Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)
Moa Jung,
Hae-Yeon Park,
Geun-Young Park,
Jong In Lee,
Youngkook Kim,
Yeo Hyung Kim,
Seong Hoon Lim,
Yeun Jie Yoo,
Sun Im
Affiliations
Moa Jung
Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Hae-Yeon Park
Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Geun-Young Park
Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Jong In Lee
Department of Rehabilitation Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Youngkook Kim
Department of Rehabilitation Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Yeo Hyung Kim
Department of Rehabilitation Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Seong Hoon Lim
Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Yeun Jie Yoo
Department of Rehabilitation Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Sun Im
Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
This study analyzed a digitized database of electronic medical records (EMRs) to identify risk factors for post-stroke infections. The sample included 41,236 patients hospitalized with a first stroke diagnosis (ICD-10 codes I60, I61, I63, and I64) between January 2011 and December 2020. Logistic regression analysis was performed to examine the effect of clinical variables on post-stroke infection. Multivariable analysis revealed that post-stroke infection was associated with the male sex (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.49–2.15), brain surgery (OR: 7.89; 95% CI: 6.27–9.92), mechanical ventilation (OR: 18.26; 95% CI: 8.49–44.32), enteral tube feeding (OR: 3.65; 95% CI: 2.98–4.47), and functional activity level (modified Barthel index: OR: 0.98; 95% CI: 0.98–0.98). In addition, exposure to steroids (OR: 2.22; 95% CI: 1.60–3.06) and acid-suppressant drugs (OR: 1.44; 95% CI: 1.15–1.81) increased the risk of infection. On the basis of the findings from this multicenter study, it is crucial to carefully evaluate the balance between the potential benefits of acid-suppressant drugs or corticosteroids and the increased risk of infection in patients at high risk for post-stroke infection.