AmsterdamUMCdb 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.
The 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.
The 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 the Amsterdam Medical Data Science website.
When 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.
The 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.
The table and field definitions are available from the AmsterdamUMCdb wiki and from Jupyter Notebooks in the tables folder.
Table name | Description |
---|---|
admissions | admissions and demographic data of the patients admitted to the ICU or MCU |
drugitems | medication orders including fluids, (parenteral) feeding and blood transfusions during the stay on the ICU |
freetextitems | observations, including laboratory results, that are based on non-numeric (text) data |
listitems | categorial observations, e.g. based on a selection from a list, like type of heart rhytm, ventilatory mode, etc. |
numericitems | numerical measurements and observations, including vital parameters, data from medical devices, lab results, outputs from drains and foley-catheters, scores etc. |
procedureorderitems | procedures and tasks, such as performing a chest X-ray, drawing blood and daily ICU nursing care and scoring |
processitems | catheters, drains, tubes, and continous non-medication processes (e.g. renal replacement therapy, hypothermia induction, etc.) |
The amsterdamumcdb
Python package contains common functions for working with the database.
Gets a dictionary of all items in AmsterdamUMCdb with translated medical concepts mapped to LOINC, SNOMED CT or ATC (work in progress).
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.
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.
np.NaN
(pandas default) and pd.NA
(used by pandas-gbq since the default is Int64
) leading to different results, particularly when finding the complement using negation.pd.Na
to be considered False
during comparisons.amsterdamumcdb
functions for consistent PostgreSQL and BigQuery results.kPa
to mmHg
) in SQL statementsseaborn-darkgrid
)reason_for_admission.ipynb
notebook into amsterdamumcdb.cohorts
module.cohorts.py
scores.py
ICUnity
project in preparation for
the The Dutch ICU Data Warehouse a number of commonly used items have been
mapped to LOINC
, SNOMED CT or ATC with data in
the vocabulary_id
, vocabulary_concept_code
, vocabulary_concept_name
columns.outliers_histogram()
when all values < 1outliers_histogram()
get_fluidbalance()
Project must be a string
error.