Purpose Emergency Laparotomy (EL) carries high mortality, necessitating accurate and timely risk prediction for patient care and quality improvement. Current risk prediction tools are developed based on variables collected for audit encompassing physiological and comorbidity markers. The Risk Estimation for Acute Laparotomy (REAL) study aims to develop a preoperative prediction model based on the current understanding of EL risk factors. Method The REAL study is a multicenter prospective cohort study on 5 major NZ hospitals. 234 variables were prospectively collected for each patient in the following categories: acute physiology, comorbidities, frailty, disability, quality of life, nutrition, and socioeconomic factors. The model was developed by sequential logistic regression. The area under the receiver operator characteristic curve (AUROC) was used to assess discrimination, and McFadden's R-square (MFR) was used for calibration. Results 1167 patients were recruited, with an inpatient mortality of 6.6%. The selected variables were the clinical frailty scale, resuscitation status, creatinine, heart failure, obstructive pulmonary disease, liver failure, confusion, diastolic bp, pulse rate and age. For simplicity, only 2 variables were continuous, and the remainder were categorical. The model achieved an AUROC of 0.90, excellent discrimination, and an MFR of 0.32, excellent calibration. Conclusion The REAL study presents a novel risk prediction tool with excellent discrimination and calibration. This model is unique as it is not constrained by variables collected for other purposes. Focusing on a parsimonious selection of variables offers a practical, easily applicable tool for preoperative risk assessment in EL patients.