Background: Acute mesenteric ischemia (AMI) is a medical condition with high levels of morbidity and mortality. However, most patients suspected of AMI will eventually have a different diagnosis. Nevertheless, these patients have a high risk for co-morbidities.

Objectives: To analyze patients with suspected AMI with an alternative final diagnosis, and to evaluate a machine learning algorithm for prognosis prediction in this population.

Methods: In a retrospective search, we retrieved patient charts of those who underwent computed tomography angiography (CTA) for suspected AMI between January 2012 and December 2015. Non-AMI patients were defined as patients with negative CTA and a final clinical diagnosis other than AMI. Correlation of past medical history, laboratory values, and mortality rates were evaluated. We evaluated gradient boosting (XGBoost) model for mortality prediction.

Results: The non-AMI group comprised 325 patients. The two most common groups of diseases included gastrointestinal (33%) and biliary-pancreatic diseases (27%). Mortality rate was 24.6% for the entire cohort. Medical history of chronic kidney disease (CKD) had higher risk for mortality (odds ratio 2.2). Laboratory studies revealed that lactate dehydrogenase (LDH) had the highest diagnostic ability for predicting mortality in the entire cohort (AUC 0.70). The gradient boosting model showed an area under the curve of 0.82 for predicting mortality.

Conclusions: Patients with suspected AMI with an alternative final diagnosis showed a 25% mortality rate. A past medical history of CKD and elevated LDH were associated with increased mortality. Non-linear machine learning algorithms can augment single variable inputs for predicting mortality.