DOI: 10.5061/dryad.0rxwdbsd5
title: Antimicrobial Resistance Microbiological Dataset (ARMD-UTSW): A deidentified collection of electronic health records, from a quaternary, academic medical center, for antimicrobial resistance research

description: Antibiotic resistance is a global public health emergency, but quality,
 real world, EHR based data sets that can be utilized for antibiotic
 resistance research are limited.  We have developed the
 Antibiotic Resistance Microbiology Dataset: UTSW (ARMD: UTSW) which
 includes microbiological culture testing and susceptibility results for
 237,258 patients at the University of Texas Southwestern Medical Center
 (UTSW) from 2005-2025.  Longitudinal demographics, prior medical
 histories, medications, procedures, and clinical specific information such
 as testing locations and recent laboratory values are also incorporated
 into the deidentified data set.  With the standardization of data
 values and careful deidentification of the ARMD: UTSW data set,
 researchers globally will be able to improve patient outcomes, increase
 awareness, and add to the collective knowledge regarding antibiotic
 resistance.
description: Our Antibiotic Resistance Microbiology Dataset: UTSW (ARMD: UTSW)
 data set comprises a longitudinal collection of Epic based EHR data from
 the University of Texas Southwestern Medical Center (UTSW) for adults (≥18
 years old) from 2005 to 2025.  It includes deidentified microbiological
 laboratory results for urine, blood, and respiratory cultures.  Also
 included is patient demographics, comorbidities, socioeconomic factors via
 the area deprivation index, and prior exposure to antibiotics and
 procedures.  All data was collected from UTSW’s Epic Clarity database via
 Microsoft’s T-SQL based SQL Server Management Studio. 
 The raw data was then transformed into standardized values to
 assist with future research uses.  This includes standardizing gender into
 two deidentified values of “0” or “1” (Null was used for missing gender
 data), bucketing patient ages at the time when the culture was taken into
 age ranges such as 18-24, 25-34, etc., and standardizing medication names
 into generic names for consistency.  Additionally, susceptibility results
 have been standardized to values of “susceptible”, “intermediate”,
 “resistant”, “synergism”, and “inconclusive” to account for the various
 reporting means from different laboratories.  Culture positivity was also
 standardized to a binary indicator of “0” or “1” based on the inclusion of
 susceptibility results for positive cultures.  In addition, we accounted
 for patients with an active infection that might have multiple cultures
 taken in a short time period by excluding patients with prior
 microbiological cultures within the two weeks before the
 encounter.  Deidentification was completed according to
 Safe Harbor regulations.  All patient identifiers were either not included
 or were anonymized.  Examples of this include the anonymization of patient
 identification numbers such as patient ID numbers, encounter ID numbers,
 and culture order ID numbers through a randomization process for each
 while keeping continuity between all individual patient records.  While
 patient zip codes were used to identify values for the area deprivation
 index, they were removed prior to the final data set.  As mentioned
 previously, patient ages were aggregated into age ranges and gender has
 been concealed with either “0”, “1”, or “Null”.  All date and time
 information has been shifted with a randomly assigned offset for each
 patient.  This allows for consistent offset values while still being able
 to account for possible temporal relationships within the
 dataset. 
description: # Antimicrobial Resistance Microbiological Dataset (ARMD-UTSW): A
 deidentified collection of electronic health records, from a quaternary,
 academic medical center, for antimicrobial resistance research Dataset
 DOI: [10.5061/dryad.0rxwdbsd5](10.5061/dryad.0rxwdbsd5) ## Description of
 the data and file structure Our dataset is comprised of longitudinal
 electronic health records from the University of Texas Southwestern
 Medical Center.  This collection includes deidentified urine, respiratory,
 and blood based microbiological culture results and susceptibilities from
 a cohort of adult patients (≥18 years old) regardless of culture
 positivity.  Additional data included in the dataset consists of prior
 medical histories such as comorbidities, socioeconomic indicators, prior
 medications, prior infections, and prior medical procedures.  The dataset
 includes 13 .csv files that can be used for future analyses such as
 machine learning predictive models.  All identifying personal information
 has been removed or anonymized according to the deidentification methods
 mentioned below.  All files are joined by the following identifiers: *
 anon_id: the deidentified patient number * pat_enc_csn_id_coded: the
 deidentified patient encounter number * order_proc_id_coded: the
 deidentified unique culture order number * order_time_jittered: the
 jittered timestamp for the unique culture order All missing values have
 been replaced with “Null” values. *Deidentification Methods* The data set
 is deidentified to comply with the Health Insurance Portability and
 Accountability Act (HIPAA) and the National Institute of Standards and
 Technology (NIST) Safe Harbor regulations.  * All identifying patient
 information has been removed from the data set.  * Any patient, encounter,
 or culture order identification numbers have been anonymized.  The anon_id
 was created by giving each patient identification number a random pair of
 letters accompanied by a random set of numbers.  The pat_enc_csn_id_coded
 and order_proc_id_coded values were created by giving each encounter
 number or culture order a prefix (10 for pat_enc_csn_id_coded and 21
 for order_proc_id_coded) accompanied by a serialized value based on the
 initial randomization of the accompanying anon_id.  This allows for each
 patient's encounter and culture order identifiers to be linked to the
 appropriate patient, but remains anonymized.   * Demographic information
 has been removed with the exception of age and gender.  Age has been
 bucketed into age ranges such as 18-24, 25-34, etc. and gender (male or
 female) has been adapted to a binary indicator of 0 or 1 without
 indication of which gender is represented by 0 or 1. * Dates and times
 have been offset (jittered) by a random number of days within a two-month
 period while keeping consistent temporal relationships. ### Files and
 variables Files and Variables 1. microbiology_cultures_adi_scores.csv 1.
 Area Deprivation Index (ADI) information for both national and state ranks
 that was mapped to patient nine digit (where available, otherwise five
 digit was used) zip codes. 2. Key Columns: 1. adi_score: ADI percentile
 score at the national level; higher values indicate higher levels of
 deprivation 2. adi_state_rank: ADI decile rankings at the state (Texas,
 USA) level; higher values indicate higher levels of deprivation 2.
 microbiology_cultures_antibiotic_class_exposure.csv 1. Prior exposure to
 various antibiotic classes 2. Key Columns: 1. medication_name: Name of
 antibiotic 2. antibiotic_class: Class of antibiotic 3.
 time_to_culturetime: Days between culture order and antibiotic exposure 3.
 microbiology_cultures_antibiotic_subtype_exposure.csv 1. Prior exposure to
 antibiotic subclasses 2. Key Columns: 1. medication_name: Name of
 antibiotic 2. antibiotic_subtype: Subclass of antibiotic 3.
 antibiotic_subtype_category: Categorical name of antibiotic subclass 4.
 medication_time_to_culturetime: Days between culture order and antibiotic
 exposure 4. microbiology_cultures_cohort.csv 1. The comprehensive listing
 of culture types, organisms, and susceptibility testing results 2. Key
 Columns: 1. culture_description: The type of culture ordered (Blood,
 Respiratory, or Urine) 2. was_positive: Binary indicator of a positive
 culture result (0 or 1) 3. organism: Name of organism 4. antibiotic: Name
 of antibiotic tested 5. susceptibility: Susceptibility results such as
 susceptible, intermediate, or resistant per antibiotic 5.
 microbiology_cultures_comorbidity.csv 1. Comorbidity information for each
 patient in the cohort 2. Key Columns: 1. comorbidity_component:
 Comorbidity component type based on either the AHRQ CCSR diagnosis or
 Elixhauser Comorbidity Index 2. comorbidity_component_start_days_culture:
 Days between the start of comorbidity and the culture order 3.
 comorbidity_component_end_days_culture: Days between the end of the
 comorbidity and the culture order with “Null” indicating an ongoing
 comorbidity and negative values indicating comorbidities that ended after
 the culture order 6. microbiology_cultures_demographics.csv 1. Basic
 demographic information for each patient in the cohort 2. Key columns: 1.
 age: The age range of the patient with values such as 18-24, 25-24, and
 above 90 2. gender: Binary indicator of 0 or 1 to indicate a particular
 gender. The indicators are purposely not identified for deidentification
 purposes. 7. microbiology_cultures_labs.csv 1. Laboratory results recorded
 for the following: 1. wbc: White blood cell count 2. neutrophils:
 Neutrophils 3. lymphocytes: Lymphocytes 4. hgb: Hemoglobin 5. plt:
 Platelets 6. na: sodium 7. hco3: bicarbonate 8. bun: blood urea nitrogen
 9. procalcitonin: Procalcitonin 2. Key columns: 1. Period_Day: Days
 between the culture order and the time range in which the laboratory
 values were measured 2. Q25_xx: Lower quartile laboratory values for
 listed measurements 3. median_xx: Median laboratory values for listed
 measurements 4. Q75_xx: Upper quartile laboratory values for listed
 measurements 5. first_xx: First laboratory value for listed measurements
 6. last_xx: Last laboratory value for listed measurements 8.
 microbiology_cultures_microbial_resistance.csv 1. Microbial resistance
 information for each culture ordered 2. Key columns: 1. organism: Name of
 organism 2. antibiotic: Name of antibiotic 3.
 Resistant_time_to_culturetime: Days between the culture order and the
 susceptibility result 9. microbiology_cultures_nursing_home_visits.csv 1.
 Information about visits to nursing facilities prior to the date of
 culture order 2. Key Columns: 1. nursing_home_visit_culture: Days between
 the culture order and nursing facility visit 10.
 microbiology_cultures_prior_infecting_organism.csv 1. Information on
 identified organisms prior to the culture order 2. Key columns: 1.
 prior_organism: Name of organism 2.
 prior_infecting_organism_days_to_culture: Days between the culture order
 and prior infection 11. microbiology_cultures_prior_med.csv 1. Information
 regarding medications used prior to the culture order 2. Key columns: 1.
 medication_name: Name of prior medication used 2.
 medication_time_to_culturetime: Days between the culture order and prior
 medication start 3. medication_category: Name of medication category 12.
 microbiology_cultures_prior_procedures.csv 1. Information on procedures
 such as mechanical ventilation, parenteral nutrition, surgical procedures,
 dialysis, or catheterization (central venous and urethral) prior to
 culture order 2. Key columns: 1. procedure_description: Name of procedure
 2. procedure_time_to_culturetime: Days between the prior procedure and the
 culture order 13. microbiology_cultures_ward_info.csv 1. Location of the
 ward in which the culture was taken such as the intensive care unit, the
 emergency department, an inpatient setting, or an outpatient setting 2.
 Key columns: 1. hosp_ward_IP: Binary indicator of the culture location in
 an inpatient setting 2. hosp_ward_OP: Binary indicator of the culture
 location in an outpatient setting 3. hosp_ward_ER: Binary indicator of the
 culture location in the emergency department 4. hosp_ward_ICU: Binary
 indicator of the culture location in the intensive care unit ##
 Code/software * No special software is needed to access the dataset.  The
 data files are all .csv (comma separated value) files which can be used
 with standard statistical methods such as SAS, R, and Python. * No
 additional scripts, or software are provided with the dataset. ## Access
 information Other publicly accessible locations of the data: * No other
 publicly accessible locations are available to download the dataset Data
 was derived from the following sources: * Data was sourced from the Epic
 based electronic health records system of the University of Texas
 Southwestern Medical Center in Dallas, Texas, USA. ## Human subjects data
 This study was approved by the IRB committee at the University of Texas
 Southwestern Medical Center (#STU-2023-0583). Patient consent was not
 required as all data will be deidentified and is secondary use in nature.
 The data set is deidentified to comply with the Health Insurance
 Portability and Accountability Act (HIPAA) and the National Institute of
 Standards and Technology (NIST) Safe Harbor regulations. • All identifying
 patient information has been removed from the data set. • Any patient,
 encounter, or culture order identification numbers have been anonymized. •
 Demographic information has been removed with the exception of age and
 gender. Age has been bucketed into age ranges such as 18-24, 25-34, etc.
 and gender (male or female) has been adapted to a binary indicator of 0 or
 1 without indication of which gender is represented by 0 or 1. • Dates and
 times have been offset (jittered) by a random number of days within a
 two-month period while keeping consistent temporal relationships.