ArticleWorld journal of surgery2022
Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions.
Article in World journal of surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Use of artificial intelligence in healthcare in South Africa: A scoping review.Health SA = SA Gesondheid · 2025Review
- Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery.International journal of nephrology and renovascular disease · 2024Article
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Authors and funding
3 authors.
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Abstract
introductionPatients undergoing laparotomy for emergency general surgery (EGS) conditions, constitute a high-risk group with poor outcomes. These patients have a high prevalence of comorbidities. This study aims to identify patient factors, physiological and time-related factors, which place patients into a group at increased risk of mortality. METHODOLOGY: In a retrospective analysis of all patients undergoing an emergency laparotomy at Greys Hospital from December 2012 to 2018, we used decision tree discrimination to identify high-risk groups.
resultsOur cohort included 1461 patients undergoing a laparotomy for an EGS condition. The mortality rate was 12.4% (181). Nine hundred and ten patients (62.3%) had at least one known comorbidity on admission. There was a higher rate of comorbidities among those that died (154; 85.1%). Patient factors found to be associated with mortality were the age of 46 years or greater (p < 0.001), current tuberculosis (p < 0.001), hypertension (p = 0.014), at least one comorbidity (0.006), and malignancy (0.033). Significant physiological risk factors for mortality were base excess less than -6.8 mmol/L (p < 0.001), serum urea greater than 7.0 mmol/L (p < 0.001) and waiting time from admission to operation (p = 0.014). In patients with an enteric breach, those younger than 46 years and a Shock Index of more than 1.0 were high-risk. Patients without an enteric breach were high-risk if operative duration exceeded 90 min (p = 0.004) and serum urea exceeding 7 mmol/dl (p = 0.016).
conclusionIn EGS patients, patient factors as well as physiological factors place patients into a high-risk group. Identifying a high-risk group should prompt consideration for an adjusted approach that ameliorates outcomes.
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