Dynamic analysis of commonly used biochemical parameters to predict common bile duct stones in patients undergoing laparoscopic cholecystectomy

Bourgouin, Stéphane; Truchet, X.; Lamblin, G.; Roulhac, J. de; Platel, J.-P.; Balandraud, Paul

doi:10.1007/s00464-017-5549-2

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…The majority of them used a combination of abnormal biochemical markers and/or imaging studies, mostly ultrasonography. 10,[13][14][15][16] By omitting any radiologic component, our score allows an estimation of choledocholithiasis risk based on what is essentially in a single test, since liver function panels almost universally include TB, ALP, and AST. It also removes any potential subjectivity introduced by ultrasonographic measurement of CBD diameter.…”

Section: Discussionmentioning

confidence: 99%

The Role of Transaminases in Predicting Choledocholithiasis. A Novel Predictive Composite Score Development in a Cohort of 1089 Patients Undergoing Laparoscopic Cholecystectomy

Kokoroskos

Peponis

Lee

et al. 2021

The American Surgeon

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Background Optimal use of interventional procedures and diagnostic tests for patients with suspected choledocholithiasis depends on accurate pretest risk estimation. We sought to define sensitivity/specificity of transaminases in identifying choledocholithiasis and to incorporate them into a biochemical marker composite score that could accurately predict choledocholithiasis. Methods All adult patients who underwent laparoscopic cholecystectomy by our Emergency Surgery Service between 2010 and 2018 were reviewed. Admission total bilirubin (TB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) was captured. Choledocholithiasis was confirmed via intraoperative cholangiogram, endoscopic retrograde cholangiopancreatography, or magnetic resonance cholangiopancreatography. Area under receiver operating characteristic curve (AUC) or C-statistic for AST, ALT, ALP, and TB as a measure of detecting choledocholithiasis was calculated. For score development, our database was randomly dichotomized to derivation and validation cohort and a score was derived. The score was validated by calculating its C-statistic. Results 1089 patients were included; 210 (20.3%) had confirmed choledocholithiasis. The AUC was .78 for TB, .77 for ALP and AST, and .76 for ALT. 545 and 544 patients were included in the derivation and the validation cohort, respectively. The elements of the derived score were TB, AST, and ALP. The score ranged from 0 to 4. The AUC was .82 in the derivation and .77 in the validation cohort. The probability of choledocholithiasis increased from 8% to 89% at scores 0 to 4, respectively. Conclusions Aspartate aminotransferase predicted choledocholithiasis adequately and should be featured in choledocholithiasis screening algorithms. We developed a biochemical composite score, shown to be accurate in preoperative choledocholithiasis risk assessment in an emergency surgery setting.

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Section: Discussionmentioning

confidence: 99%

The Role of Transaminases in Predicting Choledocholithiasis. A Novel Predictive Composite Score Development in a Cohort of 1089 Patients Undergoing Laparoscopic Cholecystectomy

Kokoroskos

Peponis

Lee

et al. 2021

The American Surgeon

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“…De otro lado, Tozzati y colaboradores (Brasil) (21) y Bourgouin y colaboradores (Francia) (22) han llegado a conclusiones similares con respecto a la elevación inicial de las aminotransferasas. De forma sorprendente, la fosfatasa alcalina mostró valores inferiores en los pacientes con CLDL.…”

Section: Discussionunclassified

Evaluación del rendimiento diagnóstico de los criterios predictivos de la sociedad británica para el diagnóstico de coledocolitiasis en una población colombiana

Gastelbondo-Morales

Regino

Gómez-Zuleta

2020

Rev Colomb Gastroenterol

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Introducción: la coledocolitiasis (CLDL) puede ser difícil de diagnosticar. Su importancia radica en sus potenciales complicaciones y en que el tratamiento se realiza mediante colangiopancreatografía retrógrada endoscópica (CPRE), un procedimiento con riesgo de generar complicaciones. Se han propuesto guías para su diagnóstico y la más empleada es la de la ASGE (American Society for Gastrointestinal Endoscopy), cuyo rendimiento no es ideal. Recientemente, se ha publicado la guía británica. Este estudio se realizó para establecer el rendimiento de ambas guías. Materiales y métodos: estudio prospectivo realizado entre agosto 1 de 2017 y julio 31 de 2018. Resultados: se incluyeron 300 pacientes para el análisis. Se realizó una CPRE en 145 pacientes y se confirmó la existencia de CLDL en 124 de ellos (85,5 %). La mediana de aspartato aminotransferasa (AST) y alanina aminotransferasa (ALT) fue mayor en los que tuvieron CLDC (207 mg/dL y 290 mg/dl, respectivamente). Entre tanto, la tasa de complicaciones posteriores a la CPRE fue del 5,5 %. El análisis multivariado no encontró una asociación significativa para alguna variable predictora de CLDL. En pacientes con alta probabilidad, las guías británicas tuvieron una sensibilidad del 65 % y una especificidad del 33 %, mientras que las guías ASGE mostraron una sensibilidad del 74 % y una especificidad del 28 %. En probabilidad intermedia fueron menos eficientes. Conclusiones: los criterios de la ASGE y la BSG (British Society of Gastroenterology) no tienen un buen desempeño en la población estudiada, a fin de discriminar la existencia o no de CLDL. La guía de la ASGE mostró un mejor rendimiento en general que las guías británicas.

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“…Missing laboratory values were replaced with median values. Different combinations of data were trialled; these were based on existing algorithms [5,8,[20][21][22]25]. The first dataset included patient demographics, first and second blood test results, the change in blood results and the ultrasound report text (where keywords indicated common bile duct stones).…”

Section: Statistical Analysis Methodsmentioning

confidence: 99%

“…Locally the IOC rate during laparoscopic cholecystectomy is 18% and there is high a rate of pre‐operative MRCP use (unpublished data) with consequent costs and delays [19]. Given this variation a number of algorithms have sought to formalise decision making [5, 6, 20–22]. Though there is data to suggest clinical algorithms are superior to clinician gestalt there is significant variance in practice [12, 23].…”

Section: Introductionmentioning

confidence: 99%

The Challenges of Implementing Artificial Intelligence into Surgical Practice

et al. 2020

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Background Artificial intelligence is touted as the future of medicine. Classical algorithms for the detection of common bile duct stones (CBD) have had poor clinical uptake due to low accuracy. This study explores the challenges of developing and implementing a machine‐learning model for the prediction of CBD stones in patients presenting with acute biliary disease (ABD). Methods All patients presenting acutely to Christchurch Hospital over a two‐year period with ABD were retrospectively identified. Clinical data points including lab test results, demographics and ethnicity were recorded. Several statistical techniques were utilised to develop a machine‐learning model. Issues with data collection, quality, interpretation and barriers to implementation were identified and highlighted. Results Issues with patient identification, coding accuracy, and implementation were encountered. In total, 1315 patients met inclusion criteria. Incorrect international classification of disease 10 (ICD‐10) coding was noted in 36% (137/382) of patients recorded as having CBD stones. Patients with CBD stones were significantly older and had higher aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin and gamma‐glutamyl transferase (GGT) levels (p < 0.001). The no information rate was 81% (1070/1315 patients). The optimum model developed was the gradient boosted model with a PPV of 67%, NPV of 87%, sensitivity of 37% and a specificity of 96% for common bile duct stones. Conclusion This paper highlights the utility of machine learning in predicting CBD stones. Accuracy is limited by current data and issues do exist around both the ethics and practicality of implementation. Regardless, machine learning represents a promising new paradigm for surgical practice.

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Dynamic analysis of commonly used biochemical parameters to predict common bile duct stones in patients undergoing laparoscopic cholecystectomy

Abstract: Commonly used biochemical parameters correctly predict CBDS when they are analysed in a dynamic setting rather than at discrete time points. The proposed model constitutes a reliable tool to decrease unnecessary ERCP and perioperative discovery rates of CBDS.

Cited by 5 publications

References 23 publications

The Role of Transaminases in Predicting Choledocholithiasis. A Novel Predictive Composite Score Development in a Cohort of 1089 Patients Undergoing Laparoscopic Cholecystectomy

The Role of Transaminases in Predicting Choledocholithiasis. A Novel Predictive Composite Score Development in a Cohort of 1089 Patients Undergoing Laparoscopic Cholecystectomy

Evaluación del rendimiento diagnóstico de los criterios predictivos de la sociedad británica para el diagnóstico de coledocolitiasis en una población colombiana

The Challenges of Implementing Artificial Intelligence into Surgical Practice

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