Dataset Information

Multiple logistic regression model to predict bile leak associated with subtotal cholecystectomy.

ABSTRACT:

Background

There are no prediction models for bile leakage associated with subtotal cholecystectomy (STC). Therefore, this study aimed to generate a multivariable prediction model for post-STC bile leakage and evaluate its overall performance.

Methods

We analysed prospectively managed data of patients who underwent STC by a single consultant surgeon between 14 May 2013 and 21 December 2021. STC was schematised into four variants with five subvariants and classified broadly as closed-tract or open-tract STC. A contingency table was used to detect independent risk factors for bile leakage. A multiple logistic regression analysis was used to generate a model. Discrimination and calibration statistics were computed to assess the accuracy of the model.

Results

A total of 81 patients underwent the STC procedure. Twenty-eight patients (35%) developed bile leakage. Of these, 18 patients (64%) required secondary surgical intervention. Multivariable logistic regression revealed two independent predictors of post-STC bile leak: open-tract STC (odds ratio [OR], 7.07; 95% confidence interval [CI], 2.191-25.89; P = 0.0170) and acute cholecystitis (OR, 5.449; 95% CI, 1.584-23.48; P = 0.0121). The area under the receiver-operating characteristic curve was 82.11% (95% CI, 72.87-91.34; P < 0.0001). Tjur's pseudo-R² was 0.3189 and the Hosmer-Lemeshow goodness-of-fit statistic was 4.916 (P = 0.7665).

Conclusions

Open-tract STC and acute cholecystitis are the most reliable predictors of bile leakage associated with STC. Future prospective, multicentre studies with higher statistical power are needed to generate more specific and externally validated prediction models for post-STC bile leaks.

SUBMITTER: Lunevicius R

PROVIDER: S-EPMC10072799 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Multiple logistic regression model to predict bile leak associated with subtotal cholecystectomy.

Lunevicius Raimundas R Nzenwa Ikemsinachi C IC

Surgical endoscopy 20230404 7

<h4>Background</h4>There are no prediction models for bile leakage associated with subtotal cholecystectomy (STC). Therefore, this study aimed to generate a multivariable prediction model for post-STC bile leakage and evaluate its overall performance.<h4>Methods</h4>We analysed prospectively managed data of patients who underwent STC by a single consultant surgeon between 14 May 2013 and 21 December 2021. STC was schematised into four variants with five subvariants and classified broadly as clos ...[more]

PMID: 37016083

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Project description:Background Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based logistic regression model to predict STAS in lung adenocarcinoma. Methods This retrospective study was approved by the institutional review board of two centers and included 578 patients (462 from center I and 116 from center II) with pathologically confirmed lung adenocarcinoma. STAS was identified from 90 center I patients (19.5%) and 28 center II patients (24.1%) from. The maximum diameter, nodule area, and area of solid components in part-solid nodules were measured. Twenty-one semantic characteristics were assessed. Univariate analysis was used to select CT characteristics, which were associated with STAS in the patient cohort of center I. Multivariable logistic regression was used to develop a CT characteristics-based model on those variables with statistical significance. The model was validated in the validation cohort and then tested in the external test cohort (patients from center II). The diagnostic performance of the model was measured by area under the curve (AUC) of receiver operating characteristic (ROC). Results At univariate analysis, age and 11 CT characteristics, including the maximum diameter of the tumor, the maximum area of the tumor, the area and ratio of the solid component, nodule type, pleural thickening, pleural retraction, mediastinal lymph node enlargement, vascular cluster sign, and lobulation, specula were found to be significantly associated with STAS. The optimal logistic regression model included age, maximum diameter and ratio of solid component with odds ratio (OR) value of 0.967 (95% CI: 0.944–0.988), 1.027 (95% CI: 1.008–1.046) and 5.14 (95% CI: 2.180–13.321), respectively. This model achieved an AUC of 0.801 (95% CI: 0.709–0.892) and 0.692 (95% CI: 0.518–0.866) in the validation cohort and the external test cohort, respectively. The difference was not statistically significant (P=0.280). Conclusions CT-based logistic regression machine learning model could preoperatively predict STAS in lung adenocarcinoma with excellent diagnosis performance, which could be supplementary to routine CT interpretation.

Dataset Information

Multiple logistic regression model to predict bile leak associated with subtotal cholecystectomy.

Background

Methods

Results

Conclusions

Publications

Multiple logistic regression model to predict bile leak associated with subtotal cholecystectomy.

Similar Datasets

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