Assessment of facility performance during mass treatment of chronic hepatitis C in Egypt: Enablers and obstacles.

BACKGROUND: The national committee for control of viral hepatitis (NCCVH) in Egypt, settled by the Ministry of health, treated over one million patients in around 60 centers with chronological changes in drug combinations. This research aims to study the health care facilities and services provided by NCCVH treatment centers in Egypt and explore hinders faced. METHODS: A cross-sectional operational research study. Multistage random sampling technique was applied for Egyptian governorates. From each stratum one governorate was chosen from which one center was randomly selected. Quality of recorded data for each center in the central server (Data-oriented parameter), newly designed score to assess the overall performance of the centers was retrieved from computer based recording system. A self-administered questionnaire was completed by the centers head. RESULTS: This study included 24 treatment centers from urban, rural areas, Upper and Lower Egypt. The Upper centers showed the best completeness of follow-up records and the least compliance rates. None of the centers had 100% completeness of follow-up data. Proportion of SVR is minimally less than proportion of patient with known outcome in all treatment centers. A novel indicator standardizing the comparisons of performance of different facilities was introduced: Total number of physicians/total number of SVR patients with completed records. The highest response rate: Monfiya Governorate (Lower Egypt), Aswan (Upper Egypt), Completeness of follow-up records: Kalyoubia (Lower Egypt), Sohag governorate (Upper Egypt). The average administrative score was 64%. CONCLUSION: Challenges of NCCVH program: overcrowdings, resistant sociocultural background among rural patients, limited accessibility for internal migrants and incompleteness of data entry are system lacking points. Strengths include, clear patient pathway, well-established database online application, well-trained physicians and treatment availability.

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.

Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy.