Surveillance of Swine Flu Influenza H1N1 by Chip Based Real Time PCR Technology from the Clinical Specimens in a Tertiary Care Hospital DC01-DC04
Dr. A Surekha,
Professor and Head, Department of Microbiology, Kurnool Medical College, Kurnool-518002, Andhra Pradesh, India.
Introduction: Influenza viruses are one of the most important viruses which have the ability to cause epidemics and pandemics. Swine flu influenza H1N1 pandemic in 2009 has seen an alarming response from all over the world. Since then continuous surveillance is ongoing to find any new swine flu case. Molecular techniques using real time Polymerase Chain Reaction (PCR) technology gained momentum for the identification of these infections.
Aim: The study aims to identify the prevalence of swine flu influenza H1N1 cases by using Truenat H1N1 micro PCR system and to understand the clinical and demographical distribution of cases attending the tertiary care hospital.
Materials and Methods: This was a prospective study done during the period of Jan 2017 to Dec 2017. All the suspected cases for influenza like illness attending our hospital were included in the study. Only samples from category C patients were subjected to laboratory testing for H1N1. Clinical specimens like nasal or throat or nasopharyngeal swabs were collected by nylon swab and transported in the viral lysis medium. Viral nucleic acid detection was done by Truenat H1N1 micro PCR system (Molbio diagnostics, Bangalore).
Results: A total of 205 samples were obtained during the study period. Out of which 15 samples (7.3%) were tested positive for swine flu influenza H1N1 by Truenat H1N1 micro PCR system. Out of these 15 cases five cases died with the case fatality rate of 33.3%. Majority of the samples were from males accounting for 65.3% followed by females 34.7%. Majority of the cases were in the age group of 30-39 year (24.8%) followed by 40-49 year age group (22.9%). Majority of the patients presented with fever (96.5%) followed by cough (75%) and cold (63.9%). Associated comorbid conditions identified were diabetes (18%), chronic kidney disease (5.3%), pre existing lung diseases (6.8%) and pregnancy (2.4%).
Conclusion: Early and accurate detection of swine flu cases is the best way to undertake any interventions in the management of any epidemic or pandemic. In order to do so, molecular techniques like Truenat H1N1 chip based real time PCR technology systems will be extremely helpful in countries like India where testing is necessary especially in the peripheral settings.