This study seeks to utilise retrospective patient data to train machine learning algorithms to predict the short term mortality and morbidity after an emergency laparotomy.

Data will be collected via the Electronic Health records system at the Queen Mary Hospital Hong Kong. Machine learning models will be compared and the best-performing one will be explored for further optimization and deployment. Upon completion, we hope that this platform will aid clinicians to identify high risk patients and aid clinical decisions and peri-operative planning, with the aim to reduce mortality and morbidity in this high risk procedure.

Emergency laparotomy (EL) is a commonly performed procedure and high risk surgery that is known to have a high mortality and morbidity rate. Despite various audits and studies to identify the risk factors and introduce protocols aimed at improving surgical outcomes, the short term mortality after EL remains high. Worldwide data demonstrates that short term (30-day) mortality ranges between 5.3-21.8%, and long term (1-year) mortality rates ranges between 15-47% (Ref 1). Older patients have been identified as the subgroup suffering from highest mortality rates, and efforts implemented in older patients undergoing EL including: the use of risk calculators for mortality prediction, increased peri-operative input from geriatrician and critical care, higher consultant surgeon and anesthetist presence in the operating theatre, and introduction of enhanced care pathways. Apart from age and specialist input, other risk factors for mortality after EL include: frailty, surgical duration, cancer-related surgery, stoma care, patient selection, pre-operative sepsis and physiological parameters, pre-existing comorbidities, ASA status (Ref 2).

Mortality prediction models currently in clinical use for EL include the Portsmouth-Physiologic and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM), Acute Physiology and Chronic Health Evaluation II (APACHE-II), American College of Surgeons National Surgical Quality Improvement (ACS-NSQIP), and the most recent addition of the (NELA) risk calculator. The National Emergency Laparotomy Audit (NELA) performed in the UK since 2012 has been a paradigm shift in evidence-based improvement for patients undergoing EL, demonstrating a reduction in national 30-day mortality rate (11.8% vs.8.7% in 2012 vs. 2012) after identification and implementation of specific recommendations (Ref 3).

Using the data from the large NELA UK cohort between 2014-2016, the NELA risk calculation tool was developed to estimate 30-day mortality, and takes into account patient demographics, ASA status, physiological parameters, vital signs, and details regarding severity and nature of surgical intervention. Multiple studies in the UK, Australia, Singapore have shown the NELA risk calculator is comparable, if not superior, to P-POSSUM for mortality prediction and risk stratification to differentiate between lowand high-risk patients undergoing EL (Ref 5, 6, 7). However, no risk scoring is perfect. The NELA risk model was shown to underpredict, and P-POSSUM to over-predict observed mortality (Ref 8). Since its introduction, NELA has been a pioneer in developing evidence-based interventions and guiding directions for future research in patients undergoing EL, but its implementation in Hong Kong has been limited by lack of validation of accuracy in our patient population.

Frailty is defined as: an objective measure of increased vulnerability and decreased physiological reserve, resulting in accumulation of physiological deficits in multiple systems, and can occur in patients of all ages, but occurs most commonly in older patients. Frailty is a well known risk factor for poor surgical outcomes in EL (Ref 9, 10), but has yet to be incorporated into commonly used risk calculators. There are many risk scoring and surrogate indices for frailty, sarcopenia and osteopenia. Clinical frailty score (CFS) is the most commonly used index for frailty, and CFS alone has been shown to provide prognostic information for patients undergoing EL, but still underperforming compared to NELA. Interestingly, addition of CFS to NELA did not increase the accuracy of the risk model prediction (Ref 11).

The application of deep learning and machine learning is gaining traction, and has been used to develop various risk prediction models and future event prediction (Ref 4). Accumulation of vast datasets from anesthetic records can prove to be a treasure trove for data scientists to uncover new trends and predictions which would previously be overlooked. Risk calculators are helpful tools for clinicians to aid in clinical decision making, but the accuracy and validation of these risk calculators have not been done in this vicinity. Using machine learning algorithms and incorporation of frailty into risk calculators, we hope to develop a novel algorithm with high accuracy and generalizability, to be introduced into clinical use.

References:

1. (Ref 1: Ng et. al, One year outcomes following emergency laparotomy: A systematic review, World J surg, 2022, https://pubmed.ncbi.nlm.nih.gov/34837122/)
2. (Ref 2: (Ref: Boyd-Carson et al, A review of surgical and perii-operative factors to consider in emergency laparotomy care, 2020, Anesthesia, https://pubmed.ncbi.nlm.nih.gov/31903572/)
3. (Ref 3: NELA Project Team. Seventh Patient Report of the National Emergency Laparotomy Audit RCoA London 2021)
4. (Ref 4: Kwon et al, Machine learning: a new opportunity for risk prediction, Korean Circ J. 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923232/)
5. (Ref 5: Lai et al, A comparison of the P-POSUSM and NELA risk score for patients undergoing emergency laparotomy in Singapore, World J Surg, 2021, https://pubmed.ncbi.nlm.nih.gov/33903953/)
6. (Ref 6: Eliezer et al, High risk emergency laparotomy in Australia: comparing NELA, P-POSSUM and ACS-NSQIP calculators, J of surg research, 2020, https://www.sciencedirect.com/science/article/abs/pii/S0022480419306584 )
7. (Ref 7: Eugene et al, Development and internal validation of a novel risk adjustment model for adult patients undergoing emergency laparotomy surgery: the national emergency laparotomy audit risk model, BJA, 2018, https://www.sciencedirect.com/science/article/pii/S0007091218305786 )
8. (Ref 8: Thahir A, Pinto-Lopes R, Madenlidou S, Daby L, Halahakoon C. Mortality risk scoring in emergency general surgery: Are we using the best tool? Journal of Perioperative Practice. 2021;31(4):153-158. doi:10.1177/1750458920920133, https://journals.sagepub.com/doi/abs/10.1177/1750458920920133)
9. (Ref 9: Fehlmann et al, Association between mortality and frailty in emergency general surgery: a systematic review and meta-analysis, Euro J Trauma Emerg Surg, 2022, https://link.springer.com/article/10.1007/s00068-020-01578-9)
10. (Ref 10: Lee et.al, 2020, https://doi.org/10.1111/jgs.16334 (https://agsjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/jgs.16334))
11. (Ref 11: Palaniappan - Comparison of the clinical frailty score CFS to the National Emergency.Palaniappan et al, Comparison of CFS to the NELA risk calculator in all patients undergoing emergency laparotomy, Colorectal disease, 2022, https://onlinelibrary.wiley.com/doi/full/10.1111/codi.16089 )

Inclusion Criteria:

• Adult patients (> 18 years old) undergoing EL in Queen Mary Hospital between January 2017 and April 2021 will be included in this study

Exclusion Criteria:

• Patients undergoing other emergency general surgical procedures (eg. laparoscopies, cholecystectomy, appendicectomy) will be excluded from this study.

All patients that meet the eligibility criteria within the timeframe of observation will be included in this study for machine learning modeling. Hence, no sample size calculation is required for this study.