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:Background: Acute respiratory distress syndrome (ARDS) is a common cause of respiratory failure in patients in intensive care unit (ICU). The therapeutic value of glucocorticoids (GCs) in the prognosis of ARDS remains controversial. The aim of this research is studying the impacts of GCs treatment on ARDS patients in ICU. Methods: We retrospectively studied 2,167 ARDS patients whose data were collected from the public eICU Collaborative Research Database, among which 254 patients who received glucocorticoid (GCs) treatment were 1:1 matched by propensity matching analysis (PSM). The primary outcome was ICU mortality. Every oxygenation index (PaO2/FiO2) measurement before death or ICU discharge was recorded. A joint model (JM) which combined longitudinal sub-model (mixed-effect model) and time-to-event sub-model (Cox regression model) by trajectory functions of PaO2/FiO2 was conducted to determine the effects of GCs treatment on both ICU mortality and PaO2/FiO2 level and further PaO2/FiO2's effect on event status. The marginal structural cox model (MSCM) adjusted the overall PaO2/FiO2 of patients to further validate the results. Results: The result of the survival sub-model showed that GCs treatment was significantly associated with reduced ICU mortality in ARDS patients [HR (95% CI) = 0.642 (0.453, 0.912)], demonstrating that GCs treatment was a protective factor of ICU mortality. In the longitudinal sub-model, GCs treatment was not correlated to the PaO2/FiO2. After adjusted by the JM, the HR of GCs treatment was 0.602 while GCs was still not significantly related to PaO2/FiO2 level. The JM-induced association showed that higher PaO2/FiO2 was a significant protective factor of mortality in ARDS patients and the HR was 0.991 which demonstrated that one level increase of PaO2/FiO2 level decreased 0.9% risk of ICU mortality. MSCM results also show that GCs can improve the prognosis of patients. Conclusion: Rational use of GCs can reduce the ICU mortality of ARDS patients in ICU. In addition to the use of GCs treatment, clinicians should also focus on the shifting trend of PaO2/FiO2 level to provide better conditions for patients' survival.

Project description:BackgroundFew interventions are known to reduce the incidence of respiratory failure that occurs following elective surgery (postoperative respiratory failure; PRF). We previously reported risk factors associated with PRF that occurs within the first 5 days after elective surgery (early PRF; E-PRF); however, PRF that occurs six or more days after elective surgery (late PRF; L-PRF) likely represents a different entity. We hypothesized that L-PRF would be associated with worse outcomes and different risk factors than E-PRF.MethodsThis was a retrospective matched case-control study of 59,073 consecutive adult patients admitted for elective non-cardiac and non-pulmonary surgical procedures at one of five University of California academic medical centers between October 2012 and September 2015. We identified patients with L-PRF, confirmed by surgeon and intensivist subject matter expert review, and matched them 1:1 to patients who did not develop PRF (No-PRF) based on hospital, age, and surgical procedure. We then analyzed risk factors and outcomes associated with L-PRF compared to E-PRF and No-PRF.ResultsAmong 95 patients with L-PRF, 50.5% were female, 71.6% white, 27.4% Hispanic, and 53.7% Medicare recipients; the median age was 63 years (IQR 56, 70). Compared to 95 matched patients with No-PRF and 319 patients who developed E-PRF, L-PRF was associated with higher morbidity and mortality, longer hospital and intensive care unit length of stay, and increased costs. Compared to No-PRF, factors associated with L-PRF included: preexisiting neurologic disease (OR 4.36, 95% CI 1.81-10.46), anesthesia duration per hour (OR 1.22, 95% CI 1.04-1.44), and maximum intraoperative peak inspiratory pressure per cm H20 (OR 1.14, 95% CI 1.06-1.22).ConclusionsWe identified that pre-existing neurologic disease, longer duration of anesthesia, and greater maximum intraoperative peak inspiratory pressures were associated with respiratory failure that developed six or more days after elective surgery in adult patients (L-PRF). Interventions targeting these factors may be worthy of future evaluation.

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.

Project description:BackgroundAnalysis of ciliary function for assessment of patients suspected of primary ciliary dyskinesia (PCD) and for research studies of respiratory and ependymal cilia requires assessment of both ciliary beat pattern and beat frequency. While direct measurement of beat frequency from high-speed video recordings is the most accurate and reproducible technique it is extremely time consuming. The aim of this study was to develop a freely available automated method of ciliary beat frequency analysis from digital video (AVI) files that runs on open-source software (ImageJ) coupled to Microsoft Excel, and to validate this by comparison to the direct measuring high-speed video recordings of respiratory and ependymal cilia. These models allowed comparison to cilia beating between 3 and 52 Hz.MethodsDigital video files of motile ciliated ependymal (frequency range 34 to 52 Hz) and respiratory epithelial cells (frequency 3 to 18 Hz) were captured using a high-speed digital video recorder. To cover the range above between 18 and 37 Hz the frequency of ependymal cilia were slowed by the addition of the pneumococcal toxin pneumolysin. Measurements made directly by timing a given number of individual ciliary beat cycles were compared with those obtained using the automated ciliaFA system.ResultsThe overall mean difference (± SD) between the ciliaFA and direct measurement high-speed digital imaging methods was -0.05 ± 1.25 Hz, the correlation coefficient was shown to be 0.991 and the Bland-Altman limits of agreement were from -1.99 to 1.49 Hz for respiratory and from -2.55 to 3.25 Hz for ependymal cilia.ConclusionsA plugin for ImageJ was developed that extracts pixel intensities and performs fast Fourier transformation (FFT) using Microsoft Excel. The ciliaFA software allowed automated, high throughput measurement of respiratory and ependymal ciliary beat frequency (range 3 to 52 Hz) and avoids operator error due to selection bias. We have included free access to the ciliaFA plugin and installation instructions in Additional file 1 accompanying this manuscript that other researchers may use.

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.

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