Is preoperative physical function testing predictive of length of stay in patients with colorectal cancer? A retrospective study

Quang, Anh Thy Le; Carli, Francesco; Prince, Femke H. M.

doi:10.1016/j.ejso.2023.06.008

Cited by 3 publications

(4 citation statements)

References 107 publications

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“…Previous studies identified age, gender, marital status, body mass index (BMI), and postoperative complications as influencing factors ( 6 , 17 , 19 ). Variables during surgery such as surgery duration, blood loss, and surgery location were also potential predictors ( 20 ).…”

Section: Discussionmentioning

confidence: 99%

“…Postoperative complications, a relatively constant risk factor affecting LOS, ranked second in variable importance, indicating a strong determinant of LOS ( 6 , 17 , 19 ). Previous research found that male colorectal cancer patients had a higher incidence of postoperative complications than females ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

“…Prolonged LOS (pLOS) was defined as greater than the average or median value ( 16 , 17 ). Due to the variability in patient care, management, and treatment responses, the median, as a measure of central tendency, was deemed more appropriate for classifying extended hospital stays than the mean.…”

Section: Methodsmentioning

confidence: 99%

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Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study

Wen,

Wang,

Chen

et al. 2024

Front. Oncol.

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ObjectiveThis study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization.MethodsWe prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm.ResultsTen significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97–0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model.ConclusionThis study successfully constructed a model predicting postoperative hospital stay duration using patients’ clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study

Wen,

Wang,

Chen

et al. 2024

Front. Oncol.

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“…Many patient factors have been identified as contributors to surgical risk, such as age, BMI, albumin levels and performance status [4,25]. More objective measures of functional capacity, such as the six-minute walk test or stair climbing assessment, have been considered as aides to surgical decision making in cancer treatments, but there is a scarcity of data when considering their use in the ovarian cancer population [26,27].…”

Section: Discussionmentioning

confidence: 99%

Is Cardiopulmonary Exercise Testing Predictive of Surgical Complications in Patients Undergoing Surgery for Ovarian Cancer?

Smits,

Agius,

Blake

et al. 2023

Cancers

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Preoperative cardiopulmonary exercise testing (CPET) provides an objective assessment of functional capability. In other intra-abdominal surgical specialties, CPET outcomes are predictive of operative morbidity. However, in ovarian cancer surgery, its predictive value remains unknown. In this study, we evaluated the association between CPET performance and surgical morbidity in ovarian cancer patients. Secondly, we assessed the association between CPET performance and other surgical outcomes (i.e., hospital stay, readmission and residual disease). This was a retrospective cohort study of patients undergoing primary surgery for ovarian cancer between 2020 and 2023. CPET performance included peak oxygen uptake (VO2 max), ventilatory efficiency (VE/VO2) and anaerobic threshold. Outcomes were operative morbidity and included intra- and postoperative complications (Clavien–Dindo), hospital stay, readmission within 30 days and residual disease. A total of 142 patients were included. A lower VO2 peak and a higher VE/VCO2 were both associated with the occurrence of postoperative complications, and a poorer anaerobic threshold was associated with more transfusions. VE/VCO2 remained significantly associated after multivariate analysis (p = 0.035). None of the CPET outcomes were associated with length of stay, readmission or residual disease. In conclusion, VE/VCO2 was significantly associated with an increased risk of all-cause postoperative complications in ovarian cancer patients undergoing primary surgery.

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284 Is cardiopulmonary exercise testing predictive of surgical complications in patients undergoing surgery for ovarian cancer?

Smits,

Agius,

Blake

et al. 2024

Poster and E-Posters

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Is preoperative physical function testing predictive of length of stay in patients with colorectal cancer? A retrospective study

Cited by 3 publications

References 107 publications

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study

Is Cardiopulmonary Exercise Testing Predictive of Surgical Complications in Patients Undergoing Surgery for Ovarian Cancer?

284 Is cardiopulmonary exercise testing predictive of surgical complications in patients undergoing surgery for ovarian cancer?

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