Email updates

Keep up to date with the latest news and content from BMC Surgery and BioMed Central.

Open Access Research article

Predictive model of biliocystic communication in liver hydatid cysts using classification and regression tree analysis

Hadj Omar El Malki123*, Yasser El Mejdoubi1, Amine Souadka1, Raouf Mohsine1, Lahcen Ifrine1, Redouane Abouqal234 and Abdelkader Belkouchi1

Author Affiliations

1 Surgery Departement "A" Ibn Sina Hospital, Rabat, Morocco

2 Medical Center of Clinical Trials and Epidemiological Study (CRECET), Medical School, University Mohammed Vth Souissi, Rabat, Morocco

3 Biostatical, clinical research and epidemiological laboratory (LBRCE), Medical School, University Mohammed Vth Souissi, Rabat, Morocco

4 Medical ICU, Ibn Sina Hospital, Rabat, Morocco

For all author emails, please log on.

BMC Surgery 2010, 10:16  doi:10.1186/1471-2482-10-16

Published: 16 April 2010

Abstract

Background

Incidence of liver hydatid cyst (LHC) rupture ranged 15%-40% of all cases and most of them concern the bile duct tree. Patients with biliocystic communication (BCC) had specific clinic and therapeutic aspect. The purpose of this study was to determine witch patients with LHC may develop BCC using classification and regression tree (CART) analysis

Methods

A retrospective study of 672 patients with liver hydatid cyst treated at the surgery department "A" at Ibn Sina University Hospital, Rabat Morocco. Four-teen risk factors for BCC occurrence were entered into CART analysis to build an algorithm that can predict at the best way the occurrence of BCC.

Results

Incidence of BCC was 24.5%. Subgroups with high risk were patients with jaundice and thick pericyst risk at 73.2% and patients with thick pericyst, with no jaundice 36.5 years and younger with no past history of LHC risk at 40.5%. Our developed CART model has sensitivity at 39.6%, specificity at 93.3%, positive predictive value at 65.6%, a negative predictive value at 82.6% and accuracy of good classification at 80.1%. Discriminating ability of the model was good 82%.

Conclusion

we developed a simple classification tool to identify LHC patients with high risk BCC during a routine clinic visit (only on clinical history and examination followed by an ultrasonography). Predictive factors were based on pericyst aspect, jaundice, age, past history of liver hydatidosis and morphological Gharbi cyst aspect. We think that this classification can be useful with efficacy to direct patients at appropriated medical struct's.