Risk factors for prolonged length of stay after colorectal surgery

Lobato, Lydia Maria; Ferreira, Patrícia Cristina Alves; Wick, Elizabeth C.; Kiran, Ravi P.; Remzi, Feza H.; Kalady, Matthew F.; Vogel, Jon D.

doi:10.1590/s2237-93632013000100005

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…We found no significant impact of stoma type on prolonging LOS; however, having stoma formation compared to not having stoma formation increases the odds of extending LOS to about 2.5 times. As seen in other published studies, our study also confirms the significance of hypertension, Eastern Cooperative Oncology Group (ECOG) performance, the American Society of Anesthesiologists (ASA) grading assessment, and grade of differentiation on LOS after colorectal surgery (31)(32)(33). Among the demographic features in this study, sex was the only one that significantly influenced prolonged LOS, and this was well described in other studies.…”

Section: Discussionsupporting

confidence: 91%

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

et al. 2021

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The aim of this pilot study was to develop logistic regression (LR) and support vector machine (SVM) models that differentiate low from high risk for prolonged hospital length of stay (LOS) in a South African cohort of 383 colorectal cancer patients who underwent surgical resection with curative intent. Additionally, the impact of 10-fold cross-validation (CV), Monte Carlo CV, and bootstrap internal validation methods on the performance of the two models was evaluated. The median LOS was 9 days, and prolonged LOS was defined as greater than 9 days post-operation. Preoperative factors associated with prolonged LOS were a prior history of hypertension and an Eastern Cooperative Oncology Group score between 2 and 4. Postoperative factors related to prolonged LOS were the need for a stoma as part of the surgical procedure and the development of post-surgical complications. The risk of prolonged LOS was higher in male patients and in any patient with lower preoperative hemoglobin. The highest area under the receiving operating characteristics (AU-ROC) was achieved using LR of 0.823 (CI = 0.798–0.849) and SVM of 0.821 (CI = 0.776–0.825), with each model using the Monte Carlo CV method for internal validation. However, bootstrapping resulted in models with slightly lower variability. We found no significant difference between the models across the three internal validation methods. The LR and SVM algorithms used in this study required incorporating important features for optimal hospital LOS predictions. The factors identified in this study, especially postoperative complications, can be employed as a simple and quick test clinicians may flag a patient at risk of prolonged LOS.

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Section: Discussionsupporting

confidence: 91%

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

et al. 2021

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“…The LOS for English patients also appears to compare relatively favorably to equivalent international figures. [8,10,25] …”

Section: Discussionmentioning

confidence: 99%

A retrospective observational study of length of stay in hospital after colorectal cancer surgery in England (1998–2010)

et al. 2016

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The National Health Service (NHS) is facing financial constraints and thus there is considerable interest in ensuring the shortest but optimal hospital stays possible. The aim of this study was to investigate patterns of postoperative length of stay (LOS) stay across the English NHS and to identify factors that significantly influence both optimal and prolonged LOS.Data were obtained from the National Cancer Data Repository (NCDR). National patterns of LOS were examined and multilevel mixed effects logistic regression was used to study factors associated with an “ideal” (≤5 days) or a prolonged (≥21 days) LOS in hospital after major resection. Funnel plots were used to examine variation across hospitals in both risk-adjusted and unadjusted LOS.All 240,873 individuals who underwent major resection for colorectal cancer were diagnosed between 1998 and 2010 in the English NHS. The overall median LOS was 10 (interquartile range [IQR] 7–14 days) days, but it fell over time from 11 (IQR 9–15) days in 1998 to 7 (IQR 5–12) days in 2010. The proportion of people experiencing “ideal” LOS increased dramatically from 4.9% in 1998 to 34.2% in 2010, but the decrease in the proportion of patients who experienced a prolonged LOS was less marked falling from 11.2% to 8.4%, respectively. Control charts showed that there was significant variation in short and prolonged LOS across NHS trusts even after adjustment for case-mix.Significant variation in LOS existed between NHS hospitals in England throughout period 1998 to 2010. Understanding the underlying causes of this variation between surgical providers will make it possible to identify and spread best practice, improve services, and ultimately reduce LOS following colorectal cancer surgery.

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“…Chiu et al state that minor and major complications were better predictors of LOS than preoperative demographic and disease parameters [ 46 ]. Sex, congestive heart failure, weight loss, Crohn’s disease, preoperative albumin < 3.5 g/dL and hematocrit < 47%, baseline sepsis, ASA class ≥ 3, open surgery, surgical time ≥ 190 min, post-operative pneumonia, failure to wean from mechanical ventilation, deep venous thrombosis, urinary tract infection, systemic sepsis, surgical site infection and reoperation within 30-days from the primary surgery were the risk factors for prolonged LOS [ 47 ]. In contrast, this study found age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications are the significant predictor variables of LOS.…”

Section: Discussionmentioning

confidence: 99%

“…Prior research has used a limited number of variables to investigate LOS, mortality and readmission following CRC surgery [7,8,10,19,[39][40][41][42]. This study includes a larger number of variables (47) including demographics, peri-and post-operative outcomes, surgical approaches, complications and mortality. These variables are used and compared in predicting LOS, mortality and readmission.…”

Section: Role Of Data Analytics and Machine Learningmentioning

confidence: 99%

Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer

et al. 2022

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Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80–96% accuracy, 84–93% sensitivity, and 75–100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.

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Risk factors for prolonged length of stay after colorectal surgery

Cited by 9 publications

References 12 publications

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

A retrospective observational study of length of stay in hospital after colorectal cancer surgery in England (1998–2010)

Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer

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