Medical Admission Prediction Score (MAPS); a simple tool to predict medical admissions in the emergency department

INTRODUCTION: Overcrowding in the emergency departments (ED) is linked to adverse clinical outcomes, a negative impact on patient safety, patient satisfaction, and physician efficiency. We aimed to design a medical admission prediction scoring system based on readily available clinical data during ED presentation. METHODS: In this retrospective cross-sectional study, data on ED presentations and medical admissions were extracted from the Emergency and Internal Medicine departments of a tertiary care facility in Qatar. Primary outcome was medical admission. RESULTS: Of 320299 ED presentations, 218772 were males (68.3%). A total of 11847 (3.7%) medical admissions occurred. Most patients were Asians (53.7%), followed by Arabs (38.7%). Patients who got admitted were older than those who did not (p <0.001). Admitted patients were predominantly males (56.8%), had a higher number of comorbid conditions and a higher frequency of recent discharge (within the last 30 days) (p <0.001). Age > 60 years, female gender, discharge within the last 30 days, and worse vital signs at presentations were independently associated with higher odds of admission (p<0.001). These factors generated the scoring system with a cut-off of >17, area under the curve (AUC) 0.831 (95% CI 0.827–0.836), and a predictive accuracy of 83.3% (95% CI 83.2–83.4). The model had a sensitivity of 69.1% (95% CI 68.2–69.9), specificity was 83.9% (95% CI 83.7–84.0), positive predictive value (PPV) 14.2% (95% CI 13.8–14.4), negative predictive value (NPV) 98.6% (95% CI 98.5–98.7) and positive likelihood ratio (LR(+)) 4.28% (95% CI 4.27–4.28). CONCLUSION: Medical admission prediction scoring system can be reliably applied to the regional population to predict medical admissions and may have better generalizability to other parts of the world owing to the diverse patient population in Qatar.