Hospital-acquired acute kidney injury (H-AKI) is a common cause of avoidable morbidity and mortality. Therefore, in the current study, we investigated whether vital signs data from patients, as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital. We analysed all emergency admissions (n=33,608) to York Hospital with NEWS data over a 24-month period. Here, we report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3), and similarly for maximum NEWS (models B0,B1,B2,B3). Of the total emergency admissions, 4.05% (1,361/33,608) had H-AKI. Models using the index NEWS had lower AUCs (0.59–0.68) than models using the maximum NEWS AUCs (0.75–0.77). The maximum NEWS model (B3) was more sensitive than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5. Based on these results, we suggest that the index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but appears to be unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload, albeit with improved sensitivity.