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Multiparameter Intelligent Monitoring in Intensive Care II: a Public-Access Intensive Care Unit Database* Mohammed Saeed, MD, Phd; Mauricio Villarroel, MBA; Andrew T

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Multiparameter Intelligent Monitoring in Intensive Care II: a Public-Access Intensive Care Unit Database* Mohammed Saeed, MD, Phd; Mauricio Villarroel, MBA; Andrew T Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* Mohammed Saeed, MD, PhD; Mauricio Villarroel, MBA; Andrew T. Reisner, MD; Gari Clifford, PhD; Li-Wei Lehman, PhD; George Moody; Thomas Heldt, PhD; Tin H. Kyaw, MEng; Benjamin Moody; Roger G. Mark, MD, PhD Objective: We sought to develop an intensive care unit re- automatically deidentified to comply with Health Insurance Portabil- search database applying automated techniques to aggregate ity and Accountability Act standards and integrated with relational high-resolution diagnostic and therapeutic data from a large, database software to create electronic intensive care unit records for diverse population of adult intensive care unit patients. This freely each patient stay. The data were made freely available in February 2010 available database is intended to support epidemiologic research through the Internet along with a detailed user’s guide and an assort- in critical care medicine and serve as a resource to evaluate new ment of data processing tools. The overall hospital mortality rate was clinical decision support and monitoring algorithms. 11.7%, which varied by critical care unit. The median intensive care unit Design: Data collection and retrospective analysis. length of stay was 2.2 days (interquartile range, 1.1–4.4 days). Accord- Setting: All adult intensive care units (medical intensive care ing to the primary International Classification of Diseases, 9th Revision unit, surgical intensive care unit, cardiac care unit, cardiac sur- codes, the following disease categories each comprised at least 5% of gery recovery unit) at a tertiary care hospital. the case records: diseases of the circulatory system (39.1%); trauma Patients: Adult patients admitted to intensive care units be- (10.2%); diseases of the digestive system (9.7%); pulmonary diseases tween 2001 and 2007. (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). Interventions: None. Conclusions: MIMIC-II documents a diverse and very large Measurements and Main Results: The Multiparameter Intelligent population of intensive care unit patient stays and contains com- Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 prehensive and detailed clinical data, including physiological intensive care unit stays. The investigators collected detailed infor- waveforms and minute-by-minute trends for a subset of records. mation about intensive care unit patient stays, including laboratory It establishes a new public-access resource for critical care data, therapeutic intervention profiles such as vasoactive medication research, supporting a diverse range of analytic studies spanning drip rates and ventilator settings, nursing progress notes, discharge epidemiology, clinical decision-rule development, and electronic summaries, radiology reports, provider order entry data, International tool development. (Crit Care Med 2011; 39:952–960) Classification of Diseases, 9th Revision codes, and, for a subset of KEY WORDS: databases; clinical decision support; hemodynamic patients, high-resolution vital sign trends and waveforms. Data were instability; information technology; patient monitoring e report the establish- (MIMIC-II) research database that is no- diverse population of intensive care unit ment of the Multiparam- table for four factors: it is publicly and (ICU) patients; it contains high temporal eter Intelligent Monitor- freely available to other research organi- resolution data, including laboratory re- W ing in Intensive Care II zations upon request; it encompasses a sults, electronic clinical documentation, and bedside monitor numeric trends and waveforms (such as the electrocardio- *See also p. 1200. This research was supported by grant R01 EB001659 gram); and it has been deidentified in a From the University of Michigan Hospitals (MS), Ann from the National Institute of Biomedical Imaging and Health Insurance Portability and Ac- Arbor, MI, the Division of Health Sciences and Technol- Bioengineering and by support from Philips Healthcare. countability Act-compliant manner. The ogy, Massachusetts Institute of Technology, Cambridge, This research was performed at the Massachu- MA, and Philips Healthcare, Andover, MA; the Division of setts Institute of Technology, Cambridge, MA, and the MIMIC-II database will support a diverse Health Sciences and Technology, Massachusetts Institute Beth Israel Deaconess Medical Center, Boston, MA. range of analytic studies spanning epide- of Technology (MV), Cambridge, MA; the Division of Dr. Saeed is employed by Philips Healthcare. Dr. miology, clinical decision-rule develop- Health Sciences and Technology, Massachusetts Institute Villarroel, Dr. Lehman, Mr. Moody, Dr. Heldt, and Dr. of Technology (ATR), and the Department of Emergency Mark received funding from the National Institutes of ment, and electronic tool development. Medicine, Massachusetts General Hospital, Boston, MA; Health (NIH). Dr. Reisner consulted with General Elec- Historically, large-scale ICU databases the Division of Health Sciences and Technology (GC), tric Healthcare and received funding from the NIH. The have been effective resources to under- Massachusetts Institute of Technology, Cambridge, MA, remaining authors have not disclosed any potential stand risk factors and natural histories of and the Department of Engineering Sciences, Institute of conflicts of interest. Biomedical Engineering, University of Oxford, Oxford, UK; For information regarding this article, E-mail: critical illness as well as the efficacy of the Division of Health Sciences and Technology (L-WL, [email protected] various treatment strategies. For in- GM, TH, BM), Massachusetts Institute of Technology, Copyright © 2011 by the Society of Critical Care stance, Acute Physiology and Chronic Cambridge, MA; AdMob Inc (THK), San Mateo, CA; and Medicine and Lippincott Williams & Wilkins Health Evaluation I–III and Project Im- the Division of Health Sciences and Technology (RGM), DOI: 10.1097/CCM.0b013e31820a92c6 Massachusetts Institute of Technology, Cambridge, MA, pact contained daily abstractions of pa- and Beth Israel Deaconess Medical Center, Boston, MA. tient data that provided new insights and 952 Crit Care Med 2011 Vol. 39, No. 5 scoring tools to relate patient outcomes Table 1. Description of clinical data classesa and lengths of stays with the patients’ conditions on admission (1, 2). Such col- Clinical Data Class Description lection and analysis of large volumes of General Patient demographics, hospital admission and discharge dates, room ICU data are invaluable to the advance- tracking, code status, hospital death dates (in or out of the ICU), ment of clinical knowledge, but it is ex- ICD-9 codes, etc tremely effort-intensive because there are Physiological Hourly nurse-verified vital signs (BP, HR, etc), SAPS, ventilator substantial challenges to the collection of settings, etc the data. Such difficulties include: dispa- Clinical laboratory tests Hematology, blood chemistries, ABGs, urinalysis, microbiology, etc rate sources of data, eg, clinical docu- Medications Detailed administration records of IV medications, provider order entry data mentation versus laboratory results; er- Fluid balance Hourly and cumulative intake (solutions, blood, etc) and output roneous or missing data; unsynchronized (urine, estimated blood loss, etc) time references; proprietary data formats; Reports Free text reports of imaging studies (x-ray, CT, MRI), 12-lead ECGs, limitations of computing power, net- echocardiograms, etc working bandwidth, and digital storage Notes Free text notes including nursing and respiratory therapist progress capacity; and concerns related to patient notes; physician hospital discharge summaries privacy. The challenge of data collection ICU, intensive care unit; ICD-9, International Classification of Diseases, 9th Revision; BP, blood has sometimes been addressed through pressure; HR, heart rate; SAPS, Simplified Acute Physiological Score; ABGs, arterial blood gases; IV, coordinated efforts by a network of clin- intravenous; CT, computed tomography; MRI, magnetic resonance imaging; ECGs, electrocardiograms. ical investigators interested in specific aA comprehensive listing of the hundreds of clinical parameters available in the MIMIC-II database problem domains such as acute respira- is available at http://physionet.org/mimic2. tory distress syndrome (ARDSNET Trial) (3), acute kidney injury (4), or septic shock (5). However, these powerful dis- hypotensive episodes. Similarly, MIM- Database Development. The data acquisi- ease specific databases were not designed IC-II enables the analysis of transient in- tion process was not visible to staff and did not to be exploited as research resources to dependent variables such as electrocar- interfere with the clinical care of patients or support other domains of ICU research diogram waveform features and their methods of monitoring. Two categories of data nor are their data widely available. associated clinical outcomes. The unique were collected: clinical data, which were ag- In 2003, under National Institutes of features of MIMIC-II are compared with gregated from ICU information systems and hospital archives, and high-resolution physio- Health funding, we established a research other major databases and we discuss the logical data (waveforms and time series of de- program with the objective of developing major challenges encountered in devel- rived physiological measurements) that were and evaluating advanced ICU monitoring
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