Key result
A clustering approach combining SWIFT variables with lactic acid and leucocyte values was proposed to identify intensive care unit patients at high risk for readmission at the time of discharge.
Applying clustering techniques to clinical and laboratory data at ICU discharge may help identify patients at high risk for readmission.
May flag readmission risk at ICU discharge; hypothesis-generating and requires prospective validation before adoption.
Decision making assumes a critical role in the Intensive Medicine. Data Mining is emerging in the clinical area to provide processes and technologies for transforming data into useful knowledge to support clinical decision makers. Appling clustering techniques to the data available on the patients admitted into Intensive Care Units and knowing which ones correspond to readmissions, it is possible to create meaningful clusters that will represent the base characteristics of readmitted patients. Thus, exploring common characteristics it is possible to prevent discharges that will result into readmissions and then improve the patient outcome and reduce costs. Moreover, readmitted patients present greater difficulty to be recovered. In this work it was followed the Stability and Workload Index for Transfer (SWIFT). A subset of variables from SWIFT was combined with the results from laboratory exams, namely the Lactic Acid and the Leucocytes values, in order to create clusters to identify, in the moment of discharge, patients that probably will be readmitted.
No takes yet. Share an insight, caveat, or question.
Veloso et al. (2014) studied Intensive Care Unit readmission. Clustering approach using SWIFT variables, Lactic Acid, and Leucocytes was evaluated on ICU readmission. A clustering approach combining SWIFT variables with lactic acid and leucocyte values was proposed to identify intensive care unit patients at high risk for readmission at the time of discharge.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: