Seasonal Trends and Antibiotic Resistance Profiles of Bacterial Pathogens in Indian Clinical Isolates.

Introduction Bacterial diseases exhibit seasonal trends, necessitating their monitoring for outbreak prediction, treatment optimization, and infection control. This study explores seasonal trends, temperature correlations, and antimicrobial resistance profiles of key pathogens in an Indian tertiary care setting. Methodology This cross-sectional study analyzed bacterial isolates from 1,562 patient samples, including , , , , , and . Monthly infection rates and seasonal patterns were visualized using heatmaps and time-series graphs. Pearson's correlation assessed the relationship between these infection rates and temperature. Antibiotic susceptibility was evaluated using VITEK2, with resistance patterns visualized in R. Results Infections peaked in April ( = 163, 10.43%) and March ( = 161, 10.30%), with as the most common pathogen ( = 271, 25.64%), followed by  ( = 201, 19.02%) and ( = 178, 16.84%). Seasonal trends showed infections peaked in summer ( = 45, 16.6%), while ( = 27, 15.2%) and ( = 24, 25.5%) peaked in winter. Temperature correlated positively with infection ( = 0.814, = 0.001) and negatively with ( = -0.845, = 0.001), and ( = -0.618, = 0.032), with no correlation observed for , , and . Multi-drug resistance (MDR), extensively drug resistance (XDR), and pandrug resistance were more prevalent in Gram-negative than in Gram-positive bacteria.  Conclusions This study reveals temperature-driven seasonal patterns in bacterial infections, aiding outbreak prediction and prevention. The findings emphasize the threat of multidrug resistance, particularly in Gram-negative bacteria, reinforcing the need for enhanced infection control and targeted antibiotic stewardship.