Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database

被引:799
作者
Saeed, Mohammed [1 ,3 ]
Villarroel, Mauricio [1 ,4 ]
Reisner, Andrew T. [1 ]
Clifford, Gari [1 ,5 ,6 ]
Lehman, Li-Wei [1 ]
Moody, George [1 ]
Heldt, Thomas [1 ]
Kyaw, Tin H. [7 ]
Moody, Benjamin [1 ]
Mark, Roger G. [1 ,2 ]
机构
[1] MIT, Div Hlth Sci & Technol, Cambridge, MA 02139 USA
[2] Beth Israel Deaconess Med Ctr, Boston, MA 02215 USA
[3] Univ Michigan Hosp, Ann Arbor, MI 48109 USA
[4] Philips Healthcare, Andover, MA USA
[5] Massachusetts Gen Hosp, Dept Emergency Med, Boston, MA 02114 USA
[6] Univ Oxford, Dept Engn Sci, Inst Biomed Engn, Oxford OX1 3PJ, England
[7] AdMob Inc, San Mateo, CA USA
基金
美国国家卫生研究院;
关键词
databases; clinical decision support; hemodynamic instability; information technology; patient monitoring; OUTCOME PREDICTION; MODEL;
D O I
10.1097/CCM.0b013e31820a92c6
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Objective: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Design: Data collection and retrospective analysis. Setting: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Patients: Adult patients admitted to intensive care units between 2001 and 2007. Interventions: None. Measurements and Main Results: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). Conclusions: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. (Crit Care Med 2011; 39: 952-960)
引用
收藏
页码:952 / 960
页数:9
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