影响手术部位感染患者再入院的因素:基于图形和预测模型的方法
Shwetha Somakumar1, Fathima Thashreefa Basheer1, Vijayanarayana K1
1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Surgical infections
|December 23, 2024
概括
这项研究开发了手术部位感染 (SSI) 再入院的预测模型,识别了关键的微生物罪祸首及其耐药性模式,以改善患者护理.
科学领域:
- 医学微生物学 医学微生物学
- 传染性疾病 传染性疾病
- 医疗保健服务研究 医疗服务研究
背景情况:
- 抗菌素耐药性的增加对治疗微生物感染构成了重大挑战.
- 手术部位感染 (SSI) 是患者再入院的主要原因,需要有效的管理策略.
- 了解耐药性模式对于优化抗菌疗法和感染控制至关重要.
研究的目的:
- 开发一个预测模型,用于在手术部位感染 (SSI) 后的再入院率.
- 确定有助于SSI再接收的流行微生物分离物.
- 分析这些分离物的抗菌素耐药性模式的趋势.
主要方法:
- 在印度 (2016-2021) 的第三级护理环境中,对549名SSI患者进行了回顾性研究.
- 使用后勤回归分析来确定再接收的预测因素.
- 分析了微生物分离物及其抗生素敏感性模式.
主要成果:
- 预测模型实现了0.77的曲线下的面积 (AUC),预测准确率为77.8%.
- 由于复发性感染导致的再入院发生在24.9%的患者中.
- 甲素耐药的黄金葡萄球菌 (MRSA) 是主要的分离物,对多种抗生素表现出显著的耐药性.
结论:
- 开发的模型可以帮助预测SSI后再接收率.
- 识别流行的耐药微生物菌株对于有针对性的治疗和感染控制至关重要.
- 这项研究为评估和管理导致SSI再录取的因素提供了一个工具.
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