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

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          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-R 2 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.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00464-023-10049-2.

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          Most cited references36

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          Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey.

          Daniel Dindo, Nicolas Demartines, Pierre-Alain Clavien (2004)
          Although quality assessment is gaining increasing attention, there is still no consensus on how to define and grade postoperative complications. This shortcoming hampers comparison of outcome data among different centers and therapies and over time. A classification of complications published by one of the authors in 1992 was critically re-evaluated and modified to increase its accuracy and its acceptability in the surgical community. Modifications mainly focused on the manner of reporting life-threatening and permanently disabling complications. The new grading system still mostly relies on the therapy used to treat the complication. The classification was tested in a cohort of 6336 patients who underwent elective general surgery at our institution. The reproducibility and personal judgment of the classification were evaluated through an international survey with 2 questionnaires sent to 10 surgical centers worldwide. The new ranking system significantly correlated with complexity of surgery (P < 0.0001) as well as with the length of the hospital stay (P < 0.0001). A total of 144 surgeons from 10 different centers around the world and at different levels of training returned the survey. Ninety percent of the case presentations were correctly graded. The classification was considered to be simple (92% of the respondents), reproducible (91%), logical (92%), useful (90%), and comprehensive (89%). The answers of both questionnaires were not dependent on the origin of the reply and the level of training of the surgeons. The new complication classification appears reliable and may represent a compelling tool for quality assessment in surgery in all parts of the world.
          Article 0 comments Cited 3153 times – based on 0 reviews     Review now
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            Assessing the performance of prediction models: a framework for traditional and novel measures.

            Ewout W. Steyerberg, Andrew J Vickers, Nancy R Cook … (2010)
            The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
            Article 0 comments Cited 869 times – based on 0 reviews     Review now
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              Measuring the accuracy of diagnostic systems.

              J Swets (1988)
              Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
              Article 0 comments Cited 845 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Raimundas Lunevicius:
                ORCID: http://orcid.org/0000-0003-3295-0142
                raimundas.lunevicius@liverpoolft.nhs.uk
                Journal
                Journal ID (nlm-ta): Surg Endosc
                Journal ID (iso-abbrev): Surg Endosc
                Title: Surgical Endoscopy
                Publisher: Springer US (New York )
                ISSN (Print): 0930-2794
                ISSN (Electronic): 1432-2218
                Publication date (Electronic): 4 April 2023
                Pages: 1-9
                Affiliations
                [1 ]GRID grid.411255.6, ISNI 0000 0000 8948 3192, Department of General Surgery, Liverpool University Hospitals NHS Foundation Trust, , Aintree Hospital, ; Lower Lane, Liverpool, L9 7AL UK
                [2 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, School of Medicine, , University of Liverpool, ; Cedar House, Ashton St, Liverpool, L69 3GE UK
                Author information
                http://orcid.org/0000-0003-3295-0142
                Article
                Publisher ID: 10049
                DOI: 10.1007/s00464-023-10049-2
                PMC ID: 10072799
                PubMed ID: 37016083
                SO-VID: a81d82be-0d47-4140-8cae-662d668608a3
                Copyright © © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 4 November 2022
                Date accepted : 26 March 2023
                Categories
                Subject: Article

                ScienceOpen disciplines: Surgery
                Keywords: subtotal cholecystectomy,bile leak,risk factors,logistic regression,multivariable model,prediction
                Data availability:
                ScienceOpen disciplines: Surgery
                Keywords: subtotal cholecystectomy, bile leak, risk factors, logistic regression, multivariable model, prediction

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