Introduction: Emergency Medical Services (EMS) are the first healthcare contact for the majority of severely ill patients. Physiologic measures collected by EMS, when incorporated into a prognostic score, may provide important information on patient illness severity. This study compares the predictive ability of 3 common prognostic scores for predicting clinical outcomes in EMS patients. Methods: Discrimination and calibration for predicting the primary outcome of hospital mortality, and secondary outcomes of 2-day mortality and ED disposition, were assessed for each of the scores using a one-year cohort of patients transported to hospital by EMS in Alberta, Canada. For each score, binary logistic regression was used to predict hospital mortality and 2-day mortality and ordinal logistic regression was used to predict ED disposition. Discrimination for each outcome was assessed using C-statistics, and calibration was assessed using calibration curves comparing predicted versus observed outcomes. Results: The Critical Illness Prediction [CIP], Modified Early Warning Score [MEWS], and National Early Warning Score [NEWS] were compared using 121,837 adult patients who were transported by paramedics. All scores had good discrimination for hospital mortality (C-statistic CIP: 0.79, MEWS: 0.71, NEWS: 0.78) and 2-day mortality (CIP:0.85, MEWS: 0.80, NEWS:0.85) but only moderate discrimination for ED disposition (CIP: 0.68, MEWS: 0.61, NEWS: 0.66). Calibration was reliable for hospital mortality in all scores but over-predicted risk for 2-day mortality at higher scores. Overall, the CIP score had the best discrimination, good calibration, and the greatest range of predicted probabilities (0.01 at a CIP score of 0 to 0.92 at a CIP score of 8) for hospital mortality. Conclusions: Prognostic scores using physiologic measures assessed by paramedics have good predictive ability for hospital mortality. These scores, particularly the CIP score, may be considered as a tool for mortality risk stratification or as a general measure of illness severity for patients included in EMS studies.
Introduction: Emergency Medical Services (EMS) are the first healthcare contact for the majority of severely ill patients. Physiologic measures collected by EMS, when incorporated into a prognostic score, may provide important information on patient illness severity. This study compares the predictive ability of 3 common prognostic scores for predicting clinical outcomes in EMS patients. Methods: Discrimination and calibration for predicting the primary outcome of hospital mortality, and secondary outcomes of 2-day mortality and ED disposition, were assessed for each of the scores using a one-year cohort of patients transported to hospital by EMS in Alberta, Canada. For each score, binary logistic regression was used to predict hospital mortality and 2-day mortality and ordinal logistic regression was used to predict ED disposition. Discrimination for each outcome was assessed using C-statistics, and calibration was assessed using calibration curves comparing predicted versus observed outcomes. Results: The Critical Illness Prediction [CIP], Modified Early Warning Score [MEWS], and National Early Warning Score [NEWS] were compared using 121,837 adult patients who were transported by paramedics. All scores had good discrimination for hospital mortality (C-statistic CIP: 0.79, MEWS: 0.71, NEWS: 0.78) and 2-day mortality (CIP:0.85, MEWS: 0.80, NEWS:0.85) but only moderate discrimination for ED disposition (CIP: 0.68, MEWS: 0.61, NEWS: 0.66). Calibration was reliable for hospital mortality in all scores but over-predicted risk for 2-day mortality at higher scores. Overall, the CIP score had the best discrimination, good calibration, and the greatest range of predicted probabilities (0.01 at a CIP score of 0 to 0.92 at a CIP score of 8) for hospital mortality. Conclusions: Prognostic scores using physiologic measures assessed by paramedics have good predictive ability for hospital mortality. These scores, particularly the CIP score, may be considered as a tool for mortality risk stratification or as a general measure of illness severity for patients included in EMS studies.
Authors: Francisco Martín-Rodríguez; Raúl López-Izquierdo; Juan F Delgado Benito; Ancor Sanz-García; Carlos Del Pozo Vegas; Miguel Ángel Castro Villamor; José Luis Martín-Conty; Guillermo J Ortega Journal: J Clin Med Date: 2020-04-18 Impact factor: 4.241
Authors: Anne Pullyblank; Alison Tavaré; Hannah Little; Emma Redfern; Hein le Roux; Matthew Inada-Kim; Kate Cheema; Adam Cook Journal: Br J Gen Pract Date: 2020-05-28 Impact factor: 5.386