Abstract
Background We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission. Methods For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program. Results The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score. Conclusion A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described.
Highlights
Acute Medicine involves the immediate and early specialist management of adult patients who require urgent care for medical conditions [1]
In this work we present a nomogram for predicting 30-day in-hospital mortality for all medical admission patients admitted via the emergency department of our centre
The nomogram is based on data that would be immediately available at the time of emergency medical admission with this model and compared with a full model which includes all predictors available retrospectively
Summary
Acute Medicine involves the immediate and early specialist management of adult patients who require urgent care for medical conditions [1]. Mortality risk predictions, incorporating the contribution of physiological or biochemical parameter combinations, may be calculated using a simple graphic calculation tool called a nomogram [12]. Such tools have been used to predict in-hospital mortality from acute poisoning in adults in the emergency department [13], for risk stratification in Acute Coronary syndrome [14, 15] and risk evaluation for Intensive Care patients [12]. We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission. A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described
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