Cerebrospinal fluid predictors of shunt-dependent hydrocephalus after hemorrhagic stroke: a systematic review and meta-analysis.
Yao-Chung YangSzu-Hao LiuYu-Hone HsuYu-Lun WuPing-Teng ChuPei-Chin LinPublished in: Neurosurgical review (2022)
Hydrocephalus is a common complication of hemorrhagic stroke and has been reported to contribute to poor neurological outcomes. Herein, we aimed to investigate the validity of cerebrospinal fluid (CSF) data in predicting shunt-dependent hydrocephalus (SDHC) in patients with hemorrhagic stroke. PubMed, CENTRAL, and Embase databases were searched for relevant studies published through July 31, 2021. The 16 studies with 1505 patient included those in which CSF data predicted risk for SDHC and reports on CSF parameters in patients in whom SDHC or hydrocephalus that was not shunt-dependent developed following hemorrhagic stroke. We appraised the study quality using Newcastle-Ottawa Scale and conducted a meta-analysis of the pooled estimates of the CSF predictors. The meta-analysis revealed three significant CSF predictors for shunt dependency, i.e., higher protein levels (mean difference [MD] = 32.09 mg/dL, 95% confidence interval [CI] = 25.48-38.70, I 2 = 0%), higher levels of transforming growth factor β1 (TGF-β1; MD = 0.52 ng/mL, 95% CI = 0.42-0.62, I 2 = 0%), and higher ferritin levels (MD = 108.87 µg/dL, 95% CI = 56.68-161.16, I 2 = 36%). The red blood cell count, lactate level, and glucose level in CSF were not significant in predicting SDHC in patients with hemorrhagic stroke. Therefore, higher protein, TGF-β1, and ferritin levels in CSF are significant predictors for SDHC in patients with hemorrhagic stroke. Measuring these CSF parameters would help in the early recognition of SDHC risk in clinical care.
Keyphrases
- cerebrospinal fluid
- atrial fibrillation
- transforming growth factor
- systematic review
- subarachnoid hemorrhage
- red blood cell
- cerebral ischemia
- pulmonary artery
- epithelial mesenchymal transition
- healthcare
- molecular dynamics
- ejection fraction
- electronic health record
- type diabetes
- end stage renal disease
- big data
- quality improvement
- clinical trial
- case control
- peritoneal dialysis
- binding protein
- signaling pathway
- meta analyses
- small molecule
- deep learning