Factors Predicting the Outcome in Patients With Acute Encephalitis Syndrome: A Retrospective Analysis of Surveillance Data in Bangladesh
Dewan Imtiaz Rahman, Faruq Abdulla, Wasik Rahman Aquib, Md Mustafizur Rahman, Muhammad Rashedul Alam, Shadman Sakib Choudhury, Mohammad Rezaul Karim, Arifa Nazneen, Kamal Ibne Amin Chowdhury, Tonmoy Sarkar, Anika Farzin, Fateha Akther Ema, Kajal Chandra Banik, Ayesha Siddika, Mohammad Enayet Hossain, Ahmed Nawsher Alam, Sharmin Sultana, Trevor Shoemaker, Christina Spiropoulou, Mohammed Ziaur Rahman, Sayera Banu, Joel M. Montgomery, Tahmina Shirin, Syed Moinuddin SatterIntroduction
Acute encephalitis syndrome (AES) is a severe inflammatory condition affecting the central nervous system, frequently leading to significant morbidity and mortality. Despite advancements in diagnostic techniques, the etiology of AES remains unidentified in a substantial number of cases, particularly in low‐ and middle‐income countries like Bangladesh. Understanding factors that predict outcomes in AES patients is crucial for improving management strategies and patient outcomes.
Methods
We used data from the Nipah surveillance in Bangladesh, analyzing patients admitted with AES to 11 surveillance hospitals across diverse geographical regions in Bangladesh from January 2021 to December 2023. Trained surveillance team members screened patients and collected data through face‐to‐face interviews using a structured questionnaire, and surveillance physicians made decisions on enrollment as an AES case. Bivariate and multivariable logistic regression analyses were used to identify predictors of mortality.
Results
A total of 12,603 AES patients were included, of whom 2,759 (22%) died. Mortality rates varied significantly across different administrative divisions, with the highest rates observed in the Rangpur division. Increased age, female sex, and delayed hospital admission were associated with higher odds of mortality. Patients from divisions like Rangpur and those aged 60 or over had notably higher odds of mortality, with adjusted odds ratios of 2.55 and 4.22, respectively. Clinical factors such as high pulse rate, ronchi, difficulty breathing, altered mental status, stiff neck, paralysis, and unconsciousness at admission were significantly associated with unfavorable outcomes.
Discussion
The findings highlight significant geographical, demographic, and clinical predictors of mortality among AES patients in Bangladesh. Older age, delayed hospital admission, and certain reported clinical symptoms on admission increase the risk of death. The study underscores the importance of timely intervention and targeted management strategies, particularly in identified aforementioned high‐risk groups.
Conclusion
Identifying predictors of adverse outcomes in AES patients can aid in developing early warning and triage systems, potentially enabling adequate resource allocation and the delivery of timely, personalized, and higher standard of care.