Package: amsterdamumcdb
WelcomeAmsterdamUMCdb is the first freely accessible European intensive care database. It is endorsed by the European Society
of Intensive Care Medicine (ESICM) and its Data Science Section. It contains de-identified health data related to tens
of thousands of intensive care unit admissions, including demographics, vital signs, laboratory tests and medications.VersionThe current version of AmsterdamUMCdb is 1.0.2, released in March 2020. This version contains data related to 23,106
intensive care unit and high dependency unit admissions of adult patients from 2003-2016. The first version of
AmsterdamUMCdb (1.0) was released in November 2019.Requesting AccessThe database, although de-identified, still contains detailed information regarding the clinical care of patients, so
must be treated with appropriate care and respect and cannot be shared without permission. To request access, go to
theAmsterdam Medical Data Sciencewebsite.AcknowledgementWhen using AmsterdamUMCdb in your research, please cite:Thoral, P. J., Peppink, J. M., Driessen, R. H., Sijbrands, E. J. G., Kompanje, E. J. O., Kaplan, L., Bailey, H.,
Kesecioglu, J., Cecconi, M., Churpek, M., Clermont, G., van der Schaar, M., Ercole, A., Girbes, A. R. J., Elbers, P. W.
G., on behalf of the Amsterdam University Medical Centers Database (AmsterdamUMCdb) Collaborators and the SCCM/ESICM
Joint Data Science Task Force (2021). Sharing ICU Patient Data Responsibly Under the Society of Critical Care
Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical
Centers Database (AmsterdamUMCdb) Example. Crit Care Med. 2021 Jun 1;49(6):e563-e577.
doi:10.1097/CCM.0000000000004916. PMID: 33625129; PMCID: PMC8132908.Facts and FiguresThe current database contains data from the clinical patient data management system of the department of Intensive Care,
a mixed medical-surgical ICU, from Amsterdam University Medical Center. The clinical data contains 23,106 admissions of
20,109 patients admitted from 2003 to 2016 with a total of almost 1.0 billion clinical observations consisting of
vitals, clinical scoring systems, device data and lab results data and nearly 5.0 million medication records.Available tablesThe table and field definitions are available from
theAmsterdamUMCdb wikiand from Jupyter Notebooks in
thetablesfolder.Table nameDescriptionadmissionsadmissions and demographic data of the patients admitted to the ICU or MCUdrugitemsmedication orders including fluids, (parenteral) feeding and blood transfusions during the stay on the ICUfreetextitemsobservations, including laboratory results, that are based on non-numeric (text) datalistitemscategorial observations, e.g. based on a selection from a list, like type of heart rhytm, ventilatory mode, etc.numericitemsnumerical measurements and observations, including vital parameters, data from medical devices, lab results, outputs from drains and foley-catheters, scores etc.procedureorderitemsprocedures and tasks, such as performing a chest X-ray, drawing blood and daily ICU nursing care and scoringprocessitemscatheters, drains, tubes, and continous non-medication processes (e.g. renal replacement therapy, hypothermia induction, etc.)amsterdamumcdb Python packageTheamsterdamumcdbPython package contains common functions for working with the database.Available functionsamsterdamumcdb.get_dictionary():Gets a dictionary of all items in AmsterdamUMCdb with translated medical concepts mapped to LOINC, SNOMED CT
or ATC (work in progress).Returns:dataframe containing dictionaryamsterdamumcdb.get_fluidbalance(admissionid, from_date, to_date, con):Calculate the fluid balance (i.e. fluid input - fluid output) for a specific time interval. Typically this will be used
to calculate daily fluid balances.Arguments:admissionid -- the admissionid of ICU admissionfrom_date -- the start of the interval, expressed as the number of milliseconds from start of this ICU admissionto_date -- the end of the interval, expressed as the number of milliseconds from start of this ICU admissioncon -- psycopg2 connection or pandas-gbq Google BigQuery configamsterdamumcdb.outliers_histogram(data, z_threshold=4.0, lower=None, upper=None, bins=None, binwidth=None, maxbins=None):Return a pyplot histogram, where the upper and/or lower outliers are binned together for a more even distribution plot.
By default, the histogram will be created with bins that are on boundaries aligned with the pyplot axis ticks.Arguments:data -- dataframe to create the histogram for.z_threshold (Optional) -- number of standard deviations from the median to determine outlierslower (Optional) -- lower threshold for binning lower outliers togetherupper (Optional) -- upper threshold for binning higher outliers togetherbins (Optional) -- int or sequence or str, allows specifying the number of bins, the actual bins or a binning
strategy
(see:https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html)binwidth (Optional) -- forces a specified size of the equally sized bins between the lower and upper thresholdmaxbins -- the maximum number of equally sized bins to createChangelogversion 0.2.0added sample cohorts (sepsis-3, mechanical ventilation, shock) incohorts.pyadded SOFA scoring toscores.pyutil functions for PostgresSQL to BigQuery translationmove SQL code from notebooks to separate SQL filesversion 0.1.5dictionary: as part of theICUnityproject in preparation for
theThe Dutch ICU Data Warehousea number of commonly used items have been
mapped toLOINC,SNOMED CTorATCwith data in
thevocabulary_id,vocabulary_concept_code,vocabulary_concept_namecolumns.dictionary:UCUMunits addedversion 0.1.4fixes binning inoutliers_histogram()when all values < 1version 0.1.3Improves binning inoutliers_histogram()version 0.1.2Allows negative dates inget_fluidbalance()version 0.1.1Fixes Google BigQueryProject must be a stringerror.version 0.1.0initial release for ESICM Datathon 2021