Critical care units are home to some of the most sophisticated patient technology within hospitals. The result- ing data have the potential to improve our understanding of disease and to improve clinical care. Critically ill patients are an ideal population for clinical database investigations because the value of many treatments and interventions they receive remains largely unproven, and high-quality studies supporting or discouraging spe- ci?c practices are relatively sparse [7]. Standardized guidelines are dependent on an evidence base that is surprisingly weak considering the amount of data generated in the ICU [15]. The MIT Laboratory for Computational Physiology (LCP) developed and maintains the publicly available Medical Information Mart for Intensive Care (MIMIC), containing highly detailed data associated with 53,423 distinct adult ICU admissions at the Beth Israel Deaconess Medical Center in Boston [27]. MIMIC is now a widely used resource worldwide and is used for clinical research studies, exploratory and validation analyses performed by pharmaceutical and medical technology companies, as well as for university, conference and online courses, tutorials and workshops. LCP recently released the open eICU Collaborative Research Database [30] in collaboration with Philips Healthcare, comprising de-identi?ed health data associated with over 200,000 critical care admissions to three hundred hospitals throughout the United States. We now intend to expand the success of our open-access, open-source approach to critical care research by releasing large new intra-operative, emergency department and imaging datasets. Importantly, we have also made exciting progress toward realizing a federated multi- center international critical care data archive in collaborations with Oxford, London, Paris, and Sao Paulo. Multi-center research is challenging, because different institutions collect and store data in (sometimes dras- tically) different formats. The adoption and harmonization of data standards is a critical requirement in order for the data to be properly archived, integrated across institutions, and shared for reuse. This proposal seeks funding to: (a) support and expand our publicly available critical care data resources into new domains including imaging; b) develop the technical infrastructure needed to integrate data from inter- national critical care centers; and c) reinforce our multidisciplinary research environment to develop algorithms, collaborations and knowledge at the interface of medicine and informatics and d) to pilot high-value decision support algorithms in the clinic.
Enormous quantities of data are collected from patients in hospital intensive care units, operating rooms and emergency departments, but most of these data are not analyzed to discover new knowledge. This research project will collect, organize and publicly distribute detailed de-identi?ed clinical and physiological data to the worldwide research community and will transform that data into new knowledge to improve patient care. To broaden and enrich the research resource, tools will be developed and processes will be established to facilitate sharing of health data across institutional and national boundaries.
| Deliberato, Rodrigo Octavio; Serpa Neto, Ary; Komorowski, Matthieu et al. (2018) An Evaluation of the Influence of Body Mass Index on Severity Scoring. Crit Care Med : |
