Prediction Model Helps Determine Risks

Wed, 08/18/2010 - 6:21am

A prediction score that includes factors such as age, blood pressure, and heart and respiratory rate has been developed for patients who received out-of-hospital emergency care associated with the development of a critical illness during their hospitalization. This could include complications such as severe sepsis, the need for mechanical ventilation or death. The study has been published in an upcoming issue of JAMA.

“Hospitals vary widely in quality of critical care. Consequently, the outcomes of critically ill patients may be improved by concentrating care at more experienced centers. By centralizing patients who are at greater risk of mortality in referral hospitals, regionalized care in critical illness may achieve improvements in outcome similar to trauma networks,” the authors write. “Early identification of non-trauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care.”

Christopher W. Seymour, M.D., M.Sc., of Harborview Medical Center, University of Washington, and colleagues conducted a study to develop a tool for prediction of critical illness during out-of-hospital care in non-injured, non-cardiac arrest patients. They hypothesized that objective, out-of-hospital factors could discriminate between patients who were and were not likely to develop critical illness during hospitalization.

Critical illness occurred during hospitalization in five percent of the development and validation cohorts. Multi-variable predictors of critical illness (which was defined as severe sepsis, delivery of mechanical ventilation or death during hospitalization) included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry (measurement of oxygenation of hemoglobin), and nursing home residence during out-of-hospital care. Using a score threshold of four or higher, sensitivity was 0.22 and specificity was 0.98.

“We demonstrate the role that simple physiologic assessment can play in risk stratification in the pre-hospital period among non-injured patients. The model provides an important foundation for future efforts to identify patients at greatest risk of critical illness using information from the out-of-hospital phase of emergency care,” the authors write. “Although improved accuracy and external validation are required, this model provides a foundation for future efforts to identify non-injured patients who may benefit from coordinated systems that regionalize emergency care.”


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