Machine learning models for prediction of postoperative ileus in patients underwent laparoscopic colorectal surgery

Li, Xueyan; Ma, Genshan; Qian, Xiaobo; Wu, Yamou; Huang, Xiaochen; Gu, Chengyong

doi:10.21203/rs.2.18587/v2

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Efficiently identifying the risk of POI to enable early intervention stands as a crucial factor in enhancing surgical outcomes. While various studies have discussed the prediction of POI 5 6 7 8 9 10 , predominantly utilizing multivariate logistic regression techniques, there exists a notable gap in research concerning deep learning-based approaches in POI prediction. This is particularly intriguing given the widespread application of deep learning in tackling other postoperative complications 11 12 13 .…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D’s Real-Time Capabilities and Cross-Hospital Transferability

Ruan,

Fu,

Jia

et al. 2024

Preprint

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Background: Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods: We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results: GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion: GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.

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Section: Introductionmentioning

confidence: 99%

Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D’s Real-Time Capabilities and Cross-Hospital Transferability

Ruan,

Fu,

Jia

et al. 2024

Preprint

View full text Add to dashboard Cite

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Machine learning prediction model for postoperative ileus following colorectal surgery

Traeger,

Bedrikovetski,

Hanna

et al. 2024

ANZ Journal of Surgery

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BackgroundPostoperative ileus (POI) continues to be a major cause of morbidity following colorectal surgery. Despite best efforts, the incidence of POI in colorectal surgery remains high (~30%). This study aimed to investigate machine learning techniques to identify risk factors for POI in colorectal surgery patients, to help guide further preventative strategies.MethodsA TRIPOD‐guideline‐compliant retrospective study was conducted for major colorectal surgery patients at a single tertial care centre (2018–2022). The primary outcome was the occurrence of POI, defined as not achieving GI‐2 (outcome measure of time to first stool and tolerance of oral diet) by day four. Multivariate logistic regression, decision trees, radial basis function and multilayer perceptron (MLP) models were trained using a random allocation of patients to training/testing data sets (80/20). The area under the receiver operating characteristic (AUROC) curves were used to evaluate model performance.ResultsOf 504 colorectal surgery patients, 183 (36%) experienced POI. Multivariate logistic regression, decision trees, radial basis function and MLP models returned an AUROC of 0.722, 0.706, 0.712 and 0.800, respectively. The MLP model had the highest sensitivity and specificity values. In addition to well‐known risk factors for POI, such as postoperative hypokalaemia, surgical approach, and opioid use, the MLP model identified sarcopenia (ranked 4/30) as a potentially modifiable risk factor for POI.ConclusionMLP outperformed other models in predicting POI. Machine learning can provide valuable insights into the importance and ranking of specific predictive variables for POI. Further research into the predictive value of preoperative sarcopenia for POI is required.

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Machine learning models for prediction of postoperative ileus in patients underwent laparoscopic colorectal surgery

Cited by 2 publications

References 19 publications

Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D’s Real-Time Capabilities and Cross-Hospital Transferability

Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D’s Real-Time Capabilities and Cross-Hospital Transferability

Machine learning prediction model for postoperative ileus following colorectal surgery

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