Survival Probabilities of Paediatric Patients Registered in ART Centre at New Civil Hospital, Surat
Author:Sridhar P Ryavanki, Jayendrakumar K Kosambiya, Sonal O Dayama, Alap Mehta, Nitin Solanki, Sangita S Trivedi
Keywords:Paediatric HIV, Survival probability, Kaplan-Meier, WHO staging, India,
Abstract:Aims and Objectives: To study the profile of paediatric HIV patients registered in ART centre of New Civil Hospital, Surat, Gujarat (India) and provide an estimate of 3 years survival probabilities of paediatric HIV patients on ART. Material and methods: Data of 175 paediatric patients (of age less than 15 years), registered from 2007 to 2010 was collected and analyzed. Kaplan Meir method for survival analysis and Log rank test to test statistical significance were used. Observations: Survival analysis of 161 patients could be done (registered from Oct 2006 to Oct 2010). The survival probability after 8 years of diagnosis of HIV is 91.7 %. After 3 year of start of ART according to WHO criteria survival probability is 85.7 %. The 3-year survival rate of paediatric HIV patients with WHO Stage 1 is 100%, Stage 2 is 75%, Stage 3 is 61.9% and Stage 4 is 40.8% which was statistically significant (p < 0.001). Conclusions: The survival probability was 91% after 3 years of diagnosis of HIV and remained same till 8 years and the probability was independent of age groups and sex. The survival probability was 85.7 % after 3 years of start of ART. There was no difference in survival probability with different baseline CD4 counts but was significantly low in patients who were in WHO stage 3 and 4 at the time of registration. Recommendations: With ART definitely proving increase in survival probability, it is now time to study different drug regimens and their respective survival probabilities. There are many studies on adverse effects of the ART drugs but there is need for research on their effect on survival. There is a scope for continuing of this study further with at least median follow up of 5 years. Larger sample and regression model can be used to understand more precisely the predictors of survival.