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Updated: Aug 16, 2025
Multimodality Diagnosis of Mesenteric Ischemia
Published on: July 21, 2023
Mortality in Patients Who Underwent Computed Tomography Angiography for a Suspected Acute Mesenteric Ischemia as a
Noy Nachmias-Peiser1, Shelly Soffer1, Nir Horesh2
1Department of Radiology, Sheba Medical Center, Tel Hashomer, Israel, Department of Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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.
Insights
Patients suspected of acute mesenteric ischemia (AMI) but with other diagnoses still face high mortality. Chronic kidney disease and elevated lactate dehydrogenase (LDH) increase risk, but machine learning models improve mortality prediction.
Area of Science:
- Medical imaging and diagnostics
- Prognostic modeling
- Gastrointestinal and hepatobiliary diseases
Background:
- Acute mesenteric ischemia (AMI) presents a diagnostic challenge, with many patients suspected of AMI ultimately receiving alternative diagnoses.
- These patients often have significant comorbidities, increasing their overall risk.
- Understanding outcomes in this specific patient group is crucial for risk stratification.
Purpose of the Study:
- To analyze the characteristics and outcomes of patients with suspected AMI but an alternative final diagnosis.
- To evaluate the efficacy of a machine learning algorithm in predicting mortality within this cohort.
Main Methods:
- Retrospective analysis of patient charts from January 2012 to December 2015 for individuals undergoing computed tomography angiography (CTA) for suspected AMI.
- Definition of the 'non-AMI' group as patients with negative CTA and a final diagnosis other than AMI.
- Evaluation of correlations between medical history, laboratory values, mortality rates, and a gradient boosting (XGBoost) model for mortality prediction.
Main Results:
- The non-AMI cohort included 325 patients, with gastrointestinal (33%) and biliary-pancreatic diseases (27%) being the most common alternative diagnoses.
- Overall mortality in this cohort was 24.6%.
- Chronic kidney disease (CKD) history (odds ratio 2.2) and elevated lactate dehydrogenase (LDH) (AUC 0.70) were significant predictors of mortality. The XGBoost model achieved an AUC of 0.82.
Conclusions:
- Patients with suspected AMI but an alternative diagnosis experience a substantial mortality rate (approximately 25%).
- Pre-existing CKD and elevated LDH levels are key indicators of increased mortality risk.
- Advanced machine learning algorithms, like gradient boosting, demonstrate superior performance in mortality prediction compared to single variables.
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