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Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions

  • Surgery in Low and Middle Income Countries
  • Published:
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Abstract

Introduction

Patients 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.

Results

Our 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).

Conclusion

In 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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Funding

No external or internal funding was utilized to generate this work.

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Authors and Affiliations

  1. Department of Surgery, University of KwaZulu-Natal, Durban, South Africa

    John L. Bruce & Damian L. Clarke

  2. Department of Anesthetics and Critical Care, Greys Hospital, University of KwaZulu-Natal, 201 Townbush road, Pietermaritzburg, 3200, South Africa

    Michelle T. D. Smith

Authors
  1. Michelle T. D. Smith
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  2. John L. Bruce
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  3. Damian L. Clarke
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Contributions

MTD Smith: primary investigator and author, data analysis and table preparation. DL Clarke assisted with the conceptualization and planning of the study and assisted with the writing of the manuscript. Dr. JL Bruce: Conceptualization of manuscript.

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Correspondence to Michelle T. D. Smith.

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Smith, M.T.D., Bruce, J.L. & Clarke, D.L. Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions. World J Surg 46, 339–346 (2022). https://doi.org/10.1007/s00268-021-06360-5

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  • DOI: https://doi.org/10.1007/s00268-021-06360-5

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