Comparison of predictive models for hepatitis C co-infection among HIV patients in Cambodia

Abstract

Background: Hepatitis C virus (HCV) infection is a major global health problem. WHO guidelines recommend screening all people living with HIV for hepatitis C. Considering the limited resources for health in low and middle income countries, targeted HCV screening is potentially a more feasible screening strategy for many HIV cohorts. Hence there is an interest in developing clinician-friendly tools for selecting subgroups of HIV patients for whom HCV testing should be prioritized. Several statistical methods have been developed to predict a binary outcome. Multiple studies have compared the performance of different predictive models, but results were inconsistent. Methods: A cross-sectional HCV diagnostic study was conducted in the HIV cohort of Sihanouk Hospital Center of Hope in Phnom Penh, Cambodia. We compared the performance of logistic regression, Spiegelhalter-Knill-Jones and CART to predict Hepatitis C co-infection in this cohort. We estimated the number of HCV co-infections that would be missed. To correct for over-optimism, the leave-one-out bootstrap estimator was used for estimating this quantity. Results: Logistic regression misses the fewest HCV co-infections (8%), but would still refer 98% of HIV patients for HCV testing. Spiegelhalter-Knill-Jones (SKJ) and CART respectively miss 12% and 29% of HCV co-infections but would only refer about 30% for HCV testing. Conclusions: In our dataset, logistic regression has the highest log-likelihood and smallest proportions of HCV co-infections missed but Spiegelhalter-Knill-Jones has the highest area under the ROC curve. The likelihood ratios estimated by Spiegelhalter-Knill-Jones might be easier to interpret for clinicians than odds ratios estimated by logistic regression or the decision tree from CART. CART is the most flexible method, and no model has to be specified regarding presence of interactions and form of the relationship between outcome and predictor variables.

Identifying the predictors of Covid-19 infection outcomes and development of prediction models

“…For example, if clinicians are capable of predicting those patients who are at high risk of disease progression, then expensive and time consuming therapies may be directed to the patients requiring urgent treatment [ 15 ]. Therefore, these risk predictive models will be useful to provide clinicians vital information to guide them to perform clinical monitoring of the patients [ 16 ].…”

Hepatitis C Prevalence and Validation of a Clinical Prediction Score for Targeted Screening among People Living with HIV in Ghana

“…The Cambodian CPS, 20 , 21 containing clinical and laboratory factors with a specific weight (age over 50 years: +1, diabetes mellitus: +1, APRI <0.45: −1, APR ≥0.45: +1, AST <30 IU/L: −1, platelets <200 ×10 9 cells/L: +1, household/partner with liver disease: +1, generalized pruritus: +1), was applied in the Ghanaian cohort based on the data collected in the cross-sectional study. Summing the weighted predictor scores of the individual’s risk factors yielded the total predictor score for each study participant.…”

“…[16][17][18] In addition to contributing to a larger evidence-base on the prevalence of HCV antibody positivity and viraemia among Ghanaian PLHIV, we also aimed to externally evaluate the discriminatory performance of a clinical prediction score (CPS) for targeted HCV testing that we previously derived from a dataset of a Cambodian HIV cohort. [19][20][21] This CPS aims to predict the risk of current hepatitis C infection, and to provide an easy-to-use tool to identify the sub-group of PLHIV who could be targeted for HCV diagnostic testing, if resources do not allow routine testing of all PLHIV.…”