Developing an AI-predictive model for predicting the likelihood of post-operative infection in surgical patients
Authors: Hassan N , Slight R , Weiand D , Morgan G , Vellinga A , Mamdouh H , Gallier S , Sapey E , Slight S 
Published: November 2023

Introduction

Antimicrobial resistance was listed among the top ten global health issues by the World Health Organization in 2019.1 By 2050, it is projected to have caused 10 million deaths, surpassing cancer, diabetes, and road traffic accidents.2 The rise in resistant bacterial strains and reduced availability of novel antibiotics makes tackling antimicrobial resistance even more challenging. Preventing infections is the most effective approach, and Artificial Intelligence (AI) has the potential to help predict the likelihood of a patient experiencing a post-operative infection.3 This can help clinicians identify those who might be at higher risk and implement clinical interventions to potentially reduce this risk, during the pre-operative stage.

Aim

To develop and internally validate an AI-predictive model for predicting the likelihood of post-operative infection in surgical patients.

Methods

A secondary care dataset from the Pioneer Research Hub was used to develop the predictive model, which included a range of data such as demographic information, vital signs, microbiological culture results, lab investigations, comorbidities, surgical details, and infection diagnoses. Machine learning techniques and clinician input were used to select predictors, both modifiable (e.g. blood glucose) and non-modifiable (e.g. age). Recursive feature elimination with cross-validation was performed, and the dataset was split into training (80%) and internal validation (20%) sets. Missing data was managed through complete case analysis and multiple imputation. Various supervised machine learning classifier algorithms were trained, and an ensemble model was created. Ethical approval was obtained (20/EM/0183, IRAS 280077).

Results

Data on 2,716 patients who underwent elective abdominal surgery were included in the model training and validation. The infection incidence in this cohort was 38%. Out of 74 initial predictors, 19 were selected. These predictors included demographic data (gender), comorbidities (diabetes, COPD, cardiac conditions, obesity, anaemia), microbiology data (positive cultures within 90 days before surgery, multi-drug resistance), and laboratory investigations within 90 days before surgery: WBCs, lymphocytes, blood glucose, platelets, haematocrit, mean cell haemoglobin, haemoglobin, total bilirubin, aspartate aminotransferase, alanine transaminase, and C-reactive protein. The gradient boosting classifier performed the best among the supervised machine learning algorithms. The ensemble model showed high performance in training (sensitivity: 85.3%, specificity: 74.6%, AUROC: 88.6%) and internal validation (sensitivity: 96.9%, specificity: 74.1%, AUROC: 85.5%). The PROBAST checklist was used to address bias before predictor selection.

Discussion/Conclusion

The developed AI-predictive model has the potential to predict the likelihood of an elective abdominal surgery patient experiencing a post-operative infection. Identifying patients at high risk of post-surgical infections can guide clinical decision making and help implement interventions during the pre-operative stage, (i.e. as part of nutritional prehabilitation screening), to reduce infection risk. However, it is important to acknowledge the limitations of this model. One limitation is the presence of missing data in the training dataset, which may introduce biases and affect the accuracy of the model. Additionally, potential inaccuracies in data entry could impact the reliability of the model's predictions. Furthermore, it is crucial to consider any historical biases that might have influenced data collection, as these biases could have adverse effects on the developed model.

References

1. Antimicrobial resistance fact sheets. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance [Accessed on 20th May 2023]

2. Kumar M, Silori R, Mazumder P, Shrivastava V, Loge F, Barceló D, Mahlknecht J. Wars and Pandemics: AMR Accelerators of the 21st Century?. Environ. Sci. Technol. Lett. 2023 Mar 26;10(4):289-91.

3. Hassan N, Slight R, Weiand D, Vellinga A, Morgan G, Aboushareb F, Slight SP. Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. IJMI. 2021 Apr 10:104457.