Prognostic model for aneurysmal subarachnoid hemorrhage patients requiring mechanical ventilation.

OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is a major cause of death and disability worldwide and imposes serious burdens on society and individuals. However, predicting the long-term outcomes in aSAH patients requiring mechanical ventilation remains challenging. We sought to establish a model utilizing the Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression to estimate the prognosis of aSAH patients requiring mechanical ventilation, based on regularly utilized and easily accessible clinical variables. METHODS: Data were retrieved from the Dryad Digital Repository. Potentially relevant features were selected using LASSO regression analysis. Multiple Cox proportional hazards analyses were performed to develop a model using the training set. Receiver operating characteristics and calibration curves were used to assess its predictive accuracy and discriminative power. Kaplan-Meier and decision curve analyses (DCA) were used to evaluate the clinical utility of the model. RESULTS: Independent prognostic factors, including the Simplified Acute Physiology Score 2, early brain injury, rebleeding, and length of intensive care unit stay, were identified and included in the nomogram. In the training set, the area under the curve values for 1-, 2-, and 4-year survival predictions were 0.82, 0.81, and 0.80, respectively. In the validation set, the nomogram exhibited excellent discrimination ability and good calibration. Moreover, DCA demonstrated that the nomogram was clinically beneficial. Finally, a web-based nomogram was constructed (https://rehablitation.shinyapps.io/aSAH). INTERPRETATION: Our model is a useful tool for accurately predicting long-term outcomes in patients with aSAH who require mechanical ventilation and can assist in making individualized interventions by providing valuable information.

Hub genes and key pathways of traumatic brain injury: bioinformatics analysis and in vivo validation.

The exact mechanisms associated with secondary brain damage following traumatic brain injury (TBI) remain unclear; therefore, identifying the critical molecular mechanisms involved in TBI is essential. The mRNA expression microarray GSE2871 was downloaded from the Gene Expression Omnibus (GEO) repository. GSE2871 comprises a total of 31 cerebral cortex samples, including two post-TBI time points. The microarray features eight control and seven TBI samples, from 4 hours post-TBI, and eight control and eight TBI samples from 24 hours post-TBI. In this bioinformatics-based study, 109 and 66 differentially expressed genes (DEGs) were identified in a Sprague-Dawley (SD) rat TBI model, 4 and 24 hours post-TBI, respectively. Functional enrichment analysis showed that the identified DEGs were significantly enriched in several terms, such as positive regulation of nuclear factor-κB transcription factor activity, mitogen-activated protein kinase signaling pathway, negative regulation of apoptotic process, and tumor necrosis factor signaling pathway. Moreover, the hub genes with high connectivity degrees were primarily related to inflammatory mediators. To validate the top five hub genes, a rat model of TBI was established using the weight-drop method, and real-time quantitative polymerase chain reaction analysis of the cerebral cortex was performed. The results showed that compared with control rats, Tnf-α, c-Myc, Spp1, Cxcl10, Ptprc, Egf, Mmp9, and Lcn2 were upregulated, and Fn1 was downregulated in TBI rats. Among these hub genes, Fn1, c-Myc, and Ptprc may represent novel biomarkers or therapeutic targets for TBI. These identified pathways and key genes may provide insights into the molecular mechanisms of TBI and provide potential treatment targets for patients with TBI. This study was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Nanchang University, China (approval No. 003) in January 2016.