Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery

Chen, Kevin A; Joisa, Chinmaya U; Stitzenberg, Karyn B.; Stem, Jonathan; Guillem, José G.; Gomez, Shawn M.; Kapadia, Muneera R.

doi:10.1007/s11605-022-05443-5

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…10 Further research is needed to better clarify the relationship between robotic surgery and pain. Lower postoperative opioid requirements may also translate into improved surgical outcomes, including an earlier return of bowel function, 16 which in fact may be a significant factor enabling such early discharge. The cohort's small ROU and early discharge nevertheless appeared to produce low rates of readmission.…”

Section: Discussionmentioning

confidence: 99%

Ambulatory Robotic Colectomy: Factors Affecting and Affected by Postoperative Opioid Use

Vu,

Franko,

Buzadzhi

et al. 2024

Surgical Laparoscopy, Endoscopy &Amp; Percutaneous Techniques

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Background: The ongoing opioid crisis demands an investigation into the factors driving postoperative opioid use. Ambulatory robotic colectomies are an emerging concept in colorectal surgery, but concerns persist surrounding adequate pain control for these patients who are discharged very early. We sought to identify key factors affecting recovery room opioid use (ROU) and additional outpatient opioid prescriptions (AOP) after ambulatory robotic colectomies. Methods: This was a single-institution retrospective review of ambulatory robotic colon resections performed between 2019 and 2022. Patients were included if they discharged on the same day (SDD) or postoperative day 1 (POD1). Outcomes of interest included ROU [measured in parenteral morphine milligram equivalents (MMEs)], AOP (written between PODs 2 to 7), postoperative emergency department presentations, and readmissions. Results: Two hundred nineteen cases were examined, 48 of which underwent SDD. The mean ROU was 29.4 MME, and 8.7% of patients required AOP. Between SDD and POD1 patients, there were no differences in postoperative emergency department presentations, readmissions, recovery opioid use, or additional outpatient opioid scripts. Older age was associated with a lower ROU (−0.54 MME for each additional year). Older age, a higher body mass index, and right-sided colectomies were also more likely to use zero ROU. Readmissions were strongly associated with lower ROU. Among SDD patients, lower ROU was also associated with higher rates of AOP. Conclusion: Ambulatory robotic colectomies and SDD can be performed with low opioid use and readmission rates. Notably, we found an association between low ROU and more readmission, and, in some cases, higher AOP. This suggests that adequate pain control during the postoperative recovery phase is a crucial component of reducing these negative outcomes.

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Section: Discussionmentioning

confidence: 99%

Ambulatory Robotic Colectomy: Factors Affecting and Affected by Postoperative Opioid Use

Vu,

Franko,

Buzadzhi

et al. 2024

Surgical Laparoscopy, Endoscopy &Amp; Percutaneous Techniques

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“…It has been reported that machine learning can be used to predict the recurrence of stage IV colorectal cancer [ 31 ]. Machine learning models can also predict readmission after colorectal surgery [ 32 ]. Additionally, they have been shown to predict the amount of bleeding in patients undergoing liver cancer resection [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery

Zhou,

Li,

Xue

et al. 2024

Heliyon

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“…A large body of literature has emphasized the utility of ML models in prognosticating adverse postoperative events. [16][17][18][19] For instance, Chen et al 27 found that neural networks more accurately predicted unplanned readmissions following colorectal surgery compared with traditional logistic regression. Tseng et al 25 developed an XGBoost classifier to predict acute kidney injury following cardiac surgery and similarly demonstrated enhanced performance relative to traditional risk scores.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Prediction of Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy

Verma,

Balian,

Hadaya

et al. 2023

Annals of Surgery

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Objective: To develop a novel machine learning (ML) model to predict clinically relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD). Summary Background Data: Accurate prognostication of CR-POPF may allow for risk stratification and adaptive treatment strategies for potential PD candidates. However, antecedent models, such as the modified Fistula Risk Score (mFRS), are limited by poor discrimination and calibration. Methods: All records entailing PD within the 2014-2018 ACS NSQIP were identified. Additionally, patients undergoing PD at our institution between 2013 and 2021 were queried from our local data repository. An eXtreme Gradient Boosting (XGBoost) model was developed to estimate the risk of CR-POPF using data from the ACS NSQIP and evaluated using institutional data. Model discrimination was estimated using the area under the receiver operating characteristic (AUROC) and precision recall curve (AUPRC). Results: Overall, 12,281 and 445 patients undergoing PD were identified within the 2014-2018 ACS NSQIP and our institutional registry, respectively. Application of the XGBoost and mFRS scores to the internal validation dataset revealed that the former model had significantly greater AUROC (0.72 vs. 0.68, P<0.001) and AUPRC (0.22 vs. 0.18, P<0.001). Within the external validation dataset, the XGBoost model remained superior to the mFRS with an AUROC of 0.79 (95% CI 0.74-0.84) versus 0.75 (95% CI 0.70-0.80, P<0.001). In addition, AUPRC was higher for the XGBoost model, compared to the mFRS. Conclusions: Our novel ML model consistently outperformed the previously validated mFRS within internal and external validation cohorts, thereby demonstrating its generalizability and utility for enhancing prediction of CR-POPF.

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Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery

Cited by 7 publications

References 33 publications

Ambulatory Robotic Colectomy: Factors Affecting and Affected by Postoperative Opioid Use

Ambulatory Robotic Colectomy: Factors Affecting and Affected by Postoperative Opioid Use

Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery

Machine Learning Based Prediction of Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy

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