Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke.

BACKGROUND: Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes. AIM: To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model. METHODS: The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed. RESULTS: Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839-0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively. CONCLUSION: Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

[Evaluation of direct digital radiography image quality in dentistry].

PURPOSE: To evaluate the quality of direct digital radiograph according to its application in dentistry. METHODS: 1195 patients with dental caries, periodontal diseases, periapical diseases, trauma of teeth or hypodontia were tested with Trophy elitys Radio Visio Graphy(RVG) and the quality of the tests was evaluated at four levels. RESULTS: It was convenient to operate RVG and easy to save the images, which reduced the amount of X-rays. 1587 pictures were taken, among which 92.3% was at level I, 6.3% was at level II,1.14% was at level III and level IV. CONCLUSIONS: The quality of the images at different positions is good, which can meet the requirement for clinical diagnosis. However, the radiography receptor should be improved for better images.