Trends, factors, and disparities associated with length of stay after lower extremity bypass for tissue loss

Nakazawa, Kenneth R.; Cornwall, James W.; Rao, Ajit; Han, Daniel; Ting, Windsor; Tadros, Rami O.; Faries, Peter L.; Vouyouka, Ageliki G.

doi:10.1016/j.jvs.2020.04.511

Cited by 8 publications

(2 citation statements)

References 12 publications

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“… 40 Similarly, in another study of 8139 patients with symptomatic vascular disease, the risk of adverse limb events was related to SBP. 41 As adverse limb events were related to extended LOS, 42 the association of elevated SBP with an increased risk of adverse limb events may account for longer LOS.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a prediction model for the prolonged length of stay in Chinese patients with lower extremity atherosclerotic disease: a retrospective study

et al. 2023

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ObjectivesThis study aims to develop and internally validate a prediction model, which takes account of multivariable and comprehensive factors to predict the prolonged length of stay (LOS) in patients with lower extremity atherosclerotic disease (LEAD).DesignThis is a retrospective study.SettingChina.Participants, primary and secondary outcomesData of 1694 patients with LEAD from a retrospective cohort study between January 2014 and November 2021 were analysed. We selected nine variables and created the prediction model using the least absolute shrinkage and selection operator (LASSO) regression model after dividing the dataset into training and test sets in a 7:3 ratio. Prediction model performance was evaluated by calibration, discrimination and Hosmer-Lemeshow test. The effectiveness of clinical utility was estimated using decision curve analysis.ResultsLASSO regression analysis identified age, gender, systolic blood pressure, Fontaine classification, lesion site, surgery, C reactive protein, prothrombin time international normalised ratio and fibrinogen as significant predictors for predicting prolonged LOS in patients with LEAD. In the training set, the prediction model showed good discrimination using a 500-bootstrap analysis and good calibration with an area under the receiver operating characteristic of 0.750. The Hosmer-Lemeshow goodness of fit test for the training set had a p value of 0.354. The decision curve analysis showed that using the prediction model both in training and tests contributes to clinical value.ConclusionOur prediction model is a valuable tool using easily and routinely obtained clinical variables that could be used to predict prolonged LOS in patients with LEAD and help to better manage these patients in routine clinical practice.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a prediction model for the prolonged length of stay in Chinese patients with lower extremity atherosclerotic disease: a retrospective study

et al. 2023

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“…Also, we chose not to include intraoperative and postoperative variables (eg, operation time, transfusion, postoperative complications, etc) even though they may strongly associate with DNHF or LOS. For instance, LOS itself can associate with DNHF 42 . This was because our models were intended to guide clinicians at a preoperative standpoint to provide a window of opportunity for appropriate planning or arrangements for risk adjustment.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

et al. 2020

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Background This study develops machine learning (ML) algorithms that use preoperative‐only features to predict discharge‐to‐nonhome‐facility (DNHF) and length‐of‐stay (LOS) following complex head and neck surgeries. Methods Patients undergoing laryngectomy or composite tissue excision followed by free tissue transfer were extracted from the 2005 to 2017 NSQIP database. Results Among the 2786 included patients, DNHF and mean LOS were 421 (15.1%) and 11.7 ± 8.8 days. Four classification models for predicting DNHF with high specificities (range, 0.80‐0.84) were developed. The generalized linear and gradient boosting machine models performed best with receiver operating characteristic (ROC), accuracy, and negative predictive value (NPV) of 0.72‐0.73, 0.75‐0.76, and 0.88‐0.89. Four regression models for predicting LOS in days were developed, where all performed similarly with mean absolute error and root mean‐squared errors of 3.95‐3.98 and 5.14‐5.16. Both models were developed into an encrypted web‐based interface: https://uci-ent.shinyapps.io/head-neck/. Conclusion Novel and proof‐of‐concept ML models to predict DNHF and LOS were developed and published as web‐based interfaces.

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A Meta-Analysis to Derive Population-Based Quality Benchmarks of the Incidence of Surgical Site Infection after Lower Limb Revascularization Surgery

Kirkham

Candeliere

Stelfox

et al. 2024

Annals of Vascular Surgery

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Trends, factors, and disparities associated with length of stay after lower extremity bypass for tissue loss

Cited by 8 publications

References 12 publications

Development and validation of a prediction model for the prolonged length of stay in Chinese patients with lower extremity atherosclerotic disease: a retrospective study

Development and validation of a prediction model for the prolonged length of stay in Chinese patients with lower extremity atherosclerotic disease: a retrospective study

Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

A Meta-Analysis to Derive Population-Based Quality Benchmarks of the Incidence of Surgical Site Infection after Lower Limb Revascularization Surgery

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