Objectives To derive and validate a risk adjustment model for predicting seven day mortality in emergency medical admissions, to test the value of including physiology and blood parameters, and to explore the constancy of the risk associated with each model variable across a range of settings.
Design Mixed prospective and retrospective cohort study.
Setting Nine acute hospitals (n=3 derivation, n=9 validation) and associated ambulance services in England, Australia, and Hong Kong.
Participants Adults with medical emergencies (n=5644 derivation, n=13 762 validation) who were alive and not in cardiac arrest when attended by an ambulance and either were admitted to hospital or died in the ambulance or emergency department.
Interventions Data were either collected prospectively or retrospectively from routine sources and extraction from ambulance and emergency department records.
Main outcome measure Mortality up to seven days after hospital admission.
Results In the derivation phase, age, ICD-10 code, active malignancy, Glasgow coma score, respiratory rate, peripheral oxygen saturation, temperature, white cell count, and potassium and urea concentrations were independent predictors of seven day mortality. A model based on age and ICD-10 code alone had a C statistic of 0.80 (95% confidence interval 0.78 to 0.83), which increased to 0.81 (0.79 to 0.84) with the addition of active malignancy. This was markedly improved only when physiological variables (C statistic 0.87, 0.85 to 0.89), blood variables (0.87, 0.84 to 0.89), or both (0.90, 0.88 to 0.92) were added. In the validation phase, the models with physiology variables (physiology model) and all variables (full model) were tested in nine hospitals. Overall, the C statistics ranged across centres from 0.80 to 0.91 for the physiology model and from 0.83 to 0.93 for the full model. The rank order of hospitals based on adjusted mortality differed markedly from the rank order based on crude mortality. ICD-10 code, Glasgow coma score, respiratory rate, systolic blood pressure, oxygen saturation, haemoglobin concentration, white cell count, and potassium, urea, creatinine, and glucose concentrations all had statistically significant interactions with hospital.
Conclusion A risk adjustment model for emergency medical admissions based on age, ICD-10 code, active malignancy, and routinely recorded physiological and blood variables can provide excellent discriminant value for seven day mortality across a range of settings. Using risk adjustment markedly changed hospitals’ rankings. However, evidence was found that the association between key model variables and mortality were not constant.
Supplementary data appendix