Prediction of Critical Illness During Out-of-Hospital Emergency Care

Abstract

OSPITALS VARY WIDELY INquality of critical care. 1 Consequently, the outcomes of critically ill patients may be improved by concentrating care at more experienced centers. [1][2][3] 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. 4 In 2006, the Institute of Medicine called for a regionalized, coordinated system of emergency care for high-risk patients, 5 one in which patients in most need of highintensity acute care are distributed to centers with the greatest expertise in caring for the critically ill.Current out-of-hospital triage of noninjured, critically ill patients uses dispatch criteria, 6 subjective emergency medical services (EMS) assessments, 7,8 coordination by medical command officers, 9 and patient preference. 10 In specific conditions such as coronary artery disease and stroke, out-of-hospital care providers use objective tools to triage and risk-stratify prehospital patients for early treatment and choice of destination. [11][12][13] However, these subjective and disease-specific assessments alone may not be sufficient for triage in general populations at risk of critical ill-ness. 8,[14][15][16] Future development of regionalized systems of acute care will require objective, routinely measured predictors that are associated with important clinical end points in a heterogeneous population. An objective triage tool may also identify patients for early treatment by out-of-hospital care providers.We sought to develop a tool for prediction of critical illness during out-ofhospital care in noninjured, non-cardiac arrest patients. Using a population-based cohort of EMS records linked to hospital discharge data, we hypothesized that objective,out-of-hospitalfactorscoulddiscriminate between patients who were and For editorial comment see p 797.

Severe Sepsis In Prehospital Emergency Care: Analysis Of Incidence, Care, And Outcome

“…These adjustments account for potential variation over time in demographics among King County residents who access the emergency care and hospital system, hospital coding practices (30), and statistical noise due to differences in sample size ("reliability adjustment") (31). We considered that trends in case fatality rates may derive from changes in illness severity, and included both the Charlson Comorbidity Index and a pre-hospital clinical risk score for critical illness in our models for case fatality (21,32). The latter includes important confounders such as initial systolic blood pressure, heart rate, Glasgow Coma Scale score, pulse oximetry, and pre-hospital location (e.g., nursing home).…”

Comparing early and delayed repair of common bile duct injury to identify clinical drivers of outcome and morbidity

“…These scores were assessed using ROC with an area under the curve (AUC) of >0.70 considered to represent a feasible model. [17][18][19] Results During the study period, 81 patients were evaluated for CBDI. Of these, 20 patients were excluded from further analysis: 17 were treated definitively with ERCP and required no surgical biliary intervention.…”

Survival After Out-of-Hospital Cardiac Arrest in Relation to Age and Early Identification of Patients With Minimal Chance of Long-Term Survival

“…[1][2][3][4][5][6] Aging influences multiple physiological processes; with increasing age, there is an increasing susceptibility to contract diseases and critical illness, including conditions such as cardiac arrest. 1,[9][10][11] Accordingly, aging is associated with a concomitant increase in OHCA incidence and a low chance of survival. [1][2][3][4][5][6][10][11][12][13][14] To improve and focus future strategies for cardiac arrest management, it is important to know how changes in survival are reflected in different age groups and whether it is possible to identify patients with minimal chance of survival.…”