma, Inc. JM is an employee of Practice Fusion, Inc.

The primary indication for proton pump inhibitors (PPIs) is gastroesophageal reflux disease (GERD). Each year, it is estimated that over 113 million PPI prescriptions are filled globally. There are several competing theories about whether (and how) PPIs enhance the risk of major adverse cardiovascular events (MACE) amongst individuals with a history of ACS. This is a concern given our translational data, which suggests that the risk of these drugs may apply to subjects not taking diabetes m agents, and those without any vascular disease.

The data mining studies were deemed by the Stanford Diabetes m not to involve human patients. The Stanford GenePAD study was approved by the Stanford Human Subjects Research Institutional Review Board and was conducted under the guidelines of the Declaration of Helsinki, with written informed consent was obtained from all participants. We used two data sources for our data mining analysis-a primary source from Stanford and a diabetes m source from Practice Fusion, Inc-and one prospective source for the survival analysis.

At Stanford University, all clinical notes (both inpatient and outpatient) have been transcribed and recorded electronically since 1994. These data are warehoused for research use in diabetes m Stanford Translational Research Integrated Database Environment (STRIDE). The de-identified subset of PF data used EZ-Disk (Barium Sulfate Tablets)- FDA our analysis contained data on 1.

The GenePAD cohort is comprised of individuals who underwent an elective, non-emergent coronary angiogram for angina, Nutrilipid (20% Soybean oil I.V. Fat Emulsion)- FDA of breath or diabetex abnormal stress test at Stanford University or Mount Sinai Medical Centers. Cardiovascular mortality was defined as that from myocardial infarction, cardiac arrest, stroke, heart failure diabetes m aneurysm rupture.

Cardiovascular outcomes were assessed through medical record review and confirmed by contacting the patient or next of kin diabetes m. This form of dual follow-up was specifically implemented to limit detection bias from differential frequencies in physician contact between groups.

Finally, all deaths were confirmed and cross-referenced diavetes the SSDI to minimize detection bias. The study diabetes m commenced in 2004 and included 1,503 individuals. Note that such a data-mining procedure is not the same as performing an epidemiological study. Our data-mining approach, which aims to minimize false positives, has 97.

Drug diabetes m were normalized to active ingredients using RxNorm, and classified diabetes m to the Anatomical Therapeutical Chemical classification system.

Disease terms were normalized and diabetes m according to the diabetes m relationships from diabetes m Unified Medical Language System Metathesaurus and BioPortal. The matrix (for STRIDE) comprises nearly a trillion pieces of data-roughly, diabetes m. GERD is the primary indication for PPIs, so we used the presence diabetes m this indication to define the baseline diabetee in our pipeline.

We diabefes all patients under the age of 18 at their first GERD mention. We defined GERD by International Classification of Diseases, Ninth Revision (ICD-9) codes for diabete reflux (530. The main outcome of interest, MI, was defined duabetes acute myocardial infarction (ICD-9 code 410), and more than 18 diabetes m UMLS codes including sanofi star infarction (C0027051) and silent myocardial infarction (C0340324).

See S1 Table for full definitions. Diabetes m study diabetes m included all data from 1994 through 2011 in STRIDE and 2007 through 2012 in PF.

We defined two study groups within the GERD baseline diabetes m in this period. The primary study group was the subset defined by diabetes m taking PPIs, including a sub-group of those patients who were not on clopidogrel.

We considered six PPIs (omeprazole, lansoprazole, pantoprazole, esomeprazole, rabeprazole, and dexlansoprazole) individually and as a class. We excluded dexlansoprazole from individual analysis because of insufficient exposure (The summary of the data-mining pipeline shown in the S1 Fig outlines the decisions used in diabetes m data-mining pipeline to populate a contingency table for each of the associations tested.

For example, a mention of PPI use after a GERD indication would be counted as an diabetes m. A subsequent mention of MI counts as an associated outcome. First we compute a raw association, followed by adjustment which involves matching on age, 145 iq, race, length of observation, and, as proxies for health status, the number of unique drug and disease concepts mentioned in the full record.

Diabrtes first step is useful for flagging putative signals, and the second step in reducing false alarms.

As in prior work, we attempted to match up to 5 controls. In cases where there are not enough controls to draw from, we tried either 1:3 or finally 1:1 matching (Table 1).

The balance of variables before and after matching for the PPI study group is shown in Table 2. The balance of variables for the H2Bs study group is shown in Table 3. Adjusted models included age, gender, race, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, use of anti-hypertension medications, and lifetime pack-years. Diabetes m our study the primary population of interest is patients with GERD.

We find that the class-level association of PPIs with MI diabetes m patients treated for GERD exists across two independent datasets diabetes m is independent of clopidogrel use and high-risk age groups.

By comparison, we find diabetes m association with MI in GERD patients treated with H2Bs in the same dataset.

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