DOI: 10.5061/dryad.jq2bvq8kp
title: Antibiotic Resistance Microbiology Dataset (ARMD): A resource for antimicrobial resistance from EHRs

description: The Antibiotic Resistance Microbiology Dataset (ARMD) is a structured and
 de-identified resource developed using electronic health records (EHR)
 from Stanford Healthcare. It provides a comprehensive overview of
 microbiological cultures including urine, respiratory, and blood cultures.
 This dataset includes 283,715 unique adult patients and features detailed
 information on culture results, identified organisms, antibiotic
 susceptibility, and associated demographic and clinical data. The dataset
 was meticulously constructed through a multi-step process designed to
 enhance data quality and relevance. By enabling the study of antimicrobial
 resistance patterns and supporting antimicrobial stewardship efforts, ARMD
 offers a valuable resource for researchers and clinicians seeking to
 improve the management of infectious diseases and combat the growing
 threat of antimicrobial resistance.
description: Cohort Selection The ARMD was created using
 de-identified EHR data from Stanford Healthcare to address this need. This
 dataset provides microbiological cultures from adult patients (≥18 years
 old) and includes key clinical data points relevant to studying
 antimicrobial resistance. The cohort construction involved the following
 features and processes: Culture
 Types: Microbiological cultures were included, specifically urine,
 respiratory, and blood cultures. 
 Temporal Adjustment: The timing of culture orders was adjusted
 for data privacy through jittering, ensuring patient confidentiality while
 retaining meaningful temporal relationships. 
 Culture Positivity: Each culture is flagged as either
 positive or negative, indicating whether an organism was identified.
 Cultures flagged as negative are represented by a null value in the
 susceptibility field. Organism
 Identification and Susceptibility: For positive cultures, the identified
 organism and its antibiotic susceptibility are recorded. Susceptibility
 values were categorized using the following logic: 
 NULL: The original susceptibility was NULL,
 indicating the culture was not positive (e.g., no growth).
 Susceptible: Includes values such as
 Susceptible, Negative, or Not Detected. 
 Resistant: Includes values such as Resistant, Non Susceptible,
 Detected, or Positive. 
 Intermediate: Includes values such as Intermediate or Susceptible
 - Dose Dependent. Inconclusive:
 Includes values such as No Interpretation, Not done, Inconclusive, or See
 Comment. Synergism: Includes
 values such as Synergy and No Synergy. 
 Antibiotic Standardization: Antibiotic names were
 cleaned and standardized to the generic form for consistency in analysis,
 allowing for accurate comparisons across records. 
 Antibiotic Susceptibility: Detailed susceptibility
 data is available for 55 different antibiotics, providing a robust
 framework for analyzing antimicrobial resistance patterns.
 The cohort was generated through a
 systematic, multi-step process to ensure high-quality data:
 Filtering for Clinical Relevance:
 Microbiological cultures associated with significant clinical outcomes
 were selected to focus on cases with actionable insights.
 Adult Patient Restriction: The dataset was
 limited to adult patients (≥18 years old) using demographic
 data. Exclusion Criteria:
 Patients with prior microbiological cultures within two weeks before the
 current culture were excluded to avoid overlapping data and ensure
 distinct clinical events. 
 Identification of Culture Positivity: Positivity was determined
 based on the presence of susceptibility results in the corresponding
 records. This rigorous cohort
 selection process ensures that the ARMD dataset is well-suited for
 research on antimicrobial resistance, supporting clinical and
 epidemiological studies aimed at improving antimicrobial stewardship and
 treatment outcomes. Implied susceptibility 
 The Implied Susceptibility table is a derived dataset created to
 provide inferred insights into antibiotic susceptibility patterns based on
 predefined relationships between antibiotics. This table captures cases
 where susceptibility to one antibiotic can imply susceptibility or
 resistance to another, based on established microbiological and
 pharmacological principles. The table is designed to enhance the
 interpretability of susceptibility data by incorporating implied
 relationships between antibiotics, which can be critical for guiding
 clinical decision-making and understanding resistance patterns.
 Additionally, we share the rules applied to derive these implied
 relationships, providing transparency and enabling researchers to
 understand and reproduce the logic behind the inferred data.
 De-Identification To ensure patient privacy
 and comply with data-sharing policies, the ARMD employs the following
 de-identification measures: 
 Unique Identifiers: 
 Each patient and culture order is assigned a unique, randomly
 generated identifier (anon_id and order_proc_id_coded). These identifiers
 are consistent across the dataset and allow linkage between associated
 data elements while preserving anonymity. 
 Temporal De-Identification:
 Dates and times are not
 included in their original format. Instead, all timestamps (e.g.,
 order_time_jittered_utc) are jittered randomly to maintain temporal
 relationships without revealing exact times. 
 The jittering process ensures the dataset retains
 analytical utility while removing direct identifiers.
 Age Censoring:
 To further ensure anonymity,
 patient ages are categorized into predefined age bins (e.g., 18–24, 25–34,
 etc.), with all patients aged 89 or older grouped into a single category
 (90+). This approach prevents re-identification of individuals based on
 age outliers. Gender
 Encoding: Gender is
 recorded as binary values (0 or 1) without defining which value
 corresponds to male or female, eliminating any interpretative bias and
 enhancing privacy. 
 Exclusion of Direct Identifiers: 
 No direct patient identifiers (e.g., names, medical
 record numbers) are included in the dataset. 
 All demographic and clinical details are provided in a
 de-identified format. 
 Ethical Approval and Patient Consent This
 study was approved by the Stanford University Institutional Review Board
 (IRB) under eProtocol #70466. The IRB determined the study involves
 minimal risk, and patient consent was waived due to the use of
 de-identified retrospective data.
description: # Antibiotic Resistance Microbiology Dataset (ARMD): A resource for
 antimicrobial resistance from EHRs ## Background Antimicrobial resistance
 (AMR) represents a pressing global health challenge, exacerbated by the
 overuse and misuse of antibiotics. Efforts to mitigate AMR require
 high-quality datasets to analyze trends in microbial susceptibility, guide
 clinical decision-making, and inform stewardship programs. Electronic
 health records (EHR) are a rich source of real-world data that can be
 leveraged to study antimicrobial use and resistance patterns. However,
 constructing meaningful datasets from EHR data requires rigorous curation
 and preprocessing to ensure accuracy, relevance, and usability. ARMD aims
 to facilitate research in antimicrobial stewardship, with applications in
 identifying resistance patterns, evaluating treatment practices, and
 informing public health interventions. By leveraging de-identified EHR
 data from Stanford Healthcare, this dataset provides a unique opportunity
 to generate insights that can help improve infectious disease management
 and curb the spread of AMR. The dataset includes detailed information on
 culture positivity, organism identification, and antibiotic susceptibility
 across 55 antibiotics. By supporting the development of algorithms for
 predicting susceptibility and selecting effective treatments, ARMD offers
 researchers the tools to optimize empiric antibiotic therapy while
 minimizing the overuse of broad-spectrum antibiotics. Additionally, ARMD
 enables the exploration of broader questions in causal inference and
 policy learning by leveraging antibiotic susceptibility testing as a proxy
 for counterfactual outcomes under different treatments. With its large
 cohort of over 283,000 adult patients and a diverse set of microbiological
 cultures, this dataset supports a range of research applications, from
 evaluating resistance patterns to improving clinical guidelines for
 antimicrobial stewardship. --- ## Description of the data and file
 structure ### Data Files #### 1. microbiology_cultures_cohort.csv
 **Description:** Contains primary information about microbiological
 cultures, including culture type, organism identified, and antibiotic
 susceptibility. **Key Columns:** * `culture_description`: Description of
 the culture (e.g., Urine, Blood). * `was_positive`: Indicates whether the
 culture was positive for an organism. * `organism`: Identified
 microorganism. * `antibiotic`: Antimicrobial agent tested. *
 `susceptibility`: Categorized susceptibility result (e.g., Susceptible,
 Resistant, Intermediate). #### 2. microbiology_cultures_ward_info.csv
 **Description:** Provides information about the ward setting where
 cultures were collected (e.g., ICU, ER). **Key Columns:** *
 `hosp_ward_IP`: Indicates if the culture was taken in an inpatient ward. *
 `hosp_ward_OP`: Indicates if the culture was taken in an outpatient
 setting. * `hosp_ward_ER`: Indicates if the culture was taken in the
 emergency department. * `hosp_ward_ICU`: Indicates if the culture was
 taken in the ICU. #### 3. microbiology_cultures_prior_med.csv
 **Description:** Details prior medication exposure for patients relative
 to the culture order. **Key Columns:** * `medication_name`: Generic name
 of the medication. * `medication_time_to_culturetime`: Days between
 medication start and culture order. * `medication_category`: Category of
 the medication. #### 4. microbiology_cultures_microbial_resistance.csv
 **Description:** Contains data on microbial resistance and the timeline of
 resistance development. **Key Columns:** * `organism`: Microorganism
 identified. * `antibiotic`: Antimicrobial agent tested. *
 `resistant_time_to_culturetime`: Days between resistance confirmation and
 culture order. #### 5. microbiology_cultures_demographics.csv
 **Description:** Includes patient demographic information at the time of
 the culture order. **Key Columns:** * `age`: Patient age categorized into
 bins (e.g., 18–24, 25–34, etc.). Patients aged 89 years or older are
 grouped into a single category (90+). * `gender`: Gender is encoded as
 binary values (0 or 1) without indicating which value corresponds to male
 or female. #### 6. microbiology_cultures_labs.csv **Description:**
 Includes detailed laboratory results recorded within specific time windows
 relative to culture orders, including summary statistics (median, Q25,
 Q75) for key laboratory metrics. **Key Columns:** * `Period_Day`: Window
 frame representing the number of days from the culture order during which
 lab measurements were recorded. * **Laboratory Metrics (summary
 statistics: Q25, Median, Q75):** * White blood cell count (wbc),
 neutrophils (neutrophils), lymphocytes (lymphocytes). * Hemoglobin (hgb),
 platelets (plt), sodium (na), bicarbonate (hco3), blood urea nitrogen
 (bun), creatinine (cr). * Lactate (lactate), procalcitonin
 (procalcitonin). * **Laboratory Metrics (first and last recorded
 values):** * `first_wbc`, `last_wbc`, `first_hgb`, `last_hgb`, `first_cr`,
 `last_cr`, etc. #### 7. microbiology_cultures_vitals.csv **Description:**
 Contains detailed vital sign measurements near the time of the culture
 order, including summary statistics and first and last recorded values.
 **Key Columns:** * **Vital Signs (summary statistics: Q25, Median, Q75):**
 * Heart rate (`heartrate`), respiratory rate (`resprate`), body
 temperature (`temp`), systolic blood pressure (`sysbp`), diastolic blood
 pressure (`diasbp`). * **Vital Signs (first and last recorded values):** *
 `first_heartrate`, `last_heartrate`, `first_resprate`, `last_resprate`,
 `first_temp`, `last_temp`, `first_sysbp`, `last_sysbp`, `first_diasbp`,
 `last_diasbp`. #### 8. microbiology_cultures_antibiotic_class_exposure.csv
 **Description:** Tracks prior exposure to antibiotic classes. **Key
 Columns:** * `antibiotic_class`: The antibiotic class. *
 `time_to_culturetime`: Days between antibiotic exposure and culture order.
 #### 9. microbiology_cultures_antibiotic_subtype_exposure.csv
 **Description:** Details prior exposure to antibiotic subclasses. **Key
 Columns:** * `antibiotic_subtype`: The antibiotic subclass. *
 `time_to_culturetime`: Days between exposure and culture order. #### 10.
 microbiology_culture_prior_infecting_organism.csv **Description:**
 Contains data on prior infecting organisms identified in previous
 microbiological cultures for each patient. **Key Columns:** *
 `prior_organism`: Indicates the presence of a prior infection caused by
 this organism. * `prior_infecting_organism_days_to_culture`: Days between
 the previously recorded infection and the culture order. #### 11.
 microbiology_cultures_comorbidity.csv **Description:** Contains detailed
 information on comorbidities. **Key Columns:** * `comorbidity_component`:
 Specific component of either the AHRQ CCSR diagnosis or Elixhauser
 comorbidity index. * `comorbidity_component_start_days_culture`: Days
 between the start of the component and the culture order. *
 `comorbidity_component_end_days_culture`: Days between the end of the
 component and the culture order (NULL indicates ongoing conditions). ####
 12. microbiology_cultures_priorprocedures.csv **Description:** Lists
 procedures performed on patients before culture orders. **Key Columns:** *
 `procedure_name`: Name of the procedure (e.g., Central Venous Catheter,
 Mechanical Ventilation). * `procedure_time_to_culturetime`: Days between
 procedure and culture order. #### 13. microbiology_cultures_adi_scores.csv
 **Description:** Contains Area Deprivation Index (ADI) data mapped to
 cohort ZIP codes. **Key Columns:** * `adi_score`: ADI score representing
 socioeconomic disadvantage. * `adi_state_rank`: State-level rank for the
 ADI score. #### 14. microbiology_cultures_nursing_home_visits.csv
 **Description:** Tracks nursing home visits relative to the culture order
 date. **Key Columns:** * `nursing_home_visit_culture`: Days between the
 nursing home visit and the culture order. #### 15.
 microbiology_cultures_implied_susceptibility.csv **Description:**
 Determines the implied susceptibility of organisms to antibiotics. **Key
 Columns:** * `Implied_susceptibility`: The inferred susceptibility of
 organisms to an antibiotic. #### 16. implied_susceptibility_rules.csv
 **Description:** Defines the rules used to infer susceptibility
 relationships between antibiotics. **Key Columns:** * `Organism`: The
 organism for which the susceptibility rules apply. * `Antibiotic`: The
 antibiotic for which susceptibility is inferred. * `Rule`: The logic or
 condition used to determine inferred susceptibility. ### Linking Across
 Files The files are linked using: * `anon_id`: De-identified patient
 identifier. * `pat_enc_csn_id_coded`: Patient encounter identifier. *
 `order_proc_id_coded`: Unique culture order identifier. *
 `order_time_jittered_utc`: Jittered timestamp. --- ## De-Identification To
 ensure compliance with HIPAA guidelines and protect patient privacy: *
 Patient IDs are anonymized, and all identifiable information is removed. *
 Ages are grouped into bins (e.g., "18–24", "25–35") to
 prevent exact identification. * Gender is anonymized as binary values (0
 and 1) without further specification. * Temporal data (e.g., culture
 collection times) are adjusted using jittering to maintain meaningful
 relationships without revealing exact dates. --- ## Sharing/Access
 Information * This dataset is publicly available on Dryad for research
 purposes. * Users are encouraged to contact the dataset creators for
 support or further clarification if needed. --- ## Code/Software * No
 scripts are included in the dataset. However, users may contact the
 authors if they require guidance on using the data or developing
 analytical workflows. * Analysis software such as Python, R, or
 statistical platforms like SPSS or SAS can be used to interpret and
 analyze the dataset. --- ## Usage Notes This dataset is ideal for
 researchers and clinicians aiming to: * Analyze trends in antimicrobial
 resistance. * Develop predictive models for empirical antibiotic
 selection. * Study the impact of comorbidities, nursing home visits, and
 clinical variables on resistance patterns. ### Handling Missing Data *
 Empty cells in the dataset are represented as "null" to ensure
 clarity. ### Ethical Considerations * This study was approved by the
 Stanford University Institutional Review Board (IRB) under eProtocol
 #70466. The IRB determined the study involves minimal risk, and patient
 consent was waived due to the use of de-identified retrospective data. ---
 ## Acknowledgments This dataset was developed with support from Stanford
 University and funded by the National Institutes of Health (NIH) R01
 grant. The authors acknowledge all contributors who assisted in creating
 and validating this dataset. --- ## Citation When using this dataset,
 please cite: Nateghi Haredasht, F., et al. *Antibiotic Resistance
 Microbiology Dataset (ARMD).* Stanford Healthcare, 2025.
 [arXiv:2503.07664](https://doi.org/10.48550/arXiv.2503.07664). ## Contact
 For questions, clarifications, or further support, please contact:
 **Fateme Nateghi Haredasht, PhD** Stanford University
 [fnateghi@stanford.edu](mailto:fnateghi@stanford.edu)