Clinical characteristics and risk stratification for late-onset herpes zoster following allogeneic hematopoietic stem cell transplantation.
Cheng-Jie FengPeng ZhaoHai-Xia FuChen-Hua YanChen-Cong WangXiao-Lu ZhuYun HeFeng-Rong WangYuan-Yuan ZhangXiao-Dong MoYuan KongWei HanJing-Zhi WangYu WangHuan ChenYu-Hong ChenXiang-Yu ZhaoYing-Jun ChangLan-Ping XuKai-Yan LiuXiao-Jun HuangXiao-Hui ZhangPublished in: Cancer letters (2024)
The incidence of herpes zoster (HZ) in allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients is significantly higher than that of the general public. Although routine antiviral prophylaxis is recommended, late-onset HZ has been highlighted, yet limited information is known about its clinical features and predictors. Here, we conducted a retrospective nested case-control study to identify patients with late-onset HZ, defined as a diagnosis of HZ after 1 year of transplantation, among allo-HSCT recipients between 2012 and 2017 at Peking University People's Hospital. Three controls were matched for each patient. A total of 201 patients developed late-onset HZ. Age over 20 years, absence of neutrophil engraftment by 14 days, mental disorders, immunosuppressant use at 1 year, and a peripheral CD4+/CD8+ ratio ≥0.5 at 1 year were independent risk factors, among which the CD4+/CD8+ ratio demonstrated good discriminative power for predicting late-onset HZ. For patients with a CD4+/CD8+ ratio <0.5, patient age, neutrophil engraftment time, mental disorders, and immunosuppressant use were potential risk factors. A stratification algorithm was accordingly established, classifying the transplant recipients into three risk groups. Whether the algorithm could facilitate the administration of posttransplant antiviral prophylaxis merits further validation.
Keyphrases
- late onset
- allogeneic hematopoietic stem cell transplantation
- risk factors
- early onset
- acute myeloid leukemia
- acute lymphoblastic leukemia
- hematopoietic stem cell
- machine learning
- healthcare
- case report
- end stage renal disease
- ejection fraction
- newly diagnosed
- deep learning
- peritoneal dialysis
- emergency department
- prognostic factors
- clinical practice
- cell therapy
- social media
- acute care
- electronic health record
- human health