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< meta property = "og:description" content = "An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This data.frame can be used to practice AMR analysis. For examples, please read the tutorial on our website." / >
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< h1 > Data set with 2,000 blood culture isolates from septic patients< / h1 >
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< div class = "hidden name" > < code > septic_patients.Rd< / code > < / div >
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< p > An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This < code > data.frame< / code > can be used to practice AMR analysis. For examples, please read < a href = 'https://msberends.gitlab.io/AMR/articles/AMR.html' > the tutorial on our website< / a > .< / p >
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< pre class = "usage" > < span class = 'no' > septic_patients< / span > < / pre >
< h2 class = "hasAnchor" id = "format" > < a class = "anchor" href = "#format" > < / a > Format< / h2 >
< p > A < code > < a href = 'https://www.rdocumentation.org/packages/base/topics/data.frame' > data.frame< / a > < / code > with 2,000 observations and 49 variables:< / p > < dl class = 'dl-horizontal' >
< dt > < code > date< / code > < / dt > < dd > < p > date of receipt at the laboratory< / p > < / dd >
< dt > < code > hospital_id< / code > < / dt > < dd > < p > ID of the hospital, from A to D< / p > < / dd >
< dt > < code > ward_icu< / code > < / dt > < dd > < p > logical to determine if ward is an intensive care unit< / p > < / dd >
< dt > < code > ward_clinical< / code > < / dt > < dd > < p > logical to determine if ward is a regular clinical ward< / p > < / dd >
< dt > < code > ward_outpatient< / code > < / dt > < dd > < p > logical to determine if ward is an outpatient clinic< / p > < / dd >
< dt > < code > age< / code > < / dt > < dd > < p > age of the patient< / p > < / dd >
< dt > < code > gender< / code > < / dt > < dd > < p > gender of the patient< / p > < / dd >
< dt > < code > patient_id< / code > < / dt > < dd > < p > ID of the patient, first 10 characters of an SHA hash containing irretrievable information< / p > < / dd >
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< dt > < code > mo< / code > < / dt > < dd > < p > ID of microorganism created with < code > < a href = 'as.mo.html' > as.mo< / a > < / code > , see also < code > < a href = 'microorganisms.html' > microorganisms< / a > < / code > < / p > < / dd >
< dt > < code > peni:rifa< / code > < / dt > < dd > < p > 40 different antibiotics with class < code > rsi< / code > (see < code > < a href = 'as.rsi.html' > as.rsi< / a > < / code > ); these column names occur in < code > < a href = 'antibiotics.html' > antibiotics< / a > < / code > data set and can be translated with < code > < a href = 'abname.html' > abname< / a > < / code > < / p > < / dd >
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< p > On our website < a href = 'https://msberends.gitlab.io/AMR' > https://msberends.gitlab.io/AMR< / a > you can find < a href = 'https://msberends.gitlab.io/AMR/articles/AMR.html' > a comprehensive tutorial< / a > about how to conduct AMR analysis, the < a href = 'https://msberends.gitlab.io/AMR/reference' > complete documentation of all functions< / a > (which reads a lot easier than here in R) and < a href = 'https://msberends.gitlab.io/AMR/articles/WHONET.html' > an example analysis using WHONET data< / a > .< / p >
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< p > Developed by < a href = 'https://www.rug.nl/staff/m.s.berends/' > Matthijs S. Berends< / a > , < a href = 'https://www.rug.nl/staff/c.f.luz/' > Christian F. Luz< / a > , < a href = 'https://www.rug.nl/staff/c.glasner/' > Corinna Glasner< / a > , < a href = 'https://www.rug.nl/staff/a.w.friedrich/' > Alex W. Friedrich< / a > , < a href = 'https://www.rug.nl/staff/b.sinha/' > Bhanu N. M. Sinha< / a > .< / p >
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