The efficacy of nursing interventions in preventing surgical site infections in patients undergoing surgery for congenital heart disease

Abstract

Surgical site infections (SSIs) pose significant risks to patients undergoing surgery for congenital heart disease (CHD), impacting recovery and increasing healthcare burdens. This study assesses the efficacy of targeted nursing interventions in reducing SSIs and enhancing wound healing in this vulnerable patient group. A prospective cohort study was conducted from January 2022 to August 2023 at a single institution, involving 120 paediatric patients divided into control (standard postoperative care) and observation (specialized nursing interventions) groups. Nursing interventions included preoperative disinfection, strategic use of antibiotics, rigorous aseptic techniques and comprehensive postoperative care. Inclusion criteria encompassed a broad spectrum of CHD patients, while exclusion criteria aimed to minimize confounders. The Institutional Ethics Committee approved the study protocols. Baseline characteristics were comparable across groups, ensuring homogeneity. The observation group exhibited significantly lower SSI rates (1.7%) compared to the control group (11.6%), with a notable increase in optimal wound healing (Grade A) outcomes (73.3% vs. 30%). The differences in healing efficacy and infection rates between the two groups were statistically significant, emphasizing the effectiveness of the targeted nursing interventions in enhancing postoperative recovery for paediatric patients undergoing CHD surgery. The study demonstrates that targeted nursing interventions can significantly reduce SSI rates and improve wound healing in paediatric CHD surgery patients. These results underscore the importance of specialized nursing care in postoperative management. Future research, including larger-scale clinical trials, is necessary to validate these findings and develop comprehensive nursing care guidelines for this population.

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events

by Yiming Li, Jianfu Li, Jianping He, Cui Tao

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs’ capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs’ potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

Etiological characteristics of wound infection in severe trauma patients and logistic regression analysis of influencing factors of infection

Abstract

To investigate the etiological characteristics of wound infection in severe trauma patients and logistic regression analysis of the influencing factors of infection. The 116 patients with severe trauma who were intervened in our hospital from 22 October 2017 to 9 September 2019 were selected as the subjects of this retrospective study and divided into a control group and an observation group according to whether they were infected or not, 58 cases each. Observe and compare the pathogenic characteristics (pathogen distribution and drug resistance) of the two groups of patients and logistic regression analysis of the influencing factors of infection. The gram-positive bacteria in the observation group were mainly Staphylococcus aureus, accounting for 17.20%; the fungi were mainly Candida tropicalis, accounting for 17.20%; and the gram-negative bacteria were mainly Acinetobacter baumannii, accounting for 20.39%; the control group was gram-positive. The main bacteria are S. aureus, accounting for 8.60%; the fungi are mainly Candida albicans, accounting for 4.3%; and the gram-negative bacteria, which are mainly Pseudomonas aeruginosa, accounting for 14.56%. Gram-positive bacteria Enterococcus faecium, S. aureus, Enterococcus faecalis, Staphylococcus epidermidis. The highest drug resistance of other gram-positive bacteria is penicillin and erythromycin at 85.00% and above. Fungi C. tropicalis, Candida parapsilosis, C. albicans, fluconazole and amphotericin B have the highest resistance to 80.00% and above. Gram-negative bacteria A. baumannii, Ps. aeruginosa, Klebsiella pneumoniae, Escherichia coli, Proteus mirabilis, Enterobacter cloacae and other gram-negative bacteria are the most resistant to ampicillin, and Piperacillin was 70.00% and above. The combined primary diseases of the two groups of patients, ventilator use ≧3 days, long-term use of glucocorticoids, catheter use days ≧5 days, fever days ≧3 days and long-term use of broad-spectrum antimicrobial drugs, the difference is statistically significant academic significance (p < 0.05). Logistic analysis showed that combined with underlying diseases, fever days ≥3 days, long-term use of glucocorticoids and catheter use days ≥5 days are the influencing factors for the occurrence of wound infections in patients with severe trauma. Trauma patients are prone to wound infections, and there are many influencing factors. Close observation of patients should be strengthened. Effective prevention and control measures should be taken for related influencing factors to reduce the incidence of infection.