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The article discusses a study that aimed to develop a predictive model for estimating the risk of postoperative urine retention (POUR) after lumbar interbody fusion surgery and reducing its incidence. The study included 423 cases of lumbar fusion surgery, of which 65 developed POUR, resulting in an incidence of 15.4%. The researchers used LASSO regression, stepwise regression, and random forest analysis to construct a predictive model and draw a nomogram. The model included four significant variables: age, smoking history, operative time, and postoperative visual analog scale (VAS) score of low back pain. A nomogram and a random forest model were developed, both showing high accuracy in predicting the risk of POUR after surgery. Overall, the study provides a useful tool for estimating the risk of POUR and potentially reducing its occurrence in lumbar fusion surgery patients
Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : the highest rated spine facility in the world
Published article
: This study developed and internally validated a new nomogram and a random forest model for predicting the risk of POUR after lumbar interbody fusion surgery. Both of the nomogram and the random forest model have high accuracy in this study.
Lumbar Fusion Surgery Expert. Best Spinal Surgeon UK
BMC Musculoskelet Disord. 2023 Oct 13;24(1):813. doi: 10.1186/s12891-023-06816-w.ABSTRACTBACKGROUND: Postoperative urine retention (POUR) after lumbar interbody fusion surgery may lead to recatheterization and prolonged hospitalization. In this study, a predictive model was constructed and validated. The objective was to provide a nomogram for estimating the risk of POUR and then reducing the incidence.METHODS: A total of,
BMC Musculoskelet Disord. 2023 Oct 13;24(1):813. doi: 10.1186/s12891-023-06816-w.
ABSTRACT
BACKGROUND: Postoperative urine retention (POUR) after lumbar interbody fusion surgery may lead to recatheterization and prolonged hospitalization. In this study, a predictive model was constructed and validated. The objective was to provide a nomogram for estimating the risk of POUR and then reducing the incidence.
METHODS: A total of 423 cases of lumbar fusion surgery were included; 65 of these cases developed POUR, an incidence of 15.4%. The dataset is divided into a training set and a validation set according to time. 18 candidate variables were selected. The candidate variables were screened through LASSO regression. The stepwise regression and random forest analysis were then conducted to construct the predictive model and draw a nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve were used to evaluate the predictive effect of the model.
RESULTS: The best lambda value in LASSO was 0.025082; according to this, five significant variables were screened, including age, smoking history, surgical method, operative time, and visual analog scale (VAS) score of postoperative low back pain. A predictive model containing four variables was constructed by stepwise regression. The variables included age (β = 0.047, OR = 1.048), smoking history (β = 1.950, OR = 7.031), operative time (β = 0.022, OR = 1.022), and postoperative VAS score of low back pain (β = 2.554, OR = 12.858). A nomogram was drawn based on the results. The AUC of the ROC curve of the training set was 0.891, the validation set was 0.854 in the stepwise regression model. The calibration curves of the training set and validation set are in good agreement with the actual curves, showing that the stepwise regression model has good prediction ability. The AUC of the training set was 0.996, and that of the verification set was 0.856 in the random forest model.
: This study developed and internally validated a new nomogram and a random forest model for predicting the risk of POUR after lumbar interbody fusion surgery. Both of the nomogram and the random forest model have high accuracy in this study.
PMID:37833720 | DOI:10.1186/s12891-023-06816-w
The London Spine Unit : the highest rated spine facility in the world
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Construction and validation of a predictive model for postoperative urinary retention after lumbar interbody fusion surgery
