Literature DB >> 30216168

Management of iatrogenic bile duct injuries: Multiple logistic regression analysis of predictive factors affecting morbidity and mortality.

Ela Ekmekcigil1, Ömer Ünalp1, Alper Uğuz1, Ruslan Hasanov1, Halil Bozkaya2, Timur Köse3, Mustafa Parıldar2, Ömer Özütemiz4, Ahmet Çoker1.   

Abstract

OBJECTIVE: Iatrogenic bile duct injuries remain a challenge for the surgeons to overcome. The predictive factors affecting morbidity and mortality are important for determining the best management modality.
MATERIAL AND METHODS: The patients who referred to Ege University Faculty of Medicine after laparoscopy associated iatrogenic bile duct injury are grouped according to Strasberg-Bismuth classification system. The type and number of prior attempts, concomitant complications, and treatment modalities are analyzed using the SPSS version 18 (IBM, Chicago, IL, USA). The variables with p<0.10 were considered for univariate analysis and then evaluated for predictive factors by forward Logistic Regression method using multiple logistic regression analysis.
RESULTS: According to the analysis of 105 patients who were referred during 2004-2014, the type and number of prior attempts are considered predictive factors in sepsis. In multiple logistic regression analysis, abscess formation, concomitant vascular injury, and serum bilirubin level are significantly effective in predicting mortality.
CONCLUSION: The management of iatrogenic bile duct injuries should be carefully planned with a multidisciplinary approach. The predictive factors affecting morbidity and mortality are important in determining the best modality for managing iatrogenic bile duct injuries. Abscess formation, vascular injury, and serum bilirubin level are the potential risk factors. Therefore, we can strongly recommend immediate assessment of patients for prompt diagnosis and referring to an HPB center, to avoid further injuries.

Entities:  

Year:  2018        PMID: 30216168      PMCID: PMC6340667          DOI: 10.5152/turkjsurg.2018.3888

Source DB:  PubMed          Journal:  Turk J Surg        ISSN: 2564-6850


  1 in total

1.  Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Authors:  Ke Li; Qinwen Shi; Siru Liu; Yilin Xie; Jialin Liu
Journal:  Medicine (Baltimore)       Date:  2021-05-14       Impact factor: 1.889

  1 in total

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