Procalcitonin/Albumin Ratio Predicts the Outcome After Severe Traumatic Brain Injury: A Propensity Score-Matched Analysis.

BACKGROUND: The procalcitonin/albumin ratio (PAR), a novel inflammation-based index, has been reported to predict the prognosis following cardiopulmonary bypass surgery and bacterial infection. However, whether PAR can predict the outcome of patients with severe traumatic brain injury (STBI) has not been fully elucidated. This study aimed to investigate the relationship between serum PAR levels and prognosis at 6 months after STBI. METHODS: We retrospectively enrolled 129 patients diagnosed with STBI and collected relevant clinical and laboratory data. Logistic regression analysis was used to estimate the association of PAR with the prognosis of STBI. The receiver operating characteristics curve was performed to examine the predictive use of PAR for prognosis. Propensity score matching (PSM) analysis was also performed to improve the reliability of the results. The primary outcome measures were expressed as a score on the modified Rankin Scale at 6 months. RESULTS: The unfavorable prognosis group had advanced age, lower Glasgow Coma Scale score, higher rate of cerebral hernia and intracranial infection, higher neutrophil/lymphocyte ratio (NLR) and C-reactive protein/albumin ratio (CAR), elevated PAR, and higher rate of pneumonia. Multivariate analysis showed that PAR (before PSM: odds ratio 3.473, 95% confidence interval 2.983-4.043, P < 0.001; after PSM: odds ratio 5.358, 95% confidence interval 3.689-6.491, P < 0.001) was independently associated with unfavorable outcome. The area under the curve of the PAR for predicting an unfavorable outcome was higher than that of the CAR and NLR. CONCLUSIONS: The PAR might be a novel independent risk factor of the outcome after STBI. Moreover, PAR was a better biomarker in predicting the outcome of patients with STBI than CAR and NLR.

Systemic immune-inflammation index predicts the outcome after aneurysmal subarachnoid hemorrhage.

Systemic inflammatory response is closely related to the pathogenesis and prognosis in critical patients. Recently, systemic immune-inflammation index (SII), an indicator of systemic inflammatory response, was proved to predict the outcome in cancerous and non-cancerous diseases. The aim of this study is to investigate the association between SII on admission and 6-month outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). The clinical data and prognosis of 76 patients with aSAH were analyzed. The 6-month outcome was assessed by the modified Rankin scale(mRS). The unfavorable outcome was defined as mRS score ≥ 3. In addition, multivariate analysis was conducted to investigate factors independently associated with the favorable outcome. Receiver operating characteristic (ROC) curve analysis was undertaken to identify the best cut-off value of SII for the discriminate between favorable and unfavorable outcome in these patients. Thirty-six patients (47.4%) in our study had an unfavorable outcome (mRS ≥ 3) at 6 months, and twenty-four (66.7%) of them were in the high-SII group. A significantly higher SII on admission was observed in patients with unfavorable functional outcome at 6 months. Binary logistic regression analysis showed that there was an independent association between SII on admission and 6-month clinical outcome (adjusted OR = 4.499, 95%CI: 1.242-16.295, P < 0.05). The AUC of the SII for predicting unfavorable outcome was 0.692 (95% CI: 0.571-0.814, P < 0.05). Systemic immune-inflammation index (SII) could be a novel independent prognostic factor for aSAH patients at the early stage of the disease.

Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.

Background: Low-grade gliomas (LGG) are one of the most prevalent types of brain cancers. The efficacy of immunotherapy in LGG is limited compared to other cancers. Immunosuppression in the tumor microenvironment (TME) of LGG is one of the main reasons for the low efficacy of immunotherapy. Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. However, their role in the TME of LGG has not been investigated. This study aimed to construct a risk model based on these TPRs and to detect the significance of immunotypes in predicting LGG prognosis and immunotherapy efficacy. Methods: A total of 688 LGGs and 202 normal brain tissues were extracted from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases. The NMF R package was used to identify TRP-related subtypes. The TPR prognostic model was established using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival of LGG samples. Results: The Subtype 2 patients had worse survival outcomes, suppressed immune function, and higher immune cell infiltration. A risk regression model consisting of 14 TPRs was established, and its performance was validated in CGGA325 cohorts. The low-risk group exhibited better overall survival, immune microenvironment, and immunotherapy response, as determined via the TIDE algorithm, indicating that increasing the level of immune infiltration can effectively improve the response to immunotherapy in the low-risk group. The risk score was determined to be an independent hazard factor (p<0.001) although other clinical features (age, sex, grade, IDH status, 1p19q codel status, MGMT status, and accepted radiotherapy) were considered. Lastly, high-risk groups in both cohorts revealed optimal drug responses to rapamycin, paclitaxel, JW-7-52-1, and bortezomib. Conclusions: Our study identified two distinct TPR subtypes and built a TPR signature to elucidate the characteristics of T cell proliferation in LGG and its association with immune status and prognosis. These findings shed light on possible immunotherapeutic strategies for LGGs.