Dataset Information

The eICU Collaborative Research Database, a freely available multi-center database for critical care research.

ABSTRACT: Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.

SUBMITTER: Pollard TJ

PROVIDER: S-EPMC6132188 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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The eICU Collaborative Research Database, a freely available multi-center database for critical care research.

Pollard Tom J TJ Johnson Alistair E W AEW Raffa Jesse D JD Celi Leo A LA Mark Roger G RG Badawi Omar O

Scientific data 20180911

Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs ...[more]

PMID: 30204154

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Project description:The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is to develop standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight common ground in a diverse landscape of different sample preparation techniques and LC-MS setups with the aim to explore differences and potential confounding factors as well as to identify areas for improvement with a direct practical benefit. With the aim to benchmark and improve current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium we performed two consecutive round robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six participating study partners were provided with two clinically relevant sample matrices: plasma, and cerebrospinal fluid (CSF). In the first round each laboratory applied their current best practice protocol for the respective matrix, covering laboratory-specific sample proteolysis, liquid chromatography, and MS measurement procedure. All resulting MS files were analyzed centrally by a standardized pipeline. Based on the achieved results and following a fully transparent exchange of all lab-specific protocols within the consortium, each laboratory could improve their methods before measuring the same samples in a second acquisition round. The results of both measurement time points are compared with respect to identifications (IDs), data completeness, retention time and quantitative precision as well as inter-laboratory reproducibility. For instance, the number of identified protein groups was increased on average by 14% for CSF and 5% for plasma from first to second measurement round across participants resulting in a median overlap number of 579 protein groups for CSF and 246 IDs for plasma across setups. The individual performances of participating study centers and the inter-laboratory reproducibility were improved in the second measurement, emphasizing the effect and importance of expert-driven exchange of best practices. In total, the study not only clearly demonstrates the beneficial effect of sharing knowledge and expertise for direct practical improvements but also highlights tendencies such as common limits of detection or preferable preparation strategies.

Dataset Information

The eICU Collaborative Research Database, a freely available multi-center database for critical care research.

Publications

The eICU Collaborative Research Database, a freely available multi-center database for critical care research.

Similar Datasets

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