Development of a national core dataset for the Iranian ICU patients outcome prediction: a comprehensive approach

OBJECTIVE: To define a core dataset for ICU Patients Outcome Prediction in Iran. This core data set will lead us to design ICU outcome prediction models with the most effective parameters. METHODS: A combination of literature review, national survey and expert consensus meetings were used. First, a literature review was performed by a general search in PubMed to find the most appropriate models for intensive care mortality prediction and their parameters. Secondly, in a national survey, experts from a couple of medical centers in all parts of Iran were asked to comment on a list of items retrieved from the earlier literature review study. In the next step, a multi-disciplinary committee of experts was installed. In 4 meetings each data item was examined separately and included/excluded by committee consensus. RESULTS: The combination of the literature review findings and experts' consensus resulted in a draft dataset including 26 data items. 92% percent of data items in the draft dataset were retrieved from the literature study and the others were suggested by the experts. The final dataset of 24 data items covers patient history and physical examination, chemistry, vital signs, oxygenations and some more specific parameters. Conclusions: This dataset was designed to develop a nationwide prognostic model for predicting ICU mortality and length of stay. This dataset opens the door for creating standardized approaches in data collection in the Iranian intensive care unit estimation of resource utility.