Predictive value of the systemic immune inflammation (SII) index for stroke-associated pneumonia.

OBJECTIVE: To investigate the predictive value of the systemic immune inflammation (SII) index on the occurrence of stroke-associated pneumonia (SAP) in patients with acute stroke. METHODS: Data of patients with or without a previous history of pulmonary who visited the First Affiliated Hospital of Kunming Medical University within 24 h of the onset of stroke were collected between January 2017 and December 2019. Patient's demographic data, stroke type, past medical history, National Institutes of Health Stroke Scale score, Glasgow Coma score, and laboratory tests were collected. Logistic regression models and receiver-operating characteristic (ROC) curves were used to investigate the predictive value of SII for the development of SAP in patients with stroke. RESULTS: We included 395 patients with acute stroke, with a mean age of 63.89 ± 13.42 years, of whom 340 (86.1%) had ischemic stroke, and 55 (13.9%) had hemorrhagic stroke. Out of 395, 113 (28.6%) had SAP and 282 (71.4%) did not, and the SII level in the SAP group was higher than that of the non-SAP group (p < .05). Logistic regression analysis of patients with stroke showed that higher SII was a risk factor for SAP in patients with stroke (per 100 units, HR = 1.081, 95% CI: 1.035-1.130, p < .001), and tertile grouping of SII showed that the risk of SAP was 5.059 times higher in the SIIQ3 group than in the SIIQ1 group (95% CI: 2.061-12.418, p < .001). ROC curve analysis indicated that the SII index had predictive value for the occurrence of SAP in patients with stroke, with an area under the curve of 0.752 (95% CI: 0.698-0.806). When the cutoff value was 861.01, the SII predicted SAP in patients with stroke with a sensitivity of 61.9% and a specificity of 76.2%. CONCLUSION: Higher SII is an independent risk factor for the development of SAP in patients with stroke and has some predictive value for the development of SAP.

Ki67 testing in the clinical management of patients with non-metastatic colorectal cancer: Detecting the optimal cut-off value based on the Restricted Cubic Spline model.

The proliferation of the biomarker Ki67 has been extensively studied in colorectal cancer (CRC). Although numerous Ki67 cut-off values have previously been reported, the optimal cut-off value remains unclear with previous studies providing contrasting results. The present retrospective cohort study aimed to determine the optimal cut-off value for CRC. Ki67 levels and the prognosis of patients with non-metastatic CRC were obtained from the Electronic Health Information System of a tertiary hospital in Kunming City. The Restricted Cubic Spline (RCS) model was used to analyze the non-linear association between Ki67 levels and the risk of patient death and metastasis. Moreover, the RCS model was used to determine the optimal cut-off value of Ki67. Cox proportional hazards models were used to verify the effects of the cut-off value. In total, 210 patients with CRC and a median age of 62.5 years (age range, 23.0-88.0 years) were studied. Results of the present study demonstrated a non-linear association between Ki67 levels and the risk of patient death based on the RCS model, and at Ki67 levels ≥60%, the hazard ratio (HR) of patient death gradually increased. Using multivariate-adjusted Cox proportional hazards models, the results of the present study demonstrated that Ki67 ≥60% indicated a high-risk ratio for both distant metastasis and death [HR, 2.640; 95% confidence interval (CI), 1.066-6.539], compared with Ki67 <60% (HR, 2.558; 95% CI, 1.079-6.064). Therefore, Ki67 ≥60% may be the optimal cut-off value for the prediction of death and metastasis in patients with CRC. Thus, Ki67 may act as a biomarker for predicting the prognosis of patients with CRC, and the optimal cut-off value for the prediction of both death and metastasis of patients with CRC is 60%.