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MIMIC-IV

Alistair Johnson Lucas Bulgarelli Tom Pollard Steven Horng Leo Anthony Celi Roger Mark

Published: June 12, 2022. Version: 2.0


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Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2022). MIMIC-IV (version 2.0). PhysioNet. https://doi.org/10.13026/7vcr-e114.

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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Abstract

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. The Medical Information Mart for Intensive Care (MIMIC)-III database provided critical care data for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (BIDMC). Importantly, MIMIC-III was deidentified, and patient identifiers were removed according to the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-III has been integral in driving large amounts of research in clinical informatics, epidemiology, and machine learning. Here we present MIMIC-IV, an update to MIMIC-III, which incorporates contemporary data and improves on numerous aspects of MIMIC-III. MIMIC-IV adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.


Background

In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. In the US, nearly 96% of hospitals had a digital electronic health record system (EHR) in 2015 [1]. Retrospectively collected medical data has increasingly been used for epidemiology and predictive modeling. The latter is in part due to the effectiveness of modeling approaches on large datasets [2]. Despite these advances, access to medical data to improve patient care remains a significant challenge. While the reasons for limited sharing of medical data are multifaceted, concerns around patient privacy are highlighted as one of the most significant issues. Although patient studies have shown almost uniform agreement that deidentified medical data should be used to improve medical practice, domain experts continue to debate the optimal mechanisms of doing so. Uniquely, the MIMIC-III database adopted a permissive access scheme which allowed for broad reuse of the data [3]. This mechanism has been successful in the wide use of MIMIC-III in a variety of studies ranging from assessment of treatment efficacy in well defined cohorts to prediction of key patient outcomes such as mortality. MIMIC-IV aims to carry on the success of MIMIC-III, with a number of changes to improve usability of the data and enable more research applications.


Methods

MIMIC-IV is sourced from two in-hospital database systems: a custom hospital wide EHR and an ICU specific clinical information system. The creation of MIMIC-IV was carried out in three steps:

  1. Acquisition. Data for patients who were admitted to the BIDMC emergency department or one of the intensive care units were extracted from the respective hospital databases. A master patient list was created which contained all medical record numbers corresponding to patients admitted to an ICU or the emergency department between 2008 - 2019. All source tables were filtered to only rows related to patients in the master patient list.
  2. Preparation. The data were reorganized to better facilitate retrospective data analysis. This included the denormalization of tables, removal of audit trails, and reorganization into fewer tables. The aim of this process is to simplify retrospective analysis of the database. Importantly, data cleaning steps were not performed, to ensure the data reflects a real-world clinical dataset.
  3. Deidentify. Patient identifiers as stipulated by HIPAA were removed. Patient identifiers were replaced using a random cipher, resulting in deidentified integer identifiers for patients, hospitalizations, and ICU stays. Structured data were filtered using look up tables and allow lists. If necessary, a free-text deidentification algorithm was applied to remove PHI from free-text. Finally, date and times were shifted randomly into the future using an offset measured in days. A single date shift was assigned to each subject_id. As a result, the data for a single patient are internally consistent. For example, if the time between two measures in the database was 4 hours in the raw data, then the calculated time difference in MIMIC-IV will also be 4 hours. Conversely, distinct patients are not temporally comparable. That is, two patients admitted in 2130 were not necessarily admitted in the same year.

After these three steps were carried out, the database was exported to a character based comma delimited format.


Data Description

MIMIC-IV is grouped into two modules: hosp, and icu. The aim of these modules is to highlight their provenance.

hosp

The hosp module contains data derived from the hospital wide EHR. These measurements are predominantly recorded during the hospital stay, though some tables include data from outside the hospital as well (e.g. outpatient laboratory tests in labevents). Patient demographics (patients), hospitalizations (admissions), and intra-hospital transfers (transfers) are recorded in the hosp module.

Notably, the patients table provides timing information for each patient through the anchor_year and anchor_year_group columns. The anchor_year is a deidentified year occurring sometime between 2100 - 2200, and the anchor_year_group is a three year long date ranges between 2008 - 2019. These pieces of information allow researchers to infer the approximate year a patient received care. For example, if a patient's anchor_year is 2158, and their anchor_year_group is 2011 - 2013, then any hospitalizations for the patient occurring in the year 2158 actually occurred sometime between 2011 - 2013. Finally, the anchor_age provides the patient age in the given anchor_year. If the patient was over 89 in the anchor_year, this anchor_age has been set to 91 (i.e. all patients over 89 have been grouped together into a single group with value 91, regardless of what their real age was).

Date of death is available within the dod column of the patients table. Date of death is derived from hospital records and state records. If both exist, hospital records take precedence. State records were matched using a custom rule based linkage algorithm based on name, date of birth, and social security number. State and hospital records for date of death were collected two years after the last patient discharge in MIMIC-IV, which should limit the impact of reporting delays in date of death.

Dates of death occurring more than one year after hospital discharge are censored as a part of the deidentification process. As a result, the maximum time of follow up for each patient is exactly one year after their last hospital discharge. For example, if a patient's last hospital discharge occurs on 2150-01-01, then the last possible date of death for the patient is 2151-01-01. If the individual died on or before 2151-01-01, and it was captured in either state or hospital death records, then the dod column will contain the deidentified date of death. If the individual survived for at least one year after their last hospital discharge, then the dod column will have a NULL value.

Other information in the hosp module includes laboratory measurements (labeventsd_labitems), microbiology cultures (microbiologyeventsd_micro), provider orders (poepoe_detail), medication administration (emaremar_detail), medication prescription (prescriptionspharmacy), hospital billing information (diagnoses_icdd_icd_diagnosesprocedures_icdd_icd_procedureshcpcseventsd_hcpcs, drgcodes), online medical record data (omr), and service related information (services).

icu

The icu module contains data sourced from the clinical information system at the BIDMC: MetaVision (iMDSoft). MetaVision tables were denormalized to create a star schema where the icustays and d_items tables link to a set of data tables all suffixed with "events". Data documented in the icu module includes intravenous and fluid inputs (inputevents), ingredients for the aforementioned inputs (ingredientevents), patient outputs (outputevents), procedures (procedureevents), information documented as a date or time (datetimeevents), and other charted information (chartevents). All events tables contain a stay_id column allowing identification of the associated ICU patient in icustays, and an itemid column allowing identification of the concept documented in d_items.


Usage Notes

The data described here are collected during routine clinical practice and reflect the idiosyncrasies of that practice. Implausible values may be present in the database as an artifact of the archival process.  Researchers should follow best practice guidelines when analyzing the data.

Up to date documentation for MIMIC-IV is available on the MIMIC-IV website [4]. We have created an open source repository for the sharing of code and discussion of the database, referred to as the MIMIC Code Repository [5, 6]. The code repository provides a mechanism for shared discussion and analysis of all versions of MIMIC, including MIMIC-IV.


Release Notes

MIMIC-IV v2.0

MIMIC-IV v2.0 was released on June 12, 2022. It focused on expanding the data elements available for patients within MIMIC-IV v1.0. Additional data available includes out-of-hospital date of death, information from the online medical record system (which includes height and weight), and more detail for continuous infusions in the ICU.

Major changes

  • The core module has been removed to simplify the schema. The admissionspatients, and transfers tables are now in the hosp module.
  • Neonates have been removed from the dataset. Neonatal data will be released in a separate project with data from the neonatal intensive care unit.

icu module

  • icustays
    • Around 700 stays (~1%) have changed due to the changes in the patients table.
  • chartevents, d_items
    • The problem list from MetaVision has been added. All problems are documented with the same itemid now present in d_items: 220001. There are just over 1,000 unique problems. Most documented problems are related to the care plan for the patient and documented during nurse shift changes (either 7am or 7pm). Less frequently, the ongoing issues are documented here.
  • ingredientevents
    • This is a new table associated with inputevents. Each intravenous administration tracked in inputevents is associated with a set of ingredients. These ingredients include water content, caloric information, and so on. The goal of the inputevents table is to support nutrition research and to provide a mechanism for estimating fluid input via summing all instances of the water ingredient. These ingredients have been separated from the inputevents table to simplify analysis and reduce the size of inputevents.
  • inputevents
    • Removed a single column which contained only null values: cancelreason.
  • procedureevents
    • Removed columns which contained only null values: totalamount, totalamountuom, cancelreason, comments_editedby, comments_canceledby, comments_date, secondaryordercategoryname.

hosp module

  • admissions
    • Fixed an issue where hospitalizations were missing edregtime and edouttime when the patient was admitted via the ED (reported in #1247, thanks @MEladawi).
  • patients
    • dod is now populated with out-of-hospital mortality from state death records. For patients admitted to the ICU, this change has increased capture of date of death from 8,223 records to 23,844 (i.e. we now have out-of-hospital mortality for an additional 15,621 ICU patients).
    • The mechanism for determining patients included in MIMIC was changed. For the most part this has resulted in an improvement, particularly regarding the logic for merging patients who had distinct medical record numbers. As a result of this change, most tables have had a change in the data content. Approximately 1% of stays were affected.
  • transfers
    • Fixed a bug where the outtime for ED stays with no associated hadm_id (i.e. an ED stay where the individual was not admitted to the hospital) was incorrect. This resulted in all transfers rows with a NULL hadm_id having an apparent stay of minutes or less. The outtime column has now been corrected.
  • labevents, d_labitems
    • The itemid for d_labitems has been changed for 43 items. These are extremely infrequently documented and each itemid has fewer than 100 observations in labevents. The exact itemid are provided in the changelog file CHANGELOG.txt.
    • Errors were found in the current values of loinc_code (reported in #938, thanks @Mauvila). In order to enable collaborative improvement, the loinc_code column has been removed, and will now be collaboratively developed in the MIMIC Code Repository. Initial values will be sourced from the hospital system.
    • A number of labs which previously had the value in the comments field now have the value in the value field (reported in #941, thanks @Mauvila). This change makes the labevents table more consistent with MIMIC-III, which had these values in the value field.
  • microbiologyevents
    • New organisms, tests, specimens, and antibiotics have been added.
  • omr
    • A new table has been added: omr. The source of this data is the Online Medical Record, and it contains miscellaneous information useful for understanding an individual's health. As of v2.0, the omr table has the following information: blood pressure, height, weight, body mass index, and Estimated Glomerular Filtration Rate (eGFR). These values are available from both inpatient and outpatient visits, and in many cases a "baseline" value from before a patient's hospitalization is available.
  • prescriptions
    • The formulary_drug_cd table has been added back (was previously in MIMIC-III). This column has the same set of values as the product_code column of emar_detail.

MIMIC-IV v1.0

MIMIC-IV v1.0 was released March 16, 2021.

core

  • admissions
    • A number (~1000, <1%) of erroneous hadm_id have been removed.
  • patients
    • dod is now populated using the patient's deathtime from their latest hospitalization (reported in #71, thanks @jinjinzhou).
    • At the moment, out-of-hospital mortality is not captured by `dod`.
  • transfers
    • Removed erroneous transfers included in the previous version.
    • transfer_id has been regenerated. transfer_id in MIMIC-IV v1.0 are not compatible with transfer_id from v0.4. We do not intend to change transfer_id when updating MIMIC-IV, but had to update it due to an error in its generation.
    • All hadm_id in transfers are also present in admissions and vice-versa (reported in #84, thanks @kokoko12305).

icu

  • icustays
    • ICU stays were inappropriately assigned in the previous version due to an error in the preprocessing code. Previously, non-ICU ward transfers were included in the ICU stays, and certain ward stays were not treated as ICU stays (reported in #67, thanks @JHLiu7 and @stefanhgm). The assignment of stay_id has been regenerated.
    • The mapping between hospital transfers and ICU stays has been updated.
    • stay_id in MIMIC-IV v1.0 are not compatible with stay_id from v0.4. We do not intend to change stay_id when updating MIMIC-IV, but had to update it due to the error identified above.
  • The change in icustays has re-assigned values to new stay_id, as a result all tables have had their content changed (due to a change in stay_id), but the structure is unchanged.

hosp

    hcpcsevents
    • Data has been added for a number of previously excluded hospitalizations.
    • The table now has a chartdate column, containing the date associated with the code. Every row is associated with a date.
  • drgcodes
    • Data has been added for a number of previously excluded hospitalizations.
    • Duplicate DRG codes have been removed from the table.
    • Descriptions have been updated using the latest dictionaries made available from mass.gov and HCUP.
  • diagnoses_icd, d_icd_diagnoses
    • Data has been added for a number of previously excluded hospitalizations (reported in #27, thanks @yugangjia).
    • The icd_code column is now trimmed and stored as a VARCHAR, i.e. codes no longer contain trailing whitespaces ('850 ' -> '850').
    • Missing ICD codes have been added to the dictionary. All ICD codes in the diagnoses_icd table have an associated reference in d_icd_diagnoses.
  • labevents
    • The comments field has been updated, fixing a bug where comments longer than 4096 characters were truncated. Due to the deidentification, it's unlikely users will see much difference, as these comments will appear as ___.
  • procedures_icd
    • Data has been added to procedures_icd for a number of previously excluded hospitalizations.
    • The table now has a chartdate column, containing the date associated with each billed procedure.
    • The icd_code column is now trimmed and stored as a VARCHAR, i.e. codes no longer contain trailing whitespaces ('850 ' -> '850').
    • Missing ICD codes have been added to the dictionary. All ICD codes in the procedures_icd table have an associated reference in d_icd_procedures.

v0.4

  • d_micro
    • This table has been removed
  • microbiologyevents
    • Added the column spec_type_desc, test_name, org_name, and ab_name columns
    • These columns contain the textual name of the organism/antibiotic/test/specimen
    • Added the comments column: this column contains information about the test, and in some cases (e.g. viral load tests), contains the result

v0.3

  • Fixed a bug in the timing between hosp and icu

v0.2

  • Updated demographics in the patient table
    • anchor_year -> anchor_year_group
    • anchor_year_shifted -> anchor_year
    • See the patients table in the MIMIC online documentation for detail on these columns
  • transfers
    • Deleted the los column
  • emar
    • mar_id -> emar_id
    • emar_id is now a composite of subject_id and emar_seq, and has form “subject_id-emar_seq”
    • emar_seq column - a monotonically increasing integer starting with the first eMAR administration
    • Added poe_id and pharmacy_id columns for linking to those tables
  • emar_detail
    • mar_id -> emar_id (changed as above)
    • Deleted the mar_detail_id column
  • hcpcsevents
    • ticket_id_seq -> seq_num
  • labevents
    • Many previously NULL values are now populated - these were removed originally due to deidentification
    • Added the comments column. This contains deidentified free-text comments with labs. PHI is replaced with three underscores (___). If an entire comment is ___, then the entire comment was scrubbed.
  • microbiologyevents
    • stay_id column removed
    • spec_id -> micro_specimen_id
  • Added the poe and poe_detail tables
    • Documentation of provider orders for various treatments and other aspects of patient management
  • Added the prescriptions table
    • Documentation of medicine prescriptions via the provider order interface
  • Added the pharmacy table
    • Detailed information regarding prescriptions provided by the pharmacy including formulary dose, route, frequency, dose, and so on.
  • inputevents
    • Fixed an error in the calculation of the amount column
  • icustays
    • Re-derived stay_id - the new stay_id are distinct from the previous version.

Ethics

The collection of patient information and creation of the research resource was reviewed by the Institutional Review Board at the Beth Israel Deaconess Medical Center, who granted a waiver of informed consent and approved the data sharing initiative.


Acknowledgements

We would like to thank the Beth Israel Deaconess Medical Center for their continued support of the MIMIC project. In particular we would like to thank Carolyn Conti, Alvin Gayles, Larry Markson, Ayad Shammout, Lu Shen, and Manu Tandon for their assistance. We would also like to thank the NIH for their gracious support.


Conflicts of Interest

None to declare.


References

  1. Henry, J., Pylypchuk, Y., Searcy T. & Patel V. (May 2016). Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015. ONC Data Brief, no.35. Office of the National Coordinator for Health Information Technology: Washington DC.+
  2. Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8-12.
  3. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.H., Feng, M., Ghassemi, M., ... & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific data, 3(1), 1-9.
  4. MIMIC Online Documentation. https://mimic.mit.edu
  5. Johnson AE, Stone DJ, Celi LA, Pollard TJ. The MIMIC Code Repository: enabling reproducibility in critical care research. Journal of the American Medical Informatics Association. 2018 Jan;25(1):32-9.
  6. Alistair Johnson, Tom Pollard, Jim Blundell, Brian Gow, erinhong, Nicolas Paris, et al. MIT-LCP/mimic-code: MIMIC Code v2.1.1. Zenodo; 2021. https://doi.org/10.5281/zenodo.821871

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Versions
  • 0.3 - Aug. 13, 2020
  • 0.4 - Aug. 13, 2020
  • 1.0 - March 16, 2021
  • 2.0 - June 12, 2022

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